Cortical Areas: Unity and Diversity
© 2002 Taylor & Francis
Conceptual Advances in Brain Research A series of books focusing on brain dynamics and information processing systems of the brain. Edited by Robert Miller, Otago Centre for Theoretical Studies in Psychiatry and Neuroscience, New Zealand (Editor-in-chief), Günther Palm, University of Ulm, Germany and Gordon Shaw, University of California at Irvine, USA.
Volume 1 Brain Dynamics and the Striatal Complex edited by R. Miller and J.R. Wickens Volume 2 Complex Brain Functions: Conceptual Advances in Russian Neuroscience edited by R. Miller, A.M. Ivanitsky and P.M. Balaban Volume 3 Time and the Brain edited by R. Miller Volume 4 Sex Differences in Lateralization in the Animal Brain by V.L. Bianki and E.B. Filippova Volume 5 Cortical Areas: Unity and Diversity edited by A. Schüz and R. Miller
This book is part of a series. The publisher will accept continuation orders which may be cancelled at any time and which provide for automatic billing and shipping of each title in the series upon publication. Please write for details.
© 2002 Taylor & Francis
Cortical Areas: Unity and Diversity
Edited by
Almut Schüz Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany
and Robert Miller University of Otago, Dunedin, New Zealand
London and New York © 2002 Taylor & Francis
First published 2002 by Taylor & Francis 11 New Fetter Lane, London EC4P 4EE Simultaneously published in the USA and Canada by Taylor & Francis Inc, 29 West 35th Street, New York, NY 10001 Taylor & Francis is an imprint of the Taylor & Francis Group © 2002 Taylor & Francis Typeset in Times by Integra Software Services Pvt. Ltd, Pondicherry, India Printed and bound in Great Britain by TJ International Ltd, Padstow, Cornwall All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Every effort has been made to ensure that the advice and information in this book is true and accurate at the time of going to press. However, neither the publisher nor the authors can accept any legal responsibility or liability for any errors or omissions that may be made. In the case of drug administration, any medical procedure or the use of technical equipment mentioned within this book, you are strongly advised to consult the manufacturer’s guidelines.
British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging in Publication Data A catalog record for this book has been requested ISBN 0–415–27723–X
© 2002 Taylor & Francis
CONTENTS
Preface
vii
List of Contributors
ix
1 Introduction: Homogeneity and Heterogeneity of Cortical Structure: A Theme and its Variations Almut Schüz
1
Part I THE EMPIRICAL STATUS OF CORTICAL MAPS 2 Cyto- and Myeloarchitectonics: Their Relationship and Possible Functional Significance Bernhard Hellwig 3 Architectonic Mapping of the Human Cerebral Cortex Katrin Amunts, Axel Schleicher and Karl Zilles 4 Topographical Variability of Cytoarchitectonic Areas Jörg Rademacher 5 Mapping of Human Brain Function by Neuroimaging Methods Rüdiger J. Seitz
15 29 53 79
Part II CORTICAL AREAS: CORRELATION WITH CONNECTIVITY 6 Regional Dendritic Variation in Primate Cortical Pyramidal Cells Bob Jacobs and Arnold B. Scheibel 7 Intrinsic Connections in Mammalian Cerebral Cortex Jonathan B. Levitt and Jennifer S. Lund 8 Thalamic Systems and the Diversity of Cortical Areas Catherine G. Cusick 9 Cortical Areas and Patterns of Cortico-Cortical Connections Jon H. Kaas v © 2002 Taylor & Francis
111 133 155 179
vi
Contents
Part III CONSTANCY AND VARIATION ACROSS SPECIES 10 The Cerebral Cortex of Mammals: Diversity within Unity Facundo Valverde, Juan A. De Carlos and Laura López-Mascaraque 11 Laminar Continuity between Neo- and Meso-Cortex: The Hypothesis of the Added Laminae in the Neocortex Robert Miller and Rupa Maitra
195
219
Part IV FUNCTIONAL EQUIVALENCE BETWEEN AREAS 12 Cross-Modal Plasticity as a Tool for Understanding the Ontogeny and Phylogeny of Cerebral Cortex Sarah L. Pallas 13 Do Primary Sensory Areas Play Analogous Roles in Different Sensory Modalities? Hubert R. Dinse and Christoph E. Schreiner 14 Plastic-Adaptive Properties of Cortical Areas Hubert R. Dinse and Gerd Boehmer
245
273 311
Part V MORPHOLOGICAL SUBSTRATES OF SEGREGATION AND INTEGRATION 15 Connectional Organisation and Function in the Macaque Cerebral Cortex Malcolm P. Young 16 The Human Cortical White Matter: Quantitative Aspects of Cortico-Cortical Long-Range Connectivity Almut Schüz and Valentino Braitenberg 17 Fundamentals of Association Cortex Stewart Shipp 18 Wheels within Wheels: Circuits for Integration of Neural Assemblies on Small and Large Scales Robert Miller
351
19 Discussion Section Robert Miller and Almut Schüz
459
© 2002 Taylor & Francis
377 387
423
PREFACE
Since the time when Bell and Magendie first showed the different functions of dorsal and ventral roots of the spinal cord, idea that different functions can be identified with different locations in the central nervous system has been central to attempts to understand the brain. The possibility that different psychological functions might in some way “reside” in different locations of the cerebral cortex was also an attractive idea, even when scientific study of the cerebral cortex was in its earliest infancy, as shown by the popularity of phrenology in the first half of the nineteenth century. This possibility came to have a firmer empirical basis in the latter half of that century, as a result of the studies of neurologists such as Broca, Wernicke and others. Development of ideas of cortical localization of function was given further impetus by results of cortical stimulation experiments, and, in the early twentieth century, from the study of cortical cytoarchitectonics. Nowadays, a localizationist view of the cortex is also favoured by the widespread use of functional imaging techniques. Throughout this long history, an alternative perspective has been advocated periodically, placing emphasis of the fact that many psychological functions appear not to be localized in specific cortical regions, or if they are associated with particular cortical areas, these areas are multiple, and distributed, rather than single and discrete. In the lesion studies of memory conducted by Lashley it was even concluded that functional loss depends more on the size of the lesions, rather than its exact location. A modern expression of this perspective comes from some of those using functional imaging methods, who are also concerned with widely distributed functions, and document networks of several cortical areas activated together when particular psychological functions are employed. Modern morphological work on the cerebral cortex, to which one of us has contributed also fits into this alternative tradition, cortical connectivity being described and analysed in terms of broad statistical constraints which might generalize across the whole neocortical mantle. These two perspectives might seem antithetical, but this is appearance rather than reality. It is not a contradiction to believe that some functions have a strict association with particular cortical areas, while others are based on more widely-distributed cortical regions. Which of these two perspectives emerges as prominent in an experiment depends on the way the experimenters frame their questions. In the chapters below, many aspects of this complex topic are explored. These include the actual evidence that the cortex can be subdivided into morphologically different areas, the correlation between such parcellation and patterns of connectivity of various sorts, the degree to which there is nevertheless an underlying uniformity to the cortex, generalizing across areas and between species, the functional equivalence of different areas, as well as the large-scale patterning of cortical functioning, and the overall integration of cortical vii © 2002 Taylor & Francis
viii
Preface
functions by interplay with other forebrain structures. all these issues have been discussed many times in the past. However, we believe it is timely to revisit them, and thus to put some of these long-standing debates in the context of modern evidence about the structure and function of the cerebral cortex. We would like to express thanks to a few people. Claudia Holt was very helpful in the handling of electronic material and Nicola Arndt in technical assistance with the manuscripts. In particular, we thank Valentino Braitenberg for valuable advice and discussions. The planning of this book was in part done at the Institute for Advanced Studies in Delmenhorst, Germany. R. Miller expresses his thanks to Professor Gareth Jones, of Otago University, and to the Schizophrenia Fellowship of New Zealand for continuing support, and to the Max Planck Institute for Biological Cybernetics, for support during visits to Tübingen, during the planning and development of this book. R. Miller, Dunedin A. Schüz, Tübingen April 2001
© 2002 Taylor & Francis
CONTRIBUTORS
Hubert R. Dinse Institute for Neuroinformatics Dept of Theoretical Biology Group Experimental Neurobiology Ruhr-University Bochum ND 04 D-44780 Bochum Germany
Katrin Amunts Institut für Medizin Forschungszentrum Jülich GmbH 52425 Jülich Germany Gerd Boehmer Institute of Physiology and Pathophysiology Gutenberg-University 55099 Mainz Germany
Jon H. Kaas Vanderbilt University Dept of Psychology 111 21st street Avenue South 301 Wilson Hall Nashville TN 37240 USA
Valentino Braitenberg Max-Planck-Institut für biologische Kybernetik Spemannstr. 38 72076 Tübingen Germany
Bernhard Hellwig Neurologische Universitätsklinik Neurozentrum Breisacher Str. 64 79106 Freiburg Germany
Catherine G. Cusick Dept of Structural and Cellular Biology and Neurosciences Programme Tulane University School of Medicine 1430 Tulane Avenue New Orleans Louisiana USA 70112
Bob Jacobs Laboratory of Quantitative Neuromorphology Dept of Psychology The Colorado College 14 East Cache La Poudre Colorado Springs CO 80903 USA
Juan A. De Carlos Instituto Cajal (CSIC) Avenida del Doctor Arce 37 28002 Madrid Spain ix © 2002 Taylor & Francis
x
Laura López-Mascaraque Instituto Cajal (CSIC) Avenida del Doctor Arce 37 28002 Madrid Spain Jennifer S. Lund Department of Ophthalmology Moran Eye Center University of Utah 50 North Medical Drive Salt Lake City UT 84132 USA Jonathan B. Levitt Dept of Biology City College of the City University of New York 138th Street & Convent Avenue New York NY10031 USA Rupa Maitra Department of Anatomic Pathology Wellington Hospital Wellington New Zealand Robert Miller Otago Centre for Theoretical Studies in Psychiatry & Neuroscience Dept of Anatomy and Structural Biology School of Medical science University of Otago PO Box 913 Dunedin New Zealand Sarah L. Pallas Dept of Biology Georgia State University PO Box 4010 Atlanta GA 30302 USA
© 2002 Taylor & Francis
Contributors
Jörg Rademacher Neurologische Klinik Heinrich-Heine-Universität Düsseldorf Moorenstrasse 5 40225 Düsseldorf Germany Arnold B. Scheibel Department of Neurobiology Brain Research Institute University of California Los Angeles CA 90024-1769 Axel Schleicher Institute of Neuroanatomy Heinrich Heine University 40225 Düsseldorf Germany Christoph E. Schreiner Coleman Laboratory W.M. Keck Center for Integrative Neuroscience Sloan Center for Theoretical Neurobiology University of California San Francisco San Francisco USA Almut Schüz Max-Planck-Institut für biologische Kybernetik Spemannstr. 38 72076 Tübingen Germany Rüdiger J. Seitz Dept of Neurology University Hospital Düsseldorf Moorenstrasse 5 40225 Düsseldorf Germany
Contributors
xi
Stewart Shipp Wellcome Dept of Cognitive Neurology University College Gower Street WC1E 6BT London England
Malcolm P. Young Neural Systems Group Dept of Psychology Claremont Place NE1 7RU Newcastle upon Tyne England
Facundo Valverde Instituto Cajal (CSIC) Avenida del Doctor Arce 37 28002 Madrid Spain
Karl Zilles C. and O. Vogt Institute of Brain Research Heinrich Heine University 40225 Düsseldorf Germany
© 2002 Taylor & Francis
Part I THE EMPIRICAL STATUS OF CORTICAL MAPS
© 2002 Taylor & Francis
2 Cyto- and Myeloarchitectonics: Their Relationship and Possible Functional Significance Bernhard Hellwig Neurologische Universitätsklinik, Neurozentrum, Breisacher Str. 64, 79106 Freiburg, Germany Tel: ++49 761 270 5001; FAX: ++49 761 270 5390 e-mail:
[email protected]
In the human cerebral cortex, a number of cortical areas can be distinguished by anatomical methods. Two classical types of cortical parcellation have been described, based on cyto- and myeloarchitectonics. In cytoarchitectonics, the definition of areas relies on variations in the sizes and packing densities of cell bodies. Myeloarchitectonic parcellation is based on the layering, the distribution and the amount of intracortical myelinated fibres. It is shown here that cyto- and myeloarchitectonics are closely related. Two simple assumptions are sufficient to transform quantitative cytoarchitectural data into the corresponding myelin picture. The rules linking cyto- and myeloarchitectonics seem to be essentially uniform throughout the neocortex. It is also well known that characteristic functional specializations can be attributed to cortical areas. However, beyond the localization of function, the functional significance of areal variability in the cortex is largely unclear. For instance, it remains to be clarified why certain areal adaptations of the basic cortical network seem to be particularly appropriate for the execution of specific tasks. It is argued that this issue will only be understood when the wiring schemes of each area are known. Since it is difficult to infer connectivity patterns from cyto- and myeloarchitectonics, their significance for a functional interpretation of cortical anatomy seems to be limited. The paper suggests, however, possible strategies that may allow one to describe cortical architectonics in terms of connectivity. KEYWORDS: areas, connectivity, cytoarchitectonics, human cerebral cortex, myelin, myeloarchitectonics
1. INTRODUCTION It has been known for a long time, i.e. since the discovery of the stripe of Gennari in the primary visual cortex (Gennari, 1782), that the cerebral cortex is not uniform. A number of histological methods allow one to distinguish cortical areas which are defined by characteristic variations of the basic cortical architecture. Interestingly, this anatomical parcellation of the cortex is not merely descriptive, but is somehow related to cortical function. Different types of information (visual, auditory, motor, etc.) are processed in different cortical areas. As yet, this relation between structure and function has been elucidated mainly in just one respect: Certain functions can be localized in certain areas. Reaching this conclusion is an important achievement, useful, for example, for a clinical neurologist who can associate symptoms in a patient with lesions visible in a CT scan. However, 15 © 2002 Taylor & Francis
Bernhard Hellwig
16
localization of function is not the whole story. Knowing where a certain type of information is processed does not explain how this is done. The functional significance of areal variations in the cortex will not be understood until the mechanisms of information processing as well as its localization can be related to cortical anatomy. For instance, it would be interesting to know why a piece of cortex that is involved in motor control looks like the motor area, and not like the primary visual cortex. The present paper will not be able to solve this problem. However, it will consider two classical approaches in cortical parcellation, cyto- and myeloarchitectonics, and discuss whether a functional interpretation is possible beyond the mere localization of function.
1.1. Cytoarchitectonics In cytoarchitectonics, cortical areas are defined on the basis of cell body stains such as the Nissl stain. Cortical parcellation relies on variations in the sizes and packing densities of neurones leading to characteristic patterns of layering (Figure 2.1). The most prominent maps of the human cerebral cortex worked out on the basis of cytoarchitectural observa1o 1a 1b 1c
I II III
1
III
2
III
3
2 1
3
2
3
IV Va Vb
3
3
4 5a
5b 6aα
VIa 6aβ VIbα
6bα
VIbβ
6bβ
Figure 2.1. Schematic drawing of a piece of association cortex: cytoarchitectonics (left) and myeloarchitectonics (right). The stripes of Baillarger correspond to the horizontal bands of myelinated fibres in layers 4 and 5b. From Vogt and Vogt (1919).
© 2002 Taylor & Francis
Cyto- and Myeloarchitectonics
17
Figure 2.2. Brodmann’s map (1909) of the human cerebral cortex (lateral view).
tions are those by Brodmann (1909) (Figure 2.2) and von Economo and Koskinas (1925) (see Figure 3.1(B) in chapter by Amunts et al., this volume). It is not the aim of this paper to present all the anatomical details of each area. These can be found in the monographs by Brodmann and von Economo mentioned above as well as in a more recent treatise by Braak (1980) which combines cyto- and myeloarchitectural observations with studies on the pigmentoarchitectonics of the human cerebral cortex. However, in order to give a basic idea of the cytoarchitectural organization of the neocortex, some general principles should be mentioned. Following a suggestion by von Economo and Koskinas (1925) the different cortical areas can be collected into larger groups. Most areas, in particular the association areas, show the typical six-layered cortex schematically illustrated in Figure 2.1. They are referred to as homotypical. Areas in which six layers cannot be clearly discerned are called heterotypical. They come in two forms. First, there is the agranular cortex in which layers 2 and 4 with small, densely packed neurones are not well developed. Examples for the agranular cortex are the Brodmann areas 4 and 6, i.e. the motor and premotor areas (Figure 2.2). The second type of heterotypical cortex is the granular cortex, which is characterized by strongly developed layers 2 and 4 with many densely packed, small neurones. This type of cortex is mainly found in the primary sensory cortices, e.g. in the Brodmann areas 17, 41 and 3 (primary visual, auditory and somatosensory cortex).
1.2. Myeloarchitectonics The myeloarchitectonics of the human cerebral cortex, based on the layering, the distribution and the amount of intracortical myelinated fibres, has been described in detail by Vogt and his co-workers (e.g. Vogt, 1910, 1911; Vogt and Vogt, 1919; Strasburger, 1937;
© 2002 Taylor & Francis
18
Bernhard Hellwig
Hopf, 1954; Batsch, 1956). Myelin preparations show three types of intracortical fibres (Figure 2.1): (1) radial fibres (vertical to the cortical surface), (2) oblique fibres, and (3) horizontal fibres (parallel to the cortical surface). The horizontal fibres are particularly useful for cortical parcellation. In most areas they form two conspicuous horizontal bands, the so-called stripes of Baillarger (Baillarger, 1840), which are usually located in layers 4 and 5b respectively (Figure 2.1). The stripes of Baillarger vary from area to area. Again, it is not the aim of this paper to describe the areal variability of myeloarchitectonic patterns in detail. The reader is referred to the papers by Vogt and his co-workers mentioned above as well as to the treatise by Braak (1980). However, some general remarks can be made. The homotypical cortex, as it was defined for cytoarchitectonics, usually exhibits both stripes of Baillarger. In some regions, such as the frontal and temporal pole, or areas located medially in the interhemispheric cleft, only the outer stripe of Baillarger may be discernible. In the heterotypical agranular cortex the stripes of Baillarger are concealed in a dense feltwork of fibres, either completely as in the primary motor cortex or partially as in the premotor cortex where only the outer stripe of Baillarger is visible. In the heterotypical granular cortex two myelin patterns can be distinguished. On the one hand, there is the primary visual cortex with its conspicuous band of horizontal myelinated fibres in layer 4b, the so-called stripe of Gennari. On the other hand, in the primary somatosensory cortex or the primary auditory cortex both stripes of Baillarger are present, the inner one being distinctly more prominent than the outer one. It is also interesting to consider the areal variability of the total amount of myelin in the cortex. The degree of myelination diminishes with increasing distance from the primary areas. It is particularly low in the region of the frontal and temporal pole as well as in areas located medially in the interhemispheric cleft. 1.3. Cyto- and Myeloarchitectonics: Different Aspects of the Same Underlying Cortical Network? Before discussing possible functional implications of the areal variability described by cyto- and myeloarchitectonics, it seems worthwhile to consider whether there is a relation between patterns of cell bodies and patterns of myelinated fibres. Finding such a relation may elucidate aspects of the underlying cortical network. According to Brodmann (1909), maps of the human cerebral cortex based on either cyto- or myeloarchitectonics are essentially identical. This is corroborated by Sanides’ monograph (1962) on the frontal lobe of the human brain. Here, the investigation of a series of sections alternatively stained by a cell body and a myelin stain yielded only one map of areal diversity in the frontal cortex. Thus, there seems to be a close relationship between cyto- and myeloarchitectonics in the sense that they obviously reflect different aspects of the same underlying cortical network. However, what is the nature of this relation? Inspection of Figure 2.1 reveals that an answer to this question is by no means obvious. While the outer stripe of Baillarger, situated in layer 4, corresponds to densely packed, small cell bodies, the inner stripe of Baillarger, located in layer 5b, coincides with less densely packed, large cell bodies. Braitenberg (1962, 1974) put forward a hypothesis as to how cyto- and myeloarchitectonics might be related. He suggested that horizontal intracortical myelinated fibres, i.e. those fibres forming the stripes of Baillarger, correspond mainly to local axonal ramifications of pyramidal neurons, the most frequent cell type in the cerebral cortex (Braitenberg,
© 2002 Taylor & Francis
Cyto- and Myeloarchitectonics
19
Figure 2.3. Camera lucida drawing of a Golgi-stained pyramidal cell in the cerebral cortex of the mouse. A number of horizontally directed axon collaterals originate slightly below the cell body. Bar, 50 µm.
1978; Braitenberg and Schüz, 1998). The bulk of horizontal axon collaterals of pyramidal cells leave the descending main axon 200 to 300 µm below the cell body (Figure 2.3) (cf. Cajal, 1911; Gilbert and Wiesel, 1979, 1983; Landry et al., 1980; Martin and Whitteridge, 1984; DeFelipe et al., 1986; Schwark and Jones, 1989). The pyramidal cells which in the majority of areas are most conspicuous in layers 3 and 5 would thus produce two maxima of horizontal fibres. These maxima, shifted downwards relative to layers 3 and 5 by 200 to 300 µm, could account for the two stripes of Baillarger. The assumption that horizontal myelinated fibres in the cortex consist mainly of local axonal ramifications of pyramidal cells (and not of thalamic or cortico-cortical afferent fibres) is supported by degeneration and tracer studies (Le Gros Clark and Sunderland, 1939; Fisken et al., 1975; Creutzfeldt et al., 1977; Gatter and Powell, 1978; Colonnier and Sas, 1978; Levitt et al., 1993). Starting out from Braitenberg’s hypothesis, Hellwig (1993) showed in a computational study that, provided quantitative data on the cell body picture of a certain area are given, two simple assumptions are sufficient to predict correctly the corresponding myelin picture. Part of this work is reviewed in the following section.
© 2002 Taylor & Francis
Bernhard Hellwig
20
2. SIMPLE RULES RELATE THE CYTO- AND MYELOARCHITECTONICS OF THE HUMAN CEREBRAL CORTEX: UNIFORMITY IN AREAL DIVERSITY 2.1. Cytoarchitectural Data It was the aim of Hellwig’s study (1993) to compute myelin pictures from quantitative data on the cytoarchitectonics of different areas. Cytoarchitectural data were taken from the treatise on the human cortex by von Economo and Koskinas (1925) which contains detailed descriptions of all areas. Three types of data were considered: (1) layer thicknesses; (2) neurone sizes in each layer (specified as the width of a cell body); (3) the volume density of neurones in each layer (specified as numbers of neurones per 0.001 mm3). 2.2. Two Basic Assumptions Two basic assumptions were used to transform von Economo’s cytoarchitectural data into myelin pictures: 2.2.1. First assumption Large neurones contribute more to the intracortical myelin content than small ones. This relation can be represented by the sigmoid curve in Figure 2.4. The assumption is hypothetical, but was inspired by observations on Nissl, myelin and Golgi stained sections through human and non-human cortices. 2.2.2. Second assumption The average distribution of horizontal axon collaterals of pyramidal neurones can be quantified by the histogram of Figure 2.5. This histogram is derived from a Golgi study on pyramidal cells in the rat visual cortex by Paldino and Harth (1977). It was used as a model of the distribution of horizontal axon collaterals with respect to the cell body. Only one modification was introduced for the computations: the histogram was scaled to the 1
myelin value
0.8 0.6 0.4 0.2 0
0
10
20
30
40
diameter of the cell body [µm] Figure 2.4.
A hypothetical curve that transforms the diameter of a neurone’s cell body into a “myelin value”.
© 2002 Taylor & Francis
Cyto- and Myeloarchitectonics
21
number of collaterals
100 80 60 40 20 0
–200
0
200
400
600
800
distance from the cell body [µm] Figure 2.5. Modified diagram from a study by Paldino and Harth (1977) on pyramidal neurones in the rat visual cortex. Distances (vertical to the cortical surface) between the endpoints of axon collaterals and the cell body were measured (positive distances: below the cell body; negative distances: above the cell body). Note that the bulk of collaterals is located below the cell body.
thickness of each area. Note that the second assumption concerns only pyramidal neurones. For the computation this means that the few neurones in layer 1 which are all of the non-pyramidal type (e.g. Peters and Kara, 1985) were discarded. In addition, below layer 1, where the non-pyramidal neurones account for only about 15% of the whole neurone population (Peters and Kara, 1985; Braak and Braak, 1986; Braitenberg and Schüz, 1998), all neurones were considered as pyramidal cells. 2.3. Procedure and Results In all, 14 neocortical areas were chosen for this study. They comprise areas focussed on by many investigators, and include the motor cortex, the primary sensory areas or the speech centres. Moreover, they give a fair impression of the variability of myeloarchitectonic patterns across the human neocortex. Here, the results of just three areas are presented, the Brodmann areas 4, 7 and 17. The architecture of area 7, a field in the parietal association cortex, is paradigmatic for the homotypical cortex. Area 4 (primary motor cortex) and area 17 (primary visual cortex), on the other hand, represent the two extremes of the heterotypical cortex (Nissl sections shown in Figure 2.6a). Myelin pictures were computed in two steps. First, using data from von Economo and Koskinas (1925), the average size of neurones in each layer was transformed into a myelin value by means of the curve in Figure 2.4. The myelin value was then multiplied by the corresponding number of neurones per unit volume. The procedure yields, for each layer, a single value which can be considered as the layer-specific contribution to the population of horizontal myelinated fibres (Figure 2.6b). In the second step of the computation, it was taken into account that myelin is distributed along axonal arborizations. This was done by convolving the diagrams in Figure 2.6b with the histogram of Figure 2.5. This simply provides for shifting the myelin into the appropriate position (Figure 2.6c). The densities of myelin thus obtained were represented as shades of grey (Figure 2.6d) in order to facilitate comparison with real myelin preparations (Figure 2.6e). The simulated
© 2002 Taylor & Francis
Bernhard Hellwig
22 a
b
c
d
e
0
Area 7
1 2 3
distance from cortical surface [mm]
Area 17
4 5 0 1 2 3 4 5 0
Area 4
1 2 3 4 5 0
50
100 0
50
100
amount of myelin [%] Figure 2.6. Computation of myelin pictures for areas 4, 7 and 17 and comparison with real myelin preparations. (a) Nissl pictures. (b) First step of the computation: the layer-specific amounts of myelin are shown as a function of the cortical depth. (c) The second step of the computation: the diagrams of Figure 2.6b are convolved with the histogram of Figure 2.5. (d) Figure 2.6c, transformed into shades of grey. (e) Real myelin preparations. Bar, 1 mm.
myelin pictures are remarkably close to the real ones. In area 7, both stripes of Baillarger are visible, in agreement with Vogt’s (1911) original description. In area 17 only one band of myelinated fibres is conspicuous, the so-called stripe of Gennari (cf. Vogt and Vogt, 1919). In area 4 the comparison between simulation and reality is complicated by the fact that the myeloarchitectonic patterns differ in two subfields. The simulation is close to the anterior part of area 4 where, according to Vogt (1910), only the outer stripe of Baillarger is visible, while the inner one grades into the white matter. 2.4. Conclusion The findings presented above support the assumption that the stripes of Baillarger consist mainly of horizontal axon collaterals of pyramidal cells. The two assumptions relating cyto- to myeloarchitectonics apply also to the other areas investigated in Hellwig’s study (1993). This suggests that the distribution of horizontal axon collaterals of pyramidal neurones and the principles of their myelination are remarkably similar in different areas. Thus, there is obviously both diversity and unity in the cortex. Despite areal variability,
© 2002 Taylor & Francis
Cyto- and Myeloarchitectonics
23
the rules linking cyto- and myeloarchitectonics seem to be essentially uniform throughout the neocortex.
3. CAN CYTO- AND MYELOARCHITECTONICS BE INTERPRETED IN FUNCTIONAL TERMS? 3.1. Connectivity and Function A functionally or computationally relevant description of cortical anatomy will focus on the connectivity between neurones. It is obvious that the wiring scheme in the cortex strongly influences how information is processed. Considering the enormous number of synapses in the cortex, connectivity certainly has to be described in a statistical way. The cortical wiring scheme can probably be adequately grasped by parameters such as connection probabilities, number of synapses involved in a connection, the amount of divergence and convergence or the relative importance of short- and long-range connections. Once these parameters are known, one should be able to specify the connections of an arbitrarily selected neurone to other neurones in the cortex in a probabilistic way. Braitenberg and Schüz (1998) have described the basic machinery of the mouse cortex on the basis of a statistical analysis of its components. To some extent, these data can be extrapolated to the human cerebral cortex. However, it is still largely unclear how the basic cortical wiring scheme varies from area to area. This leads to the question discussed in the next section: Can the variability of the connectivity scheme in different areas of the human cerebral cortex be inferred from cyto- and myeloarchitectonics and does this lead to a better understanding of the mechanisms of information processing in these areas?
3.2. Discussion In cytoarchitectonics, the local variations of size and packing density of cell bodies are used for cortical parcellation. The number of synapses on cell bodies is small, rarely exceeding 200 (Peters and Kaiserman-Abramof, 1970; White and Rock, 1980; Müller et al., 1984). This is not much compared to the overall number of synapses carried by a cortical neurone: about 8000 in the mouse and about 40 000 in the human cortex (Braitenberg and Schüz, 1998). In other words, a method that stains the cell bodies of a neurone cannot be very helpful for elucidating cortical connectivity. Connections are predominantly located in the neurophil, i.e. in those parts of the cortical tissue that remain unstained in cell body preparations. Nevertheless, a few general statements about connectivity can be made, since the size of a cell body is positively correlated to the length of its dendritic arborizations (Bok, 1959). For instance, small perikarya which are densely packed indicate that the dendritic processes are relatively short, thus occupying only a small volume. This applies to layer 4 of the primary sensory areas where the thalamic afferents arrive. The dense packing of cell bodies points to a local preprocessing of the incoming thalamic information. Some layers contain large cell bodies which are not so densely packed, e.g. layers 3 and 5 in Figure 2.1. This indicates large and richly ramified dendritic trees, i.e. information is sampled from a relatively extended piece of cortex. Pyramidal neurones in layers 3 and 5 are the origin of important long-range projections to other cortical or subcortical structures.
© 2002 Taylor & Francis
24
Bernhard Hellwig
Their large dendrites seem to ensure that the information which is projected contains a relatively general overview of cortical activity, and not some specialized data about the processes in small cortical patches. Beyond this, cytoarchitectonics does not tell us much about patterns of dendritic or axonal arborizatons which carry the bulk of synapses and are thus most important for cortical connectivity. In this respect, myeloarchitectonics might be more interesting because it shows patterns of intracortical fibres. The main function of myelin is probably to increase conduction velocities. However, it is doubtful that this is an important property for all intracortical axonal fibres. Many of them are so short that the actual conduction times are within the range of a few milliseconds, even if the whole variability of conduction velocities encountered in the nervous system is taken into consideration. This is unlikely to be significant (Hellwig, 1993). An important function of intracortical myelin might be its ability to insulate axonal fibres, in the sense that myelinated segments of axonal ramifications are unable to form synapses. This is an interesting property of myelin, since it means that myelination imposes a spatial structure on axonal trees: in some places they are capable of interacting with other neurones, in others they are not. The distribution of myelin over the axonal tree is probably not random. The time course of maturation in the primary visual cortex of the cat suggests that myelination is related to early learning processes. In the first postnatal weeks, there is a period of extraordinary plasticity in the visual cortex, the so-called critical period. Plasticity, for example the susceptibility to the effects of monocular deprivation, is high until some time between the sixth or eighth week after birth (Hubel and Wiesel, 1970; Olson and Freeman, 1980). On the level of pyramidal neurones this period is characterized by the emergence and refinement of axonal arborizations (Callaway and Katz, 1990). By pruning of inappropriate axon collaterals, axonal ramifications are formed in which long horizontal axonal fibres give off clusters of axon collaterals that preferably contact certain target regions, namely columns of similar orientation preference (Gilbert and Wiesel, 1989). The process of shaping axonal trees is experience-dependent (Löwel and Singer, 1992). Axonal arborizations attain an adult appearance by the end of the critical period, i.e. about 7 weeks after birth (Callaway and Katz, 1990). Interestingly, this is the time when myelination starts. The first myelinated fibres in the primary visual cortex of the cat appear by the end of the sixth postnatal week, myelination is moderate until the end of the eighth week, and then undergoes an enormous, almost explosive increase (Haug et al., 1976). In conclusion, two processes seem to coincide at the end of the critical period in the visual cortex of the cat: the termination of the experience-dependent shaping of axonal branching patterns and the onset of myelination. Observations on individual pyramidal cells suggest that the myelinated parts of axonal trees are mainly those horizontal fibre segments that interconnect clusters of collaterals (DeFelipe et al., 1986) (Figure 2.7). Thus, myelin would insulate predominantly axonal segments which failed to establish functional relations with other cortical neurones during the critical period. In other words, myelin would be a sort of memory trace, a tool to store information about early learning processes. The interpretation of myelin as a memory trace by which early experiences are fixed may explain why the overall amount of myelin is higher in the heterotypical areas, i.e. in the primary motor and sensory cortices, than in the homotypical association cortex. The primary areas are in a close relation to the outside world, and a repertoire of information
© 2002 Taylor & Francis
Cyto- and Myeloarchitectonics
25
dendrite
myelin axon
Figure 2.7. Schematic drawing of a cortical pyramidal cell. Long myelinated horizontal axon collaterals emanate below the cell body and give off clusters of non-myelinated collaterals.
processing steps fixed by early experiences may be an efficient way to deal with ever-recurring standard tasks. In the association cortex the tasks to be expected are less predictable. Thus, a less rigid wiring scheme may be useful in which associations of all kinds can be learned. In this context, it is also interesting to note that the onset of myelination is much earlier in the primary areas than in the association cortex (Flechsig, 1920, 1927). All in all, it is, however, most difficult to interpret areal variability as revealed by myeloarchitectonics in functional terms. This is mainly due to the fact that myelin preparations, although showing axonal fibres, do not reveal the cortical wiring scheme, since those axonal fibre segments are stained that, insulated by myelin, are unable to contact other neurones. In a way, myelin preparations display the “negative” of intracortical connectivity. 3.3. Outlook It is an important task for neuroanatomists (and for neuroscientists in general) to relate structure to function. As far as the parcellation of the cortex into areas is concerned, this goal has been achieved mainly in one respect: Certain functional specializations can be attributed to certain areas. However, beyond the localization of function, the functional interpretation of areal variability in the cortex is largely unclear. In particular, it remains to be clarified why areal adaptations of the basic cortical network seem to be particularly appropriate for the execution of specific tasks. For instance, one wonders why the structure of the motor cortex is obviously useful for the control of movements, but not for other tasks such as the processing of visual information. In other words, the relation between the mechanisms of information processing and the areal variability of cortical anatomy is
© 2002 Taylor & Francis
26
Bernhard Hellwig
unclear. This is due to the fact that the connectivity patterns in each area are largely unknown. As pointed out above, cyto- and myeloarchitectonics are not very helpful in this respect. How can variations of wiring schemes in different areas be elucidated? Knowing the variability of neuronal arborizations in different areas would in itself be helpful. Unfortunately, this type of information is scarce. For the human cortex, one of the main sources is still Cajal’s (1911) treatise on the nervous system, which yields some qualitative, but no quantitative data on the variability of Golgi-stained neurones in different areas. More recent material is reviewed in this book in chapters by Jacobs and Scheibel (2002) and Valverde et al. (2002). In all, studying the architectonics of the cortex as revealed by Golgi or similar methods is still a worthwhile research program. An approach by which the local connectivity between pyramidal neurones in a given area can be quantitatively estimated has been suggested by Hellwig (2000). Pyramidal neurones in layers 2 and 3 of the rat visual cortex were intracellularly stained and threedimensionally reconstructed using a computer-based camera lucida system. In a computer experiment, pairs of pre- and postsynaptic neurones were formed and potential synaptic contacts, i.e. spatial contacts between axons and dendrites, were calculated. For each pair, the calculations were carried out for a whole range of distances (0 to 500 µm) between the pre- and the postsynaptic neurone, in order to describe cortical connectivity as a function of the spatial separation of neurones. It was also possible to differentiate whether neurones were situated in the same or in different cortical layers. The data thus obtained were used to compute connection probabilities, the average number of contacts between neurones or the frequency of specific numbers of contacts. It could be shown by comparison with independent data that the local cortical connectivity between pyramidal neurones estimated in this way was a good approximation to reality. In principle, this approach can be extended to other layers as well as to other areas. This makes it possible to investigate cortical architectonics in terms of connectivity. The interpretation of functional processes in cortical areas will certainly be promoted by knowledge about the underlying wiring scheme. However, data on connectivity could also be important in another context: They could actually be used to build artificial neuronal networks with a biologically realistic structure. In such networks, the areal variability of information processing could be studied. Thus, describing cortical architectonics in terms of connectivity would not just be an analytic undertaking, but could also serve as a basis for a synthetic approach.
REFERENCES Baillarger, J.G.F. (1840) Recherches sur la structure de la couche corticale des circonvolutions du cerveau. Mémoires de l’academie royale de Médecine, 8, 149–183. Batsch, G. (1956) Die myeloarchitektonische Untergliederung des Isocortex parietalis beim Menschen. Journal für Hirnforschung, 2, 225–270. Bok, S.T. (1959) Histonomy of the Cerebral Cortex. Amsterdam, London: Elsevier. Braak, H. (1980) Architectonics of the Human Telencephalic Cortex. Berlin, Heidelberg, New York: Springer Verlag. Braak, H. and Braak, E. (1986) Ratio of pyramidal cells versus non-pyramidal cells in the human frontal isocortex and changes in ratio with ageing and Alzheimer’s disease. In: D.F. Swaab, E. Fliers. M. Mirmiran, W.A. van Gool and F. van Haaren (eds), Aging of the brain and Alzheimer’s disease (Progress in Brain Research, Vol. 70), Amsterdam: Elsevier, pp. 185–212. Braitenberg, V. (1962) A note on myeloarchitectonics. Journal of Comparative Neurology, 118, 141–146.
© 2002 Taylor & Francis
Cyto- and Myeloarchitectonics
27
Braitenberg, V. (1974) Thoughts on the cerebral cortex. Journal of Theoretical Biology, 46, 421–447. Braitenberg, V. (1978) Cortical architectonics: general and areal. In: M.A.B. Brazier and H. Petsche (eds), Architectonics of the Cerebral Cortex, New York: Raven Press, pp. 443–465. Braitenberg, V. and Schüz, A. (1998) Cortex: Statistics and Geometry of Neuronal Connectivity. Berlin, Heidelberg, New York: Springer Verlag. Brodmann, K. (1909) Vergleichende Lokalisationslehre der Großhirnrinde. Leipzig: Johann Ambrosius Barth. Cajal, S Ramón y (1911) Histologie du système nerveux de l’homme et des vertébrés. Madrid: Consejo superior de investigaciones cientificas, Instituto Ramón y Cajal. Callaway, E.M. and Katz, L.C. (1990) Emergence and refinement of clustered horizontal connections in cat striate cortex. Journal of Neuroscience, 10, 1134–1153. Colonnier, M. and Sas, E. (1978) An anterograde degeneration study of the tangential spread of axons in cortical areas 17 and 18 of the squirrel monkey (Saimiri sciureus). Journal of Comparative Neurology, 179, 245–262. Creutzfeldt, O.D., Garey, L.J., Kuroda, R. and Wolff, J.-R. (1977) The distribution of degenerating axons after small lesions in the intact and isolated visual cortex of the cat. Experimental Brain Research, 27, 419–440. DeFelipe, J., Conley, M. and Jones, E.G. (1986) Long-range focal collateralization of axons arising from corticocortical cells in monkey sensory-motor cortex. Journal of Neuroscience, 6, 3749–3766. Fisken, R.A., Garey, L.J. and Powell, T.P.S. (1975) The intrinsic, association and commissural connections of area 17 of the visual cortex. Philosophical Transactions of the Royal Society London, Series B, 272, 487–536. Flechsig, P. (1920) Anatomie des menschlichen Gehirns und Rückenmarks auf myelogenetischer Grundlage. Leipzig: Thieme. Flechsig, P. (1927) Meine myelogenetische Hirnlehre. Berlin: Springer. Gatter, K.C. and Powell, T.P.S. (1978) The intrinsic connections of the cortex of area 4 of the monkey. Brain, 101, 513–541. Gennari, F. (1782) De peculiari structura cerebri nonnullisque eius morbus. Parma. Gilbert, C.D. and Wiesel, T.N. (1979) Morphology and intracortical projections of functionally characterised neurons in the cat visual cortex. Nature, London, 280, 120–125. Gilbert, C.D. and Wiesel, T.N. (1983) Clustered intrinsic connections in cat visual cortex. Journal of Neuroscience, 3, 116–1133. Gilbert, C.D. and Wiesel, T.N. (1989) Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex. Journal of Neuroscience, 9, 2432–2442. Haug, H., Kölln, M. and Rast, A. (1976) The postnatal development of myelinated nerve fibres in the visual cortex of the cat. Cell and Tissue Research, 167, 265–288. Hellwig, B. (1993) How the myelin picture of the human cerebral cortex can be computed from cytoarchitectural data. A bridge between von Economo and Vogt. Journal für Hirnforschung, 34, 387–402. Hellwig, B. (2000) A quantitative analysis of the local connectivity between pyramidal neurons in layers 2/3 of the rat visual cortex. Biological Cybernetics, 82, 111–121. Hopf, A. (1954) Die Myeloarchitektonik des Isocortex temporalis beim Menschen. Journal für Hirnforschung, 1, 208–279. Hubel, D.H. and Wiesel, T.N. (1970) The period of susceptibility to the physiological effects of unilateral eye closure in kittens. Journal of Physiology (London), 206, 419–436. Jacobs, B. and Scheibel, A.B. (2002) Regional dendritic variation in primate cortical pyramidal cells. In: A. Schüz and R. Miller (eds), Cortical areas: unity and diversity (Conceptual Advances in Brain Research series), London: Taylor and Francis Publishers. Landry, P., Labelle, A. and Deschênes, M. (1980) Intracortical distribution of axonal collaterals of pyramidal tract cells in the cat motor cortex. Brain Research, 191, 327–336. Le Gros Clark, W.E. and Sunderland, S. (1939) Structural changes in the isolated visual cortex. Journal of Anatomy, 73, 563–574. Levitt, J.B., Lewis, D.A., Yoshioka, T. and Lund, J.S. (1993) Topography of pyramidal neuron intrinsic connections in macaque monkey prefrontal cortex (areas 9 and 46). Journal of Comparative Neurology, 338, 360–376. Löwel, S. and Singer, W. (1992) Selection of intrinsic horizontal connections in the visual cortex by correlated neuronal activity. Science, Washington, 255, 209–212. Martin, K.A.C. and Whitteridge, D. (1984) Form, function and intracortical projections of spiny neurones in the striate visual cortex of the cat. Journal of Physiology (London), 353, 463–504. Müller, L.J., Verwer, R.W.H., Nunes Cardoso, B. and Vrensen, G. (1984) Synaptic characteristics of identified pyramidal and multipolar non-pyramidal neurons in the visual cortex of young and adult rabbits. A quantitative Golgi-electron microscope study. Journal of Neuroscience, 12, 1071–1087. Olson, C.R. and Freeman, R.D. (1980) Profile of the sensitive period for monocular deprivation in kittens. Experimental Brain Research, 39, 17–21. Paldino, A. and Harth, E. (1977) A computerized study of Golgi-impregnated axons in rat visual cortex. In: R.D. Lindsay (ed.), Computer Analysis of Neuronal Structures, New York: Plenum Press, pp. 189–207.
© 2002 Taylor & Francis
28
Bernhard Hellwig
Peters, A. and Kaiserman-Abramof, I.R. (1970) The small pyramidal neuron of the rat cerebral cortex. The perikaryon, dendrites and spines. American Journal of Anatomy, 127, 321–355. Peters, A. and Kara, D.A. (1985) The neuronal composition of area 17 of rat visual cortex. II. The nonpyramidal cells. Sanides, F. (1962) Die Architektonik des menschlichen Stirnhirns. In: M. Müller, H. Spatz and P. Vogel (eds), Monographien aus dem Gesamtgebiete der Neurologie und Psychiatrie, Vol. 98, Berlin, Göttingen, Heidelberg: Springer Verlag. Schwark, H.D. and Jones, E.G. (1989) The distribution of intrinsic cortical axons in area 3b of cat primary somatosensory cortex. Experimental Brain Research, 78, 501–513. Strasburger, E.H. (1937) Die myeloarchitektonische Gliederung des Stirnhirns beim Menschen und Schimpansen. I. Teil. Myeloarchitektonische Gliederung des menschlichen Stirnhirns. Journal für Psychologie und Neurologie, 47, 461–491. Valverde, F., De Carlos, J.A. and López-Mascaraque, L. (2002) The cerebral cortex of mammals: diversity within unity. In: A. Schüz and R. Miller (eds), Cortical areas: unity and diversity (Conceptual Advances in Brain Research series), Taylor and Francis Publishers, London, New York. Vogt, C. and Vogt, O. (1919) Allgemeinere Ergebnisse unserer Hirnforschung. Journal für Psychologie und Neurologie, 25, 279–461. Vogt, O. (1910) Die myeloarchitektonische Felderung des menschlichen Stirnhirns. Journal für Psychologie und Neurologie, 15, 221–232. Vogt, O. (1911) Die Myeloarchitektonik des Isocortex parietalis. Journal für Psychologie und Neurologie, 18, 379–390. von Economo, C. and Koskinas, G.N. (1925) Die Cytoarchitektonik der Hirnrinde des erwachsenen Menschen. Wien, Berlin: Springer Verlag. White, E.L. and Rock, M.P. (1980) Three-dimensional aspects and synaptic relationships of a Golgi-impregnated spiny stellate cell reconstructed from serial thin sections. Journal of Neurocytology, 9, 615–636.
© 2002 Taylor & Francis
3 Architectonic Mapping of the Human Cerebral Cortex Katrin Amunts1, Axel Schleicher3 and Karl Zilles1,2,3 1
Institut für Medizin, Forschungszentrum Jülich, Germany and 2C. and O. Vogt Institute of Brain Research and 3Institute of Neuroanatomy, Heinrich Heine University, Düsseldorf, Germany Correspondance: Dr. Katrin Amunts, Institut für Medizin, Forschungszentrum Jülich, GmbH, D-52425 Jülich, Germany Tel: +49 2461 614300; FAX: +49 2461 618307 e-mail:
[email protected]
The classical cyto- and myeloarchitectonic maps of the human cerebral cortex considerably influenced the concept of localization of function. Presently, these maps serve as anatomical references in functional imaging studies. However, the classical maps suffer from drawbacks such as the highly observer-dependent definition of areal borders; the fact that they present only a single aspect of architectonic organization of the cortex (e.g. only cytoarchitecture), and the lack of information on intersubject variability of location and size of a cortical area in a spatial reference system. Recent methodological progress in computerized image analysis of histological specimens, the introduction of markers which reflect various architectonic aspects of cortical organization (e.g. receptor autoradiography), and the development of warping techniques to compensate for intersubject variability of brain structure in 3D made it possible to overcome these drawbacks. We propose a new concept of architectonic mapping which is based on: (i) a definition of areal borders by using multivariate statistical analysis, and not by highly subjective judgements; (ii) a quantitative analysis of similarity and dissimilarity in architecture between cortical areas; and (iii) a multimodal characterization of cortical organization based on cyto-, myelo- and receptor-architectonic mapping. The comparison of architectonic maps with functional imaging data in a common standard reference space allows, for the first time, a direct analysis of correlations between structure and function in the living human brain, and provides new insights into the architecture of the cerebral cortex. KEYWORDS: architecture, brain mapping, human cerebral cortex, intersubject variability, transmitter receptors
1. INTRODUCTION The classical cytoarchitectonic maps of the human cerebral cortex published by Brodmann (1909), Campbell (1905), Elliot Smith (1907), von Economo and Koskinas (1925) and the Vogts (Vogt and Vogt, 1919) have recently gained considerable attention, since they present mandatory structural data for the microanatomical interpretation of functional imaging data. These maps, however, do not fulfil the requirements of an anatomical reference system for functional human brain mapping. For instance, they present only schematic, simplified drawings of a single, individual brain or hemisphere in a two-dimensional view without any descriptions of the intersubject variability of cortical architecture. The same is true for more recent architectonic maps, e.g. by the Russian school (Sarkisov et al., 1949), 29 © 2002 Taylor & Francis
30
Katrin Amunts, Axel Schleicher and Karl Zilles
Sanides (1962, 1964), Bailey and von Bonin (1951) and Braak (1979). Moreover, these maps differ between each other with respect to the number, location and extent of cortical areas (Zilles, 1990). Campbell subdivided the human cerebral cortex on the basis of cell body- and myelinstaining into 14 regions, amongst them precentral, frontal, visuo-sensory, and the auditopsychic areas (Campbell, 1905). Elliot Smith studied the regional and laminar distribution of myelinated fibers in unstained sections, and proposed a different map containing about 50 areas (Elliot Smith, 1907). Nowadays, the most widely used map is that of Brodmann, which relies on extensive studies of cell body-stained (Nissl-stain) histological sections (Brodmann, 1903, 1905, 1908, 1909). He subdivided the cortex on the basis of cytoarchitectonic criteria into approximately 40 cortical areas. Unfortunately, he never described his criteria for parcellation of most of the areas in sufficient detail. This is true in particular for so-called higher associative areas like areas of the prefrontal cortex, posterior parietal lobe, and of the inferior temporal cortex. Brodmann’s schematic surface drawing of an architectonic map was used by Talairach and Tournoux as basis of the architectonic parcellation in their stereotaxic atlas (Talairach and Tournoux, 1988). They simply transferred Brodmann’s areas to their own brain atlas by trying to identify corresponding sulcal patterns in both brains, assuming a strong association between the sulcal pattern and borders of cortical areas. Such an association, however, was already doubted by Brodmann. He mentioned that “ . . . a schematic drawing can reflect only the major spatial relationships, and therefore, precise topographical associations1 cannot be considered in general or only in a distorted manner; this is true in particular for all those cortical regions which have borders in the neighborhood of sulci and those regions which are located in the depth of such a cortical region” (Brodmann, 1908). The basis of Brodmann’s research was the working hypothesis that the cerebral cortex is composed of numerous cortical areas, each of them characterized by a distinct cytoarchitecture and function. Following this concept, the cytoarchitecture of a cortical area should be more or less constant within a cortical area, but changes considerably at its border. For example, Brodmann’s area 4 was conceptualized as the anatomical equivalent of the primary motor cortex which guides voluntary movements (Fritsch and Hitzig, 1870) and Broca’s region was regarded as the anatomical correlate of the functionally defined center of speech (Broca, 1861). Although for the vast majority of cortical areas such as microstructural-function relationship could not be rigorously tested at that time, both Brodmann and Campbell took architectonic localization of function for granted. The strict localizationist approach culminated in a map of the human cortex of Kleist (1934) in which complex functions were assigned to a distinct cytoarchitectonic area. Brodmann’s area 18 (for instance) was associated with visual attention, perception of spatial position, and eye movements toward the upper and lower visual field. Brodmann himself did not represent such an extreme localizational concept (Brodmann, 1909). In order to avoid a confusion of histological data and unproven evolutionary and functional speculations, he created his system of a “neutral” nomenclature by numbering different cytoarchitectonic areas mainly according to their dorso-ventral sequence. Older studies (Vogt and Vogt, 1919) and more recent electrophysiological studies in nonhuman primates have demonstrated that the basic idea of Brodmann was true: Neurones with similar receptive fields and
1
i.e. between sulci and areal borders [Au].
© 2002 Taylor & Francis
Architectonic Mapping
31
A
B
C
Figure 3.1. Cytoarchitectonic maps of the lateral surface of human brain adapted from [A] Brodmann (1909), [B] von Economo and Koskinas (1925) and [C] the Russian school (Sarkisov et al., 1949). Cytoarchitectonic areas are marked by different hatches and classified according to Brodmann’s nomenclature by Arabic numerals [A, C] or according to that of von Economo and Koskinas by letters and numerals [B]. Note differences in sulcal pattern as well as in shape and extent of the areas (e.g. in the frontal lobe with respect to areas 45, 9 and 46; compare A with C).
© 2002 Taylor & Francis
32
Katrin Amunts, Axel Schleicher and Karl Zilles
response properties lie within the same cytoarchitectonic area, as found when the same brain is sectioned and cell-stained following recording experiments, and correlations are sought between penetration sites and the cytoarchitectonic pattern. Conversely, response properties of neurones change across cytoarchitectonic borders (Luppino et al., 1991; Matelli et al., 1991; Tanji and Kurata, 1989). Although later studies extended and supplemented the maps of Brodmann and Campbell, they followed the concept of a cortical area implied by these maps. von Economo and Koskinas (1925) introduced an even more complex subdivision of the human cortex into cortical areas with regional peculiarities (= subareas). They defined cortical areas on the basis of their topography (frontal, parietal, occipital, etc.), and in terms of their cytoarchitecture and local peculiarities. As an example, area FAg is characterized by its location in the frontal lobe (“F”), an agranular cytoarchitecture (“A”) with giant pyramidal cells (“γ ”). Area FCB, e.g. has common features of area FB and FC, etc. Discussion of this concept, however, raised controversy in the scientific community. von Economo and Koskinas applied quantitative criteria (e.g. size of cells, thickness of layers) in order to provide a more precise characterization of a cortical area, to formalize the cytoarchitectonic description of cortical areas, and to make it more independent of the experience of the observer. Finally, the Russian school published another map of the human cerebral cortex which was, however, based mainly on Brodmann’s approach. Additionally, they tried to overcome one of the unsolved problems of Brodmann’s map, i.e. the neglect of intersubject variability. Their atlas considered intersubject variability in the extent and position of cortical areas by analyzing a sample of dozens of hemispheres (Filimonoff, 1932; Kononova, 1935, 1938; Sarkisov et al., 1949). The increasing number of available architectonic atlases revealed a further problem of architectonic mapping. Although all the cytoarchitectonic maps were based on the same concept of a cortical area as an architectonically distinct and homogeneous region, and all were the result of the same methodical approach, their areal patterns do not match, for example, with respect to the number of cortical areas, their relationship to sulci and gyri, as well as to the neighbouring cortical areas. Even if we compensate for interindividual differences in the macroscopical anatomy of the brains, numerous differences between the maps can hardly be explained. Thus, in the frontal lobe, area 46 has a common border with areas 44 and 45 in Brodmann’s map (Brodmann, 1909), but this border is absent in the map of the Russian school, since here area 9 separates completely area 46 from 44 and 45 (Sarkisov et al., 1949). In a more recent study, transitional areas were defined which exhibited mixed architectonic features of areas 46 and 45 (Rajkowska and Goldman-Rakic, 1995b). Considerable differences between the maps can also be found with respect to the anterior border of area 4, the extrastriate visual cortex, and the parietal cortex, where Brodmann found only a few areas, but recent observations revealed a much higher number of areas. What might be the reasons for differences between the maps? One reason concerns differences in parcellation criteria of the different observers. The most important criteria used in all studies are the density and size of nerve cells, their distribution within cortical layers, the absolute and relative thicknesses of cortical layers, the radial and horizontal arrangement of neurones, the presence of special cells (e.g. giant Betz cells of Brodmann’s area 4), and locally specific subdivisions of layers into sublayers (e.g. the subdivision of layer IV of Brodmann’s area 17 into sublayers IVA–C). For the vast majority of cortical areas, not only one, but a whole complex of criteria is used for its definition. Very often,
© 2002 Taylor & Francis
Architectonic Mapping
33
these criteria are weighted relative to each other, in a different way by each observer. In addition, the criteria are sometimes difficult to formalize objectively. This can be illustrated by the example of such a “simple” area as Brodmann’s area 4. Typical for this area are giant pyramidal cells in layer V (Betz cells), which were discovered by Betz as characteristic cells of the motor area of man, chimpanzee, other primates and dog (Betz, 1874). However, how big is a Betz cell? The height of these cells may vary between different individuals from 60–120 µm, their width from 30–60 µm. Moreover, comparably largesized cells can be found outside area 4 in the area postcentralis gigantopyramidalis (von Economo and Koskinas, 1925). Furthermore, the distance between single Betz cells increases towards subarea 4a (Geyer et al., 1996; Zilles et al., 1995). Thus, the border between area 4 and the rostrally adjoining area 6 is difficult to define on the basis of the Betz cells-criterion. If giant pyramidal cells are defined not by their absolute size, but by their relative size (i.e. comparison with cells in neighbouring areas), such cells can be also found in layers III and V of areas 44 and 45, in the extrastriate visual cortex and in the temporal cortex (Bailey and von Bonin, 1951). Consequently, a reliable definition of area 4 requires not only this, but also additional criteria, e.g. the absence of an inner granular layer. Bailey and von Bonin further followed this line of discussion and asked if there is any objective basis for a detailed cytoarchitectonic map at all. They came to the final conclusion that “ . . . vast areas are so closely similar in structure as to make any attempt at subdivisions unprofitable, if not impossible”. As a consequence, their cytoarchitectonic map is based only on a parcellation into a few main types of cortical regions: regions with numerous granular cells (koniocortex), without granular cells (agranular cortex), with large pyramids in layer III, and the allocortex, as well as 4 combinations between these main types. In contrast to the previously mentioned maps of Brodmann and others, their map does not show sharp borders but gradual transitions between areas (Bailey and von Bonin, 1951). The question arises about which cortical map is the most appropriate. Is it that of Bailey and von Bonin with 8 subdivisions, that of Campbell, Brodmann and the Russian school with about 20 to 40 subdivisions, or that of von Economo and Koskinas with about 100 areas and subareas? One way to answer this questions may be the combination of cytoarchitectonic mapping with other architectonic mapping techniques (multimodal mapping). Flechsig was the first to gave a detailed subdivision of the neocortex into 40 cortical areas by his myelogenetic method, i.e. by studying the heterochronous development of myelination in the white matter immediately below the cortex during foetal and early postnatal periods (Flechsig, 1898). The Vogts and their co-workers subdivided the human cortex on the basis of myeloarchitectonic criteria (distribution and density of myelinated axons within the cortex) into more than 150 fields (Lungwitz, 1937; Riegele, 1931; Strasburger, 1938; Vogt and Vogt, 1919; Vogt, 1919). Their map and the underlying nomenclature were quite complex and difficult to verify for other observers. This might be one reason why it did not reach general acceptance in subsequent years. More recent methods of cortical mapping, e.g. by immunohistochemistry (Bidmon et al., 1997; Campbell and Morrison, 1989; Hendry et al., 1994; Tootell and Taylor, 1995; Zilles et al., 1991c), histochemistry (Burkhalter and Bernardo, 1989; Clarke, 1994; Wong-Riley et al., 1993), pigmentoarchitecture (Braak, 1977, 1979) and regional and laminar distribution of different transmitter receptor binding sites (Dietl et al., 1987; Jansen et al., 1989; Zilles and Clarke, 1997; Zilles and Schleicher, 1995; Zilles et al., 1988, 1991d) proved to be valuable alternatives in architectonic research. Most importantly, the maps based on different histological and histochemical techniques frequently show a perfect spatial coincidence of
© 2002 Taylor & Francis
34
Katrin Amunts, Axel Schleicher and Karl Zilles
many areal borders, thus corroborating the position of an areal border by multimodal imaging. Moreover, since a single receptor may not reveal all borders demonstrated by other markers, this finding can be used to define a family of neurochemically related areas by studying the regional pattern of one transmitter receptor, and comparing its distribution with the maps revealed by other receptors or by cytoarchitecture. We think that such a multimodal concept of cortical mapping improves and supplements classical cytoarchitectonic analysis. We will show below, that the architectonic analysis of any histological or histochemical specimen can also be improved considerably by using quantitative measurements and statistically testable image analysis procedures: (i) Borders between cortical areas can be identified by observer-independent statistical analysis of local changes in cytoarchitecture (Schleicher et al., 1999). We will illustrate this approach in cytoarchitectonic specimens, although it can also be applied to receptor architectonic and myeloarchitectonic specimens (Zilles and Schleicher, 1993). (ii) We will present a method for quantifying cytoarchitectonic differences between cortical areas. This method defines the similarity or dissimilarity between cortical areas in terms of numerical distance measures. Using this approach, it can be tested statistically whether differences in cytoarchitecture (or any other architecture) are significant. Furthermore, it allows one to test the long-standing hypothesis of the gradual, rather than distinct character of the majority of cytoarchitectonic borders (Bailey and von Bonin, 1951). (iii) In contrast to previous cortical maps which were based on only one technique, multimodal architectonic analysis will be performed. We will discuss the correspondence and differences of architectonic borders which are revealed by receptor autoradiography of numerous different receptor binding sites, as well as by cytoarchitecture. Human striate and extrastriate areas, as well as Brodmann’s areas 44 and 45 (Broca’s region) will serve as examples for multimodal mapping of the cerebral cortex. (iv) We will conclude with some perspectives on the application of these maps in a threedimensional probabilistic atlas system.
2. OBSERVER-INDEPENDENT DEFINITION OF CYTOARCHITECTONIC BORDERS One of the key features of the neocortex is its organization in layers running parallel to the pial surface. Cortical layers differ by their absolute and relative widths and cell densities. The laminar pattern of a cortical area is represented by its sequence of layers, varying in cell density. Our observer-independent approach to the definition of cortical borders considers these architectonic features. It is based on the assumption that each area has a unique, homogeneous laminar pattern, which distinguishes it from those of neighbouring cortical areas. Several methods have been applied in the past for quantifying the laminar pattern. An early approach was described by Hudspeth and colleagues (1976). They analyzed optical density profiles to describe the distribution of staining intensity across cortical layers in the human primary visual cortex. Although the optical density is an easy and fast measurable parameter, it has the major disadvantage of being sensitive to differences in staining intensity of nerve cells (and of the background) in different brains and sections.
© 2002 Taylor & Francis
Architectonic Mapping
35
For technical reasons, such differences are almost inevitable in histological specimen. Variations in intensity are influenced by factors like age, clinical history, cause of death, post mortem delay, autopsy conditions, and histological techniques (Blinkov and Glezer, 1968; Haug, 1980; Skullerud, 1985; Vierordt, 1893). Based on this experience, we used the volume density of nerve cells in order to quantify the laminar pattern, a parameter with a long tradition in quantitative neurobiology (Haug, 1956; von Economo and Koskinas, 1925). It has the advantage that, within reasonable limits, it is not affected by either staining, or anisotropy (Weibel, 1979). The volume density of nerve cells was estimated as the areal fraction of all stained cellular profiles in square measuring fields of 20–30 µm and defined as gray level index (GLI) ranging from 0% to 100% (Schleicher and Zilles, 1990). Other stereological parameters (e.g. the numerical density) are also available, but we focussed on the volume density since this robust stereological parameter can be automatically estimated from existing histological series in large samples. This parameter is highly correlated with the volume density of neurones, since the density of endothelial and glial cells does not vary systematically throughout the cortical layers (Wree et al., 1982). Using a computerized image analyzer, the GLI was measured in cortical regions of interest (Figure 3.2). GLI images were achieved from which GLI profiles (= density profiles) reaching from the border between layers I and II to the border between cortex and white matter were extracted. The shape of these density profiles describes quantitatively the laminar pattern, i.e. the cytoarchitecture of a cortical area. Dissimilarities between cortical areas and their laminar patterns were reflected by differences in shape of the density profiles. The shape of a profile was numerically described by a set of ten features: the mean of the amplitude (i.e. the mean GLI; meany.o), the center of gravity in the x-direction (meanx.o), the standard deviation (sd.o), the skewness (skew.o), the kurtosis (kurt.o), and the analogous parameters for the first derivative of each profile (meany.d, meanx.d, sd.d, skew.d, kurt.d). Features are based on central moments (Dixon et al., 1988) of the original density profile, and on its first derivative, by treating the profile as a frequency distribution, whereby the cortical depth is the x-value and the GLI is the frequency value at that x-value. Features were normalized in order to weight them equally. Some features can be interpreted directly in terms of cytoarchitecture: the mean GLI increases with increasing density of cell bodies. The feature meanx.o will be smaller than 50% if the supragranular layers have a higher GLI than the infragranular layers. Vice versa, if the infragranular layers show more densely packed cell bodies than the supragranular layers, the meanx.o will be shifted to a value greater than 50%. Multivariate statistical analysis was then used in order to quantify differences in shape between profiles. The Mahalanobis distance D was used as a multivariate measure of differences in shape between neighbouring profiles for detecting cytoarchitectonic borders (Schleicher et al., 1998, 1999). The basic idea was that profiles are more or less similar in shape within a cortical area (homogeneity criterion), and the shape changes abruptly at the border of two neighbouring areas (Schleicher et al., 1995). In order to detect the position of the border, cortical regions of interest were covered by a sequence of equidistant density profiles (Figure 3.2A). The Mahalanobis distance was then calculated between two neighboring sets (= blocks) of profiles (Figure 3.2C). If these two blocks belong to one and the same area, the Mahalanobis distance was small, since differences in the laminar pattern between these two groups of profiles were small. Vice versa, if these two blocks were located exactly at opposite sides of a cortical border, the Mahalanobis distance was maximal since differences in the laminar pattern of these two groups of profiles were
© 2002 Taylor & Francis
36
Katrin Amunts, Axel Schleicher and Karl Zilles
Figure 3.2. Observer-independent definition of cytoarchitectonic borders of the visual cortex. The GLI as a measure of neuronal packing density (Wree et al., 1982) was obtained in a histological section stained for cell bodies (Zilles and Schleicher, 1980; Zilles et al., 1986; Amunts et al., 2000; Schleicher and Zilles, 1990). As a result, a GLI image [A] was produced, in which each pixel corresponds to a GLI value measured with a spatial resolution of 25 µm. Light pixels correspond to a low packing density, dark pixels to a high density. The cortical region of interest was covered by a sequence of profiles, indexed consecutively from 1 to 242 [A]. Each profile quantifies the course of the GLI from the border between layers I and II to the border between the cortex and the white matter (along a line perpendicular to the cortical surface). A multivariate distance measure, the Mahalanobis distance D, was calculated (Schleicher et al., 1998) [C]. D is a measure of difference in profile shape between neighbouring blocks of profiles; e.g. D at the position of profile 20 was calculated as the difference in shape between profiles 1–20 and profiles 21–40 [C]. Since 20 profiles of one block were compared with 20 profiles of the neighbouring block, the block size in this case was 20. D was calculated for different block sizes ranging from 8 to 24 [D]. The dots mark the positions of significant Mahalanobis distances for each block size and each position of the profile. For block size 20, significant distances were obtained from the graph of [C]. Significant values of the Mahalanobis distance are marked by red circles and lines. In this histological section, borders were quantitatively defined between areas V1 and V2d (large arrowhead at position 61), within area V2d (small arrowheads at positions 103 and 153), and between areas V2d and V3 (large arrowhead at position 173), and transferred to the original histological section [B]. The border between areas V1 and V2d corresponds to the border between Brodmann’s areas 17 and 18, that between V2d and V3 to the border between Brodmann’s areas 18 and 19 (Amunts et al., 2000; Gattass et al., 1981; Newsome and Allman, 1980; Newsome et al., 1986; Zilles and Clarke, 1997). Scal—Sulcus calcarinus. (see Color Plate 1)
© 2002 Taylor & Francis
Architectonic Mapping
37
large. After the calculation of the Mahalanobis distance for the two adjacent blocks of profiles, both blocks were shifted simultaneously by ≈128 µm (i.e. by the width of one profile) to the next position. In this manner, the Mahalanobis distance was calculated continuously for all sequential positions of all possible blocks of profiles in the region studied (Figure 3.2C). Distances were calculated for different block sizes ranging from 8 to 24 profiles per block (Figure 3.2D). They were calculated between blocks of profiles, and not between single profiles, in order to improve the signal to noise ratio. A subsequent Hotelling’s T2 test (with a Bonferroni correction of the p-values) was applied for testing the significance of each value of the Mahalanobis distance. Borders were defined at those positions of profiles where the following criteria were fulfilled. The Mahalanobis distance D is significant (Hotelling’s T2-test; α = 5%), positions with significant D were stable across different block sizes and could be followed up through neighboring histological sections. In the histological section shown in Figure 3.2, the Mahalanobis distance reaches significant values at the areal borders between areas V2d and V1 at position 61, and between areas V2d and V3 at position 173. Within area V2, the distance shows local maxima at positions 103 and 153. In our sample, internal subparcellations of V2 were associated with the presence or absence of large pyramidal cells in deep layer III. Borders within area V2 have been described in the past by several authors using cytoarchitectonic criteria (Amunts et al., 2000; von Economo and Koskinas, 1925), myeloarchitecture (Lungwitz, 1937; Sanides and Vitzthum, 1965a,b) as well as on the basis of cytochrome oxidase staining (Burkhalter and Bernardo, 1989; Clarke, 1993; Clarke and Miklossy, 1990; Gattass et al., 1997; Lewis and Olavarria, 1995; Merigan et al., 1993; Tootell and Taylor, 1995). Thus, this approach not only confirms borders between well known cytoarchitectonic areas according to Brodmann’s map, but it also detects new subdivisions. A further example is the subdivision of Brodmann’s area 4 into an anterior and a posterior part (Geyer et al., 1996). Recently, areas 3a and 3b were confirmed in cytoarchitectonic specimens (Geyer et al., 1999) using this method. These areas were first mentioned by Brodmann (1909) and later explicitly described by the Vogts in their myeloarchitectonic map (Vogt and Vogt, 1919).
3. HOW DIFFERENT ARE TWO CORTICAL AREAS IN THEIR CYTOARCHITECTURE? Whether a cortical region is homogeneous in architecture and thus constitutes a single cortical area or, alternatively, consists of two or more cortical areas, has been a matter of controversy between different observers. Consequently, the different cortical maps display a different number of cortical fields. The number reaches from 8 (Bailey and von Bonin, 1951) to more than 100 (von Economo and Koskinas, 1925; Vogt and Vogt, 1919). The analysis of interareal differences in cytoarchitectonics becomes even more complicated due to intersubject variability in architecture of the same cortical area in different brains. Cytoarchitectonic variability has been described since the early days of architectonic research (Kononova, 1938; von Economo and Koskinas, 1925). Other authors mentioned it as “considerable”, but the degree of variability was not quantified. Intersubject variability in microstructure makes it often difficult or even impossible to detect reliably subtle differences in cytoarchitecture between areas. Finally, the statement of whether several © 2002 Taylor & Francis
38
Katrin Amunts, Axel Schleicher and Karl Zilles
cortical areas are more similar (or different) in cytoarchitecture cannot be verified by using pure visual inspection. Thus, analysis of interareal differences has to be based on measurements. Most studies in the past relied on the measurement of single morphometric parameters within a certain sample, e.g. the dendritic length (Hayes and Lewis, 1996; Huttenlocher, 1979), cell sizes (Blinkov and Glezer, 1968; Hayes and Lewis, 1996; von Economo and Koskinas, 1925), the layer thickness (Amunts et al., 1995; Harasty et al., 1996; Zilles et al., 1986), the sizes of cortical areas, subcortical structures and fibre bundles (Andrews et al., 1997; Filimonoff, 1932; Geyer et al., 1999; Haug, 1987a; Kononova, 1935, 1938; Rajkowska and GoldmanRakic, 1995b; Stensaas et al., 1974). Stereological parameters have also been applied successfully (Brody, 1955; Gundersen et al., 1988; Haug, 1984, 1987a,b; Henderson et al., 1980; Pakkenberg and Gundersen, 1997; Schmitz et al., 1999; Terry et al., 1987; West, 1993; Zilles et al., 1986). Altogether, these parameters represent important, quantitative data of cortical microstructure. However, they often reflect only a single aspect of cortical microstructure (e.g. cell density) and do not consider that the cortex is a layered structure with local changes in cell density, size and number within cortical layers and sublayers. In a more recent study on the human frontal lobe, several cytoarchitectonic parameters of areas 9 and 46 were analyzed (Rajkowska and Goldman-Rakic, 1995a,b). Hereby, a three-dimensional counting method (Williams and Rakic, 1988) was applied to measure total cortical and relative laminar thicknesses, neuronal packing density per 0.001 mm3 in individual cortical layers, and sizes of neuronal somata in selected cortical layers. The analysis of these morphometric parameters revealed differences between both areas in the thickness of layer IV, in the packing density of neurones as well as in the size distribution of neurones. The authors concluded that objective cytometric methods can clearly distinguish two adjacent areas within the human prefrontal lobe. In this study, different morphometric parameters were treated and interpreted separately. It is also possible to combine cytoarchitectonic parameters and to analyze them by multivariate statistical analysis. Such an approach has been used by us in defining areal borders (see above). The multivariate approach offers the advantage that different morphometric parameters can be normalized by compensating for different scales and can be combined into one feature vector. The feature vectors are then used in a comprehensive statistical test. In addition, multivariate analyses (e.g. discriminant analysis) take into account the correlations – often high – between parameters and offer procedures to detect those parameters which contribute most to the dissociation between areas. We illustrate this multivariate approach and discuss its implication for a group of five cortical areas: Brodmann’s areas 6, 44, 45, V1 and V2. These areas were selected by the following considerations: (i) The terms Broca’s region and Broca’s area are based on functional concepts. They are used inconsistently with respect to cytoarchitecture. It is widely accepted that areas 44 and 45 constitute Broca’s region (Aboitiz and Garcia, 1997; Amunts et al., 1999; Kononova, 1949; Petrides and Pandya, 1994; Roland, 1993; Uylings et al., 1999), but the terms Broca’s region and Broca’s area are also applied for areas 44, 45 and 47 (Riegele, 1931; Vogt, 1910), as well as for area 44 only (Galaburda, 1980; von Economo and Koskinas, 1925). In some recent functional imaging studies, Broca’s region (or area) refers to a cortical region which includes area 44 and, sometimes, adjacent area 6 (Paulesu et al., 1993; Petrides et al., 1993). Recent fMRI studies
© 2002 Taylor & Francis
Architectonic Mapping
39
have provided evidence that the posterior part of Broca’s region, area 44, and the homologue region of the right hemisphere might be involved in imagery of movement (Binkofski et al., 1999, 2000). This would associate area 44 functionally with area 6. Originally, the definition was based on gross macroscopical markers, i.e. by gyri and sulci (Broca, 1861; Herve, 1888). In this context, we wanted to check whether areas 44 and 45 can be grouped together on the basis of cytoarchitectonic similarity. (ii) If areas 44 and 45 constitute Broca’s region, they should be more similar in cytoarchitecture to each other than to neighboring cortical areas, e.g. to the adjacent ventral part of area 6. (iii) It was expected that areas 44 and 45 would differ even more from areas V1 and V2 than from area 6, because V1 and V2 belong to the visual cortex and are characterized by a completely different organization of their input and output, reflected by different laminar patterns. (Dis-)similarities between these areas were quantified by calculating a multivariate distance measure between the density profiles in these areas. Profiles were obtained from cytoarchitectonic areas 6, 44, 45, V1 and V2 of ten human post mortem brains (Amunts et al., 1999, 2000). Fifteen to thirty profiles were obtained from three randomly selected sections of each area, hemisphere and brain. Thus, a total of about 3000 profiles were processed. Ten features were extracted from each of the profiles, as described above, for the definition of borders. In contrast to the latter approach, the Euclidean distance (and not the Mahalanobis distance) was used as multivariate distance measure. The advantage of the Euclidean distance for this type of analysis is that it is more sensitive in detecting the dissimilarity in architecture between cortical areas. i.e. the Euclidean distance measures the absolute distance between two centroids of the ten-dimensional space (= dissimilarity between areas), whereas the Mahalanobis distance depends not only on this distance, but also on the variability within an area. Thus, the Mahalanobis distance becomes smaller with increasing variance (Schleicher et al., 2000). The Euclidean distance was calculated in each individual brain for all ten possible combinations of two areas from the areas 44, 45, 6, V1 and V2 (= interareal differences). It was also calculated between profiles from corresponding areas of the left and the right hemisphere (= interhemispheric differences). Corresponding distances were averaged across the whole sample size. Multidimensional scaling (Systat® for Windows, Version 9, SPSS, USA) was applied for data reduction and visualization of distances between the cortical areas. Interhemispheric differences were tested statistically against differences between randomly selected profiles from one and the same area. The results are shown in Figure 3.3. The analysis showed a high degree of similarity in cytoarchitecture of areas 44 and 45. Both areas differed considerably from areas 6 as well as V1 and V2 (large distances between the centroids). Area 6 showed shorter distances to areas 44 and 45 than to V1 and V2, which may correspond to the close topographical and functional relationship between areas 44/45 and 6. On the basis of the cytoarchitectonic similarity of areas 44 and 45, these data provide an anatomical argument to combine areas 44 and 45, but not 44 and 6 into a region. Based on classical cytoarchitectonic descriptions, area 44 (which is dysgranular) takes a transitional position between area 45 (which is granular) and area 6 (which is agranular). This relationship is kept in the arrangement of the centroids in the graphs.
© 2002 Taylor & Francis
40
Katrin Amunts, Axel Schleicher and Karl Zilles
Figure 3.3. Dissimilarities of Brodmann’s areas 44, 45, 6, as well as areas V1 and V2 based on quantitative cytoarchitecture (K. Amunts, unpublished observations). Euclidean distances were calculated as multivariate measures of dissimilarity between profiles of different cortical areas (= interareal differences) and of the two hemispheres of one and the same area (= interhemispheric differences). Euclidean distance is based on features, which characterize the shape of the profiles of an area (see text). Multidimensional scaling was applied for visualization of interareal differences and for data reduction to a two-dimensional plane defined by dimension-1 and dimension-2 (Schleicher et al., 2000). The larger the dissimilarity in cytoarchitecture between two areas, the larger the distance between them in the graph. Error bars indicate standard errors. Results in this upper graph quantify interareal differences of the left hemisphere. Whereas areas 44 and 45 were found to be very similar in cytoarchitecture, both areas differed considerably from area 6 as well as from visual areas V1 and V2. Interhemispheric differences in cytoarchitecture (lower graph) were significant (marked by an asterisk) for areas 44 and 45, but not for areas 6, V1 and V2. The line marks the level of intersubject variability in cytoarchitecture. It was calculated as the average Euclidean distance between corresponding areas across different subjects (i.e. the distances in shape within the sample of 10 brains between all areas 44, all areas 45, etc. were calculated and then averaged across the brains and areas). The analysis supplemented previous findings on asymmetry in volume of area 44 (Amunts et al., 1999) and demonstrates that cytoarchitectonic asymmetry in areas 44 and 45 might be a microstructural correlate of brain lateralization and dominance for language, in particular.
© 2002 Taylor & Francis
Architectonic Mapping
41
Finally, interhemispheric distances between corresponding areas of both hemispheres were significant for areas 44 and 45, but not for areas 6, V1 and V2. That is, areas 44 and 45 revealed significant left/right differences in cytoarchitecture. Thus, in addition to asymmetry in volume of area 44 which was reported previously (Amunts et al., 1999; Galaburda, 1980), interhemispheric asymmetry was demonstrated at a microstructural level. This cytoarchitectonic asymmetry may contribute to the functional phenomenon of cerebral lateralization and dominance for language, in particular. Information obtained from analysis of interareal differences might be applied for creating hierarchies and families of cortical areas, as described for the cortex of nonhuman primates (Fellemann et al., 1997; Fellemann and Van Essen, 1991; Gattass et al., 1997; Hubel and Wiesel, 1972; Kaas, 1989; Kötter and Sommer, 2000; Nakamura et al., 1993; Peterhans and von der Heydt, 1993; Stephan et al., 2000; Xiao et al., 1999; Zeki, 1978; Zilles and Clarke, 1997). Multivariate distance analysis has been applied to detect interhemispheric cytoarchitectonic differences of the motor cortex (Amunts et al., 1996), its developmental changes (Amunts et al., 1997), interareal, interhemispheric and intersubject differences of Broca’s region (Amunts et al., 1999), as well interareal differences in receptor architecture of the mesial motor and premotor cortex in the macaque (Geyer et al., 1998) and of the human the somatosensory cortex (Geyer et al., 1997, 1999). A quantitative analysis of interareal differences in cytoarchitecture is also relevant for detecting architectonic differences between normal and pathologically altered cortical tissue. Finally, the criterion of similarity in architecture might be valuable with respect to a comparative analysis of homologies between humans and non-human primates (Petrides and Pandya, 1994).
4. MULTIMODAL MAPPING – CORRESPONDENCES AND DIFFERENCES BETWEEN CYTOARCHITECTONIC AND RECEPTOR ARCHITECTONIC BORDERS Architectonic analysis using multivariate statistics can be even more decisive when they incorporate other modalities of architecture, e.g. receptor architecture. The comparisons of receptor- and cytoarchitectonic maps provided evidence that in several cortical regions receptor architecture reveals similar architectonic parcellations as compared to cytoarchitecture. We will present here recent data from our analysis of the human visual cortex for discussing the correspondence between cytoarchitectonic and receptor-architectonic parcellation schemes. Numerous observations on the regional and laminar distribution of transmitter receptors in the human primary (V1) and secondary visual cortex (V2) have been published. Reports on receptors in other extrastriate areas are rare. (For an overview see Zilles and Clarke, 1997). This is due to the facts, that (i) human extrastriate areas are difficult to identify in cytoarchitectonic sections, (ii) the classical architectonic maps of the human occipital lobe show a much less detailed parcellation than corresponding maps of nonhuman primates, and (iii) receptor architectonics of the human occipital lobe require extraordinary large cryostat sections of unfixed, frozen brains, which are difficult to handle. Our own observations in nonhuman primates and human post-mortem brains, as well as recent data from the literature, provide evidence that receptor architectonic mapping is a promising approach to human brain mapping (Bonaventure et al., 2000; d’Argy et al., 1988; Geyer et al., 1997,
© 2002 Taylor & Francis
42
Katrin Amunts, Axel Schleicher and Karl Zilles
1998; King et al., 1995; Pazos et al., 1987a,b; Thoss et al., 1996; Zezula et al., 1988; Zilles et al., 1991a,b,d, 1995, 1988). As an example, results of receptor-architectonic mapping of receptor binding sites of the glutamatergic kainate receptor (ligand: [3H]kainate), the muscarinic M1 (ligand: [3H]pirenzipine) and M2 (ligand: [3H]oxotremorine-M) receptors as well as the 5-HT2 receptor (ligand: [3H]ketanserin) of human areas V1, V2v, V2d, V3d and V3a are shown (Figure 3.4, K. Zilles, unpublished observations). We used standardized protocols for the visualization of a variety of different receptors and of cytoarchitecture in a series of adjacent sections (Zilles et al., 1995), thus providing reliable data for cortical parcellation and for evaluation of areal borders. After contrast enhancement and colour coding, differences in regional and laminar receptor distributions reveal borders between visual areas which match to those in cyto- and myeloarchitecture, and provide insight into the neurochemical aspects of cortical organization in the occipital lobe. In detail, the border between areas V1 and V2 was characterized by a prominent change in density and laminar pattern in almost all ligands as well as in cyto- and myeloarchitecture (Figure 3.4A–F). In correspondence to cytoarchitectonic mapping, V1 was located on the upper and the lower bank of the calcarine sulcus and extended to the mesial surface of the cuneus (Amunts et al., 2000; Filimonoff, 1932; Stensaas et al., 1974). The cuneal extension was highly variable between different brains. For kainate receptors (Figure 3.4A), the infragranular layers displayed a higher density in V1 than in V2, whereas the supragranular layers had a lower density in V1 than in V2. The dorsal and ventral borders of V1 coincided precisely with the borders found in the autoradiographs of 5-HT2 (Figure 3.4B), M1 (Figure 3.4C) and M2 (Figure 3.4D) receptors as well as in cyto- (Figure 3.4E) and myeloarchitecture (Figure 3.4F). The border between areas V2d and V3d was clearly established by kainate, 5-HT2 and M2 receptor autoradiography, but less obviously by the M1 receptor. M2 and 5-HT2 receptor densities and laminar patterns showed a clear border between V2v and the adjacent ventral V3, whereas more subtle changes were seen in kainate and M1 receptor autoradiographs. M2 receptors revealed a distinct subdivision of area V3 into V3d and V3A, which was absent in the other receptor autoradiographs and detected only with difficulty by simple visual inspection in cytoarchitectonic sections. In the myeloarchitectonic section, this border could be identified by a higher myelin density in supragranular layers of area V3d than of V3A (Figure 3.4F). The different receptors revealed corresponding areal borders, which coincided with the cyto- or myeloarchitectonic borders. Coincidence in position of borders defined by receptor autoradiography has been proven in an observer-independent fashion using the same method as described above for cytoarchitecture (Schleicher et al., 1998). Some receptors, however, did not reveal all borders seen with other receptors. The 5-HT2 receptor and the 5-HT1A receptors (images of the regional distribution of the latter are not presented here) are examples, which display a more homogenous distribution in the occipital lobe than that of other receptors. For instance, only minor differences in receptor density were found between areas V2 and VP/V3. These receptors, however, showed a pronounced heterogeneity in the motor cortex, where they make visible the border between motor and premotor areas (Zilles et al., 1995). In contrast, the GABAa receptor is heterogeneously distributed even within area V1 (Zilles and Schleicher, 1993). It shows periodically distributed patches in layers II/IVc, which might be related to cytochrome oxidase blobs and ocular dominance columns (Hendrickson et al., 1981). © 2002 Taylor & Francis
Architectonic Mapping 3
[ H] kainate binding sites
3
M1 receptor [ H] pirenzepin binding sites
43
3
5-HT2 receptor [ H] ketanserin binding sites
3
M2 receptor [ H] oxotremorine binding sites
Figure 3.4. Regional distributions of kainate [A], 5-HT2 [B] and muscarinic M1 [C] and M2 [D] receptors (demonstrated with [3H]kainate, [3H]ketanserin, [3H]pirenzipine and [3H]oxotremorine-M binding; K. Zilles, unpublished observations) in coronal sections through the human occipital lobe. Grey value images were scaled to receptor densities, enhanced in contrast, and colour coded. The colour scales indicate the receptor densities in fmol/mg protein. The receptors are heterogeneously distributed, and therefore allow mapping of striate and extrastriate areas including V1, V2v, V2d, V3d, V3A. Borders obtained by receptor achitectonic mapping were compared with borders revealed by cytoarchitectonic [E] and myeloarchitectonic [F] mapping. Note the pronounced differences in receptor density [A–D] as well as in the laminar pattern of cell packing density [E] and myeloarchitecture [F] at the borders of area V1 to V2. The border between area V2d and V3d is most pronounced in A, B and D mapping. In E and F, it is recognizable only at higher magnification; it can be detected by the observer-independent approach for border definition. The border between V3d and V3A is clearly marked in D and F and can be verified quantitatively also in E. Thus, different architectonic techniques supplement each other and are the basis of multimodal architectonic mapping. SCal –Sulcus calcarinus, SLin—Sulcus lingualis, SCol—Sulcus collateralis, SOS—Sulcus occipitalis superior. (see Color Plate 2) © 2002 Taylor & Francis
44
Katrin Amunts, Axel Schleicher and Karl Zilles
Finally, the parcellations revealed by quantitative cyto-, myelo- and receptor architectonic techniques revealed a more subtle subdivision of the human occipital lobe than shown in the classical architectonic maps from the beginning of the 20th century. They seem to correspond to parcellations observed in functional imaging (DeYoe et al., 1996; Engel et al., 1997; Gulyas and Roland, 1994; Hadjikhani and Tootell, 2000; Haxby et al., 1994; Larsson et al., 1999; Sereno et al., 1995; Shipp et al., 1995; Tootell et al., 1995, 1997; Watson et al., 1993) and nonhuman primate studies (Fellemann et al., 1997; Fellemann and Van Essen, 1991; Gattass et al., 1997; Hubel and Wiesel, 1972; Kaas, 1989, 1993; Nakamura et al., 1993; Peterhans and von der Heydt, 1993; Xiao et al., 1999; Zeki, 1978; Zilles and Clarke, 1997).
5. CONCLUSION AND PERSPECTIVES Although the parcellation of the human cerebral cortex is still far from complete, receptor and quantitative cytoarchitectonic findings provide new criteria for a detailed mapping, which cannot be achieved by cytoarchitectonic analysis alone. If the borders between receptor architectonic areas are established, receptor densities for distinct areas and layers can be calculated. This information can be used to create receptor “fingerprints” of cortical areas (Geyer et al., 1998), which represent the densities of a set of several receptors in a defined cortical area as a multivariate polar plot of receptor densities. First results have shown that these fingerprints have a similar shape in functionally-related areas, but a different shape if functionally differing areas are compared. What is a cortical area? This question provides an important argument for multimodal architectonic mapping, since not all subparcellations of the cerebral cortex constitute a cortical area. For instance, the subdivision of areas V1 and V2 into blob and interblob regions (Livingstone and Hubel, 1987; Roe and Tso, 1995; Tootell et al., 1983; Wong-Riley et al., 1993) reflects differences in colour and orientation selectivity (V1) and receptive field properties (V2), but these regions do not constitute cortical areas. Additional examples are the somatotopy of the motor and somatosensory cortex, the tonotopical organisation of the auditory cortex, each of which represents a functional segregation without representing an architectonic entity. The isolated analysis of only one aspect of cortical organization without consideration of other mapping techniques, would lead to an over-parcellation of the cerebral cortex. The multimodal approach proposed here avoids this problem by providing an overview of the different hierarchical levels (e.g. cytoarchitectonic or receptor architectonic families of cortical areas) of the cortical organization. An important perspective for a functionally-relevant architectonic parcellation of the cortex arises from the integration of architectonic maps with recent PET, fMRI and MEG studies in a common spatial reference system and database, e.g., the European Computerized Human Brain Database ECHBD (Roland et al., 1999; Roland and Zilles, 1994, 1996a,b, 1998). Borders verified in an observer-independent manner for the sensorimotor (Geyer et al., 1996, 1999; Grefkes et al., 2001) and auditory cortex (Morosan et al., 2001), Broca’s region (Amunts et al., 1999), and the visual cortex (Amunts et al., 2000) have been defined in ten human post mortem brains. The areal borders were labeled in corresponding digitized histological sections, and subjected to 3-D reconstruction. These reconstructions of the histological sections and of the cytoarchitectonic areas were warped to the format of the standard reference brain of the ECHBD (Roland et al., 1999; Roland
© 2002 Taylor & Francis
Architectonic Mapping
45
and Zilles, 1996a,b; Schormann et al., 1999b). A modified “principal axes” theory and a movement model for large deformations were applied for the warping of the 3-D datasets of post mortem brains to the standard brain, in order to compensate for intersubject variability in extent, shape and sulcal pattern of the brains, and to achieve a maximal anatomical overlap between the different post mortem brains and the reference brain (Schormann et al., 1997, 1999a; Schormann and Zilles, 1997, 1998). Individual cortical maps were superimposed in the standard reference brain (Schormann et al., 1999b). The overlapping cortical areas of individual brains and their architectonic areas in the standard reference brain result in probability or population maps, which display the intersubject variability in the extent, shape and topography of cortical areas. Variability of macroscopical features (e.g. hemispheric shape or sulcal pattern) has been analyzed in numerous previous studies (Dumoulin et al., 1998; Kennedy et al., 1998; Rademacher et al., 1993; Thompson et al., 1996, 1998; Westbury et al., 1999; Zilles et al., 1997). (For a more comprehensive review concerning this aspect see chapter by Rademacher in this book.) In contrast to traditional brain atlases and to the atlas of Talairach and Tournoux (1988), the ECHBD is (i) a real 3-D representation of cortical areas, (ii) it is based on an observerindependent architectonic definition of cortical areas, and (iii) it provides quantitative information on intersubject variability of the topography of each cortical area. Using its common spatial reference system for combined analysis of PET and cytoarchitectonic data, it has been shown that the processing of both real and illusory contours activates area V2 (Larsson et al., 1999). Another example is the cytoarchitectonic subdivision of area 4 into an anterior and a posterior part, which were first observed in receptor architectonic sections (Zilles et al., 1995), then defined in cytoarchitectonic sections and superimposed with PET data, which allowed one to correlate these areas with functional differences (Geyer et al., 1996). Further combined cytoarchitectonic/functional imaging studies have been performed in the sensorimotor and visual cortices (Bodegard et al., 2000a,b; Naito et al., 1999, 2000). Since the ECHBD has an open structure, it allows the integration of additional modalities of architectonic data (e.g. receptor architectonic, myeloarchitectonic) and of further functional imaging data. In conclusion, architectonic brain mapping has become more objective and less observerdependent. Recent marker techniques, e.g. receptor-autoradiography of different receptors, immuno- and enzyme-histochemical methods have added functional meaningful information. Multimodal mapping and quantitative analysis of interareal differences promote a new and more complex concept of a cortical area. Finally, by applying recent 3-D probabilistic atlas systems, the combined analysis of architectonic maps and functional imaging studies allows the testing of the functional significance of architectonic parcellations and a systematic search for new, functionally relevant cortical areas. Thus, architectonic brain mapping has changed considerably during the last 100 years and opens exciting perspectives for the future.
ACKNOWLEDGEMENTS Published and unpublished work by the authors reviewed in this chapter was supported by the Deutsche Forschungsgemeinschaft (SFB 194/A6), the BioTech program of the EC and the Human Brain Project (P20-MHDA52176) funded by the National Institute of Mental Health, National Institute for Drug Abuse, and the National Cancer Institute. The authors
© 2002 Taylor & Francis
46
Katrin Amunts, Axel Schleicher and Karl Zilles
thank Aleksandar Malikovic, Nicola Palomero-Gallagher, Ursula Blohm, Brigitte Machus and Renate Dohm for assistance in preparing the histological sections and the autoradiographs.
REFERENCES Aboitiz, F. and Garcia, G.L. (1997) The evolutionary origin of language areas in the human brain. A neuroanatomical perspective. Brain Research Reviews, 25, 381–396. Amunts, K., Istomin, V.V., Schleicher, A. and Zilles, K. (1995) The postnatal development of the human primary motor cortex: a quantitative cytoarchitectonic analysis. Anatomy and Embryology, 192, 557–571. Amunts, K., Schlaug, G., Schleicher, A., Steinmetz, H., Dabringhaus, A., Roland, P.E. and Zilles, K. (1996) Asymmetry in the human motor cortex and handedness. Neuroimage, 4, 216–222. Amunts, K., Schmidt-Passos, F., Schleicher, A. and Zilles, K. (1997) Postnatal development of interhemispheric asymmetry in the cytoarchitecture of human area 4. Anatomy and Embryology, 196, 393–402. Amunts, K., Schleicher, A., Bürgel, U., Mohlberg, H., Uylings, H.B.M. and Zilles, K. (1999) Broca’s region revisited: Cytoarchitecture and intersubject variability. Journal of Comparative Neurology, 412, 319–341. Amunts, K., Malikovic, A., Mohlberg, H., Schormann, T. and Zilles, K. (2000) Brodmann’s areas 17 and 18 brought into stereotaxic space – where and how variable? Neuroimage, 11, 66–84. Andrews, T.J., Halpern, S.C. and Purves, D. (1997) Correlated size variations in human visual cortex, lateral geniculate nucleus, and optic tract. Journal of Neuroscience, 17, 1859–2868. Bailey, P. and von Bonin, G. (1951) The isocortex of man. Urbana: University of Illinois Press. Betz, W. (1874) Anatomischer Nachweis zweier Gehirncentra. Centralblatt für die medizinischen Wissenschaften, 12, 578–580, 594–599. Bidmon, H.J., Wu, J.Y., Godecke, A., Schleicher, A., Mayer, B. and Zilles, K. (1997) Nitric oxide synthaseexpressing neurons are area-specifically distributed within the cerebral cortex of the rat. Neuroscience, 81, 321–330. Binkofski, F., Buccino, G., Posse, S., Seitz, R., Rizzolatti, G. and Freund, H.-J. (1999) A fronto-parietal circuit for object manipulation in man: evidence from an fMRI-study. European Journal of Neuroscience, 11, 3276–3286. Binkofski, F., Amunts, K., Stephan, K.M., Posse, S., Schormann, T., Freund, H.-J., Zilles, K. and Seitz, R. (2000) Broca’s region subserves recognition of action. Human Brain Mapping, 11, 273–285. Blinkov, S.M. and Glezer, I.I. (1968) Das Zentralnervensystem in Zahlen und Tabellen. Jena: Fischer. Bodegard, A., Geyer, S., Naito, E., Zilles, K. and Roland, P.E. (2000a) Somatosensory areas in men activated by moving stimuli: cytoarchitectonic mapping and PET. NeuroReport, 11, 187–191. Bodegard, A., Ledberg, A., Geyer, S., Naito, E., Zilles, K. and Roland, P.E. (2000b) Object shape differences reflected by somatosensory cortical activation in human. Journal of Neuroscience, 20 RC 51, 1–5. Bonaventure, P., Hall, H., Gommeren, W., Cras, P., Langlois, X., Jurzak, M. and Leysen, J.E. (2000) Mapping of serotonin 5-HT(4) receptor mRNA and ligand binding sites in the post-mortem human brain. Synapse, 36, 35–46. Braak, H. (1977) The pigment architecture of the human occipital lobe. Anatomy and Embryology, 150, 229–250. Braak, H. (1979) The pigment architecture of the human frontal lobe. Anatomy and Embryology, 157, 35–68. Broca, M.P. (1861) Remarques sur le siége de la faculté du langage articulé, suivies d’une observation d’aphemie (Perte de la Parole). Bulletins et memoires de la Societe Anatomique de Paris, 36, 330–357. Brodmann, K. (1903) Beiträge zur histologischen Lokalisation der Grosshirnrinde. II: Der Calcarinustyp. Journal für Psychologie und Neurologie, II, 133–159. Brodmann, K. (1905) Beiträge zur histologischen Lokalisation der Groβhirnrinde. V: Über den allgemeinen Bauplan des Cortex pallii bei den Mammaliern und zwei homologe Rindenfelder im besonderen. Zugleich ein Beitrag zur Furchenlehre. Journal für Psychologie und Neurologie, VI, 275–303. Brodmann, K. (1908) Beiträge zur histologischen Lokalisation der Groβhirnrinde. VI: Die Cortexgliederung des Menschen. Journal für Psychologie und Neurologie, X, 231–246. Brodmann, K. (1909) Vergleichende Lokalisationslehre der Groβhirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues. Leipzig: Barth JA. Brody, H. (1955) Organisation of the cerebral cortex. Part 3. A study of aging in the human cerebral cortex. Journal of Comparative Neurology, 102, 551–556. Burkhalter, A. and Bernardo, K.L. (1989) Organization of cortico-cortical connections in human visual cortex. Proceedings of the National Academy of Scence, U.S.A., 13, 1916–1931. Campbell, A.W. (1905) Histological studies on the localisation of cerebral function, Cambridge: Cambridge University Press.
© 2002 Taylor & Francis
Architectonic Mapping
47
Campbell, M. and Morrison, J.H. (1989) Monoclonal antibody to neurofilament protein (SMI-32) labels a subpopulation of pyramidal neurons in the human and monkey neocortex. Journal of Comparative Neurology, 282, 191–205. Clarke, S. (1993) Callosal connections and functional subdivision of the human occipital lobe. In: B. Gulyas, D. Ottoson and P. Roland (eds), Functional organization of the human visual cortex. Oxford: Pergamon Press, pp. 137–149. Clarke, S. (1994) Modular organization of human extrastriate visual cortex: Evidence from cytochrome oxidase pattern in normal and macular degeneration cases. European Journal of Neuroscience, 6, 725–736. Clarke, S. and Miklossy, J. (1990) Occipital cortex in man: Organization of callosal connections, related cytoand myeloarchitecture, and putative boundaries of functional visual areas. Journal of Comparative Neurology, 298, 188–214. d’Argy, R., Gillberg, P.G., Stalnacke, C.G., Persson, A., Bergstrom, M., Langstrom, B., Schoeps, K.O. and Aquilonius, S.M. (1988) In vivo and in vitro receptor autoradiography of the human brain using an 11Clabelled benzodiazepine analogue. Neuroscience Letters, 85, 304–310. DeYoe, E.A., Carman, G.J., Bandettini, P., Glickman, S., Wieser, J., Cox, R., Miller, D. and Neitz, J. (1996) Mapping striate and extrastriate visual areas in human cerebral cortex. Proceedings of the National Academy of Science, U.S.A., 93, 2382–2386. Dietl, M.M., Probst, A. and Palacios, J.M. (1987) On the distribution of cholecytokinin receptor binding in the human brain: an autoradioraphic study. Synapse, 1, 169–183. Dixon, W.J., Brown, M.B., Engelman, L., Hill, M.A. and Jennrich, R.I. (1988) BMDP. Statistical software manual, Berkley: University of California Press. Dumoulin, S.O., Bittar, R.G., Kabani, N.J., Baker, C.L., LeGoualher, G., Pike, G.B. and Evans, A.C. (1998) Quantification of the variability of human area v5/MT in relation to the sulcal pattern in the parietotemporo-occipital cortex: a new anatomical landmark. Neuroimage, 7, S319-S319. Elliot Smith, G. (1907) A new topographical survey of the human cerebral cortex, being an account of the distribution of the anatomically distinct cortical areas and their relationship to the cerebral sulci. Journal of Anatomy, 41, 237–254. Engel, S.A., Glover, G.H. and Wandell, B.A. (1997) Retinotopic organization in human visual cortex and the spatial precision of functional MRI. Cerebral Cortex, 7, 181–192. Fellemann, D.J. and Van Essen, D.C. (1991) Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1–47. Fellemann, D.J., Burkhalter, A. and Van Essen, D.C. (1997) Cortical connections of areas V3 and VP of Macacque monkey. Extrastriate visual cortex. Journal of Comparative Neurology, 379, 21–47. Filimonoff, I.N. (1932) Über die Variabilität der Groβhirnrindenstruktur. Mitteilung II – Regio occipitalis beim erwachsenen Menschen. Journal für Psychologie und Neurologie, 44, 2–96. Flechsig, P. (1898) Neue Untersuchungen über die Marbildung in den menschlichen Grosshirnlappen. Neurologisches Zentralblatt (Leipzig), 21, 977–996. Fritsch, G. and Hitzig, E. (1870) Über die elektrische Erregbarkeit des Grosshirns. Archiv für Anatomie, Physiologie und wissenschaftliche Medizin, Jg. 1870, 300–332. Galaburda, A.M. (1980) La region de Broca: Observations anatomiques faites un siècle après la mort de son découvreur. Revue Neurologique (Paris), 136, 609–616. Gattass, R., Gross, C.G. and Sandell, J.H. (1981) Visual topography of V2 in the macaque. Journal of Comparative Neurology, 201, 519–539. Gattass, R., Sousa, A.P., Mishkin, M. and Ungerleider, L.G. (1997) Cortical projections of area v2 in the macaque. Cerebral Cortex, 7, 110–129. Geyer, S., Ledberg, A., Schleicher, A., Kinomura, S., Schormann, T., Bürgel, U., Klingberg, T., Larsson, J., Zilles, K. and Roland, P.E. (1996) Two different areas within the primary motor cortex of man. Nature (London), 382, 805–807. Geyer, S., Schleicher, A. and Zilles, K. (1997) The somatosensory cortex of human: Cytoarchitecture and regional distributions of receptor-binding sites. Neuroimage, 6, 27–45. Geyer, S., Matelli, M., Luppino, G., Schleicher, A., Jansen, Y., Palomero-Gallagher, N. and Zilles, K. (1998) Receptor autoradiographic mapping of the mesial and premotor cortex of the macaque monkey. Journal of Comparative Neurology, 397, 231–250. Geyer, S., Schleicher, A. and Zilles, K. (1999) Areas 3a, 3b, and 1 of human primary somatosensory cortex: I. Microstructural organisation and interindividual variability. Neuroimage, 10, 63–83. Grefkes, C., Geyer, S., Schormann, T., Roland, P. and Zilles, K. (2001) Human somatosensory area 2: Observerindependent cytoarchitectonic mapping, interindividual variability, and population map. Neuroimage, 14, 617–632. Gulyas, B. and Roland, P.E. (1994) Processing and analysis of form, color and binocular disparity in the human brain: functional anatomy by positron emission tomography. European Journal of Neuroscience, 6, 725–736.
© 2002 Taylor & Francis
48
Katrin Amunts, Axel Schleicher and Karl Zilles
Gundersen, H.J.G., Bendtsen, T.F., Korbo, L., Marcussen, N., Moeller, A., Nielsen, K., Nyengaard, Pakkenberg, B., Sörensen, F.B., Vesterby, A. and West, M.J. (1988) Some new, simple and efficient stereological methods and their use in pathological research and diagnosis. Acta Pathologica Microbiologica et Immunologica Scandinavica, 96, 379–394. Hadjikhani, N.K. and Tootell, R.B.H. (2000) Projection of rods and cones within human visual cortex. Human Brain Mapping, 9, 55–63. Harasty, J., Halliday, G.M. and Kril, J.J. (1996) Reproducible sampling regimen for specific cortical regions: application to speech-related areas. Journal of Neuroscience Methods, 67, 43–51. Haug, H. (1956) Remarks on the determination and significance of the gray cell coefficient. Journal of Comparative Neurology, 104, 473–492. Haug, H. (1980) Die Abhängigkeit der Einbettungsschrumpfung des Gehirngewebes vom Lebensalter. Verhandlungen der Anatomisches Gesellschaft, 74, 699–700. Haug, H. (1984) Macroscopic and microscopic morphometry of the human brain and cortex. A survey in the light of new results. Brain Pathology, 1, 123–149. Haug, H. (1987a) Brain sizes, surfaces, and neuronal sizes of the cortex cerebri: a stereological investigation of man and his variability and a comparison with some mammals (primates, whales, marsupials, insectivores, and one elephant). American Journal of Anatomy, 180, 126–142. Haug, H. (1987b) Die menschliche Hirnrinde—Architektur und Daten. In: E.H. Graul, S. Pütter and D. Loew (eds), Natürliche und künstliche Intelligenz, Kunstfehler—Regresse—Sterbehilfe, (series: Das Gehirn und seine Erkrankungen, Vol. 17). Iserlohn: Medice, pp. 137–164. Haxby, J.V., Horrwitz, B., Ungerleider, L.G., Maisog, J.M., Pietrini, P. and Grady, C.L. (1994) The functional organization of human extrastriate cortex: a PET-rCBF study of selective attention to faces and locations. Journal of Neuroscience, 14, 6336–6353. Hayes, T.L. and Lewis, D.A. (1996) Magnopyramidal neurons in the anterior motor speech region. Archives of Neurology, 53, 1277–1283. Henderson, G., Tomlinson, B.E. and Gibson, P.H. (1980) Cell count in human cerebral cortex in normal adults throughout life using a image analysing computer. Journal of Neurological Science, 46, 113–136. Hendrickson, A.E., Hunt, S.P. and Wu, J.Y. (1981) Immunocytochemical localization of glutamic acid decarboxylase in monkey striate cortex. Nature (London), 292, 605–607. Hendry, S.H.C., Huntsman, M., Vinuela, A., Möhler, H., DeBlas, A. and Jones, E. (1994) GABA a receptor subunit immunoreactivity in primate visual cortex: Distribution in macaques and humans and regulation by visual input in adulthood. Journal of Neuroscience, 10, 2438–2450. Herve, G. (1888) La circonvolution de Broca. Etude de morphologie cerebrale. Paris: Davy, A. Hubel, D.H. and Wiesel, T. (1972) Laminar and columnar distribution of geniculo-cortical fibers in the macaque monkey. Journal of Comparative Neurology, 146, 421–450. Hudspeth, A.J., Ruark, J.E. and Kelly, J.P. (1976) Cytoarchitectonic mapping by microdensitometry. Proceedings of the National Academy of Science, U.S.A., 73, 2928–2931. Huttenlocher, P.R. (1979) Synaptic density in human frontal cortex—developmental changes and effects of aging. Brain Research, 163, 195–205. Jansen, K.L.R., Faull, R.L.M. and Dragunow, M. (1989) Excitatory amino acid receptors in the human cerebral cortex: A quantitative auroradiographic study comparing the distribution of [3H]TCP, [3H]glycine, L-[3H]glutamate, [3H]AMPA and [3H]kainic acid binding sites. Journal of Neuroscience, 32, 587–607. Kaas, J.H. (1989) Why does the brain have so many visual areas? Journal of Cognitive Neuroscience, 1, 121–135. Kaas, J.H. (1993) The organization of visual cortex in primates: problems, conclusions, and the use of comparative studies understanding the human brain. In: B. Gulyas, D. Ottoson and P.E. Roland (eds), Functional organization of the human visual cortex. Oxford: Pergamon Press, pp. 1–12. Kennedy, D.N., Lange, N., Makris, N., Bates, J., Meyer, J. and Caviness, V.J. (1998) Gyri of the human neocortex: an MRI-based analysis of volume and variance. Cerebral Cortex, 8, 372–384. King, P.R., Gundlach, A.L. and Louis, W.J. (1995) Quantitative autoradiographic localization in rat brain of alpha 2-adrenergic and non-adrenergic I-receptor binding sites labelled by [3H]rilmenidine. Brain Research, 675, 264–278. Kleist, K. (1934) Gehirnpathologie. Leipzig: Barth. Kononova, E.P. (1935) Structural variability of the cortex cerebri. Inferior frontal gyrus in adults (Russian). In: S.A. Sarkisov and I.N. Filimonoff (eds), Annals of the Brain Research Institute. Vol. I. Moscow-Leningrad: State Press for Biological and Medical Literature, pp. 49–118. Kononova, E.P. (1938) Variability in the structure of the human cerebral cortex. Frontal lobe of adult man (Russian). In: S.A. Sarkisov and I.N. Filimonoff (eds), Annuals of the Brain Research Institute. Vol. III–IV., Moscow-Leningrad: State Press for Biological and Medical Literature, pp. 213–274. Kononova, E.P. (1949) The frontal lobe (Russian). In: S.A. Sarkisov, I.N. Filimonoff and N.S. Preobrashenskaya, The Cytoarchitecture of the Human Cortex Cerebri. Moscow: Medgiz, pp. 309–343.
© 2002 Taylor & Francis
Architectonic Mapping
49
Kötter, R. and Sommer, F.T. (2000) Global relationship between anatomical connectivity and activity propagation in the cerebral cortex. Philosophical Transactions of the Royal Society, London, series B, 355, 127–134. Larsson, J., Amunts, K., Gulyas, B., Malikovic, A., Zilles, K. and Roland, P.E. (1999) Neuronal correlates of real and illusory contour perception: functional anatomy with PET. European Journal of Neuroscience, 11, 4024–4036. Lewis, J.W. and Olavarria, J.F. (1995) Two rules for callosal connectivity in striate cortex of the rat. Journal of Comparative Neurology, 361, 119–137. Livingstone, M.S. and Hubel, D.H. (1987) Connections between layer 4B of area 17 and the thick cytochrome oxidase stripes of area 18 in the squirrel monkey. Journal of Neuroscience, 7, 3371–3377. Lungwitz, W. (1937) Zur myeloarchitektonischen Untergliederung der menschlichen Area praeoccipitalis (Area 19 Brodmann). Journal für Psychologie und Neurologie, 47, 607–638. Luppino, G., Matelli, M., Camarda, R.M., Gallese, V. and Rizzolatti, G. (1991) Multiple representations of body movements in mesial area 6 and the adjacent cingulate cortex: An intracortical microstimulation study in the macaque monkey. Journal of Comparative Neurology, 311, 463–482. Matelli, M., Luppino, G. and Rizzolatti, G. (1991) Architecture of superior and mesial area 6 and the adjacent cingulate cortex in the macaque monkey. Journal of Comparative Neurology, 311, 445–462. Merigan, W.H., Nealey, T.A. and Maunsell, J.R. (1993) Visual effects of lesions of cortical area v2 in macacques. Journal of Neuroscience, 13, 3180–3191. Morosan, P., Rademacher, J., Schleicher, A., Amunts, K., Schormann, T. and Zilles, K. (2001) Human primary auditory cortex: Cytoarchitectonic subdivisions and mapping into a special reference system. Neuroimage, 13, 684–701. Naito, E., Ehrsson, H.H., Geyer, S., Zilles, K. and Roland, P.E. (1999) Illusory arm movements activate cortical motor areas: a positron emission tomography study. Journal of Neuroscience, 19, 6134–6144. Naito, E., Kinomura, S., Geyer, S., Kawashima, R., Roland, P.E. and Zilles, K. (2000) Fast reaction to different sensory modalities activate common fields in the motor areas, but the anterior cingulate cortex is involved in the speed of reaction. Journal of Neurophysiology, 83, 1701–1709. Nakamura, H., Gattass, R., Desimone, R. and Ungerleider, L.G. (1993) The modular organization of projections from areas V1 and V2 to areas V4 and TEO in macaques. Journal of Neuroscience, 13, 3681–3691. Newsome, W.T. and Allman, J.M. (1980) Interhemispheric connections of the visual cortex in the owl monkey, Aotus trivirgatus, and the bushbaby. Journal of Comparative Neurology, 194, 209–233. Newsome, W.T., Maunsell, J.H.R. and Van Essen, D.C. (1986) Ventral posterior visual area of the macaque: visual topography and areal boundaries. Journal of Comparative Neurology, 252, 139–153. Pakkenberg, B. and Gundersen, H.J.G. (1997) Neocortical neuron number in humans: Effect of sex and age. Journal of Comparative Neurology, 384, 312–320. Paulesu, E., Frith, C.D. and Frackowiak, R.S.J. (1993) The neural correlates of the verbal component of working memory. Nature (London), 362, 342–345. Pazos, A., Probst, A. and Palacios, J.M. (1987a) Serotonin receptors in the human brain—III. Autoradiographic mapping of serotonin-1 receptors. Neuroscience, 21, 97–122. Pazos, A., Probst, A. and Palacios, J.M. (1987b) Serotonin receptors in the human brain—IV. Autoradiographic mapping of serotonin-2 receptors. Neuroscience 21, 123–139. Peterhans, E. and von der Heydt, R. (1993) Functional organization of area V2 in the alert monkey. European Journal of Neuroscience, 5, 509–524. Petrides, M., Alivisatos, B., Meyer, E. and Evans, A.C. (1993) Functional activation of the human frontal cortex during the performance of verbal working memory tasks. Proceedings of the National Academy of Science, U.S.A., 90, 878–882. Petrides, M. and Pandya, D.N. (1994) Comparative architectonic analysis of the human and the macaque frontal cortex. In: F. Boller and J. Grafman (eds), Handbook of Neuropsychology. New York: Elsevier, pp. 17–58. Rademacher, J., Caviness, J., Steinmetz, H. and Galaburda, A.M. (1993) Topographical variation of the human primary cortices: implications for neuroimaging, brain mapping, and neurobiology. Cerebral Cortex, 3, 313–329. Rajkowska, G. and Goldman-Rakic, P.S. (1995a) Cytoarchitectonic definition of prefrontal areas in the normal human cortex: I. Remapping of areas 9 and 46 using quantitative criteria. Cerebral Cortex, 5, 307–322. Rajkowska, G. and Goldman-Rakic, P.S. (1995b) Cytoarchitectonic definition of prefrontal areas in the normal human cortex: II. Variability in locations of areas 9 and 46 and relationship to the Talairach coordinate system. Cerebral Cortex, 5, 323–337. Riegele, L. (1931) Die Cytoarchitektonik der Felder der Broca’schen Region. Journal für Psychologie und Neurologie, 42, 496–514. Roe, A.W. and Tso, D.Y. (1995) Visual topography in primate v2: multiple representation across functional stripes. Journal of Neuroscience, 15, 3689–3715. Roland, P.E. (1993) Brain activation. New York: Wiley-Liss. Roland, P.E. and Zilles, K. (1994) Brain atlases – a new research tool. Trends in Neuroscience, 17, 458–467.
© 2002 Taylor & Francis
50
Katrin Amunts, Axel Schleicher and Karl Zilles
Roland, P.E. and Zilles, K. (1996a) Functions and structures of the motor cortices in human. Current Opinion in Neurobiology, 6, 773–781. Roland, P.E. and Zilles, K. (1996b) The developing European computerized human brain database for all imaging modalities. Neuroimage, 4, S39–S47. Roland, P.E. and Zilles, K. (1998) Structural divisions and functional fields in the human cerebral cortex. Brain Research Reviews, 26, 87–105. Roland, P.E., Fredriksson, J., Svensson, P., Amunts, K., Cavada, C., Hari, R., Cowey, A., Crivello, F., Geyer, S., Kostopoulos, J., Mazoyer, B., Poppelwell, D., Schleicher, A., Schormann, T., Seppa, M., Uylings, H., deVos, K. and Zilles, K. (1999) ECHBD, A Database for functional-structural and functional-functional relations. Neuroimage, 8, 128. Sanides, F. (1962) The architecture of the human frontal lobe and the relation to its functional differentiation. International Journal of Neurology, 5, 247–261. Sanides, F. (1964) The cyto-myeloarchitecture of the human frontal lobe, and its relation to phylogenetic differentiation of the cerebral cortex. Journal für Hirnforschung, 6, 269–282. Sanides, F. and Vitzthum, H.G. (1965a) Die Grenzerscheinungen am Rande der menschlichen Sehrinde. Deutsche Zeitschrift für Nervenheilkunde, 187, 708–719. Sanides, F. and Vitzthum, H.G. (1965b) Zur Architektonik der menschlichen Sehrinde und den Prinzipien ihrer Entwicklung. Deutsche Zeitschrift für Nervenheilkunde, 187, 680–707. Sarkisov, S.A., Filimonoff, I.N. and Preobrashenskaya, N.S. (1949) Cytoarchitecture of the human cortex cerebri (Russ.). Moscow: Medgiz. Schleicher, A. and Zilles, K. (1990) A quantitative approach to cytoarchitectonics: analysis of structural inhomogeneities in nervous tissue using an image analyser. Journal of Microscopy, 157, 367–381. Schleicher, A., Amunts, K., Geyer, S., Simon, U., Zilles, K. and Roland, P.E. (1995) A method of observerindependent cytoarchitectonic mapping of the human cortex. Human Brain Mapping, Suppl. 1, 77. Schleicher, A., Amunts, K., Geyer, S., Kowalski, T. and Zilles, K. (1998) An observer-independent cytoarchitectonic mapping of the human cortex using a stereological approach. Acta Stereologia, 17, 75–82. Schleicher, A., Amunts, K., Geyer, S., Morosan, P. and Zilles, K. (1999) Observer-independent method for microstructural parcellation of cerebral cortex: a quantitative approach to cytoarchitectonics. Neuroimage, 9, 165–177. Schleicher, A., Amunts, K., Geyer, S., Kowalski, T., Schormann, T., Palomero-Gallagher, N. and Zilles, K. (2000) A stereological approach to human cortical architecture: Identification and delineation of cortical areas. Journal of Chemical Neuroanatomy, 20, 31–47. Schmitz, C., Schuster, D., Niessen, P. and Korr, H. (1999) No difference between estimated mean nuclear volumes of various types of neurons in the mouse brain obtained on either isotrophic uniform random sections or conventional frontal or sagittal sections. Journal of Neuroscience Methods, 88, 71–82. Schormann, T. and Zilles, K. (1997) Limitations of the principle axes theory. IEEE Transactions in Medical Imaging, 16, 942–947. Schormann, T. and Zilles, K. (1998) Three-dimensional linear and nonlinear transformations: an integration of light microscopical and MRI data. Human Brain Mapping, 6, 339–347. Schormann, T., Dabringhaus, A. and Zilles, K. (1997) Extension of the Principal axis theory for the determination of affine transformations. In Informatik aktuell, edited by Anonymous, pp. 384–391. Springer. Schormann, T., Henn, S. and Zilles, K. (1999a) Ein sequentielles System zur Anpassung medizinischer Bilddaten. In Proceedings of the DAGM, Berlin: Springer. Schormann, T., Posse, S. and Zilles, K. (1999b) The new reference brain of the ECHBD database. Neuroimage, 9, 40. Sereno, M.I., Dale, A.M., Reppas, J.B., Kwong, K.K., Belliveau, J.W., Brady, T.I., Rosen, B.R. and Tootell, R.B.H. (1995) Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging. Science (Washington), 268, 889–893. Shipp, S., Watson, J.D.G., Frackowiak, R.S.J. and Zeki, S. (1995) Retinotopic maps in human prestriate visual cortex: the demarcation of areas V2 and V3. Neuroimage, 2, 125–132. Skullerud, K. (1985) Variations in the size of the human brain. Acta Neurologica Scandinavica, 71, 1–94. Stensaas, S.S., Eddington, D.K. and Dobelle, W.H. (1974) The topography and variability of the primary visual cortex in man. Journal of Neurosurgery, 40, 755. Stephan, K.E., Hilgetag, C.C., Burns, G.A., O’Neill, M.A., Young, M.P. and Kotter, R. (2000) Computational analysis of functional connectivity between areas of primate cerebral cortex. Philosophical Transactions of the Royal Society, London series B, 355, 111–126. Strasburger, E.H. (1938) Vergleichende myeloarchitektonische Studien an der erweiterten Brocaschen Region des Menschen. Journal für Psychologie und Neurologie, 48, 477–511. Talairach, J. and Tournoux, P. (1988) Coplanar stereotaxic atlas of the human brain, Stuttgart: Thieme. Tanji, J. and Kurata, K. (1989) Changing concepts of motor areas of the cerebral cortex. Brain Development, 11, 374–377.
© 2002 Taylor & Francis
Architectonic Mapping
51
Terry, R.D., De Teresa, R. and Hansen, L.A. (1987) Neocortical cell counts in normal human adult aging. Annals of Neurology, 21, 530–539. Thompson, P., Schwartz, C. and Toga, A.W. (1996) High-resolution random mesh algorithms for creating a probabilistic 3D surface atlas of the human brain. Neuroimage, 3, 19–34. Thompson, P.M., Moussai, J., Zohoori, S., Goldkorn, A., Khan, A.A., Mega, M.S., Small, G.W., Cummings, J.L. and Toga, A.W. (1998) Cortical variability and asymmetry in normal aging and Alzheimer’s disease. Cerebral Cortex, 8, 492–509. Thoss, V.S., Piwko, O. and Hoyer, D. (1996) Somatostatin receptors in the rhesus monkey brain: localization and pharmacological characterization. Naunyn Schmiedebergs Archives of Pharmacology, 353, 648–440. Tootell, R.B.H., Silverman, M.S., De Valois, R.J. and Jacobs, G.H. (1983) Functional organization of the second cortical visual area im primates. Science, Washington, 220, 737–739. Tootell, R.B.H., Reppas, J.B., Kwong, K.K., Malach, R., Born, R.T., Brady, T.J., Rosen, B.R. and Belliveau, J.W. (1995) Functional analysis of human MT and related visual cortical areas using magnetic resonance imaging. Journal of Neuroscience, 15, 3215–3230. Tootell, R.B.H. and Taylor, J.B. (1995) Anatomical evidence for MT and additional cortical visual areas in humans. Cerebral Cortex, 5, 39–55. Tootell, R.B.H., Mendola, J.D., Hadjikhani, N.K., Ledden, P.J., Liu, A.K., Reppas, J.B., Sereno, M.I. and Dale, A.M. (1997) Functional analysis of V3A and related areas in human visual cortex. Journal of Neuroscience, 17, 7060–7078. Uylings, H.B.M., Malofeeva, L.I., Bogolepova, I.N., Amunts, K. and Zilles, K. (1999) Broca’s language area from a neuroanatomical and developmental perspective. In: P. Hagoort and C. Brown (eds), Neurocognition of language processing, Oxford: Oxford University Press, pp. 319–336. Vierordt, H. (1893) Anatomische, physiologische und physikalische Daten und Tabellen zum Gebrauch für Mediziner, Jena: Fischer Verlag. Vogt, C. (1910) Die myeloarchitektonische Felderung des menschlichen Stirnhirns. Journal für Psychologie und Neurologie, 15, 221–238. Vogt, C. and Vogt, O. (1919) Allgemeinere Ergebnisse unserer Hirnforschung. Journal für Psychologie und Neurologie, 25, 292–398. Vogt, O. (1919) Die myeloarchitektonische Felderung des menschlichen Stirnhirns. Journal für Psychologie und Neurologie, XV, 221–232. von Economo, C. and Koskinas, G.N. (1925) Die Cytoarchitektonik der Hirnrinde des erwachsenen Menschen, Berlin: Springer. Watson, J.D.G., Frackowiak, R.S.J. and Zeki, S. (1993) Functional separation of colour and motion centres in human visual cortex. In: B. Gulyas, D. Ottoson and P.E. Roland (eds), Functional organization of the human visual cortex, Oxford: Pergamon Press, pp. 317–328. Weibel, E. (1979) Stereological methods. Vol 1: Practical methods for biological morphometry, London: Academic Press. West, M.J. (1993) New stereological methods for counting neurons. Neurobiology of Aging, 14, 275–285. Westbury, C.F., Zatorre, R.J. and Evans, A.C. (1999) Quantifying variability in the planum temporale: A probability map. Cerebral Cortex, 9, 392–405. Williams, R.W. and Rakic, P. (1988) Three-dimensional counting: an accurate and direct method to estimate numbers of cells in sectioned material. Journal of Comparative Neurology, 278, 344–352. Wong-Riley, M.T., Hevner, R.F., Cutlan, R., Earnest, M., Egan, R., Frost, J. and Nguyen, T. (1993) Cytochrome oxidase in the human visual cortex: distribution in the developing and the adults brain. Visual Neuroscience, 10, 41–58. Wree, A., Schleicher, A. and Zilles, K. (1982) Estimation of volume fractions in nervous tissue with an image analyzer. Journal of Neuroscience Methods, 6, 29–43. Xiao, Y., Zych, A. and Fellemann, D.J. (1999) Segregation and convergence of functionally defined V2 thin stripe and interstripe compartment projections to area V4 of Macaques. Cerebral Cortex, 9, 792–804. Zeki, S. (1978) Functional specialization of the visual cortex of the rhesus monkey. Nature (London), 274, 423–428. Zezula, J., Cortés, R., Probst, A. and Palacios, J.M. (1988) Benzodiazepine receptor sites in the human brain: autoradiographic mapping. Neuroscience, 25, 771–795. Zilles, K. (1990) Cortex. In: G. Paxinos (ed.), The human nervous system. San Diego: Academic Press, pp. 757–802. Zilles, K. and Schleicher, A. (1980) Quantitative Analyse der laminären Struktur menschlicher Cortexareale. Verhandlungen der Anatomisches Gesellschaft, 74, 725–726. Zilles, K. and Schleicher, A. (1993) Cyto- and myeloarchitecture of human visual cortex and the periodical GABA-A receptor distribution. In: B. Gulyas, D. Ottoson and P. Roland (eds), Functional organization of the human visual cortex. Oxford: Pergamon Press, pp. 111–120. Zilles, K. and Schleicher, A. (1995) Correlative imaging of transmitter receptor distributions in human cortex. In: W. Stumpf and H. Solomon (eds), Autoradiography and correlative imaging. San Diego: Academic Press, pp. 277–307.
© 2002 Taylor & Francis
52
Katrin Amunts, Axel Schleicher and Karl Zilles
Zilles, K. and Clarke, S. (1997) Architecture, connectivity and transmitter receptors of human extrastriate visual cortex. Comparison with non-human primates. In: K.S. Rockland, J.H. Kaas and A. Peters (eds), Cerebral Cortex. Vol. 12. New York: Plenum Press, pp. 673–742. Zilles, K., Schleicher, A., Rath, M. and Bauer, A. (1988) Quantitative receptor autoradiography in the human brain. Histochemistry, 90, 129–137. Zilles, K., Werner, L., Qü, M., Schleicher, A. and Gross, G. (1991a) Quantitative autoradiography of 11 different transmitter binding sites in the basal forebrain region of the rat—evidence of heterogenity in distribution patterns. Neuroscience, 42, 473–481. Zilles, K., Gross, G., Schleicher, A., Schildgen, S., Bauer, A., Bahro, M.S., Zech, K. and Kolassa, N. (1991b) Regional and laminar distribution of alpha-adrenoreceptors and their subtypes in human and rat hippocampus. Neuroscience, 40, 307–320. Zilles, K., Hajos, F., Kalman, M. and Schleicher, A. (1991c) Mapping of glial fibrillary acidic protein-immunoreactivity in the rat forebrain and mesencephalon by computerized image analysis. Journal of Comparative Neurology, 308, 340–355. Zilles, K., Qü, M., Schröder, H. and Schleicher, A. (1991d) Neurotransmitter receptors and cortical architecture. Journal für Hirnforschung, 32, 343–356. Zilles, K., Schlaug, G., Matelli, M., Luppino, G., Schleicher, A., Qü, M., Dabringhaus, A., Seitz, R. and Roland, P.E. (1995) Mapping of human and macaque sensorimotor areas by integrating architectonic, transmitter receptor, MRI and PET data. Journal of Anatomy, 187, 515–537. Zilles, K., Schleicher, A., Langemann, C., Amunts, K., Morosan, P., Palomero-Gallagher, N., Schormann, T., Mohlberg, H., Bürgel, U., Steinmetz, H., Schlaug, G. and Roland, P.E. (1997) A quantitative analysis of sulci in the human cerebral cortex: development, regional heterogeneity, gender difference, asymmetry, intersubject variability and cortical architecture. Human Brain Mapping, 5, 218–221. Zilles, K., Werners, R., Büsching, U. and Schleicher, A. (1986) Ontogenesis of the laminar structures in area 17 and 18 of the human visual cortex. A quantitative study. Anatomy and Embryology, 174, 129–144.
© 2002 Taylor & Francis
4 Topographical Variability of Cytoarchitectonic Areas Jörg Rademacher Neurologische Klinik, Heinrich-Heine-Universität, Düsseldorf, Moorenstrasse 5, 40225, Düsseldorf, Germany Tel: (0049)2461-612107; FAX: (0049)211-81-18485; e-mail:
[email protected]
There is growing interest in the pattern of convolutions in the human brain, in the context of modern neuroimaging studies of relationships between structure and function, because the sulcal landmarks can be reliably visualized in vivo by magnetic resonance imaging techniques. It is generally assumed that the pattern of gyri and sulci is a morphological feature of the brain which is strictly related to the structural constraints of functional organization. However, the assumption that macroanatomic topographic landmarks may serve as a guide to functional imaging is problematic, because such a system depends upon constant relationships of cytoarchitectonic field boundaries to the gyral pattern and sulci of the brain. Although consistent correlations between the positions of certain architectonic field boundaries and the primary brain sulci have been reported occasionally, the classical templates, including the Brodmann map, do not provide such information. At the very best, these templates include general guidelines for using macroanatomic landmarks that provide the framework for specific cytoarchitectonic areas. They lack information about the course and size of gyri and sulci, and do not permit prediction of how these landmarks relate to the architectonic areas. Thus, the early students of cortical cytoarchitecture do not explain the extent to which cortical areas correlate with the individual gyral and sulcal pattern. Individual anatomic variations have not been systematically charted, but there is evidence suggesting that, at least for some cytoarchitectonic areas, there is considerable variability. For practical purpose, one can distinguish “class 1 variability”, which is closely predictable from the gyral and sulcal landmarks, and “class 2 variability” which is not predictable from the visible landmarks. The latter introduces a significant error to the localization of function, if the mapping technique is based on gross anatomical landmarks. KEYWORDS: brain development, brain mapping, cerebral cortex, cytoarchitecture, gyrification, morphometry
1. INTRODUCTION The gyrencephalic human cerebral cortex is distributed over a folded cerebral surface, thereby allowing for a larger area of cortical surface in the same volume than in case of a lissencephalic brain with a smooth cerebral surface (Zilles, 1990). Early neuroanatomical and electrophysiological studies supported the hypothesis that cytoarchitectonically-defined cortical areas may represent distinct functional units, and that the cerebral sulci may coincide with the areal borders (Brodmann, 1909; Vogt and Vogt, 1919). Consequently, the Brodmann map has become the most influential anatomical reference system for the analysis of structure-function correlations in the human brain. It is used as a twodimensional template for a cytoarchitectonically-based parcellation of the cerebral cortex, which is defined by regional heterogeneities of the cellular and laminar organization. 53 © 2002 Taylor & Francis
54
Jörg Rademacher
Modern neuroimaging techniques, including positron emission tomography and functional magnetic resonance imaging, subdivide the human cerebral cortex with increasing spatial resolution. For analysis, the resulting foci of activation are related to visible macroanatomic landmarks, because even the most advanced imaging protocols do not permit direct visualization of the laminar heterogeneities which define the cytoarchitectonic pattern. It is the general assumption of many brain mapping studies that these gyral and sulcal landmarks coincide with the borders of cytoarchitectonic areas, as shown in the Brodmann map. It has also been hypothesized that the intrasulcal cortices may play a distinctive role in higher cognitive processing, because the most rapid changes in neuronal activity are frequently observed in the sulcal fundi (Markowitsch and Tulving, 1995). However, the available knowledge about the precise relationship between the topography of specific cytoarchitectonic fields and the sulcal and gyral patterns in the general population is not adequate compared to the demands of structural and functional brain mapping techniques (Rademacher et al., 1992; Zilles et al., 1995; Geyer, 1996; van Essen et al., 1998; Roland and Zilles, 1998; Schormann and Zilles, 1998; Amunts et al., 1999). Anatomic variability may obscure and distort structure-function relationships in several ways, if the range of individual macroanatomic variations (Eberstaller, 1890; Cunningham, 1892; Galaburda et al., 1987; Steinmetz et al., 1989; Ono et al., 1990; Paus et al., 1996; Penhune et al., 1996; Thompson et al., 1996; Westbury et al., 1999) and microanatomic variations (Tables 4.1 and 4.2) is underestimated. First, interindividual gross anatomic variation regarding the course and the extent of the major (primary) gyri and sulci may lead to mismatching of anatomy with function (Steinmetz et al., 1990). Caution has to be urged where map locations from a coordinate system such as the Talairach atlas (Talairach and Tournoux, 1988) or the Damasio atlas (Damasio and Damasio, 1989) are referenced to a “standard” brain. This caution is needed especially because the transfer of Brodmann areas to a three-dimensional reference system represents a “best guess” topography, derived from Brodmann’s two-dimensional template of one hemisphere and is not based on systematic anatomical data. Second, while there is considerable overlap in the cytoarchitectonic parcellation of the human brain between the published brain maps (Brodmann, 1909; von Economo and Koskinas, 1925; Sarkisov et al., 1949; Braak, 1980), there is also considerable variation (Zilles, 1990). These discrepancies between schemes of parcellation in the identification of areal borders may result from the use of descriptive non-quantitative criteria by different authors, but they can also be expected to reflect microanatomic variability. Such variation will probably increase the range of mismatching between anatomy and function. Third, interhemispheric asymmetries have been shown to exist for many macroanatomically or cytoarchitectonically defined cortical regions, both in the general population and in individuals (Geschwind and Levitsky, 1968; Galaburda et al., 1978; Falzi et al., 1982; Eidelberg and Galaburda, 1984; Steinmetz et al., 1990; Witelson and Kigar, 1992; Ide et al., 1996; Hutsler et al., 1998; Amunts et al., 1999; Rademacher et al., 1999). In contrast, the classical cytoarchitectonic maps and the Talairach atlas are based on the assumption that one hemisphere always represents the topographical mirror image of its contralateral “homologue”. As a consequence, asymmetries of functional activation are difficult to interpret. They may reflect different cognitive strategies between the hemispheres, if bilateral but topographically discrepant foci map onto different cytoarchitectonic areas. However, they may also represent identical functional units, if they simply follow asymmetries in cytoarchitectonic topography and map onto the same areas.
© 2002 Taylor & Francis
Topographical Variability of Cytoarchitectonic Areas
55
In the following sections evidence will be presented on the topographical relationship between the visible gyri and sulci and the borders of cytoarchitectonic areas, including developmental aspects and experimental data on gyrus formation. Essentials of gyrus formation will be described first, because they relate to the more general principles that underlie this aspect of cortical organization. Results from the making of maps are discussed thereafter. This dual strategy is suggested to represent a useful concept for imaging studies, because one may want not only to construct detailed anatomic maps, but also to characterize the principles that govern them (Friston, 1998).
2. GYRUS FORMATION Knowledge of the phylogenetic and ontogenetic mechanisms of gyrus formation may help to improve understanding of the relationship between sulcal landmarks and architectonic areal borders, as well as the biological range of variations. Several questions arise: Do these anatomic parameters vary independently or do they show a strict covariation? Are there systematic differences in the degree of correlation between distinct cortical regions? If so, does this difference reflect organizational principles, for example, a difference between the primary somatosensory areas and the association areas? Is there a fundamental difference between the primary (high incidence rate) and tertiary (low incidence rate) brain sulci with respect to cytoarchitectonic areal borders? Can cytoarchitectonic (and functional) homologies or asymmetries be deduced from the sulcal topography? To what extent are these patterns genetically determined? The ultimate answers to these questions cannot be given yet, but the increasing research interest in these issue, which is important to neuroimaging, have already provided interesting new insights. Cortical folding has been proposed to represent the brain’s solution to the problem of packing a phylogenetically increasing cortical surface into the restricted volume of the cerebral vault (Zilles et al., 1988; Welker, 1990). Similar to the general phylogenetic pattern from lissencephalic to gyrencephalic brains (Zilles et al., 1989), the human cortex changes from a smooth surface to a highly convoluted structure during ontogenesis (Chi et al., 1977; Armstrong et al., 1995). The continuous increase in gyrification begins in ontogenetic week 22 after the majority of neurones have finished their migration into the cortex. In general, the degree of cortical folding appears to be closely associated with the size of the brain, if different species are compared (Armstrong et al., 1995). Nevertheless, anatomical data from primates have shown that the size of individual cytoarchitectonic areas cannot be predicted simply by regression analysis on the basis of brain weight (Holloway, 1979) suggesting that additional intrinsic mechanisms may be active. In the human brain, interindividual differences in the size of a given cytoarchitectonic area have a wide range, up to a factor ten (Filimonoff, 1932; Stensaas et al., 1974; Galaburda et al., 1978; Rademacher et al., 1993; Rajkowska and Goldman-Rakic, 1995; Amunts et al., 1999) and obviously cannot be explained by the size of the individual brain. It has also been speculated that gyrus formation correlates with the topography of cytoarchitectonic areas and their respective borders, which tend to coincide with the course of the cerebral sulci (Welker, 1990). In this model, gyri and sulci (i.e. macroanatomic parameters) are interpreted as the expression of specific structural principles and constraints reflecting intracortical organization (Sisodiya et al., 1996). In fact, the degree of cortical folding found in adult human brains appears to be a rather constant phenomenon,
© 2002 Taylor & Francis
56
Jörg Rademacher
with distinct and stable local changes in the anterior-to-posterior direction, showing the highest values over the prefrontal and parieto-temporo-occipital association cortices and the lowest values over the primary sensory cortices (Zilles et al., 1988). This finding could imply that the cytoarchitectonic patterns show a similar constancy in their distribution and that their relationship to the cerebral gyri is fixed genetically. However, the formation of cytoarchitectonically defined cortical areas in the brains of mammals does not necessarily lead to sulcus formation. Lissencephalic monkeys show the same main architectonic subdivisions as do other primate brains, including the human cortex (Brodmann, 1909), and the highly convoluted brains of dolphins or whales do not reflect an increase in cytoarchitectonic areas. Phylogenetically, cytoarchitectonic areal formation and the development of a convoluted brain are not interdependent. They probably represent distinct processes with partially overlapping genetic and epigenetic mechanisms, thereby allowing for variations not only of their respective patterns, but also of their relationship to each other. Is this phylogenetic evidence paralleled by similar insights from studies of ontogenesis? Shortly after birth, the convolutedness of the human brain reaches its adult configuration while the brain itself continues to grow (Chi et al., 1977). Consequently, the postnatal changes in the degree of cortical folding match those of brain growth thereby maintaining a constant degree of convolutedness. On the basis of this observation, Armstrong et al. (1995) have made a qualitative distinction between the primary and secondary brain sulci, which together establish the degree of convolutedness that characterizes the human brain, and the tertiary sulci which appear postnatally and only maintain the degree of convolutedness that was previously established. Interestingly, only the topography of the deep and ontogenetically early primary sulci can be reliably observed in all brains (Ono et al., 1990) and this appears to be strongly determined genetically (Lohmann et al., 1999). However, the heritability of the precise overall gyral pattern may be less than 20% (Bartley et al., 1997), and surprising discordances for sulcal shape have been found among monozygotic twins (Steinmetz et al., 1995). Thus, one may postulate that an ontogenetic process exists that can produce profound morphological shifts as determined by random environmental factors without much genetic change. If this principle is also valid for the relationship between sulci and cytoarchitectonic borders, one would expect a topographical coincidence in only a few examples, i.e. by chance. In contrast, the available evidence shows that the rate of a topographical overlap between these two anatomical parameters is much higher than would be expected, if such relationship was governed by chance (Sanides, 1962; Rademacher et al., 1993; White et al., 1997a,b; Amunts et al., 1999; Geyer et al., 1999). However, dissociations between the sulcal topography and the borders of the cytoarchitectonic areas would not be surprising. In this context, it is a major challenge to analyze how genetically determined intrinsic control and non-genetically determined extrinsic control have a complementary or concurrent influence on the final amount of overlap between the cytoarchitectonic topography and the individual gyral pattern. Three standard theories have been proposed. First, the “mechanical model” of brain development of brain convolutions (Richman et al., 1975) which can explain the general degree of cortical folding, but not the distinct placement and orientation of gyri and sulci (Armstrong et al., 1995). In brief, the mechanical model assumes that the relative amount of cortical folding is the result of the mechanical forces internal to the cortex and generated by having two differently sized cortical strata (i.e. supragranular layers I–III vs granular and infragranular layers IV–VI). Second, a “tensionbased” theory of mammalian brain morphogenesis has been proposed that stresses the
© 2002 Taylor & Francis
Topographical Variability of Cytoarchitectonic Areas
57
importance of mechanical tension along axons and dendrites in the white matter (van Essen, 1997). According to this model, tension along axons can explain how and why the cortex folds in a distinct pattern. The underlying phylogenetic (and ontogenetic) principle would be to keep total axonal length low, thus contributing to the compactness of neural connectivity in the adult brain. Third, an active process termed “gyrogenesis” is postulated, whereby internal growth processes including cytoarchitectonic differentiation, ingrowth of thalamic and cortical afferents, selective neuronal death and progressive myelination move the gyral crowns outward (Rakic, 1988; Kostovic and Rakic, 1990; Welker, 1990). The concept of gyrogenesis suggests that the formation of gyri and sulci must bear a close relationship to the cytoarchitectonic parcellation of the cerebral cortex. It has also been hypothesized to explain better the individual placement, orientation, and depth of the cerebral convolutions (Armstrong et al., 1995) and this has been supported by experimental data on gyrus formation (Smart and McSherry, 1986; Ferrer et al., 1988). The comparative experimental evidence for genetic control of these mechanisms responsible for mammalian cortical development has been presented recently by Rubenstein and Rakic (1999). In agreement with the concept of gyrogenesis, it takes the complex and ordered array in functionally distinct cytoarchitectonic areas as depending on species-specific interactions between intrinsic properties of cortical cells and connectivity between cortical or subcortical structures (Rubenstein et al., 1999). Defective genes are recognized as a cause for cortical abnormalities at the microanatomic (cytoarchitecture) and macroanatomic (gyri and sulci) level (Raymond et al., 1995). For example, bilateral enucleation in the foetal monkey leads to a decrease of Brodmann area 17 (primary visual cortex) and to an increase of Brodmann area 18 (visual association cortex), paralleled by the induction of supplementary sulci and gyri of the occipital lobe (Dehay et al., 1996). The modified borders of the striate cortex often coincided with a new and deep sulcus. While these and the aforementioned results support the concept that the architectonic subdivisions and the sulcal pattern may show a high degree of anatomical (and functional) plasticity, it also supports the hypothesis that there is some degree of covariation between the cytoarchitectonic borders and the framework of sulcal landmarks. To date, it is impossible to clarify the precise impact of the genotype on these biological parameters. Quantitative analyses of the anatomical phenotype are mandatory to improve our understanding of the relationship between cytoarchitecture and macroanatomy.
3. CORTICAL MAPS A comprehensive analysis of the relationship of cytoarchitectonic areal borders to the individual gyral pattern and surrounding sulci needs to be based on larger series of brain specimens in order to consider both interhemispheric and interindividual variations. The most relevant anatomic studies which fullfill this criterion are summarized in Tables 4.1 and 4.2. Based on this criterion the present selection of cytoarchitectonic areas is focused on the primary areas, their direct neighbours, and the language regions. There is a lack of comparable data relating to other brain regions such as the superior parietal lobe, the inferomedial temporal lobe, and others. To evaluate the findings, two classes of variability were distinguished. Class 1 variability is closely predictable from visible landmarks. It reflects topographical variations which can be predicted by the framing gyri and sulci even if these landmarks are variable in a stereotactic system. It does not degrade the
© 2002 Taylor & Francis
58
Jörg Rademacher
confidence of mapping of a system keyed to landmarks visible in MR images. Class 2 variability, is not predictable from visible landmarks, thereby degrading the confidence of mapping. The incidence rates in % (IR) of the relevant sulci (for their topography see Figures 4.1 and 4.2) were taken from the atlas of Ono et al. (1990) which is based on 25 autopsy specimen brains. Given the individual variability described above, attention has also been payed to differences in the location of specific cytoarchitectonic areas between the classical maps. The systematic presentation follows well-established criteria of subdividing the cerebral cortex. On the basis of neuroanatomical studies in primates including the human brain, the cortex is subdivided into the frontal, parietal, temporal and occipital lobes which contain the primary sensorimotor areas and the association areas (Zilles, 1990). Cytoarchitectonic areas are labeled according to the Brodmann (1909) nomenclature (i.e. numbers), unless otherwise stated. In addition, the nomenclature of von Economo and Koskinas (1925) is given in brackets (i.e. letters). Anatomical descriptions are kept short, and extensive definitions can be found in the original literature (see Tables 4.1 and 4.2; Ono et al., 1990; Rademacher et al., 1992). 3.1. Frontal Lobe: Primary Motor Cortex 3.1.1. Cytoarchitectonic criteria and macroanatomic landmarks Area 4 (area FAg) is distinctive for the presence of giant pyramidal cells in layer V, relatively large cells in sublayer IIIc, and a low cell packing density through all layers without sharp laminar definition. Related macroanatomic landmarks (Figures 4.1 and 4.2) include the precentral gyrus which lies between the precentral (IR 100%) and the central sulci (IR 100%). On the medial hemispheric surface, the paracentral lobule extends from the ascending and descending paracentral sulci (IR 92%) anteriorly to the terminal upswing of the cingulate sulcus (IR 100%) posteriorly. 3.1.2. Class 1 variability The identification of area 4 with an observer-independent method (Schleicher et al., 1999) has shown that it always occupies the posterior bank of the precentral gyrus (Geyer et al., 1996) thereby supporting earlier observations (Rademacher et al., 1993). Its presence on the exposed surface of the lateral convexity is restricted to the dorsal-most portion of the precentral gyrus. The superior frontal sulcus represents the inferior level at which area 4 may be found on the convexity surface. There is no extension of area 4 onto the postcentral gyrus and little or no extension onto the superior frontal gyrus, so that the central and precentral sulci represent the posterior and anterior borders of dorsolateral area 4, respectively. More ventrally, area 4 is exclusively localized in the depth of the central sulcus where it shows a constant extent on its anterior bank (White et al., 1997a). Medially, area 4 is located in the central portion of the paracentral lobule, anterior to the termination of the central sulcus. The bihemispheric surface topography of area 4 shows roughly symmetric patterns (shapes) in individual brains (Rademacher et al., 1993; White et al., 1997a,b). It has also been shown that certain features visible in MR images provide reliable evidence for structural-functional organization. The functionally-defined motor hand area can be localized to a knob on the middle third of the precentral gyrus (Yousry et al., 1997) and the asymmetry in the depth of the central sulcus may represent a morphological
© 2002 Taylor & Francis
Topographical Variability of Cytoarchitectonic Areas
59
Figure 4.1. Sulci of the lateral brain surface. aar, anterior ascending ramus of the Sylvian fissure; ag, angular sulcus; ahr, anterior horizontal ramus of the Sylvian fissure; ao, anterior occipital sulcus; ce, central sulcus; if, inferior frontal sulcus; im, intermediate sulcus; ip, intraparietal sulcus; it, inferior temporal sulcus; lo, lateral occipital sulcus; par, posterior ascending ramus of the Sylvian fissure; phr, posterior horizontal ramus of the Sylvian fissure; poc, postcentral sulcus; prc, precentral sulcus; sf, superior frontal sulcus; st, superior temporal sulcus.
Figure 4.2. Sulci of the medial brain surface. ca, callosal sulcus; calc, calcarine sulcus; cc, corpus callosum; ce, central sulcus; ci, cingulate sulcus; ma, marginal sulcus; pa, paracingulate sulcus; po, parietooccipital sulcus; sp, subparietal sulcus; sr, superior rostral sulcus.
© 2002 Taylor & Francis
60
Jörg Rademacher
correlate of asymmetry in the size of the motor cortex and hand preference (Amunts et al., 1996). 3.1.3. Class 2 variability While the general topography of area 4 appears to be predictable from the gyral landmarks, the precise borders for example between primary motor area 4 and premotor area 6 differ between hemispheres and do not always match macrostructural landmarks (Rademacher et al., 1993; White et al., 1997b). Medially, on the paracentral lobule, the ventral border of area 4 varies considerably with respect to the cingulate sulcus and there is no constant limiting sulcal landmark for the anterior border of area 4. The ascending and descending paracentral sulci are mostly located within area 6. Laterally, the intrasulcal size of area 4 on the anterior lip of the central sulcus shows individual variations by a factor of up to two. Consequently, the total cortical volume of area 4 cannot be inferred reliably from the gyral pattern. Similarily, the subdivision of area 4 into areas 4a and 4p, which has been recognized by Geyer et al. (1996) is not indicated by sulcal borders. 3.1.4. Differences between classical maps On the dorsal portion of the precentral gyrus area 4 correlates well with Sanides’ motor area 42 (Sanides, 1962). In contrast, the ventral portions of their homologues in the maps of Brodmann and von Economo and Koskinas have been depicted with a much larger surface extent towards the Sylvian fissure. This discrepancy exceeds the range of the observed anatomic variations as described above and may result from the authors’ intention to visualize the intrasulcal extent of area 4 on the two-dimensional brain templates. Medially, area 4 may be relatively large and extends down to the cingulate sulcus (Brodmann, 1909) or it may be relatively small (i.e. <50%) with only a triangular tongue reaching the sulcus (von Economo and Koskinas, 1925). In this case, similar discrepancies can be expected from the maximal range of normal variability (Rademacher et al., 1993). A location of area 4 beyond the cingulate sulcus onto the cingulate gyrus has not been described. The results of pigmentoarchitectonic studies are within the range of these observations (Braak, 1979, 1980). There are conflicting reports on a putative asymmetry in the human primary motor cortex (Rademacher et al., 1993; Amunts et al., 1996; White et al., 1997b). 3.2. Frontal Lobe: Broca’s Region 3.2.1. Cytoarchitectonic criteria and macroanatomic landmarks Areas 44 and 45 (areas FCBm and FDG) are characterized by large pyramidal cells in the depth of layer III. Area 44 is relatively thick, shows distinct cellular columns, a thin layer II, and a blurring of the border between gray matter and white matter. Area 45 is thinner than area 44, its layer IV is distinct and layer V is bilaminar and quite narrow. Macroanatomically, Broca’s region is localized at the posterior end of the inferior frontal gyrus. It is subdivided by the ascending branch of the Sylvian fissure (IR 88%) into its triangular portion anteriorly and its opercular portion posteriorly. Reliable local sulcal landmarks (IR 100%) include the inferior frontal sulcus, the precentral sulcus and the anterior hori-
© 2002 Taylor & Francis
Topographical Variability of Cytoarchitectonic Areas
61
zontal branch of the Sylvian fissure (IR 88%) (Figure 4.1). The diagonal sulcus is a more variable landmark (68%). 3.2.2. Class 1 variability Constant relationships between the variable sulcal topography of Broca’s region and the underlying cytoarchitectonic parcellation have been described in the classical literature (Stengel, 1930; Riegele, 1931; Sanides, 1962). The borders of areas 45 and 44 have also been reproduced with an observer-independent method (Amunts et al., 1999). Area 45 is always localized on the triangular part of the inferior frontal gyrus, anterior to the ascending part of the Sylvian fissure. The anterior horizontal branch of the Sylvian fissure represents the anterior and ventral borders of area 45 in most of the brains. Area 44 is constantly found posterior to area 45 on the opercular part of the inferior frontal gyrus and anterior to the precentral sulcus. The inferior frontal sulcus always marks the dorsal border of both areas, and they do not reach the convexity surface of the middle frontal gyrus. Macroanatomically defined asymmetries of the inferior frontal gyrus towards the left side (Falzi et al., 1982) are paralleled by a leftward asymmetry of area 44 (Galaburda, 1980; Amunts et al., 1999) in a given population. 3.2.3. Class 2 variability The precise localization of cytoarchitectonically defined Broca’s region is not always indicated by the surrounding sulci (Stengel, 1930; Riegele, 1931; Amunts et al., 1999). For example, its dorsal border may be found in the ventral or dorsal lip of the inferior frontal sulcus and its posterior border may be detected in the anterior or posterior wall of the precentral sulcus. With varying frequencies, area 45 reaches onto the orbital surface of the inferior frontal gyrus. The border between areas 44 and 45 varies up to 2 cm around the fundus of the ascending branch of the Sylvian fissure. With respect to the interareal border, the diagonal sulcus does not represent a useful anatomical landmark. The volumes of area 44 differ across subjects by a factor up to 10, while such variability cannot be observed for gyral size (Amunts et al., 1999). In addition, it cannot be expected that the aforementioned class 1 asymmetries towards the left side in a given population show a strict correlation between micro- and macroanatomy in every individual. For example, no systematic interhemispheric differences have been detected for area 45 because half of the brains had larger volumes on the left side and half of the brains showed an inverse asymmetry (Amunts et al., 1999). However, consistent volumetric asymmetries (left larger than right) have been found for area 44 in the same brains. This shows that one gyral region of interest (here: posterior inferior frontal gyrus) may contain divergent asymmetry patterns of two cytoarchitectonic areas (i.e. area 45 vs area 44). 3.2.4. Differences between classical maps The available cytoarchitectonic maps differ considerably in the topography, sizes and sulcal borders of areas 44 and 45. Brodmann (1909) localized area 46 dorsal to area 45, and area 9 dorsal to area 44, while Sarkisov et al. (1949) localized area 9 dorsal to area 45, and area 8 dorsal to area 44. While these and other authors (von Economo and Koskinas, 1925) agree on a subdivision of Broca’s area into two cytoarchitectonic areas, those of the
© 2002 Taylor & Francis
62
Jörg Rademacher
Vogt school (Vogt and Vogt, 1919; Riegele, 1931) described up to five areas in the same region, defined by myelo- or cyto-architectonics. These differences may result from an observer-dependent bias in the interpretation of smaller cellular and laminar changes as reliable borders of distinct architectonic areas. Furthermore, the relative proportions of areas 44 and 45 vary from apparently equal sizes (Brodmann, 1909; von Economo and Koskinas, 1925) to a significant preponderance of area 45 (Sarkisov et al., 1949). This inconsistent pattern can be expected to reflect the normal range of variability in the population (Amunts et al., 1999). 3.3. Frontal Lobe: Prefrontal Association Cortex 3.3.1. Cytoarchitectonic criteria and macroanatomic landmarks Area 9 (area FD) is thick and characterized by a low cell-packing density, a blurring of the laminar borders of layer IV, and a pale sublayer Vb. The border of layer VI to the underlying white matter is indistinct. Compared to area 9, area 46 (area FDd) shows a higher neuronal packing density and its horizontal lamination has sharper borders between all layers. Characteristically, there is a pronounced radial arrangement of cells and the cortex/ white matter border is sharper than in area 9. Macroanatomically, the superior frontal gyrus and the middle frontal gyrus serve as landmarks. They are separated by the superior frontal sulcus (IR 100%). The middle frontal sulcus (IR 86%) subdivides the middle frontal gyrus into two portions. Laterally, the inferior frontal sulcus (IR 100%) is the ventral border of the middle frontal gyrus and medially, the cingulate sulcus (IR 100%) is the ventral border of the superior frontal gyrus (Figures 4.1 and 4.2). The frontomarginal sulcus (IR 100%) separates the prefrontal and fronto-orbital cortices. 3.3.2. Class 1 variability Typically, area 9 is located on the middle third of the superior frontal gyrus, covering its dorsolateral and dorsomedial portions, while area 46 occupies the central portions of the middle frontal gyrus (Rajkowska and Goldman-Rakic, 1995). Area 46 is always surrounded by area 9, and never extends onto the medial brain surface, where the cingulate sulcus is a reliable ventral border for area 9. Laterally, the inferior frontal sulcus represents a reliable border for the maximal ventral extent of both areas, and the frontomarginal sulcus may serve as the border for their maximal anterior extent. 3.3.3. Class 2 variability The exact position and size of areas 9 and 46 are characterized by considerable differences between individuals (Rajkowska and Goldman-Rakic, 1995). Area 9 may also occupy varying portions of the middle frontal gyrus and its length may vary by a factor of up to two. The size of area 46 on the exposed surface of the middle frontal gyrus may vary by a factor of up to five. It also has a variable extent in the depth of the middle frontal sulcus, including cases with an almost exclusively intrasulcal distribution. Rarely, area 46 occupies small portions of the superior frontal or inferior frontal gyri. These variations in size of area 9 cannot be predicted from those of area 46 and vice versa. Needless to say, the size of the gyri, and the course of the sulci do not permit precise localization or measurement
© 2002 Taylor & Francis
Topographical Variability of Cytoarchitectonic Areas Table 4.1.
63
Variability of cytoarchitectonic areas: frontal and parietal lobes
Brain region Frontal lobe Primary motor cortex
Broca’s area
Prefrontal cortex
Study
Hemispheres Brodmann areas (new parcellation)
Rademacher et al., 1993 Geyer et al., 1996 Zilles et al., 1996 Amunts et al., 1997 White et al., 1997a,b Riegele, 1931 Kononova, 1949 Galaburda, 1980 Amunts et al., 1999 Kononova, 1949 Rajkowska and Goldman-Rakic, 1995
20 9 12 40 40 16 20 20 20 20 10
4 4(4a, 4p) 4 4 4 44, 45(57–66) 44, 45 44 44, 45 9, 46 9, 46
20 40 20 6 16
3b 3 3a, 3b, 1 40(88, 89), 39(90) 40(PF, PFG), 39(PG, PEG)
Parietal lobe Primary somatosensory Rademacher et al., 1993 cortex White et al., 1997a,b Geyer, Schleicher and Zilles, 1999 Inferior parietal cortex Schulze, 1962 Eidelberg and Galaburda, 1984
of the underlying cytoarchitecture. Furthermore, inconsistent left-right asymmetries have been observed in individual brains for areas 9 and 46 (Kononova, 1949). 3.3.4. Differences between classical maps Variations in location and size of areas 9 and 46 have also been documented in the classical brain maps. In the Brodmann map, area 46 is located ventrally on the middle frontal gyrus and extends onto the inferior frontal gyrus, bordering area 45. In contrast, the von Economo and Koskinas template maps area 46 dorsally on the middle frontal gyrus, surrounded by area 9. Nevertheless, the interindividual differences observed here between cases exceeds the variability noted in the aforementioned classical maps (Kononova, 1949; Amunts et al., 1999). Consequently, it can be assumed that most of the variability results from biological diversity. Inter-observer variability may result from the fact that the cytoarchitectonic borders between areas 9 and 46 exhibit transitional features with the surrounding areas, but these phenomena appear to introduce only a minor source of variability. 3.4. Parietal Lobe: Primary Somatosensory Cortex 3.4.1. Cytoarchitectonic criteria and macroanatomic landmarks Area 3 (area PB) is recognized by the high packing density of the small neurones of layers II–IV, extremely low density of neurones in layer V, and a sharp border between gray and white matter. Area 3 can be subdivided into areas 3a and 3b (areas PA and PB) with the
© 2002 Taylor & Francis
64
Jörg Rademacher
latter having a higher cell density and lack of columnar arrangement. Large pyramidal cells of lower layer III are characteristic of area 1 (area PC). This band of layer III pyramids is reduced in area 2 (area PD). Precentral gyrus, postcentral gyrus and the central sulcus represent the relevant topographic landmarks (Figures 4.1 and 4.2). 3.4.2. Class 1 variability The borders of cytoarchitectonic areas 3a, 3b, 1 and 2 can be recognized by statistically significant changes in cytoarchitecture (Geyer et al., 1999). Area 3a is located in the fundus of the central sulcus and area 3b is located in the anterior bank of the postcentral gyrus (IR 100%). Neither area extends onto the convexity surface of the postcentral gyrus or onto major parts of the precentral gyrus. The locations of the borders of area 3 (combined areas 3a and 3b) along the central sulcus are remarkably consistent among individual brains (White et al., 1997a). Posteriorly, area 1 is consistently found on the crown of the postcentral gyrus and area 2 is always located in the depth of the postcentral sulcus, usually on the posterior bank of the postcentral gyrus. The total amount of primary somatosensory cortex can be expected to correlate with the size of the postcentral gyrus, because there is only a small degree of topographical variation and the surface areas usually show symmetry in size and shape (Rademacher et al., 1993; White et al., 1997a,b). Functionally, the interdigitation of the inner walls of the central sulcus halfway between the midline and the Sylvian fissure seems to be reliably related to digit- and hand-related activity (White et al., 1997a). Interestingly, this “presumptive hand region” can be characterized macroanatomically, but distinct cytoarchitectonic features have not been observed. 3.4.3. Class 2 variability The cytoarchitectonic borders between areas 3, 1 and 2 do not always match the macrostructural landmarks. For example, the extent of area 3 onto the medial hemispheric surface shows the highest degree of interindividual variability in the primary somatosensory cortex (Rademacher et al., 1993; White et al., 1997a). Area 1 may be restricted to the crown of the postcentral gyrus or it may also extend up to 1cm into the depth of the postcentral sulcus in some cases (Geyer et al., 1999). 3.4.4. Differences between classical maps On the lateral brain surface, the primary somatosensory areas are depicted in a relatively uniform pattern on the postcentral gyrus (Brodmann, 1909; von Economo and Koskinas, 1925; Sarkisov et al., 1949). Their topography does not appear to vary in the anteriorto-posterior or dorsal-to-ventral direction. On the templates, the intrasulcal areas 3 and 2 (areas PA, PB, PD) seem to be localized on the crown of the postcentral gyrus, but again this is an artefact of the two-dimensional drawings. The authors describe an exclusively intrasulcal extent for these areas. On the medial brain surface, Brodmann (1909) and Sarkisov et al. (1949) depict areas 3, 1, and 2 in the anterior-to-posterior direction. In contrast, von Economo and Koskinas (1925) do not map the putative homologue of area 2, i.e. area PD, onto the medial surface. Their map characterizes area PA, i.e. area 3a, as horseshoe-shaped and surrounding areas PB and PC, i.e. areas 3b and 1, while the Brodmann map shows area 5 in the same location.
© 2002 Taylor & Francis
Topographical Variability of Cytoarchitectonic Areas
65
3.5. Parietal Lobe: Inferior Parietal Cortex 3.5.1. Cytoarchitectonic criteria and macroanatomic landmarks In comparison with the superior parietal lobe, areas 40 and 39 belong to the more granular cortices with a relative accentuation of layer IV. Area 40 (area PF including areas PFop, PFT, PFcm, PFm; areas 88 and 89 of Schulze, 1962) is characterized by a prominent horizontal layering, a thick layer III with small pyramidal cells, decreasing cell volumes in layers V and VI, and the appearance of radial columns perpendicular to the cortical surface extending from layers III to VI. Area 39 (area PG; combined areas PEG and PG of Eidelberg and Galaburda, 1984) is thinner with a granular but narrow layer IV, has larger cells in layer III, a lower cell density in layer V, and a more prominent horizontal layering. Macroanatomically, the opercularizing intraparietal sulcus (IR 100%) takes a transaxial course which represents the dorsal border of the inferior parietal lobe (Figure 4.1). The posterior segment of the superior temporal sulcus (IR 100%) marks its ventral border. Anteriorly, the postcentral sulcus (IR 100%) indicates the border. The intermediate sulcus of Jensen (IR 52%) takes its origin from the intraparietal sulcus and separates supramarginal and angular gyri.
3.5.2. Class 1 variability The depth of the intraparietal sulcus always represents the dorsal border of areas 40 and 39, and the superior temporal sulcus is never crossed in the ventral direction (Brodmann, 1909; Schulze, 1962; Eidelberg and Galaburda, 1984). Consequently, the cytoarchitectonic areas of the inferior parietal lobe are not found on the convolutions of the superior parietal lobe or the middle temporal gyrus. The major portion of area 40 is localized on the supramarginal gyrus including its opercular portion in the depth of the Sylvian fissure, and most of area 39 is localized on the angular gyrus. The postcentral sulcus represents the anterior border of area 40, and posteriorly the border is marked by the descending portion of the intraparietal sulcus. The overall size of combined areas 40 and 39 can be estimated from the size of the inferior parietal lobe with some confidence.
3.5.3. Class 2 variability There is no reliable sulcal landmark indicating the precise cytoarchitectonic border between areas 40 and 39, and their relative sizes compared with each other vary considerably. When present, the intermediate sulcus is roughly correlated with the interareal border (Eidelberg and Galaburda, 1984), but up to three downward-projecting sulci of the intraparietal sulcus may make the interpretation difficult (Ono et al., 1990). Anteriorly, area 40 has been found to cross or to fall short of the postcentral sulcus in single hemispheres. Posteriorly, the cytoarchitectonic differentiation between area 39 and the occipital cortices appears to be difficult because of rather small and gradual changes (Schulze, 1962). Ventrally, an inconstant portion of area 40 occupies the superior temporal gyrus. Minor variations have also been noted dorsally where both areas are localized at varying positions of the ventral lip of the intraparietal sulcus (Eidelberg and Galaburda, 1984). Furthermore, macroanatomic and cytoarchitectonic asymmetries of varying extent and directions exist in individual brains (Schulze, 1962; Eidelberg and Galaburda, 1984). Although there
© 2002 Taylor & Francis
66
Jörg Rademacher
are no systematic side differences, mirror images in size and topography of both areas between the hemispheres of one brain are exceptional (Schulze, 1962). 3.5.4. Differences between classical maps Brodmann has described two areas on the inferior parietal lobe, area 40 anteriorly and area 39 posteriorly. In contrast, von Economo and Koskinas (1925) and Sarkisov et al. (1949) have subdivided the homologue of area 40 into four subareas and Schulze (1962) described two areas in the same region. Area 39 has been subdivided into two or three areas by Eidelberg and Galaburda (1984) and Sarkisov et al. (1949), respectively. All classical maps lack information on the intrasulcal extent of area 40 into the Sylvian fissure, and of areas 40 and 39 into the intraparietal sulcus. The extent of area 39 onto the superior temporal gyrus and onto the occipital lobe varies considerably between the maps, probably due to the normal range of variations and difficulties in the identification of sharp borders in these locations (Eidelberg and Galaburda, 1984). Nevertheless, the relative sizes of both areas appear to be quite similar, occupying approximately equal portions of the inferior parietal lobe (Brodmann, 1909; von Economo and Koskinas, 1925; Sarkisov et al., 1949). 3.6. Temporal Lobe: Primary Auditory Cortex 3.6.1. Cytoarchitectonic criteria and macroanatomic landmarks Area 41 (areas TC and TD) is characterized by the extremely high packing density of small cells in layers II–IV and the paler-staining, low-density neurones in layer V. It lacks prominent pyramidal cells in layer III. Heschl’s gyrus represents the relevant macroanatomic landmark (Figure 4.3). It is the rostral-most transverse gyrus on the superior
Figure 4.3. Posterior superior temporal plane. H, Heschl’s gyrus; left and right cytoarchitectonic areas 41 (line marks on Heschl’s gyrus), 42 (light hatch marks on the central planum temporale), 22 (dark hatch marks on the lateral planum temporale and lateral Heschl gyrus) and medial prokoniocortex (black); view from above, the left hemisphere is on the left side; anterior is at the top.
© 2002 Taylor & Francis
Topographical Variability of Cytoarchitectonic Areas
67
temporal plane and extends from the retroinsular region to the lateral rim of the superior temporal gyrus. Heschl’s gyrus is bordered anteriorly by the first temporal transverse sulcus (IR 100%) and posteriorly by Heschl’s sulcus (IR 100%). An intermediate sulcus (IR 41%) may indent Heschl’s gyrus along its long axis. 3.6.2. Class 1 variability In all brains, area 41 is localized on the central portion of Heschl’s gyrus in the depth of the Sylvian fissure (Rademacher et al., 1993; Morosan et al., 1996), confirming earlier observations on the relationship between primary auditory cortex and the anterior transverse gyrus (Pfeifer, 1920). Its major portion is limited by the first transverse sulcus rostrally and Heschl’s sulcus posteriorly. Laterally, area 41 does not extend upon the convexity surface of the temporal lobe and medially, it does not extend beyond the insular margin (Figure 4.3). The most medial part of Heschl’s gyrus is consistently occupied by prokoniocortex (Galaburda and Sanides, 1980). Area 41 never extends beyond the second transverse gyrus (Rademacher et al., 1993).
Table 4.2.
Variability of cytoarchitectonic areas: temporal and occipital lobes
Brain region Temporal lobe Primary auditory cortex
Wernicke’s region
Study
Hemispheres
Brodmann areas (new parcellation)
von Economo and Horn, 1930 Galaburda and Sanides, 1980 Rademacher et al., 1993 Morosan et al., 1996 Clarke and Rivier, 1998 von Economo and Horn, 1930 Braak, 1978
14 6 20 20 10 14 12
Galaburda, Sanides and Geschwind, 1978 Galaburda and Sanides, 1980
8
41(TC, TD) 41(KAm, KAlt) 41 41(Te1.1, 1.0, 1.2) 41 42(TB), 22(TA1) 42(magn.py.m.), 22(magn.py.c.) 22(Tpt)
6
Witelson, Glezer and Kigar, 1995 18 Rivier and Clarke, 1997 10
Occipital lobe Primary visual cortex
Visual association cortex
© 2002 Taylor & Francis
Rademacher et al., 1999
22
Brodmann, 1903 Filimonoff, 1932 Stensaas et al., 1974 Rademacher et al., 1993 Amunts et al., 2000 Filimonoff, 1932 Heinze, 1954 Clarke and Miklossy, 1990
4 13 52 20 20 13 12 6
Amunts et al., 2000
20
42(PaAi, PaAe), 22(Tpt) 22(TA1) 42, 22(AA, PA, LA, MA, AIA, STA) 42, 22 17 17 17 17 17 18, 19 18, 19 18, 19(V2, V3, VP, V4, V5) 18
68
Jörg Rademacher
3.6.3. Class 2 variability The hypothesis that there is a typical “standard” brain with two transverse gyri on the right side and one on the left (Pfeifer, 1920) does not prove to be a useful guideline for topographic mapping, because variations in the position and configuration of Heschl’s gyrus complicate the framing of area 41 (Rademacher et al., 1993; Morosan et al., 1996). The position of Heschl’s sulcus with respect to the temporal pole may vary up to 35 mm (von Economo and Horn, 1930). Most importantly, there can be multiple transverse gyri which are frequently indented by an intermediate transverse sulcus. In some hemispheres, the intermediate sulcus represents the posterior border of area 41, but area 41 may extend much further in the posterior direction, beyond Heschl’s sulcus. Bilateral symmetry of areal extent is present only in a minority of cases, while in the majority (60%), there is an interhemispheric size difference of approximately one third towards the larger left side (Rademacher et al., 1993). There appears to be no relationship between asymmetry in the number of transverse gyri and asymmetry of architectonic area 41, i.e. the side differences in the number of transverse gyri are not correlated with similar asymmetries in the size or topography of area 41.
3.6.4. Differences between classical maps The location of the primary auditory cortex may vary according to the architectonic method used to define it. Thus, compared to the pigmentoarchitectonically-defined granular core area that is reported exclusively on the medial half of Heschl’s gyrus (Braak, 1980), the cytoarchitectonically defined primary auditory cortex extends more laterally and covers about two thirds of Heschl’s gyrus (von Economo and Koskinas, 1925). Obviously, considerable individual variations in the mediolateral extent of area 41 have also been reported (von Economo and Horn, 1930). In the Brodmann map, the primary auditory cortex is represented by a single cytoarchitectonic area, while in the map of von Economo and Koskinas (1925) it contains two cytoarchitectonic fields (TC and TD). Two koniocortical fields within the primary auditory cortex have also been differentiated by Galaburda and Sanides (1980). With an observer-independent cytoarchitectonic method, three distinct areas have been recognized in the same region (Morosan et al., 1996) and Clarke and Rivier (1998) described up to eight additional compartments of unknown functional significance based on cytochrome oxidase staining.
3.7. Temporal Lobe: Wernicke’s Region 3.7.1. Cytoarchitectonic criteria and macroanatomic landmarks Wernicke’s region includes areas 42 and 22 and is classically regarded as the morphological equivalent of the auditory association cortex. Area 42 (area TB) is characterized by bulky pyramidal cells in layer IIIc, a well granularized layer IV, and a cell poor layer V. Area 22 (area TA1) is characterized by cell dense layers IV and V with large pyramidal cells in layer IIIc and smaller pyramids in layer V. There are continuous radial stripes from layer II to V. Macroanatomically, Wernicke’s region includes the planum temporale between Heschl’s sulcus and the terminal horizontal (left sided IR 56%; right sided IR 8%) or ascending (left sided IR 44%; right sided IR 92%) segment of the Sylvian fissure
© 2002 Taylor & Francis
Topographical Variability of Cytoarchitectonic Areas
69
(Figure 4.3). It also contains the posterior portion of the superior temporal gyrus above the superior temporal sulcus (IR 100%). 3.7.2. Class 1 variability Typically, area 42 occupies the major portion (>90%) of the planum temporale (von Economo and Horn, 1930). Heschl’s sulcus represents in most cases its anterior border and posteriorly, the end of the horizontal ramus of the Sylvian fissure may serve as the border for its maximal variation zone (Rademacher et al., 1999). Area 42 does not extend onto the insular cortex medially and it does not occupy the convexity of the superior temporal gyrus laterally (Figure 4.3). Area 22 covers a cortical stripe of varying extent along the lateral border of the planum temporale. Its major portion is always localized on the convexity of the superior temporal gyrus where the superior temporal sulcus represents its ventral border. The posterior end of the horizontal portion of the Sylvian fissure marks the posterior border of area 22. Anteriorly, area 22 always reaches the coronal level which passes through the anterolateral end of Heschl’s sulcus. 3.7.3. Class 2 variability The precise cytoarchitectonic borders of areas 42 and 22 do not coincide with gyral or sulcal landmarks. Bordering on the medial boundary of area 42 parainsular prokoniocortex occupies the planum temporale without a limiting sulcus (Galaburda and Sanides, 1980). Similarily, lateral and immediately adjacent to area 42, area 22 is found without a limiting sulcus. The anterior and posterior borders of area 22 are also not directly delineated by brain sulci. In individual brains, area 22 may cross the coronal levels which indicate the anterolateral end of Heschl’s sulcus or the posterior end of the horizontal segment of the Sylvian fissure. In addition, the shape of areas 42 and 22 varies considerably and does not always follow the expected rostrocaudal orientation as described by Galaburda and Sanides (1980). The cortical volumes of area 42 vary by up to a factor of five and these intersubject differences cannot generally be inferred from the size of the planum temporale (Rademacher et al., 1999). In the modern literature, gross left-right asymmetry of the planum temporale has first been described by Geschwind and Levitsky (1968). In a meta-analysis of 520 adult brains from various studies, a left area larger than the right was found in 74% of all brains, with left/right ratios ranging from 1.20 to 2.0 (values from Steinmetz et al., 1990). From their cytoarchitectonic study in four brains Galaburda et al. (1978) concluded that there is a strong correlation between asymmetry of the planum temporale and asymmetry of area 22 which explains the full structural asymmetry. However, a leftward asymmetry has also been observed for area 42 in more than two thirds of cases in another brain series while area 22 showed leftward asymmetry in only half of the brains (Rademacher et al., 1999). On the planum temporale, area 42 was considerably larger than area 22 thus indicating that macroanatomic asymmetries may not simply be reflections of the underlying asymmetry of one architectonic area. 3.7.4. Differences between classical maps In general, there is good overlap between the classical maps in the topography of area 42 on the planum temporale and area 22 on the middle and posterior third of the superior
© 2002 Taylor & Francis
70
Jörg Rademacher
temporal gyrus (Brodmann, 1909; von Economo and Koskinas, 1925). Anteriorly, at the level of Heschl’s gyrus and posteriorly, at the caudal end of the planum temporale, area 22 extends into the depth of the Sylvian fissure. In contrast, Galaburda and Sanides (1980) also depicted a portion of area 22 (area Tpt) which covers the parietal operculum and postulated a parietal extension of the auditory region. Topographically, this region is identical with the posterior portion of area 40 (area PFcm). Witelson et al. (1995) reported that area 22 may in fact occupy the terminal ascending segment of the Sylvian fissure. These discrepancies are further obscured by the finding of macroanatomic Sylvian fissure asymmetries and planum temporale variability which have usually not been referred to in the cytoarchitectonic maps (Witelson and Kigar, 1992; Ide et al., 1996; Westbury et al., 1999). In addition the parcellation of cytoarchitectonic areas on the planum temporale varies between different brain maps. For example, area 42 has been subdivided into three parakoniocortical areas (i.e. caudo-dorsal, internal and external) by Galaburda and Sanides (1980). The external parakoniocortex (so-defined) is supposed to share a major portion of the superior temporal gyrus with area 22. Therefore, the new parcellation scheme includes changes in the number of areas per macroanatomic unit and significant differences in the local topography which cannot be explained by the range of biological variability. Rivier and Clarke (1997) have described six auditory areas which are located in the posterior temporal region. Significant controversies over the structural organization of the auditory cortex can be expected to emerge if results from myeloarchitectonics (Hopf, 1954), pigmentoarchitectonics (Braak, 1978), and cytochrome oxidase staining (Rivier and Clarke, 1997) are included in a comparison between different areal maps of the superior temporal lobe. 3.8. Occipital Lobe: Primary Visual Cortex 3.8.1. Cytoarchitectonic criteria and macroanatomic landmarks Area 17 (area OC) is defined by its characteristic laminar composition with the granular layer IV having three distinct sublaminae IVa, IVb, and IVc (stripe of Gennari). This pattern ceases abruptly at its border with area 18. Macroanatomically, the calcarine sulcus (IR 100%) is the ventral border of the cuneus’ and the parieto-occipital sulcus (IR 100%) is its anterior border (Figure 4.2). The cuneal point marks the intersection of both sulci. The sagittal sulci of the cuneus (IR 46%) and the intralingual sulci (IR 34%) course horizontally across the cuneus and the lingual gyrus, respectively. The collateral sulcus (IR 100%) represents the boundary between lingual gyrus and fusiform gyrus. On the cerebral convexity, the lateral occipital sulcus (IR 96%) takes a sagittal course (Figure 4.1). 3.8.2. Class 1 variability Typically, area 17 is almost exclusively (>90%) localized posteriorly to the cuneal point on the medial occipital brain surface, where it lies within the region outlined by the parietooccipital sulcus anteriorly and the occipital pole posteriorly (Rademacher et al., 1993) (Figure 4.4). Bilaterally, approximately two thirds of striate cortex are buried in the calcarine sulcus, confirming earlier results (Stensaas et al., 1974). With the relative size of the intracalcarine portion of area 17 compared to the total amount of striate cortex being relatively constant, the length and depth of the calcarine sulcus can serve as an indicator for the overall size of area 17 (Filimonoff, 1932). Direct visualization and identification of
© 2002 Taylor & Francis
Topographical Variability of Cytoarchitectonic Areas
71
Figure 4.4. Surface rendering of the medial occipital lobe. Left and right (L and R) cytoarchitectonic areas 17 (red) and 18 (green); dorsal is at the top; arrowheads, parietooccipital sulcus; (modified from Amunts et al., 2000). (see Color Plate 3)
the calcarine sulcus are also mandatory for studies of structural-functional mapping, because its variability zone in a stereotactic reference system measures 2 cm in the vertical axis (Steinmetz et al., 1990). 3.8.3. Class 2 variability Given the diversity of patterning of the cuneal, lingual, collateral and lateral occipital sulci, it requires some surmise to distinguish the homologies between different hemispheres. The exact topography of area 17 with regard to the occipital gross anatomical landmarks and the absolute amount of area 17 show a considerable amount of individual variation (Filimonoff, 1932; Stensaas et al., 1974; Rademacher et al., 1993; Amunts et al., 2000). For example, functional activation which maps onto the cuneus may relate either to area 17 or to area 18, depending on the individual anatomy (Figure 4.4). Relative to the size of the cuneus, the dorsal portion of area 17 may constitute between 12% and 74% of its area (Rademacher et al., 1993). Also, the varying relationship between the cuneal and lingual portions of area 17, showing up to fivefold individual differences, cannot be inferred from the surface relief (Amunts et al., 2000). The total size of area 17 appears to be symmetrical between the hemispheres for individual brains (Rademacher et al., 1993; Amunts et al., 2000), but in a minority of cases there may be a relevant asymmetry, with differences between sides of up to 400% (Stensaas et al., 1974). Interhemispheric differences have been described for the spatial position of area 17, which is located more medially and caudally on the left than on the right. Whether area 17 extends onto the lateral convexity or not cannot be deduced from the sulcal pattern. When present, this portion usually comprises less than 10% of the total striate cortex (Rademacher et al., 1993). 3.8.4. Differences between classical maps The Brodmann map shows an area 17 which takes a sagittal course parallel to the calcarine sulcus from the cuneal point anteriorly to the occipital pole posteriorly. The von
© 2002 Taylor & Francis
72
Jörg Rademacher
Economo and Koskinas map shows the same anterior-to-posterior extent of area 17, but its shape is triangular with the base at the occipital pole. In contrast with the Brodmann map, which describes a close relationship between the cuneal and lingual extent of area 17 and the course of the cuneal and lingual sulci respectively, the von Economo and Koskinas map excludes such an overlap between limiting sulci and cytoarchitectonic topography. In general, the modern maps show more irregular patterns in the shape of area 17 than the classical maps (Rademacher et al., 1993; Amunts et al., 2000) (Figure 4.4). 3.9. Occipital Lobe: Visual Association Cortex 3.9.1. Cytoarchitectonic criteria and macroanatomic landmarks Areas 18 and 19 (areas OB and OA) represent larger parts of the extrastriate visual cortex. Area 18 has a very prominent layer III and a lower cell packing density than area 17. There is no stripe of Gennari. Delineation of the border between areas 18 and 19 by visual inspection alone is problematic in Nissl-stained sections (Zilles, 1990). Filimonoff (1932) reported that the most typical difference is a more compact layer III with less radial stripes in area 18. At the parietal border, the remaining radial stripes of area 19 disappear and there are larger cells in layer III. At the border with area 37 there is a characteristic increase in the cell packing density of layer V. Recently, area 18 has been analyzed on the basis of quantitative cytoarchitecture and multivariate statistics (Amunts et al., 2000). Macroanatomically, the occipitotemporal sulcus (IR 100%) marks the border between the fusiform gyrus and the inferior temporal gyrus on the ventral brain surface. On the dorsolateral brain convexity the external parieto-occipital sulcus (IR 98%) separates the superior parietal lobe and the occipital lobe. The terminal downward projection of the intraparietal sulcus (IR 100%) and the anterior occipital sulcus (IR 98%) mark the approximate border between the inferior parietal lobe and the occipital lobe (Figure 4.1). The lateral occipital (IR 96%) sulci take a horizontal or oblique course across the lateral occipital lobe and, when present, the transverse occipital sulcus (IR 62%) is characterized by its vertical extent near the occipital pole. 3.9.2. Class 1 variability Typically, areas 18 and 19 are localized dorsally and ventrally to area 17 on the medial hemispheric surface. Anteriorly, the parieto-occipital sulcus is a reliable border of area 18 (Amunts et al., 2000) (Figure 4.4) and it may also be so for area 19 (Filimonoff, 1932; Braak, 1980). Ventrally, the collateral sulcus frequently represents the border between area 19 and area 37. Considerable amounts of areas 18 and 19 are localized on the lateral brain surface near the occipital pole with the external parieto-occipital sulcus and the terminal descending segment of the intraparietal sulcus as the anterior borders of area 19 (Filimonoff, 1932). 3.9.3. Class 2 variability There are no sulcal landmarks on the ventral and inferolateral brain surface which represent the precise macroanatomic or cytoarchitectonic border between the occipital and the temporal cortices. On the lateral brain surface, the lateral occipital sulci do not represent
© 2002 Taylor & Francis
Topographical Variability of Cytoarchitectonic Areas
73
useful landmarks for the borders of areas 18 and 19 (Filimonoff, 1932; Amunts et al., 2000). Similarily, the anterior extent of area 19 onto the parieto-occipital sulcus and the intraparietal sulcus varies considerably (Filimonoff, 1932). For area 18, topographical variations appear to be most prominent on the medial brain surfaces (Amunts et al., 2000). Compared to area 17, the topographical variability of area 18 is considerably larger (Amunts et al., 2000) and the extent of area 18 is underestimated in the stereotaxic atlas of Talairach and Tournoux (1988). On the ventral brain surface, the occipitotemporal sulcus does not appear to coincide systematically with cytoarchitectonic areal borders (Braak, 1980). The mean volumes of area 18 do not differ significantly between the hemispheres (Amunts et al., 2000), but individual size differences of up to 100% have been reported (Heinze, 1954). It is not known whether the consistent differences in gyrification between the occipital association cortex (higher degree of cortical folding) and the occipital pole (lower degree of cortical folding) may serve as a marker for the indirect localization of visual association areas 18 and 19 (Gebhard et al., 1993). 3.9.4. Differences between classical maps Typically, the parieto-occipital sulcus is depicted as the anterior border of the visual association cortices on the medial brain surface and the external parieto-occipital sulcus and the posterior intraparietal sulcus are presented as the anterior border of the visual association cortices on the lateral brain surface. However, there are also differences between the classical studies (Brodmann, 1909; von Economo and Koskinas, 1925; Sarkisov et al., 1949). While the Brodmann map shows that the superior half of the parieto-occipital sulcus is bordered by area 19 and its inferior half is bordered by area 18, the other maps indicate that more than 80% of the length of the parieto-occipital sulcus borders area 19. The relative size of area 18 with respect to area 19 on the cuneus shows all possible variations including symmetry (Sarkisov et al., 1949), asymmetry in favour of area 18 (Brodmann, 1909) and asymmetry in favour of area 19 (von Economo and Koskinas, 1925). These differences and asymmetries can be expected to result from the normal range of variations (Filimonoff, 1932; Amunts et al., 2000). While the Brodmann map shows the sagittal lingual sulcus as the dorsal border of area 18 and the collateral sulcus as the ventral border of area 18, such a clear relationship between the sulcal landmarks and the cytoarchitectonic borders cannot be deduced from the other classical or modern maps (Amunts et al., 2000). Even more important, the concept of a single area 19 is not supported by the modern literature on human brain mapping (Clarke and Miklossy, 1990; Zilles, 1990; Zeki, 1993). Braak (1980) subdivided the same region into 10 pigmentoarchitectonically defined areas. With few exceptions, the borders of the occipital association cortices do not show a systematic relationship to the local sulcal pattern (Amunts et al., 2000).
4. ANATOMIC LANDMARKS AND FUNCTIONAL ZONES Since cortical areas reflect the organization of the cerebral cortex, the issue of parcellating the cortex is fundamental for human brain mapping (Roland and Zilles, 1994). As described above, the gyral and sulcal landmarks frequently indicate the approximate location of a cortical area in the individual brain (class 1 variability), but the precise cytoarchitectonic borders do not match reliably with gyral or sulcal landmarks (class 2 variability).
© 2002 Taylor & Francis
74
Jörg Rademacher
The aforementioned data from various brain maps indicate that neither do the macro- and microanatomic parameters vary independently nor do they show a strict covariation. In this context, there is a fundamental difference between the primary (high incidence rate) and tertiary (low incidence rate) brain sulci with respect to cytoarchitectonic areal borders. With varying frequencies, only the former showed relevant relationships with areal borders. The data also appeared to reflect other systematic differences, in that there was a higher degree of class 1 variability for the primary somatosensory areas, and a higher degree of class 2 variability for the association areas. A higher biological variability of the association cortices compared to relatively strict patterns for the primary cortices may reflect a useful ontogenetic principle which allows for competition in the final development of individual cortical organization. Interindividual discrepancies have been observed for various anatomic parameters such as areal topography, size, laterality and parcellation. Between individual brains these differences could vary up to a factor 10, the functional implications of such enormous variations being unknown. In this context, it seems to be at least problematic, if cytoarchitectonic and functional homologies or asymmetries are to be deduced from the sulcal topography and the size of regions of interest as defined macroanatomically. Functional imaging studies have traditionally relied on a “standard” topography derived from a single brain (Damasio and Damasio, 1989) or on stereotactic reference systems (Talairach and Tournoux, 1988) for estimating the location of regions of interest. Substantial anatomical variation must be ignored by these approaches, with inevitable inconsistencies and mismatching between anatomy and function (Steinmetz et al., 1990). The significance of intersubject variability in the topography of functional signal changes remains unclear, because it may reflect either individual differences in cognitive strategy, or in anatomic topography. Obviously, a probabilistic atlas of the human brain including macroanatomic and microanatomic population maps could account for the variance in position of structures between individuals and hemispheres (Roland and Zilles, 1994; Maziotta et al., 1995; Zilles et al., 1995). Population maps are defined as three-dimensional representations of cytoarchitectonic areas in standard anatomical format in a population of subjects. Probabilistic cytoarchitectonic maps provide overlay maps for each area showing the degree of interindividual microstructural variability in any point of the brain’s threedimensional space (Morosan et al., 1996; Amunts et al., 1999, 2000; Geyer et al., 1999; Rademacher et al., 1999). The hypothesis that the cerebral sulcal patterns provide practically no information about the underlying anatomical organization of the neocortex (Killackey, 1995) is not supported by these studies. However, also cytoarchitectonic and macroanatomic parcellations are only interesting if they can be attributed to cerebral function. For example, many brains show asymmetries in the size of a multitude of gyri and of cytoarchitectonic areas. Despite these findings, the mean asymmetry scores are not so frequently asymmetrical, and the large variability of individual side differences indicates that cortical morphological asymmetry may be present even in the absence of clear functional asymmetry (Hutsler et al., 1998). Other problems relate to the homologies between different parcellation systems. The cytoarchitectonically based Brodmann map (1909) depicts about 50 cortical areas while the myeloarchitectonically based Vogt map shows up to 200 cortical areas (Vogt and Vogt, 1919). Similarily, it is not known how these subdivisions relate to those of Braak’s pigmentoarchitectonic method (Braak, 1980). In the case of the human visual association
© 2002 Taylor & Francis
Topographical Variability of Cytoarchitectonic Areas
75
areas, the classical cortical maps do not describe as many subdivisions as in the wellestablished map of the macaque (Zeki, 1993). In contrast, functional and anatomical (Clarke and Miklossy, 1990) evidence suggests that the human primary visual cortex (V1) is also surrounded by multiple functionally distinct association areas which can be mapped onto the gyral pattern and the primary sulci. The visual colour area (V4) maps onto the posterior portion of the fusiform gyrus (Allison et al., 1993) and the visual motion area (V5) bears a constant relationship to the posterior segment of the inferior temporal sulcus and the lateral occipital sulcus (Watson et al., 1993). This shows that discrepancies exist between the results of any pair of mapping techniques. Therefore, it seems reasonable to combine cytoarchitectonic and macroanatomic parcellation systems in order to attribute functional properties to distinct cortical areas.
REFERENCES Allison, T., Begleiter, A., McCarthy, G., Roessler, E., Nobre, A.C. and Spencer, D.D. (1993) Electrophysiological studies of color processing in human visual cortex. Electroencephalography and Clinical Neurophysiology, 88, 343–355. Amunts, K., Schlaug, G., Schleicher, A., Steinmetz, H., Dabringhaus, A., Roland, P.E. and Zilles, K. (1996) Asymmetry in the human motor cortex and handedness. NeuroImage, 4, 216–222. Amunts, K., Schleicher, A., Bürgel, U., Mohlberg, H., Uylings, H.B.M. and Zilles, K. (1999) Broca’s region revisited: cytoarchitecture and intersubject variability. Journal of Comparative Neurology, 412, 319–341. Amunts, K., Malikovic, A., Mohlberg, H., Schormann, T. and Zilles, K. (2000) Brodmann’s areas 17 and 18 brought into stereotaxic space—Where and how variable? NeuroImage, 11, 66–84. Armstrong, E., Schleicher, A., Omran, H., Curtis, M. and Zilles, K. (1995) The ontogeny of human gyrification. Cerebral Cortex, 5, 56–63. Bartley, A.J., Jones, D.W. and Weinberger, D.R. (1997) Genetic variability of human brain size and cortical gyral patterns. Brain, 120, 257–269. Braak, H. (1978) On magnopyramidal temporal fields in the human brain—probable morphological counterparts of Wernicke’s sensory speech region. Anatomy and Embryology, 152, 141–169. Braak, H. (1979) The pigment architecture of the human frontal lobe. I. Precentral, subcentral and frontal region. Anatomy and Embryology, 157, 35–68. Braak, H. (1980) Architectonics of the Human Telencephalic Cortex. Berlin: Springer-Verlag. Brodmann, K. (1909) Vergleichende Lokalisationslehre der Grosshirnrinde. Leipzig: Barth. Chi, J.G., Dooling, E.C. and Gilles, F.H. (1977) Gyral development of the human brain. Annals of Neurology, 1, 86–93. Clarke, S. and Miklossy, J. (1990) Occipital cortex in man: organization of callosal connections, related myeloand cytoarchitecture, and putative boundaries of functional visual areas. Journal of Comparative Neurology, 298, 188–214. Clarke, S. and Rivier, F. (1998) Compartments within human primary auditory cortex: evidence from cytochrome oxidase and acetylcholinesterase staining. European Journal of Neuroscience, 10, 741–745. Cunningham, D.J. (1892) Contribution to the Surface Anatomy of the Cerebral Hemispheres. Dublin: Royal Irish Academy. Damasio, H. and Damasio, A.R. (1989) Lesion Analysis in Neuropsychology. New York: Oxford University Press. Dehay, C., Giroud, P., Berland, M., Killackey, H. and Kennedy, H. (1996) Contribution of thalamic input to the specification of cytoarchitectonic cortical fields in the primate: effects of bilateral enucleation in the fetal monkey on the boundaries, dimensions, and gyrification of striate and extrastriate cortex. Journal of Comparative Neurology, 367, 70–89. Eberstaller, O. (1890) Das Stirnhirn. Ein Beitrag zur Anatomie der Oberfläche des Gehirns. Wien: Urban & Schwarzenberg. Eidelberg, D. and Galaburda, A.M. (1984) Inferior parietal lobule. Divergent architectonic asymmetries in the human brain. Archives of Neurology, 41, 843–852. Falzi, G., Perrone, P. and Vignolo, L.A. (1982) Right-left asymmetry in anterior speech region. Archives of Neurology, 39, 239–240. Ferrer, I., Hernandez-Marti, M., Bernet, E. and Galofre, E. (1988) Formation and growth of the cerebral convolutions. I. Postnatal development of the median-suprasylvian gyrus and adjoining sulci in the cat. Journal of Anatomy, 160, 89–100.
© 2002 Taylor & Francis
76
Jörg Rademacher
Filimonoff, I.N. (1932) Über die Variabilität der Grosshirnrindenstruktur. Mitteilung II: Regio occipitalis beim erwachsenen Menschen. Journal of Psychology and Neurology, 44, 1–96. Friston, K.J. (1998) Imaging neuroscience: principles or maps? Proceedings of the National Academy of Sciences U.S.A., 95, 796–802. Galaburda, A.M. (1980) La région de Broca: observations anatomiques faites un siècle après la mort de son découvreur. Revue Neurologique, 136, 609–616. Galaburda, A. and Sanides, F. (1980) Cytoarchitectonic organization of the human auditory cortex. Journal of Comparative Neurology, 190, 597–610. Galaburda, A.M., Sanides, F. and Geschwind, N. (1978) Human brain: cytoarchitectonic left-right asymmetries in the temporal speech region. Archives of Neurology, 35, 812–817. Galaburda, A.M., Corsiglia, J., Rosen, G.D. and Sherman, G.F. (1987) Planum temporale asymmetry, reappraisal since Geschwind and Levitsky. Neuropsychologia, 25, 853–868. Gebhard, R., Zilles, K. and Armstrong, E. (1993) Gross anatomy and gyrification of the occipital cortex in human and non-human primate. In: B. Gulyas, D. Ottoson and P.E. Roland (eds), Functional Organization of the Human Visual Cortex. Oxford: Pergamon Press, pp. 101–109. Geschwind, N. and Levitsky, W. (1968) Human brain: Left-right asymmetries in temporal speech region. Science, Washington, 161, 186–187. Geyer, S., Ledberg, A., Schleicher, A., Kinomura, S., Schormann, T., Bürgel, U., Klingberg, T., Larsson, J., Zilles, K. and Roland, P.E. (1996) Two different areas within the primary motor cortex of man. Nature, London, 382, 805–807. Geyer, S., Schleicher, A. and Zilles, K. (1999) Areas 3a, 3b, and 1 of human primary somatosensory cortex. NeuroImage, 10, 63–83. Heinze, G. (1954) Zytoarchitektonische Untergliederung der Area occipitalis. Journal für Hirnforschung, 1, 173–189. Holloway, R.L. (1979) Brain size, allometry, and reorganization: toward a synthesis. In: M.E. Hahn, C. Jensen and B.C. Dudek (eds), Development and Evolution of Brain Size. New York: Academic Press, pp. 59–88. Hopf, A. (1954) Die Myeloarchitektonik des Isocortex temporalis beim Menschen. Journal für Hirnforschung, 1, 208–279. Hutsler, J.J., Loftus, W.C. and Gazzaniga, M.S. (1998) Individual variation of cortical surface area asymmetries. Cerebral Cortex, 8, 11–17. Ide, A., Rodríguez, E., Zaidel, E. and Aboitiz, F. (1996) Bifurcation patterns in the human Sylvian fissure: hemispheric and sex differences. Cerebral Cortex, 6, 717–725. Killackey, H.P. (1995) Evolution of the human brain: a neuroanatomical perspective. In: M.S. Gazzaniga (ed.), The Cognitive Neurosciences. Cambridge: MIT Press, pp. 1243–1253. Kononova, E.P. (1949) The frontal lobe [in Russian]. In: S.A. Sarkisov, I.N. Filimonoff and N.S. Preobrashenskaya (eds), The Cytoarchitecture of the Human Cortex Cerebri, Moscow: Medgiz, pp. 309–343. Kostovic, I. and Rakic, P. (1990) Developmental history of the transient subplate zone in the visual and somatosensory cortex of the macaque monkey and human brain. Journal of Comparative Neurology, 297, 441–470. Lohmann, G., von Cramon, Y. and Steinmetz, H. (1999) Sulcal variability of twins. Cerebral Cortex, 9, 754–763. Markowitsch, H.J. and Tulving, E. (1995) Cognitive processes and cerebral cortical fundi. NeuroReport, 15, 413–418. Mazziotta, J.C., Toga, A.W., Evans, A., Fox, P. and Lancaster, J. (1995) A probabilistic atlas of the human brain: theory and rationale for its development. NeuroImage, 2, 89–101. Morosan, P., Schleicher, A., Schormann, T. and Zilles, K. (1996) Cytoarchitectonic mapping of cortical areas on the first transverse temporal gyrus and intersubject variability. NeuroImage, 3, S141. Ono, M., Kubik, S. and Abernathey, C.D. (1990) Atlas of the Cerebral Sulci. Stuttgart-New York: Georg Thieme Verlag. Paus, T., Tomaiuolo, F., Otaky, N., MacDonald, D., Petrides, M., Atlas, J., Morris, R. and Evans, A.C. (1996) Human cingulate and paracingulate sulci: pattern, variability, asymmetry, and probabilistic map. Cerebral Cortex, 6, 207–214. Penhune, V.B., Zatorre, R.J., MacDonald, J.D. and Evans, A.C. (1996) Interhemispheric anatomical differences in human primary auditory cortex: probabilistic mapping and volume measurement from magnetic resonance scans. Cerebral Cortex, 6, 661–672. Pfeifer, R.A. (1920) Myelogenetisch-anatomische Untersuchungen über das kortikale Ende der Hörleitung. Abhandlungen der mathematisch physikalischen Klasse der sächsischen Akademie der Wissenschaften, 37, 1–54. Rademacher, J., Galaburda, A.M., Kennedy, D.N. and Caviness, Jr., V.S. (1992) Human cerebral cortex: localization, parcellation, and morphometry with magnetic resonance imaging. Journal of Cognitive Neuroscience, 4, 352–374. Rademacher, J., Caviness, Jr., V.S., Steinmetz, H. and Galaburda, A.M. (1993) Topographical variation of the human primary cortices and its relevance to brain mapping and neuroimaging studies. Cerebral Cortex, 3, 313–329.
© 2002 Taylor & Francis
Topographical Variability of Cytoarchitectonic Areas
77
Rademacher, J., Werner, C., Schleicher, A. and Zilles, K. (1999) Planum temporale and language: cytoarchitectonic variability. NeuroImage, 9, S1045. Rajkowska, G. and Goldman-Rakic, P.S. (1995) Cytoarchitectonic definition of prefrontal areas in the normal human cortex. II. variability in locations of areas 9 and 46 and relationship to the Talairach coordinate system. Cerebral Cortex, 5, 323–337. Rakic, P. (1988) Specification of cerebral cortical areas. Science, Washington, 241, 170–176. Raymond, A.A., Fish, D.R., Sisodiya, S.M., Alsanjayri, N., Stevens, J.M. and Shorvon, S.D. (1995) Abnormalities of gyration, heterotopias, tuberous sclerosis, focal cortical dysplasia, microdysgenesis, dysembryoplastic neuroepithelial tumour and dysgenesis of the archicortex in epilepsy. Clinical, EEG and neuroimaging features in 100 adult patients. Brain, 118, 629–660. Richman, D.P., Stewart, R.M., Hutchison, J.W. and Caviness, V.S. (1975) Mechanical model of brain convolutional development. Science, Washington, 189, 18–21. Riegele, L. (1931) Die Cytoarchitektonik der Felder der Brocaschen Region. Journal of Psychology and Neurology, 42, 496–514. Rivier, F. and Clarke, S. (1997) Cytochrome oxidase, acetylcholinesterase, and NADPH-diaphorase staining in human supratemporal and insular cortex: evidence for multiple auditory areas. NeuroImage, 6, 288–304. Roland, P.E., and Zilles, K. (1994) Brain atlases—A new research tool. Trends in Neurosciences, 17, 458–467. Roland, P.E. and Zilles, K. (1998) Structural divisions and functional fields in the human cerebral cortex. Brain Research Brain Research Reviews, 26, 87–105. Rubenstein, J.L.R., Anderson, S., Shi, L., Miyashita-Lin, E., Bulfone, A. and Hevner, R. (1999) Genetic control of cortical regionalization and connectivity. Cerebral Cortex, 9, 524–532. Rubenstein, J.L.R. and Rakic, P. (1999) Genetic control of cortical development. Cerebral Cortex, 9, 521–523. Sanides, F. (1962) Die Architektonik des menschlichen Stirnhirns. Berlin: Springer-Verlag. Sarkisov, S.A., Filimonoff, I.N. and Preobrashenskaya, N.S. (1949) Cytoarchitecture of the Human Cortex Cerebri [in Russian]. Moscow: Medgiz. Schleicher, A., Amunts, K., Geyer, S., Simon, U., Zilles, K. and Roland, P.E. (1999) Observer-independent method for microstructural parcellation of cerebral cortex: a quantitative approach to cytoarchitectonics. NeuroImage, 9, 165–177. Schormann, T. and Zilles, K. (1998) Three-dimensional linear and nonlinear transformations: an integration of light microscopical and MRI data. Human Brain Mapping, 6, 339–347. Schulze, H.A.F. (1962) Quantitative Untersuchungen zur Frage der individuellen Variation und der Hemisphärendifferenzen der corticalen Areale des unteren Parietalläppchens. Journal für Hirnforschung, 5, 345–376. Sisodiya, S., Free, S., Fish, D. and Shorvon, S. (1996) MRI-based surface area estimates in the normal adult human brain: evidence for structural organisation. Journal of Anatomy, 188, 425–438. Smart, I.H. and McSherry, G.M. (1986) Gyrus formation in the cerebral cortex of the ferret. II. Description of the internal histological changes. Journal of Anatomy, 147, 27–43. Steinmetz, H., Rademacher, J., Huang, Y., Hefter, H., Zilles, K., Thron, A. and Freund, H.-J. (1989) Cerebral asymmetry: MR planimetry of the human planum temporale. Journal of Computer Assisted Tomography, 13, 996–1005. Steinmetz, H., Fürst, G. and Freund, H.-J. (1990) Variation of perisylvian and calcarine anatomic landmarks within stereotaxic proportional coordinates. American Journal of Neuroradiology, 11, 1123–1130. Steinmetz, H., Rademacher, J., Jäncke, L., Huang, Y., Thron, A. and Zilles, K. (1990) Total surface of temporoparietal intrasylvian cortex: diverging left-right asymmetries. Brain and Language, 39, 357–372. Steinmetz, H., Herzog, A., Schlaug, G., Huang, Y. and Jäncke, L. (1995) Brain (a)symmetry in monozygotic twins. Cerebral Cortex, 5, 296–300. Stengel, E. (1930) Morphologische und cytoarchitektonische Studien über den Bau der unteren Frontalwindung bei Normalen und Taubstummen. Ihre individuellen und Seitenunterschiede. Zeitschrift für Experimentelle und Angewandte Psychologie, 130, 630–677. Stensaas, S.S., Eddington, D.K. and Dobelle, W.H. (1974) The topography and variability of the primary visual cortex in man. Journal of Neurosurgery, 40, 747–755. Talairach, J. and Tournoux, P. (1988) Coplanar Stereotaxic Atlas of the Human Brain. Stuttgart: Thieme. Thompson, P.M., Schwartz, C., Lin, R.T., Khan, A.A. and Toga, A.W. (1996) Three-dimensional statistical analysis of sulcal variability in the human brain. Journal of Neuroscience, 16, 4261–4274. van Essen, D.C. (1997) A tension-based theory of morphogenesis and compact wiring in the central nervous system. Nature, London, 385, 313–318. van Essen, D.C., Drury, H.A., Joshi, S. and Miller, M.I. (1998) Functional and structural mapping of human cerebral cortex: solutions are in the surfaces. Proceedings of the National Academy of Sciences, U.S.A., 95, 788–795. Vogt, O. and Vogt, C. (1919) Ergebnisse unserer Hirnforschung. Journal of Psychology and Neurology, 25, 277–462. von Economo, C. and Koskinas, G.N. (1925) Die Cytoarchitektonik der Hirnrinde des erwachsenen Menschen. Berlin: Springer Verlag.
© 2002 Taylor & Francis
78
Jörg Rademacher
von Economo, C. and Horn, L. (1930) Über Windungsrelief, Maße und Rindenarchitektonik der Supratemporalfläche, ihre individuellen und ihre Seitenunterschiede. Zeitschrift für Neurologie und Psychiatrie, 130, 678–757. Watson, J.D.G., Myers, R., Frackowiack, R.S.J., Hajnal, J.V., Woods, R.P., Mazziotta, J.C., Shipp, S. and Zeki, S. (1993) Area V5 of the human brain: evidence from a combined study using positron emission tomography and magnetic resonance imaging. Cerebral Cortex, 3, 79–94. Welker, W. (1990) Why does cerebral cortex fissure and fold? A review of determinants of gyri and sulci. In: E.G. Jones and A. Peters (eds), Cerebral Cortex, Vol 8B, Comparative Structure and Evolution of Cerebral Cortex, Part II. New York: Plenum Press, pp. 3–136. Westbury, C.F., Zatorre, R.J. and Evans, A.C. (1999) Quantifying variability in the planum temporale: a probability map. Cerebral Cortex, 9, 392–405. White, L.E., Andrews, T.J., Hulette, C., Richards, A., Groelle, M., Paydarfar, J. and Purves, D. (1997a) Structure of the human sensorimotor system. I: morphology and cytoarchitecture of the central sulcus. Cerebral Cortex, 7, 18–30. White, L.E., Andrews, T.J., Hulette, C., Richards, A., Groelle, M., Paydarfar, J. and Purves, D. (1997b) Structure of the human sensorimotor system. II: Lateral symmetry. Cerebral Cortex, 7, 31–47. Witelson, S.F. and Kigar, D.L. (1992) Sylvian fissure morphology and asymmetry in men and women: bilateral differences in relation to handedness in men. Journal of Comparative Neurology, 323, 326–340. Witelson, S.F., Glezer, I.I. and Kigar, D.L. (1995) Women have greater density of neurons in posterior temporal cortex. Journal of Neuroscience, 15, 3418–3428. Yousry, T.A., Schmid, U.D., Alkadhi, H., Schmidt, D., Peraud, A., Buettner, A. and Winkler, P. (1997) Localization of the motor hand area to a knob on the precentral gyrus. A new landmark. Brain, 120, 141–157. Zeki, S. (1993) The multiple visual areas of the cerebral cortex. In: S. Zeki (ed.), A Vision of the Brain. Oxford: Blackwell Scientific Publications, pp. 87–93. Zilles, K. (1990) Cortex. In: G. Paxinos (ed.), The Human Nervous System. San Diego: Academic Press, pp. 757–802. Zilles, K., Armstrong, E., Schleicher, A. and Kretschmann, H.-J. (1988) The human pattern of gyrification in the cerebral cortex. Anatomy and Embryology, 179, 173–179. Zilles, K., Armstrong, E., Moser, K.H., Schleicher, A. and Stephan, H. (1989) Gyrification in the cerebral cortex of primates. Brain, Behavior and Evolution, 34, 143–150. Zilles, K., Schlaug, G., Matelli, M., Luppino, G., Schleicher, A., Qü., Seitz, R. and Roland, P.E. (1995) Mapping of human and macaque sensorimotor areas by integrating architectonic, transmitter receptor, MRI and PET data. Journal of Anatomy, 187, 515–537.
© 2002 Taylor & Francis
5 Mapping of Human Brain Function by Neuroimaging Methods Rüdiger J. Seitz Department of Neurology, Heinrich-Heine-University Düsseldorf Correspondence: Department of Neurology, University Hospital Düsseldorf Moorenstraße 5, D-40225 Düsseldorf Tel: 0049-211 81-18974; FAX: 0049-211 81-18485 e-mail:
[email protected]
This chapter gives an account of functional neuroanatomy of the human brain as revealed by neuroimaging methods. Metabolic autoradiography and optical imaging are the foundations of functional brain mapping, providing robust insights into the topography and temporal dynamics of cerebral metabolism in laboratory animals. The tomographic imaging methods provide excellent localising information about activation-related haemodynamic changes, but cannot resolve the temporal evolution of the underlying electromagnetic brain activity. Further, imaging of human brain function is hampered by inter-subject variability of brain structure and function as well as by the “partial volume effect” inherent in tomographic imaging. Nevertheless, the neuroimaging techniques allow one to study brain function non-invasively in healthy people, making feasible the construction of a physiological atlas of the different aspects of brain function. New technical developments have opened avenues for exploiting the temporal aspect of brain activity and analysing functional– structural relationships with reference to microanatomy. Examples will be described showing a good correspondence of specific activation studies in healthy subjects to lesion studies in patients with circumscribed neurological deficits. In contrast, activations which are abnormal in terms of quantitation and localisation have been observed in pathological conditions, suggesting that cerebral reorganisation can affect functional brain maps. Finally, evidence will be presented showing that the cerebral activations are influenced by learning and follow remarkable temporal modulations upon task performance. It is concluded that, the cortical representations of function appear as distributed computation nodes with widespread cortical and subcortical connections, processing information in task-related networks. KEYWORDS: autoradiography, brain activation, brain lesions, cerebral blood flow, cerebral metabolizm, cerebral reorganisation, deoxyhaemoglobin, electromagnetic activity, optical imaging, partial volume effect, structural variability, tomographic imaging
1. INTRODUCTION Functional neuroimaging is a powerful technology for mapping human brain function, and has undergone enormous development during the past decade (Fox, 1997; Seitz et al., 2000). Most widely used are measurements of stimulation-related haemodynamic changes. These changes can be assessed with measurements of the regional cerebral blood flow (rCBF) using positron emission tomography (PET), and of the blood oxygenation level-dependent changes (BOLD) using functional magnetic resonance imaging (fMRI). These tomographic 79 © 2002 Taylor & Francis
80
Rüdiger J. Seitz
imaging tools can localize brain activity changes with relatively good spatial resolution of approximately 5 to 9 mm (Frackowiak et al., 1994; Calamante et al., 1999). The temporal resolution of PET and fMRI, however, is relatively poor, being in the range of approximately 6 s to 1 min, due, respectively, to the tracer kinetics and the haemodynamic characteristics of the measurements. Nevertheless, the reconstructed tomographic imaging data allow one to detect activity changes occurring simultaneously in different parts of the brain, including the different parts of the cerebral cortex, subcortical structures as the basal ganglia and thalamus, and the cerebellum. It should be born in mind, however, that the observed haemodynamic changes represent only indirect measures of brain activity. Although, under physiological conditions, there is a tight coupling of activation-related metabolic and haemodynamic changes to increases in neural activity (Fox et al., 1984; Blomqvist et al., 1994; Bandettini et al., 1997; Hoge et al., 1999), bioelectric neural activity has a time-course in the range of several milliseconds, faster than the haemodynamic measures by three orders of magnitude. Therefore, one of the assumptions underlying functional imaging with PET and fMRI is that a state of activation has to be kept constant over a sufficiently long period of time in order to capture functional changes in the different parts of the brain, during a condition approaching a steady state. Bioelectric activity of the human brain can be recorded directly from the surface of the head using electroencephalography (EEG) and magnetoencephalography (MEG), the latter measuring the magnetic fields induced by electrical current flow. Temporal resolution of these techniques lies in the range of milliseconds, optimally reflecting the dynamics of brain activity (Hari and Lounasmaa, 1989; Näätänen et al., 1994). In comparison, spatial resolution is relatively poor, being determined by the number and distribution of the recording electrodes or sensors covering the head. The electrical potentials or magnetic fields recorded on the surface of the head, respectively with EEG and MEG, do not however reflect the localization of the real electrical activity in the brain, because the regionally-varying degrees of volume conductance in the cerebrospinal fluid compartment, the meninges, and in particular the skull severely distort the recorded data. Therefore, spatial analysis of the recorded data is based on biomathematical models that explain the data, recorded from the surface of the brain statistically, in terms of intracerebral sources (Wood et al., 1985; Romani and Rossini, 1988; Scherg, 1990; Kristeva et al., 1991; Snyder, 1991; de Peralta et al., 1997). In other words, the uncertainty of localization of the calculated source, the so-called inverse problem, critically depends on the model assumptions of head shape and volume conduction. Recently, information obtained from structural magnetic resonance imaging (MRI) has been used to create realistic head models for the analysis of the cortical generators of bioelectric activity as recorded with EEG (Dale and Sereno, 1993; Gevins et al., 1994; Marin et al., 1998). Magnetic fields are virtually unaffected by volume conduction. Therefore, even sources as deep in the brain as the thalamus or the hippocampal formation can be picked up by MEG (Ribary et al., 1991; Ebersole, 1997). However, dependent on the type of measuring devices, radial or tangential magnetic fields remain undetected in the MEG measurements, and this can profoundly effect the interpretation of the recorded data (Hämäläinen et al., 1993). Furthermore, due to the limited statistical power inherent in the biomathematical models, only a limited number of equivalent dipoles can be identified to explain the recorded data. In consequence, either well-determined experimental stimuli, such as electrical stimuli which evoke potentials, or simple, phase-locked sensory stimuli or movements have been studied extensively in brain research. More recently, localization of functional activity changes, as demonstrated by
© 2002 Taylor & Francis
Functional Brain Mapping
81
rCBF or fMRI measurements, as well as anatomical constraints, have been used for interpreting as well as restricting the “search space” in MEG recordings (Heinze et al., 1994; George et al., 1995; Gerloff et al., 1996; Kinsces et al., 1999; Korvenoja et al., 1999). Nevertheless, the basic assumption underlying these measures is that the task-specific, regional neuronal activity has the same temporal and spatial pattern across successive trials during the event-related recordings of EEG and MEG. Thereby, these methods will allow one to capture the temporal sequence of events in the brain. In spite of their methodological limitations, the neuroimaging tools can provide new insights into the topographic and temporal organization of human brain functions. Changes of brain activity were originally conceptualized as increased activity in a taskspecific stimulation condition, as compared to a specific control condition (Raichle, 1987). Later, task-specific decreases of brain activity also came into focus (Seitz and Roland, 1992a; Drevets et al., 1995; Shulman et al., 1997). Such categorical comparisons were based on the simplified model of a hierarchical implementation of activity in the human brain, that could be disentangled by psychological subtraction (Petersen et al., 1988; Kosslyn et al., 1995). Recently, more complex experimental designs have been addressed analytically to assess the effect of a number of different effective factors. In these factorial designs, main effects of each variable, as well as the interaction between these variables in the different psychophysical tasks, can be measured explicitly (Price et al., 1997). Also, biomathematical approaches have been developed to account for coherent brain activity in different brain regions, in relation to defined task conditions (Alexander and Moeller, 1994; Friston, 1994; McIntosh and Gonzalez-Lima, 1994; Büchel et al., 1997). These statistical approaches can be subdivided into those that are hypothesis-driven and region-based, thus restricting the search space by imposing an external model (McIntosh et al., 1994; Azari et al., 1999) and those using “omnibus” statistics, evaluating the functional connectivity in the entire set of image data (Friston et al., 1993; Seitz et al., 2001). Most recently, fMRI has been developed further to accommodate also activity recordings in an event-related fashion (D’Esposito et al., 1997; Buckner et al., 1998; Friston et al., 1998; Beauchamp et al., 1999). These measurements combine high spatial resolution with high temporal resolution (Menon and Kim, 1999). Common to these different types of image data analysis, however, is the fundamental idea that human brain function can be localized in topographically defined maps. The physiological foundations, technical limitations of functional neuroimaging, and the perspectives of the functional parcellation of the human brain will be discussed in this chapter.
2. PHYSIOLOGICAL FOUNDATIONS Microelectrode recordings in non-human primates have provided evidence for the modular organization of the cerebral cortex. Hubel and Wiesel (1963) as well as Mountcastle and collaborators (1969) were the first to report that neuronal populations assemble in vertical units extending from the upper to the lower laminae of the cerebral cortex. These so-called cortical columns are spatially distinct, representing receptive fields of the different sensory modalities or motor output modules (Hubel et al., 1978; Phillips et al., 1988; Fetz, 1993). A cortical area represents a local network of neuronal populations which are anatomically organized in cortical units, as evident physiologically by their correlated neuronal activity (Peters and Kara, 1987; Gray et al., 1990). In primary cortical areas these units
© 2002 Taylor & Francis
82
Rüdiger J. Seitz
follow a topographical map of their receptive fields (van Essen, 1985; Kaas, 1993). In motor cortex, however, representations of neighbouring units are intermingled such that sub-regions represent parts of local networks with highly organized patterns of corticocortical and cortico-subcortical afferents, and efferent connections allowing for specificity of movement rather than of muscles (Schieber and Hibbard, 1993; Lemon, 1988). More recently, it has been shown that correlated firing in neighbouring neurones, as recorded with multielectrodes, provides higher-order features of neural activity and thus far more information than the mere neuronal firing rate (Maynard et al., 1999). Furthermore, evidence was obtained showing that adjacent receptive fields are not invariant, but can be reshaped in relation to experimental manipulations. For example, repetitive stimulation of a receptive unit induces a local expansion of the corresponding cortical area, while conversely denervation results in shrinkage and even loss of the corresponding cortical representation (Jenkins and Merzenich, 1987; Eysel, 1992; Nudo et al., 1996; Kaas and Florence, 1997). In the motor cortex, the locally interconnected sub-fields provide avenues for reorganization subsequent to local cortical damage (Stepniewska et al., 1993; Weiss and Keller, 1994). Autoradiography provides quantitative means of studying the regional glucose consumption (rCMRGLc), the rCBF, and neurotransmitter and neuroreceptor distributions in laboratory animals. Such studies, which were pioneered by Sokoloff (Sokoloff et al., 1977) revealed that the cerebral cortex metabolizes the glucose analogue deoxyglucose in a highly organized, stimulus-related pattern (Kennedy et al., 1976; Juliano et al., 1981). Figure 5.1 shows a column-like cortical labelling in the striate cortex, with the highest intensity in cortical layer IV, during visual stimulation of one eye. In comparison to binocular whole-field visual stimulation, which induced a homogenous labelling of the occipital cortex, the regular metabolic pattern appeared to correspond to the ocular dominance columns. Exceptions were the areas with exclusively monocular input probably representing the blind spot (arrows). Likewise, cortical columns in somatosensory cortex were mapped autoradiographically in relation to simple and complex somatosensory stimuli (Juliano et al., 1983). Thus, it became obvious that metabolic mapping provided a means of studying the functional organization of the brain in vivo, with high spatial resolution. Also, autoradiography was used to demonstrate cortical plasticity in response to experimental brain lesions and peripheral deafferentation (Dietrich et al., 1985; Gilman et al., 1987; Kossut et al., 1988; Welker et al., 1992). It should be emphasized that these autoradiographic recordings demonstrate the total amount of glucose consumption of brain tissue, which is brought about by the neurones, the surrounding glial cells and to a small part by the endothelium of the intracerebral vessels. In addition to the topographic information, autoradiography also provided information about the driving forces of enhanced metabolic activity. Evidence was obtained showing that labelling intensity of glucose metabolism correlated with stimulus intensity (Kadekaro et al., 1985; Yarowsky et al., 1983), as did the intensity of glucose metabolism with the rCBF (Kuschinski et al., 1981; Cremer et al., 1983; Ueki et al., 1988). More specifically, regional metabolic activity was shown to reflect potassium ion fluxes at synaptic clefts (Mata et al., 1980; Kadekaro et al., 1985). Apart from neurones, astrocytes are predominantly responsible for metabolizing glucose (Magistretti and Pellerin, 1999). Recently, it was shown that excitation and inhibition are spatially closely related in the cerebral cortex, both resulting in a graded increase of glucose metabolism (Brühl and Witte, 1995). By applying optical imaging techniques, it was found that electrically active cortical areas induce a locally enhanced blood flow and enhanced oxygen consumption (Frostig et al., 1990; Malonek
© 2002 Taylor & Francis
Functional Brain Mapping
83
A
B
C
5.0 mm Figure 5.1. Autoradiographic mapping of the ocular dominance columns using 2D-deoxy-glucose in the rhesus monkey. The coronal sections at the level of the striate cortex show intensive metabolic activity during binocular vision (A), low metabolic activity during bilateral visual deprivation (B), and a patterned metabolic activity after right eye occlusion (C). Note the maximal metabolic activity in cortical Layer IV and the alternate dark and light striations during one-eye visual stimulation. The arrows indicate the location of the blind spot with only monocular input. Taken from Kennedy et al. (1976) with permission.
and Grinvald, 1996). Thus, glucose, the only energy substrate of the primate brain, is metabolized in the presence of oxygen, which is supplied rapidly to the area of enhanced brain activity by a local rise in rCBF, that exceeds quantitatively and spatially the area of enhanced metabolism (Grinvald et al., 1991). In addition, due to the high temporal resolution of the
© 2002 Taylor & Francis
84
Rüdiger J. Seitz
optical imaging techniques it became possible to uncover the sequential events of oxygen supply, oxygen extraction, and haemodynamic response after stimulation onset (Frostig et al., 1990). Accordingly, oxygen transport from the cerebral blood vessels into brain tissue is the initial event followed by a graded haemodynamic response. In accord with theoretical predictions these results support the view that a local rise in blood flow is such that it allows sufficient regional oxygen supply (Kislyakov and Ivanov, 1986; Buxton and Frank, 1997). These experimental recordings with nearly microscopical resolution opened detailed insight into the spatially and temporally organized metabolic-hemodynamic changes related to brain activity. The mediators for these events are manifold, including potassium ions, nitrous oxide, and possibly lactate but their individual contributions are still unclear (Erecinska and Silver, 1989; Iadecola, 1993). The functional imaging techniques, such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), provide means to study the active human brain. However, they present only indirect indicators of neural activity, as they measure the stimulation-related haemodynamic response. PET activation studies rely essentially on the tracer technology measuring the local task-specific cerebral accumulation of the rCBF tracer throughout the entire brain (Raichle, 1987). Depending on the biomathematical quantification model chosen, sampling intervals of 40 to 100 seconds are used in rCBF studies. Tracers labelled with positron-emitting isotopes such as [15O]-water or [15O]butanol allow one to estimate the rCBF in quantitative units by computation with the time-activity curve of the arterial tracer concentration (Herzog et al., 1996) or semiquantitatively after normalizing the cerebral tracer uptake to the global normal reference value of 50 ml/100g/min (Friston et al., 1994; Votaw et al., 1999). With respect to the neuronal processes that evolve in the time frame of milliseconds, an inherent limitation of the rCBF technique is therefore the long duration of the measuring time needed. In fMRI, the endogenous blood oxygenation level-dependent (BOLD) contrast is used as an indirect marker of cerebral blood flow changes (Calamante et al., 1999). Due to the enhanced blood flow in activated brain areas the amount of diamagnetic oxygenated blood becomes locally enhanced (see above). Thus, the paramagnetic deoxy-haemoglobin decreases which results in a signal increase in the fMRI images. In proportion to the fast evolving haemodynamics (Sitzer et al., 1994; Deppe et al., 2000), this signal builds up in some 8 seconds which can be followed with fast MR sequences including echo-planar imaging (Stehling et al., 1991; Frahm et al., 1992; Bandettini et al., 1997). While image evaluation of rCBFPET capitalizes on identifying significant changes that persist in the steady-state during the scanning interval compared to the control steady-state, fMRI exploits the consistency of activation-related changes over a couple of subsequent activation-control cycles. The areas that survive the different steps of image analysis (see also above) are converted to pseudocoloured hot spots, which thereafter can be superimposed on co-registered structural MR images or anatomical templates (Steinmetz et al., 1992a; Frackowiak, 1994). These hot spots indicate those areas in the brain that are specifically activated by a given task. However, such an area is not uniquely specialized for this task but rather may subserve also related operations. Nevertheless, the more routinely a task is performed, the more focal are the areas of cerebral activation (Roland, 1993). Interestingly, there seems to be a good correlation between brain electrical activity and the haemodynamic response as measured with rCBF-PET and fMRI, both during physiological stimulation (Heinze et al., 1994; Gerloff et al., 1996; Korvenoja et al., 1999) and under pathological conditions (Volkmann et al., 1998; Krakow et al., 1999). Nevertheless,
© 2002 Taylor & Francis
Functional Brain Mapping
85
the mode of stimulation has been shown to affect the hemodynamic response profoundly. In the visual cortex, a relation between the haemodynamic response and the stimulation frequency was observed (Fox et al., 1984; Hoge et al., 1999). In contrast, evoked electrical brain activity, as measured with EEG and MEG, becomes more clearly discernible in relation to an increasing interstimulus interval (Ibánez et al., 1995; Schnitzler et al., 1999). Recently, it was shown by simultaneous electrophysiological recordings and fMRI scanning during visual stimulation in the primate that the BOLD signal closely correlates with the local field potentials but not with the activity of single neurones (Logothetis et al., 2001). These data support the idea that neuroimaging can record information processing in the human brain due to concerted activity of neuronal populations. While these findings hold not only for the different lobes of the cerebral cortex and most likely also for the cerebellum, task-related activations may be elusive in the basal ganglia, thalamus, and brainstem structures. This failure may be due to the diversity of neurotransmitter systems, the higher anatomical packing density of diversified functional units in these structures (Chesselet et al., 2000; Schmahmann, 2000), as well as to the need to apply specialized approaches to image analysis (Bohm et al., 1991; Kleinschmidt et al., 1994). In addition, the reader should be alerted to the observation that there are gender differences of the rCBF and oxygenation response to physiological stimuli (Kastrup et al., 1999). Finally, it should be pointed out that the regional activation-induced changes of metabolism and blood flow, though strongly linked to each other (Roland et al., 1987; Fox et al., 1988; Seitz and Roland, 1992a; Blomqvist et al., 1994; Hoge et al., 1999), have different time constants of return to baseline, that can be picked-up by neuroimaging techniques (Madsen et al., 1999).
3. INTER-INDIVIDUAL VARIABILITY OF THE HUMAN BRAIN The most critical issue concerning the transposition of the results of animal experiments to tomographic imaging of the human brain is the inter-individual variability of human brain anatomy. This factor influences group image data which have been conceptualized as probabilistic representations of brain functions as well as the anatomic correspondence of activation foci related to identical tasks among different subjects. Inter-individual variability of the human brain has recently come into focus again in relation to in vivo morphometry from MRI studies of healthy subjects. Qualitative analysis of MRI images had already revealed profound inter-individual differences in gyrus formation, even in the brains of monozygotic twins (Steinmetz et al., 1995). By in vivo MRI, it was shown by quantitative means that gyral configuration of the human brain is highly variable among different individuals, and largely determined by epigenetic factors (Steinmetz et al., 1992b; Amunts et al., 1997; Kennedy et al., 1998). Also, there are interhemispheric differences within and between the sexes, probably related to differences in handedness, skills, and capacities (Schlaug et al., 1995; Gur et al., 1999). Still, after proportional scaling of the brains of different individuals into stereotactic reference space, inter-individual variability of the major cerebral sulci at the cerebral surface has a range of 1–2 cm (Steinmetz et al., 1989). These in vivo studies on high-resolution MR images validated previous findings by Talairach and colleagues obtained from post-mortem investigations (Talairach et al., 1967). Likewise, other approaches of spatial standardization using non-linear transformation procedures in addition to linear (affine) ones have also revealed residual inter-subject variability of a similar magnitude, both in terms of structural
© 2002 Taylor & Francis
86
Rüdiger J. Seitz
and functional inter-subject variability (Evans et al.., 1988; Seitz et al., 1990; Friston et al., 1994; Thurfjell et al., 1995; Roland et al., 1997). This is illustrated in Figure 5.2. After spatial standardization using affine and non-linear transformations, residual variability of the central sulcus was approximately 4 mm. The structural variability was similar to the variability of the rCBF increases related to sequential finger movements after applying the same spatial standardization parameters to the rCBF images as to the MR images. This residual functional variability was shown to be of such a magnitude that the areas of activation related to finger movement in eight of nine different subjects overlapped in only one pixel (Schlaug et al., 1994). In addition, there is considerable inter-subject variability of the cerebral activation pattern, that appeared to be related to the individual task performance (Schlaug et al., 1994). Similar data were also reported by other groups using other methods for normalization of spatial image data (Hunton et al., 1996; Hasnain et al., 1998). Thus, large rCBF changes in tomographic images appeared to be consistent across subjects, this being different from small rCBF changes that had a relatively large inter-subject variability (Figure 5.2). This finding is a reflection of the observation that the variance of rCBF changes is not stationary within the pixel matrix (Grabowski et al., 1996). That is, in the case of high mean rCBF due to consistent performance across the subjects, the interindividual location of the activation focus is more similar than in case of low mean rCBF related to low and less consistent performance. The problem of inter-subject variability cannot be discussed without mentioning the limited spatial resolution, the so-called partial volume effect in tomographic images. The partial volume effect obscures anatomic resolution and attenuates quantitation of measured activity (Mazziotta et al., 1981; Bohm et al., 1991). Figure 5.3 shows that the partial volume effect severely attenuates the signals recorded with functional imaging: The smaller the object the smaller the signal. The activity of objects with a dimension greater than three-times the optimal image resolution is recovered at 95% of the true activity only. The problem of the partial volume effect is of an even greater implication for imaging the human brain, because the cerebral cortex, and subcortical structures, in particular, are far smaller than the image resolution of current rCBF and fMRI image data. In addition, the cerebral gyri are lined by the cerebral sulci that contain inactive cerebrospinal fluid which further accentuates the partial-volume effect in functional imaging data. The same holds because of the close neighbourhood of the basal ganglia, thalamus and brain stem nuclei to the ventricular system. Figure 5.3 also shows that the smaller the signal compared to the image noise, the greater is the underestimation of the signal in magnitude and spatial dimension due to the partial volume effect (Knorr et al., 1993). Both, image noise and the partial volume effect may contribute to false negatives in functional imaging. Thus, the true activity changes in cerebral cortex and subcortical nuclei are underestimated by functional imaging, even if the partial volume effect is in the range of a few pixels, as is the case in fMRI (Calamante et al., 1999). Consequently, the partial volume effect adds to inter-subject variability of brain configuration, by diminishing stimulation-induced signal changes in functional imaging data. Thus, group image data underestimate the real changes, calculated by taking the peak changes in the individual subjects, by about of 30% (Seitz et al., 1990). Further, Figure 5.2 shows that inter-subject averaging may result in a slightly different position of the mean activation centre compared to the arithmetic mean of the peak changes in the individual subjects. Thus, inter-subject averaging can affect the resulting data not only quantitatively but also qualitatively with respect to anatomic validity. The danger of mislocalization
© 2002 Taylor & Francis
Functional Brain Mapping
87
Figure 5.2. Influence of the inter-individual variability on the delineation of an activation area in spatially standardized images of a group of subjects. (A) A great mean rCBF change has a small inter-individual variance (expressed as standard error of mean rCBF change) in identical pixel locations suggesting consistency of the activation-induced mean rCBF changes across subjects. Note that pixels with low activation-induced rCBF changes are quite variable (including also the largest SEM), reflecting a high degree of functional variability across subjects. (B) Comparison of structural and functional variability expressed as standard deviations. The tip of the central sulcus (left) had a range of variability of 4 mm compared to the variability of the mean rCBF increase related to finger movements of 5 to 10 mm (right). The arithmetic mean (♦) of the peak activations in the spatially standardized images of the individual subjects (●) deviated from the location of the peak of the mean rCBF increase in the statistical group image (䊊) by approximately 3 mm. For further details see Seitz et al. (1990).
© 2002 Taylor & Francis
88
Rüdiger J. Seitz
Figure 5.3. Influence of the functional image data on the delineation of an activation area. (A) Profile of the mean rCBF increase along a continuous row of pixels in the left motor cortex in a group of healthy subjects during right-hand finger movements. The mean rCBF increase at a threshold of p < 0.01 (28.7%) is lower than the peak change (41.2%) and smaller in extent than the entire area of mean rCBF increase. The entire area of the mean rCBF change had an increase of 23.4% compared to the resting control state. (B) Attenuated recovery of real activity in individual data in relation to object size and image noise. Real activity is recovered at approximately 95% for large objects with a mean background activity of 10% (open columns). Increasing image noise progressively impairs object detection and object recovery progressively: 60% background activity (hatched columns), 78% background activity (black column). Object size is given in relation to image resolution (FWHM, full width at half maximum). A complete description of the simulation study is given by Knorr et al. (1993).
© 2002 Taylor & Francis
Functional Brain Mapping A
89
B
Figure 5.4. Superimposition of the mean rCBF increases related to finger movement imagery as compared to preparation in 10 subjects (A) as yellow area onto the cytoarchitectonic subarea 4a of left motor cortex (B). Common areas of overlap of the anatomical site of cytoarchitectonic areas 4a (red) and 4p (violet) of two brains (n = 2) after standardization into reference space of the Human Brain Atlas (Roland et al., 1997). The centres of gravity (COG) are indicated. Note, the additional activation in the supplementary motor area and the lack of activation in subarea 4p of motor cortex (A). The location of the central sulcus is indicated as undulated yellow (A) and black (B) line, respectively. Preliminary data have been communicated by Stephan et al. (1995b) and were kindly provided by the authors. Left in the image is the lateral surface of the brain, right in the image the interhemispheric cleft. (see Color Plate 4)
increases with the partial volume effect in the image data. It is well recognized that heavy image smoothing may enhance the detectability of functional changes, but at the expense of creating virtual activation centres by merging of small closely adjacent real activity changes (Worsley et al., 1992). Increasing the partial volume effect artificially by image smoothing may therefore result in activation areas in possibly unexpected locations. To get a more realistic view of human brain function, in particular in cognitive tasks, it is mandatory to use improved methods of image standardization and optimal image resolution, as is feasible in the latest generation of PET scanners, and in high-Tesla fMRI scanners. These technical refinements will enhance the tomographic reflection of true activity changes, reducing the liability to false negatives and false positives. What will remain is the inter-individual variability due to anatomic differences in the microscopical dimension. As is highlighted in the chapter by Rademacher in this volume, there is not even a definite correspondence of cytoarchitectonic and chemoarchitectonic areas with macroanatomic landmarks in the human brain. This has been shown for the central sulcus, the lateral and the calcarine fissure (Rademacher et al., 1993). Accordingly, inter-subject variability also has a microscopical dimension which can be revealed only when macroscopical variability can be fully accounted for by spatial standardization procedures. To this end, new methods have been developed which allow one to transform different brains into the same reference with a point-to-point correspondence (Christensen et al., 1994; Schormann and Zilles, 1998; Fischl et al., 1999). These processes allow one to create maps of true microanatomic variability between different individuals, thus increasing the areas of rCBF overlap in different subjects in group image data, and improving the signal-to-background relation in activated areas dramatically (Schormann, personal communication).
© 2002 Taylor & Francis
90
Rüdiger J. Seitz
By these means it will also become possible to study functional subdivisions of cortical areas. For example, electrophysiological evidence in the somatosensory domain suggests that the cortical areas 3a and 2 are concerned with proprioceptive information, while areas 3b and 1 are processors of cutaneous input (Kaas, 1993). Likewise, area 4 of the motor cortex was shown to be made up by two cytoarchitectonically different sub-fields (Geyer et al., 1996). Co-registration of these cytoarchitectonic data with functional imaging maps suggest that area 4p in the depth of the central sulcus is concerned with motor execution while area 4a at the lateral outer aspect of the precentral gyrus is more concerned with preparatory aspects of movement (Figure 5.4). A question closely related to the issue of subdivisions within areas is whether identical neuronal populations have the capacity to assemble in a task-related manner to different subsets affording different functions. In conclusion, at the microscopical and microelectrode level, respectively, the issue of structural-functional correspondence is unsettled. Further discussion of this topic is given in the chapter by Amunts et al., in this volume.
4. LOCALIZATION OF HUMAN BRAIN FUNCTION Study of localization of human brain function has a long tradition. Until the advent of computed tomography (CT) this could only be done retrospectively by correlating neurological deficits with brain lesions at autopsy. CT provided for the first time a means of making in vivo correlations of brain lesions with clinical deficits, allowing for prospective analyses. However, since only a few Hounsfield units differentiate tissue compartments within the brain, and because of the partial volume effect produced by the skull and the geometrically complicated base of the skull, anatomical resolution is compromised in CT. Thus, small brain lesions, particularly in white matter structures and the brain stem, are usually elusive in CT scans. The lesion-based approach for studying the brain was dramatically improved by MRI which is far more sensitive and less prone to the confounds of the partial-volume effect than CT (Young et al., 1982; Prichard and Brass, 1992). Accordingly, MRI has become the method of choice for study of correlations between lesions and deficits. Brain lesions, however, do not follow functional divisions, but develop within the pathogenetic framework of the underlying disease. For example, brain infarctions have an individual configuration following the territories of the cerebral arteries or their branches (Bogousslavski et al., 1986; Ringelstein et al., 1992). Thus, superimposition of brain lesions of different patients introduces noise into the group data. Nevertheless, a common area of lesion overlap is expected to demonstrate a brain area critical for a certain function. It was therefore surprising that lesion mapping of syndromes like hemineglect was shown not to be functionally revealing but rather resulted in a reflection of the territory of the middle cerebral artery (Kertezs and Ferro, 1984). Similarly, motor aphasia was not a sufficient description of deficit to pinpoint the representative speech area in the brain (Poeck et al., 1984; Alexander et al., 1990). One explanation for this failure could be the distributed localization of brain function involving networks of different brain structures—a concept pioneered by Mesulam (1981, 1990). Accordingly, only damage to a critical node within such a network interferes with the function of the network. However, there is a large number of different nodes within such a network, subserving different sub-functions and allowing for partial rewiring after damage to one or a few of these nodes. Combined
© 2002 Taylor & Francis
Functional Brain Mapping
91
behavioural, electrophysiological and anatomical studies have shown the multiplicity and diversity of such networks, for example for voluntary control of action (Rizzolatti et al., 1998). This concept would explain variability of lesions across different subjects presenting with a closely similar deficit. However, as was evidenced recently, these functional deficits have to be fairly well defined in neurophysiological or neuropsychological terms to reflect a cortical module or a critical node. Examples are the clinically similar but clearly differentiable syndromes of mirror agnosia, mirror ataxia, and visuomotor ataxia (Rondot et al., 1977; Binkofski et al., 1999a). Thus, slightly different lesion locations are characterized by slightly different patterns of neurophysiological or neuropsychological deficits, which can be mapped to non-overlapping brain lesions even within one lobe. A different approach to localizing human brain function is to stimulate the brain or to record brain activity directly during open brain surgery. Careful documentation of observed brain lesions and the results of intra-operative cortical stimulation studies have been the basis for the elaboration of the topographical organization of motor and sensory representations by Foerster (1936) and subsequently by Penfield and Jasper (1954). More recently such a systematic approach was used to map brain structures relevant for production of speech and music (Ojemann et al., 1989; Creutzfeld and Ojemann, 1989; Haglund et al., 1992). This type of investigation has, however, a severe inherent confounding factor, which is the fact that studies are performed on abnormal brains. As lesions interfere with function, compensatory mechanisms are evoked, resulting in functional reorganization which is greater the earlier the lesion was acquired and the more slowly it expands (Seitz and Azari, 1999; Gadian et al., 1999). Recently, hypothesis-driven stimulation of the normal cortex using transcranial magnetic stimulation (TMS) has been shown to be appropriate to map the cortical representations of individual finger muscles in human motor cortex (Wassermann et al., 1996; Classen et al., 1998a). Quite differently, TMS has also been used as a probe to interfere with brain function. Thereby, the role of posterior brain areas for mediating visuospatial functions and visual imagery can be evaluated (Beckers and Hömberg, 1991; Paus et al., 1996; Kosslyn et al., 1999). The localizing capacity of TMS is limited however, due to the uncertainty of the exact location and spatial extent of the stimulated part of the brain. The most direct approach to the study of human brain function is to measure brain activity during brain work. This has become possible since the advent of the functional imaging techniques such as PET, fMRI, and MEG. In most experimental designs and data analyses of functional neuroimaging, a hierarchical concept of functional representation has been assumed (Petersen et al., 1988; Kosslyn et al., 1995). This approach follows the idea that neuropsychological processes can be identified by subtracting from a more complex task, a similar task in which the cerebral computations differ by one aspect. Thereby, functional units subserving this additional demand should become identifiable. In an ideal situation, task A would require areas 1 and 2 and task B areas 1 and 3, while the control task used in both comparisons would engage area 1 to the same degree. It is clear, however, that area 1 represents a specific activation in task A as in task B thus being elusive by direct comparisons of tasks A and B. Conversely, since area 1 is of critical importance for both tasks, it could be demonstrated as an area of a main effect by a conjunction analysis (Friston, 1997; Price et al., 1997). When area 1 is activated in a different manner in task A and B, there is a region-specific interaction. When delineating activation areas in functional imaging data, it is usually tacitly assumed that the activation area reflects a functional brain unit activated by a given task. However, stimulation-induced activity changes
© 2002 Taylor & Francis
92
Rüdiger J. Seitz
obtained by matrix statistics of groups of subjects are critically influenced by the image noise (Figure 5.3). The peak mean activity change exhibits the point with the greatest significance level, while the area of change at a predefined significance threshold is smaller than the entire area of change (Lueck et al., 1989). As evident from Figure 5.3, definition of what is considered to represent the stimulation-related change is a critical determinant of the spatial dimension and magnitude of this change. The currently available image processing methods yield astonishingly similar results with an advantage for larger compared to small samples (Grabowski et al., 1996; Missimer et al., 1999). In addition to the categorical type of data analysis, multivariate statistics have been applied for accounting for the concept of distributed cerebral functions, in the sense of task-related networks involving sub-units in different cortical and subcortical areas. Approaches of this sort without a priori assumptions are correlation and principal components analyses, requiring additional statistical testing to make inferences about the identified patterns (Friston et al., 1993; Alexander and Moeller, 1994; Friston, 1994). These results can be displayed as image data, but do not provide any clue about the direction of information flow among the constituting areas, or about the strength of connectivity among them. For this goal, the approaches of structural equation modelling can be applied. These types of analysis incorporate external models to describe inter-regional interactions (McIntosh and Gonzalez-Lima, 1994; Büchel and Friston, 1997). These methods are, however, biased by the model-related selection of areas included in the calculation. Also, in these approaches the number of regions should be less than the number of study subjects, since otherwise the calculated solutions are not stable.
5. CORRESPONDENCE OF LESION AND ACTIVATION STUDIES It is widely accepted that brain lesions, by definition, interfere with brain function but do not necessarily show the site where a certain brain function is represented. On the contrary, brain imaging usually shows a number of brain areas activated in relation to a certain task. This becomes evident from neuroimaging studies on visual information processing (see Gulyas, 1997, for review). Accordingly, network approaches to analysis of image data have provided evidence that a specific function involves an entire network of brain structures. For example, object vision involves a number of striate and extrastriate cortical areas (Horwitz, 1994; Büchel and Friston, 1997). The question, though, is whether a certain brain structure which is essential for a given function can be identified by the correspondence of lesion and activation studies. A lesion to such a structure would then lead to a permanent deficit and coincide with the activation site related to the corresponding activation paradigm. In the motor system, it is well established that the mid-dorsal portion of the precentral gyrus contains the cortical representation of finger and hand movements of the opposite side of the body. The first evidence for this was obtained from intra-operative stimulation studies showing that low threshold electrical stimulation of this cortical area induces muscle twitches of the contralateral forearm (Foerster, 1936; Penfield and Boldrey, 1953). Likewise, in neuroimaging studies, circumscribed rCBF increases were observed in this location during hand and finger movements of the contralateral arm (Seitz and Roland, 1992b; Rao et al., 1993; Stephan et al., 1995; Seitz et al., 1996, 1997; Rijntjes et al., 1999). More importantly, even muscle relaxation activates the same cortical areas (Toma
© 2002 Taylor & Francis
Functional Brain Mapping
93
et al., 1999). There are anatomical landmarks such as the omega-shaped configuration of the central sulcus on axial MR images, as well as the endpoint of the superior frontal fissure touching the precentral sulcus at this point, to indicate the location of the motor hand representation (Ebeling et al., 1992; Yousry et al., 1995). Conversely, combined morphological and electrophysiological evidence was obtained recently showing that the degree of destruction of part of the precentral gyrus or its corticospinal projection is a quantitative indicator of both the loss of individual finger movements of the contralateral hand, and the capacity for recovery (Binkofski et al., 1996; Pendlebury et al., 1999). It should also be emphasized that lesion effects are topographically specific. For instance, leg movements were affected to a lesser degree, or not at all, when precentral lesions affected arm function, and vice versa (Schneider and Gautier, 1994; Azari et al., 1996). Furthermore, motor impairment does not correlate with somatosensory impairment of the same body part (Azari et al., 1996). These results have their counterpart in the rCBF increases in the frontomesial cortex related to leg movements and in the postcentral gyrus related to somatosensory stimulation in healthy volunteers (Fox et al., 1987a; Fukuyama et al., 1997). Similarly, bimanual co-ordination was shown to be significantly impaired in patients with circumscribed lesions of the mid-part of the cingulate gyrus, while activation studies in healthy volunteers demonstrated large areas of activation both in the supplementary motor area and in the mid-cingulate cortex, probably involving the cingulate motor area as well as the lateral premotor cortex (Stephan et al., 1999). Quantitative investigations revealed persistent motor deficits in patients or primates with lesions within the precentral gyrus in spite of apparently complete clinical recovery (Friel and Nudo, 1998; Seitz et al., 1999). Apparently, clinical restoration goes along with measurable minute deficits of the arm contralateral and ipsilateral to the lesion, and is often accompanied by subtle, but clear-cut changes of task performance related to alternative strategies of task performance (Winstein and Pohl, 1995). It should be emphasized that cortical lesions subsequent to focal ischemia affect not only the local cortical machinery but also their cortical and cortico-subcortical afferents and efferents. This widespread effect of cortical lesions can be visualized by metabolic mapping (Seitz et al., 1994, 1999). Consequently, it can be argued that persistent neurological impairments result not only from the lesion itself but also from suppression or deafferentation of the affected cortical node from other parts of the network (von Giesen et al., 1994; Classen et al., 1995). By contrast, circumscribed experimental lesions of cortex sparing subcortical fibre tracts remain free from associated remote effects (Buchkremer-Ratzmann et al., 1997). Similar observations have also been made in the visual system, showing a mirror-like correspondence of the retinotopic activation of the visual cortex upon visual stimulation and the central or peripheral homonymous visual field defects after circumscribed lesions of the calcarine cortex or the optic tract (Fox et al., 1987b; Zihl and von Cramon, 1985). The correspondence of lesion and activation studies also appears to hold for higher order visual areas. Specifically, there appears to be a close correspondence between clinical deficits in motion and colour perception related to circumscribed occipito-temporal brain lesions and the cerebral activations upon motion perception and colour vision in healthy volunteers (Zihl et al., 1991; Watson et al., 1993; Ungerleider and Haxby, 1994; Tootell et al., 1995). Recently, it was demonstrated that such a close correspondence of lesions and sites of activation is also pertinent for the parietal lobe. Evidence was obtained that unilateral disturbances of forming a precision grip formation for grasping are associated with lesions
© 2002 Taylor & Francis
94
Rüdiger J. Seitz
Figure 5.5. Topographic correspondence of an area of lesion overlap among different patients and the site of activation in healthy volunteers. The patients had a severe impairment of object prehension, while the healthy subjects specifically activated the same region during prehension movements as contrasted with reaching (Binkofski et al., 1998). The lesion and the activation foci are localized in the lateral bank of the cortex lining the anterior part of the intraparietal sulcus (IPS) thus probably corresponding to the anterior intraparietal area. Note that the lesions of the patients differed in extent (shaded area). CS = central sulcus.
of the parietal cortex lining the anterior portion of the intraparietal sulcus (Binkofski et al., 1998). Conversely, healthy volunteers required to perform grasping movements showed a specific activation of the cortex lining the interior part of the intraparietal sulcus in surprisingly close correspondence to the cortical lesions inducing the grasping deficit (Figure 5.5). Similarly, lesions of this superior parietal lobule induce severe deficits of object manipulation, resulting in tactile agnosia (Binkofski et al., 2001). Conversely, neuroimaging studies in healthy volunteers showed circumscribed activations of the superior parietal cortex in a closely corresponding location, related to tactile information processing during exploration of large geometric objects, as well as of textures (Seitz et al., 1991; O’Sullivan et al., 1994; Binkofski et al., 1999b). Lesions of the inferior parietal cortex including the parietal operculum have been shown to abolish the ability to identify objects upon tactile exploration, a syndrome which was termed object agnosia (Caselli et al., 1993). Recently, fMRI data in healthy volunteers provided evidence that the inferior parietal cortex, including the secondary somatosensory area, becomes activated during tactile exploration and identification of large complex geometric objects (Binkofski et al., 1999b). These data support the notion that the secondary somatosensory area is involved in processing intrinsic object information necessary for object recognition. A specific location for speech production was demonstrated recently in a study on language recovery, after post-stroke aphasia (Heiss et al., 1999). Patients with frontal and subcortical infarctions recovered well within 8 weeks after stroke, while patients with an infarction involving the superior temporal cortex did not. The cortical activation patterns related to a verb generation task showed activation of the superior temporal cortex bilaterally, and of the right inferior frontal cortex in the patients who recovered, whereas the patients who did not recover showed activation of the inferior frontal cortex in both hemispheres but only of the right superior temporal cortex. These patterns demonstrated the impact of the left superior temporal cortex for language recovery in post-stroke aphasia. They correspond to the activation patterns related to verb generation in healthy volunteers, as sampled across different imaging centres (Poline et al., 1996). Of particular interest in this connection are also the observations by Friston et al. (1991) who showed that the superior temporal activations were negatively correlated to the dorsolateral prefrontal
© 2002 Taylor & Francis
Functional Brain Mapping
95
activations, suggesting a modulatory action of the dorsolateral prefrontal cortex on the temporal language areas. Conversely, a remote functional disturbance in the superior temporal cortex impaired language abilities in patients with left medial temporal lobe epilepsy (Arnold et al., 1996). These examples indicate that circumscribed structural lesions (as well as functional irritations remote from the brain lesions) can interfere with functions that are represented in exactly these locations as evident from functional imaging. In other words, lesion studies can provide specific information about critical cortical nodes, whereas corresponding activation studies demonstrate a number of activated cortical areas inclusive of the critical area as implicated from lesion studies. Accordingly, the notion that cerebral lesions interfere with the network required to produce a neuronal function, while the network becomes visible as a whole in neuropsychological activation studies in healthy volunteers, is supported by the evidence available from combined lesion and activation studies.
6. DISCREPANCY OF LESION AND ACTIVATION STUDIES In contrast to complete destruction of a cortical module, a partial lesion of this module can be compensated by reorganization of the remaining parts (Nudo et al, 1996; Xerri et al., 1998). Similarly, a lesion to one node within a cortical network can be compensated by reshaping of the remaining parts of the network. Such mechanisms of plastic reorganisation are supposed to underlie the clinical recovery of function. If so, cerebral lesions and activation studies are expected to be discrepant. Examples include slowly progressive brain lesions. Indeed, evidence was obtained showing that low grade gliomas distort the representation of a given brain function to such an extent that it is mapped to an atypical location (Wunderlich et al., 1998). This kind of cortical reorganization took place predominantly within a functional unit, such as the motor cortex. Morphometric measures performed in parallel to the activation studies revealed that the functional displacement exceeded the mass effect of the growing tumor (Figure 5.6). In cortical lesions that are acquired in utero, neonatally or during early infancy, reorganization of function has been shown even to exceed the limits of functional borders, allowing for abnormal functional representation in grossly abnormal locations including even the contralateral cerebral hemisphere (Müller et al., 1998; Seitz and Azari, 1999). In temporal lobe epilepsy in which a structural abnormality evolved during adolescence, there may be also a discrepancy between a circumscribed cerebral lesion and the clinical deficit. It was shown that patients with left temporal lobe epilepsy exhibiting atrophy and sclerosis of the hippocampal formation are impaired in verbal recall, verbal fluency and verbal intelligence (Frisk and Milner, 1990; Tranel, 1991). The metabolic changes related to this disease condition occurred not only in the mesiotemporal region but also in remote locations in the superior temporal and inferior frontal language areas and in prefrontal cortex (Arnold et al., 1996; Jokeit et al., 1997). Network approaches to analysis of the data revealed large-scale abnormalities in these patients involving a weakened relationship of the bilateral prefrontal cortex but strengthened connectivity of the language areas and the right thalamus (Figure 5.6). These observations are in accord with the finding that patients with left temporal lobe epilepsy enjoy recovery of their language functions following selective anterior temporal lobectomy and thus excision of the irritative lesion (Regard et al., 1994).
© 2002 Taylor & Francis
96
Rüdiger J. Seitz
Figure 5.6. Local and large-scale reorganization of functional representations in chronic brain lesions. (A) Three-dimensional vector displacements of the rCBF increases in the affected cerebral hemisphere of five patients with precentral gliomas. Origin corresponds to the location of motor hand area as determined for the normal hand in the contralesional hemisphere. The displacement of the vectors from origin indicates the structural displacement of the central sulcus. d = dorsal, f = frontal, l = lateral, o = occipital, m = medial, v = ventral. (B) Abnormal metabolic interactions in patients suffering from left mesiotemporal epilepsy and impairment of verbal abilities. Compared to healthy controls, weak interhemispheric coupling of prefrontal cortex and enhanced coupling between the metabolically depressed inferior frontal and superior temporal cortex and the unaffected right thalamus. The individual image data have been spatially standardized to the stereotactice space (Talairach and Tournoux, 1988) to allow for inter-individual comparisons.
Another condition in which brain lesions compromise brain function in a remote fashion is neglect. In motor neglect the somatosensory afferent and motor efferent projections as well as the corresponding primary cortical processing areas were shown to be functional. However, circumscribed lesions in quite variable locations interfered with the network mediating conscious behaviour, resulting in a widespread pattern of metabolic deficits including premotor, parietal, cingulate and thalamic locations (von Giesen et al., 1994). Interestingly, this behavioural deficit regressed as the exaggerated cortical inhibition of the motor cortical apparatus after stroke normalized, as evidenced by transcranial magnetic stimulation (Classen et al., 1997). These findings have been supplemented by network analysis approaches showing that post-stroke motor recovery involves large-scale networks inclusive of brain structures remote from the infarct lesion. Within the motor system there was a resetting of the pathway from cerebellum to thalamus and supplementary motor area in the sub-acute post-infarct period (Azari et al., 1996). Furthermore, a network including thalamus and visual cortex, active with a more prolonged time course after stroke, suggested supramodal compensation of the deficit (Seitz et al., 1999). In acute stroke magnetic resonance imaging is the method of choice for demonstrating perfusion abnormalities (Prichard, 1992; Calamante et al., 1999). It should be pointed out that in such situations conventional and even modern neuroimaging techniques (as for instance diffusion-weighted imaging) may reveal only small lesions or even be normal (Tong et al., 1998; Neumann-Haefelin et al., 1999). In these situations, the affected cerebral cortex is electrically inexcitable as demonstrated in humans and in animal experiments, remaining profoundly abnormal for a long period of time (Dominkus et al., 1990; Heald et al., 1993; Bolay and Dalkara, 1998). Specifically, after incomplete
© 2002 Taylor & Francis
Functional Brain Mapping
97
ischemia, the motor cortex may be excitable with transcranial magnetic stimulation in patients who recovered from stroke, while the haemodynamic response related to functional activation remains negative (Seitz et al., 1998). Initial data suggest that this electricalhaemodynamic decoupling develops in the subacute stage after infarction (Binkofski et al., 1999c). At present it is unclear whether, and if so, when the electrical-haemodynamic coupling will become restored again. As outlined above, the assumption underlying rCBF and fMRI measurements is that brain function (as characterized by the changing demands of electric activity upon regionally enhanced metabolism) can be picked up indirectly by the haemodynamic response. Changes of the threshold of neuronal excitability have however been shown to remain elusive in such measurements, while they can be recognized by transcranial magnetic stimulation and MEG recordings (Schnitzler et al., 1995; Stephan and Frackowiak, 1996). Specifically, it was shown that imagery of motor activity fails to induce rCBF changes in the motor cortex of a magnitude, spatial extent or significance comparable to that in premotor, cingulate, and parietal cortical areas (Stephan et al., 1995a; Porro et al., 1996; Roth et al., 1996; Seitz et al., 1997). Rather, only a portion in the lateral subarea of motor cortex appears to be activated (Figure 5.4). Nevertheless, excitability of the motor cortex after motor imagery was enhanced, threshold for excitation being reduced at the same time (Schnitzler et al., 1995; Stephan and Frackowiak, 1996). Likewise, visual imagery induces only minute haemodynamic changes in primary visual cortex (Roland and Gulyas, 1994). Furthermore, it was shown that transcranial magnetic stimulation over the occipital cortex eliminated visual imagery while it was ineffective in posterior temporal application (Kosslyn et al., 1999). These data show that haemodynamic measurements may only partially reflect activity changes in the brain, adding to possible dissociations of lesion and activation studies.
7. CORTICAL REPRESENTATIONS OF FUNCTION The cortical representations of function have been shown to be affected remarkably by physiological learning. Using fMRI it was shown that learning of sequential finger movements induces a spatial increase of the cortical finger representation as learning progressed (Karni et al., 1995). Group data of motor learning provided evidence of a common area of representation of hand-finger movement, with a clear rate effect on the rCBF increase, and irrespective of the degree of learning of finger movement (Seitz and Roland, 1992; Grafton et al., 1992; Jenkins et al., 1994). Using transcranial magnetic stimulation, it was however demonstrated that the threshold for cortical excitability varies in relation to task acquisition. The excitable area of motor cortex was most extensive when the task was explicitly and routinely performed (Pascual-Leone et al., 1994, 1995). Moreover, it was shown that these changes coded movement direction (Classen et al., 1998b). Also, during skill learning the motor cortical activation becomes progressively lateralized (Seitz and Roland, 1992b). This change was accompanied by a temporal reorganization of EMG activity from the typical three-phasic burst discharges related to individual finger movements to a two-burst discharge pattern after learning, probably reflecting the establishment of a tremor-like activity in the cortico-subcortical loop. Event-related fMRI has provided evidence that the cortical representations of function may also be modulated in a highly organized fashion in the temporal dimension. For
© 2002 Taylor & Francis
98
Rüdiger J. Seitz
example, it was shown that in working memory conditions the haemodynamic response, in those cortical areas concerned with stimulus perception, preceded those in areas related to response generation, while prefrontal areas showed enhanced haemodynamic responses during the entire delay period between stimulus presentation and the response execution (D’Esposito et al., 1997; Menon and Kim, 1999). Likewise, the perception of ambiguous stimuli resulted in antagonistic response curves in different visual sub-fields, which were related to the actual perception of the stimuli (Kleinschmidt et al., 1998). As outlined in this chapter, activation studies in healthy subjects and lesion studies in patients with well-defined clinical deficits provide means for elucidating the modular organization of the human brain. Thus, construction of a physiological atlas of the different brain functions appears feasible. One should bear in mind that anatomical afferents and efferents converge at the observed, activated nodes allowing the processing of information gathered from different sources. This view of a task-related assembly of cerebral networks comprising different critical nodes would apply to the so-called primary areas and also to the higher order associative cortices. The view is also capable of accounting for plastic topographical and temporal reorganization related to physiological learning and to adaptation to pathological conditions. Finally, this view would explain the relative paucity of corticofugal efferent and corticopetal afferent projections in the presence of a plethora of cortical-cortical connections, which can be estimated from the large dimension of cerebral white matter compared to the small diameter of the pyramidal tract, the posterior spinal columns, and the optic nerves.
REFERENCES Alexander, M.P., Naeser, M.A. and Palumbo, C. (1990) Broca’s area aphasia: aphasia after lesions including the frontal operculum. Neurology, 40, 353–362. Alexander, G.E. and Moeller, J.R. (1994) Application of the subprofile model to functional imaging in neuropsychiatric disorders: A principal component approach to modeling brain function in disease. Human Brain Mapping, 2, 79–94. Amunts, K., Schmidt-Passos, F., Schleicher, A. and Zilles, K. (1997) Postnatal development of interhemispheric asymmetry in the cytoarchitecture of human area 4. Anatomy and Embryology (Berlin), 196, 393–402. Arnold, S., Schlaug, G., Niemann, H., Ebner, A., Lüders, H., Witte, O.W. and Seitz, R.J. (1996) Topography of interictal metabolic depressions in mesiotemporal lobe epilepsy. Neurology, 46, 1422–1430. Azari, N.P., Binkofski, F., Pettigrew, K.D., Freund, H.-J. and Seitz, R.J. (1996) Enhanced regional cerebral metabolic interactions in thalamic circuitry predicts motor recovery in hemiparetic stroke. Human Brain Mapping, 4, 240–253. Azari, N.P., Arnold, S., Schlaug, G., Niemann, H., Ebner, A., Lüders, H., Witte, O.W. and Seitz, R.J. (1999) Reorganized patterns of metabolic interactions in temporal lobe epilepsy. Neuropsychologia, 37, 625–636. Bandettini, P.A., Kwong, K.K., Davis, T.L., Tootell, R.B.H., Wong, E.C., Fox, P.T., Belliveau, J.W., Weisskoff, R.M. and Rosen, B.R. (1997) Characterization of cerebral blood oxygenation and flow changes during prolonged brain activation. Human Brain Mapping, 5, 93–109. Beauchamp, M.S., Haxby, J.V., Jennings, J.E. and DeYeo, E.A. (1999) An fMRI version of the FarnsworthMunsell 100Hue test reveals multiple color-sensitive areas in human ventral occipitotemporal cortex. Cerebral Cortex, 9, 257–263. Beckers, G. and Hömberg, V. (1991) Impairment of visual perception and visual short term memory scanning by transcranial magnetic stimulation of occipital cortex. Experimental Brain Research, 87, 421–432. Binkofski, F., Seitz, R.J., Arnold, S., Claßen, J., Benecke, R. and Freund, H.-J. (1996) Thalamic metabolism and integrity of the pyramidal tract determine motor recovery in stroke. Annals of Neurology, 39, 460–470. Binkofski, F., Dohle, C., Posse, S., Stephan, K.M., Hefter, H., Seitz, R.J. and Freund, H.-J. (1998) Human anterior intraparietal area subserves prehension. A combined lesion and functional MRI activation study. Neurology, 50, 1253–1259.
© 2002 Taylor & Francis
Functional Brain Mapping
99
Binkofski, F., Buccino, G., Dohle, C., Seitz, R.J. and Freund, H.-J. (1999a) Mirror agnosia and mirror ataxia constitute different parietal lobe disorders. Annals of Neurology, 46, 51–61. Binkofski, F., Buccino, G., Posse, S., Seitz, R.J., Rizzolatti, G. and Freund, H.-J. (1999b) A fronto-parietal circuit for object manipulation in man: evidence from an fMRI study. European Journal of Neuroscience, 11, 3276–3286. Binkofski, F., Fink, G.R., Wittsack, H.-J., Buccino, G., Seitz, R.J. and Freund, H.-J. (1999c) Lesion location determines cortical activation patterns in stroke recovery. Neuroimage, 9, S726. Binkofski, F., Kunesch, E., Dohle, C., Seitz, R.J., Freund, H.-J. (2001) Tactile Apraxia: unimodal apractic disorder of tactile object exploration associated with parietal lobe lesions. Brain, 124, 132–144. Blomqvist, G., Seitz, R.J., Sjögren, I., Halldin, C., Stone-Elander, S., Widen, L., Solin, O. and Haaparanta, M. (1994) Regional cerebral oxidative and total glucose consumption during rest and activation studied with positron emission tomography. Acta Physiologica Scandinavica, 151, 29–43. Bohm, C., Greitz, T., Seitz, R.J. and Eriksson, L. (1991) Specification and selection of regions of interest (ROIs) in a computerized brain atlas. Journal of Cerebral Blood Flow and Metabolism, 11 (Suppl 1), A64–A68. Bogousslavsky, J., Regli, F. and Assal, G. (1986) The syndrome of unilateral tuberothalamic artery territory infarction. Stroke, 17, 434–441. Bolay, H. and Dalkara, T. (1998) Mechanisms of motor dysfunction after transient MCA occlusion: persistent transmission failure in cortical synapses is a major determinant. Stroke, 29, 1988–1994. Brühl, C. and Witte, O.W. (1995) Cellular activity underlying altered brain metabolism during focal epileptic activity. Annals of Neurology, 38, 414–420. Büchel, C. and Friston, K.J. (1997) Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. Cerebral Cortex, 7, 768–778. Buchkremer-Ratzmann, I. and Witte, O.W. (1997) Extended brain disinhibition following small photothrombotic lesions in rat frontal cortex. NeuroReport, 8, 519–522. Buckner, R.L., Goodman, J., Burock, M., Rotte, M., Koutstaal, W., Schacter, D., Rosen, B. and Dale, A.M. (1998) Functional-anatomic correlates of object priming in humans revealed by rapid presentation of event-related fMRI. Neuron, 20, 285–296. Buxton, R.B. and Frank, L.R. (1997) A model for the coupling between cerebral blood flow and oxygen metabolism during neural stimulation. Journal of Cerebral Blood Flow and Metabolism, 17, 64–72. Calamante, F., Thomas, D.L., Pell, G.S., Wiersma, J. and Turner, R. (1999) Measuring cerebral blood flow using magnetic resonance imaging techniques. Journal of Cerebral Blood Flow and Metabolism, 9, 701–735. Caselli, R.J. (1993) Ventrolateral and dorsomedial somatosensory association cortex damage produces distinct somesthetic syndromes in humans. Neurology, 43, 762–771. Chesselet, M.F. (2000) Mapping the basal ganglia. In: A.W. Toga and J.C. Mazziotta (eds), Brain Mapping: the systems. Academic Press: San Diego, pp. 177–206. Christensen, G.E., Rabbitt, R.D. and Miller, M.I. (1994) 3-D brain mapping using deformable neuroanatomy. Physics in Medicine and Biology, 39, 609–618. Classen, J., Kunesch, E., Binkofski, F., Hilperath, F., Schlaug, G., Seitz, R., Glickstein, M. and Freund, H.-J. (1995) Subcortical origin of visuomotor apraxia. Brain, 118, 1365–1374. Classen, J., Schnitzler, A., Binkofski, F., Werhahn, K.J., Kim, Y.-S., Kessler, K.R. and Benecke, R. (1997) The motor syndrome associated with exaggerated inhibition within the primary motor cortex. Brain, 120, 605–619. Classen, J., Knorr, U., Werhahn, K.J., Schlaug, G., Kunesch, E., Cohen, L.G., Seitz, R.J. and Benecke, R. (1998a) Output mapping of human central motor representation: multiple modalities and multiple spatial scales. Journal of Physiology (London), 512, 163–179. Classen, J., Liepert, J., Wise, S.P., Hallett, M. and Cohen, L.G. (1998b) Rapid plasticity of human cortical movement representation induced by practice. Journal of Neurophysiology, 79, 1117–1123. Cremer, J.E., Cunninham, V.J. and Seville, M.P. (1983) Relationsphips between extraction and metabolism of glucose, blood flow, and tissue blood volume in regions of rat brain. Journal of Cerebral Blood Flow and Metabolism, 3, 291–302. Creutzfeld, O. and Ojemann, G. (1989) Neuronal activity in the human lateral temporal lobe. I. Responses to speech. Experimental Brain Research, 77, 451–475. Dale, A.M. and Sereno, M.I. (1993) Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. Journal of Cognitive Neuroscience, 5, 162–176. Deppe, M., Knecht, S., Papke, K., Lohmann, H., Fleischer, H., Heindel, W., Ringelstein, E.B. and Henningsen, H. (2000) Assessment of hemispheric language lateralization: a comparison between fMRI and fTCD. Journal of Cerebral Blood Flow and Metabolism, 20, 263–268. de Peralta Menendez, R.G., Hauk, O., Andino, S.G., Vogt, H. and Michel, C. (1997) Linear inverse solutions with optimal resolution kernels applied to electromagnetic tomography. Human Brain Mapping, 5, 454–467. D’Esposito, M., Aguirre, G.K., Zarahn, E. and Ballard, D. (1997) Functional MRI studies of spatial and nonspatial working memory. Cognitive Brain Research, 7, 1–13. Dietrich, W.D., Ginsberg, M.D., Busto, R. and Smith, D.W. (1985) Metabolic alterations in rat somatosensory cortex following unilateral vibrissal removal. Journal of Neuroscience, 5, 874–880.
© 2002 Taylor & Francis
100
Rüdiger J. Seitz
Dominkus, M., Grisold, W. and Jelinek, V. (1990) Transcranial electrical motor evoked potentials as a prognostic indicator for motor recovery in stroke patients. Journal of Neurology Neurosurgery and Psychiatry, 53, 745–748. Drevets, W.C., Burton, H., Videen, T.O., Snyder, A.Z., Simpson, J.R.Jr. and Raichle, M.E. (1995) Blood flow changes in human somatosensory cortex during anticipated stimulation. Nature (London), 373, 249–252. Ebeling, U., Schmid, U.D., Ying, H. and Reulen, H.J. (1992) Safe surgery of lesions near the motor cortex using intra-operative mapping techniques: a report on 50 patients. Acta Neurochirurgia, 119, 23–28. Ebersole, J.S. (1997) Magnetoencephalography/magnetic source imaging in the assessment of patients with epilepsy. Epilepsia, 38 (Suppl 4), S1–S5. Erecinska, M. and Silver, I.A. (1989) ATP and Brain Function. Journal of Cerebral Blood Flow and Metabolism, 9, 2–19. Evans, A.C., Beil, C., Marrett, S., Thompson, C.J. and Hakim, A. (1988) Anatomical-functional correlation using an adjustable MRI-based region of interest atlas with positron emission tomography. Journal of Cerebral Blood Flow and Metabolism, 8, 513–530. Eysel, U.T. (1992) Cortical plasticity: remodelling receptive fields in sensory cortices. Current Biology, 2, 389–391. Fetz, E.E. (1993) Cortical mechanisms controlling limb movement. Current Opinion in Neurobiology, 3, 932–939. Fischl, B., Sereno, M.I., Tootell, R.B.H. and Dale, A.M. (1999) High-resolution intersubject averaging and a coordinate system for the cortical surface. Human Brain Mapping, 8, 272–284. Foerster, O. (1936) Motorische Felder und Bahnen. In: O. Bumke and O. Foerster (eds), Handbuch der Neurologie, Vol. 6, Allgemeine Neurologie. Berlin: Julius Springer Verlag, pp. 1–357. Fox, P.T. and Raichle, M.E. (1984) Stimulus rate dependence of regional cerebral blood flow in human striate cortex, demonstrated by positron emission tomography. Journal of Neurophysiology, 51, 1109–1120. Fox, P.T., Burton, H. and Raichle, M.E. (1987a) Mapping human somatosensory cortex with positron emission tomography. Journal of Neurosurgery, 67, 34–43. Fox, P.T., Miezin, F.M., Allman, J.M., van Essen, D.C. and Raichle, M.E. (1987b) Retinotopic organization of human visual cortex mapped with positron emission tomography. Journal of Neuroscience, 7, 913–922. Fox, P.T., Raichle, M.E., Mintun, M.A. and Dence, C. (1988) Nonoxidative glucose consumption during focal physiologic neural activity. Science, (Washington), 241, 462–464. Fox, P.T. (1997) The growth of human brain mapping. Human Brain Mapping, 5, 1–2. Frackowiak, R.S.J. (1994) Functional mapping of verbal memory and language. Trends in Neuroscience, 17, 109–115. Frahm, J., Bruhn, H., Merboldt, K.-D. and Hänicke, W. (1992) Dynamic MR imaging of human brain oxygenation during rest and photic stimulation. Journal of Magnetic Resonance Imaging, 2, 501–505. Friel, K.M. and Nudo, R.J. (1998) Recovery of motor function after focal cortical injury in primates: compensatory movement patterns used during rehabilitative training. Somatosensory and Motor Research, 15, 173–189. Frisk, V. and Milner, B. (1990) The role of the left hippocampal region in the acquisition and retention of story content. Neuropsychologia, 28, 349–359. Friston, K.J., Frith, C.D., Liddle, P.F. and Frackowiak, R.S.J. (1991) Investigating a network model of word generation with positron emission tomography. Proceedings of the Royal Society of London, B, 244, 101–106. Friston, K.J., Frith, C.D., Liddle, P.F. and Frackowiak, R.S.J. (1993) Functional connectivity: the principalcomponent analysis of large (PET) data sets. Journal of Cerebral Blood Flow and Metabolism, 13, 5–14. Friston, K.J. (1994) Functional and effective connectivity in neuroimaging: a synthesis. Human Brain Mapping, 2, 56–78. Friston, K.J., Holmes, A.P., Worsley, K.J., Poline, J.-P., Frith, C.D. and Frackowiak, R.S.J. (1994) Statistical parametric maps in functional imaging: a generalized linear approach. Human Brain Mapping, 2, 189–210. Friston, K.J. (1997) Imaging cognitive anatomy. Trends in Cognitive Science, 1, 21–27. Friston, K.J., Fletcher, P., Josephs, O., Holmes, A., Rugg, M.D. and Turner, R. (1998) Event-related fMRI: Characterizing differential responses. Neuroimage, 7, 30–40. Frostig, R.D., Lieke, E.E., Ts’o, D. and Grinvald, A. (1990) Cortical functional architecture and local coupling between neuronal activity and the microcirculation revealed by in vivo high-resolution optical imaging of intrinsic signals. Proceedings of the National Academy of Science, U.S.A., 87, 6082–6086. Fukuyama, H., Ouchi, Y., Matsuzaki, S., Nagahama, Y., Yamauchi, H., Ogawa, M., Kimura, J. and Shibasaki, H. (1997) Brain functional activity during gait in normal subjects: a SPECT study. Neuroscience Letters, 228, 183–186. Gadian, D.G., Mishkin, M. and Vargha-Khadem, F. (1999) Early brain pathology and its relation to cognitive impairment: the role of quantitative magnetic resonance techniques. In: P. Stefan, F. Andermann, S.A. Shorvon and P. Chauvel (eds), Plasticity and epilepsy. (Advances in Neurology, Volume 81). New York: Raven Press, pp. 307–315. Gevins, A., Cutillo, B., DuRousseau, D., Le, J., Leong, H., Martin, N., Smith, M.E., Bressler, S., Bricket, P., McLaughlin, J., Barbero, N. and Laxer, K. (1994) Imaging the spatiotemporal dynamics of cognition with high-resolution evoked potential methods. Human Brain Mapping, 1, 101–116. George, J.S., Aine, C.J., Mosher, J.C., Schmidt, D.M., Ranken, D.M., Schlitt, H.A., Wood, C.C., Lewine, J.D., Sanders, J.A. and Belliveau, J.W. (1995) Mapping function in the human brain with magnetoencephalo-
© 2002 Taylor & Francis
Functional Brain Mapping
101
graphy, anatomical magnetic resonance imaging, and functional magnetic resonance imaging. Journal of Clinical Neurophysiology, 12, 406–431. Gerloff, C., Grodd, W., Altenmüller, E., Kolb, R., Naegele, T., Klose, U., Voigt, K. and Dichgans, J. (1996) Coregistration of EEG and fMRI in a simple motor task. Human Brain Mapping, 4, 199–209. Geyer, S., Ledberg, A., Schleicher, A., Kinomura, S., Schormann, T., Bürgel, U., Klingberg, T., Larsson, J., Zilles, K. and Roland, P.E. (1996) Two different areas within the primary motor cortex of man. Nature (London), 382, 805–807. Gilman, S., Dauth, G.W., Frey, K.A. and Penney, J.B. (1987) Experimental hemiplegia in the monkey: basal ganglia glucose activity during recovery. Annals of Neurology, 22, 370–376. Grabowski, T.J., Frank, R.J., Brown, C.K., Damasio, H., Boles Ponto, L.L., Watkins, G.L. and Hichwa, R.D. (1996) Reliability of PET activation across statistical methods, subject groups, and samples sizes. Human Brain Mapping, 4, 23–46. Grafton, S.T., Mazziotta, J.C., Presty, S., Friston, K.J., Frackowiak, R.S.J. and Phelps, M.E. (1992) Functional anatomy of human procedural learning determined with regional cerebral blood flow and PET. Journal of Neuroscience, 12, 2542–2548. Gray, C.M., Engel, A.K., König, P. and Singer, W. (1990) Stimulus-dependent neuronal oscillations in cat visual cortex: receptive field properties and feature dependence. European Journal of Neuroscience, 2, 607–619. Grinvald, A., Frostig, R.D., Siegel, R.M. and Bartfeld, E (1991) High-resolution optical imaging of functional brain architecture in the awake monkey. Proceedings of the National Academy of Science, U.S.A., 88, 11559–11563. Gulyas, B. (1997) Functional organization of human visual cortical areas. In: K.S. Rockland, J.H. Kaas and A. Peters (eds), Cerebral Cortex, Vol 12, New York: Plenum Press, pp. 743–775. Gur, R.C., Turetsky, B., Matsui, M., Yan, M, Bilker, W., Hughett, P. and Gur, R.E. (1999) Sex differences in brain gray and white matter in healthy young adults: correlations with cognitive performance. Journal of Neuroscience, 15, 4065–4072. Haglund, M.M., Ojemann, G.A. and Hochman, D.W. (1992) Optical imaging of epileptiform and functional activity in human cerebral cortex. Nature (London), 358, 668–671. Hämäläinen, M., Hari, R., Ilmoniemi, R.J., Knuutila, J. and Lounasmaa, O.V. (1993) Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain. Reviews of Modern Physics, 65, 413–497. Hari, R. and Lounasmaa, O.V. (1989) Recording and interpretation of cerebral magnetic fields. Science, (Washington), 244, 432–436. Hasnain, M.K., Fox, P.T. and Woldorff, M.G. (1998) Intersubject variability of functional areas in the human visual cortex. Human Brain Mapping, 6, 301–315. Haxby, J.V., Horwitz, B., Ungerleider, L.G., Maisog, J.M., Pietrini, P. and Grady, C.L. (1994) The functional organization of human extrastriate cortex: a PET-rCBF study of selective attention to faces and locations. Journal of Neuroscience, 14, 6336–6353. Heald, A., Bates, D., Cartlidge, N.E.F., French, J.M. and Miller, S. (1993) Longitudinal study of motor conduction time following stroke. 2. Central motor conduction measured within 72 h after stroke as a predictor of functional outcome at 12 months. Brain, 116, 1371–1385. Heinze, H.J., Mangun, G.R., Burchert, W., Hinrichs, H., Scholz, M., Münte, T.F., Gös, A., Scherg, M., Johannes, S., Hundeshagen, H., Gazzaniga, M.S. and Hillyard, S.A. (1994) Combined spatial and temporal imaging of brain activity during visual selective attention in humans. Nature (London), 372, 543–546. Heiss, W.D., Kessler, J., Thiel, A., Ghaemi, M. and Karbe, H. (1999) Differential capacity of left and right hemispheric areas for compensation of poststroke aphasia. Annals of Neurology, 45, 430–438. Herzog, H., Seitz, R.J., Tellmann, L. and Müller-Gärtner, H.-W. (1996) Quantification of regional cerebral blood flow with 15O-butanol and positron emission tomography in humans. Journal of Cerebral Blood Flow and Metabolism, 16, 645–649. Hoge, R.D., Atkinson, J., Gill, B., Crelier, G.R., Marrett, S. and Pike, G.B. (1999) Linear coupling between cerebral blood flow and oxygen consumption in activated human cortex. Proceedings of the National Academy of Science, U.S.A., 96, 9403–9408. Horwitz, B. (1994) Data analysis paradigms for metabolic-flow data: combining neural modeling and functional neuroimaging. Human Brain Mapping, 2, 112–122. Hubel, D.H. and Wiesel, T.N. (1963) Shape and arrangement of columns in cat’s striate cortex. Journal of Physiology (London), 165, 559–568. Hubel, D.H., Wiesel, T.N. and Stryker, M.P. (1978) Anatomical demonstration of orientation columns in macaque monkey. Journal of Comparative Neurology, 177, 361–380. Hunton, D.L., Miezin, F.M., Buckner, R.L., van Mier, H.I., Raichle, M.E. and Petersen, S.E. (1996) An assessment of functional-anatomical variability in neuroimaging studies. Human Brain Mapping, 4, 122–139. Ibánez, V., Deiber, M.P., Sadato, N., Toro, C., Grissom, J., Woods, R.P., Mazziotta, J.C., and Hallett, M. (1995) Effects of stimulus rate of regional cerebral blood flow after median nerve stimulation. Brain, 118, 1339–1351.
© 2002 Taylor & Francis
102
Rüdiger J. Seitz
Iadecola, C. (1993) Regulation of the cerebral microcirculation during neural activity: is nitric oxide the missing link? Trends in Neuroscience, 16, 206–214. Jenkins, W.M. and Merzenich, M.M. (1987) Reorganization of neocortical representations after brain injury: a neurophysiological model of the bases of recovery from stroke. In: F.J. Seil, E. Herbert and B.M. Carlson (eds), Neural regeneration. (Progress in Brain Research, Vol. 71). Amsterdam: Elsevier, pp. 249–266. Jenkins, I.H., Brooks, D.J., Nixon, P.D., Frackowiak, R.S.J. and Passingham, R.E. (1994) Motor sequence learning: a study with positron emission tomography. Journal of Neuroscience, 14, 3775–3790. Jokeit, H., Seitz, R.J., Markowitsch, H.J., Neumann, N., Witte, O.W. and Ebner, A. (1997) Prefrontal asymmetric interictal glucose hypometabolism and cognitive impairment in patients with temporal lobe epilepsy. Brain, 120, 2283–2294. Juliano, S.L., Hand, P.J. and Whitsel, B.L. (1981) Patterns of increased metabolic activity in somatosensory cortex of monkeys macaca fascicularis, subjected to controlled cutaneous stimulation: a 2-deoxyglucose study. Journal of Neurophysiology, 46, 1260–1284. Juliano, S.L., Hand, P.J. and Whitsel, B.L. (1983) Patterns of metabolic activity in cytoarchitectural area SII and surrounding cortical fields of the monkey. Journal of Neurophysiology, 50, 961–980. Kaas, J.H. (1993) The functional organization of somatosensory cortex in primates. Anatomische Anziege, 175, 509–518. Kadekaro, M., Crane, A.M. and Sokoloff, L. (1985) Differential effects of electrical stimulation of sciatic nerve on metabolic activity in spinal cord and dorsal root ganglion in the rat. Proceedings of the National Academy of Science, U.S.A., 82, 6010–6013. Karni, A., Meyer, G., Jezzard, P., Adams, M.M., Turner, R. and Ungerleider, L.G. (1995) Functional MRI evidence for adult motor cortex plasticity during motor skill learning. Nature (London), 377, 155–158. Kastrup, A., Li, T.-Q., Glover, G.H., Krüger, G. and Moseley, M.E. (1999) Gender differences in cerebral blood flow and oxygenation response during focal physiological neural activity. Journal of Cerebral Blood Flow and Metabolism, 19, 1066–1071. Kennedy, C., DesRosiers, M.H., Skurada, O., Shinohara, M., Reivich, M., Jehle, W. and Sokoloff, L. (1976) Metabolic mapping of the primary visual system of the monkey by means of the autoradiographic [14C]deoxyglucose technique. Proceedings of the National Academy of Science, U.S.A., 73, 4230–4234. Kennedy, D.N., Lange, N., Makris, N., Bates, J., Meyer, J. and Caviness, Jr., V.S. (1998) Gyri of the human neocortex: an MRI-based analysis of volume and variance. Cerebral Cortex, 8, 372–384. Kertezs, A. and Ferro, J.M. (1984) Lesion size and location in ideomotor apraxia. Brain, 107, 921–933. Kincses, W.E., Brain, C., Kaiser, S. and Elbert, T. (1999) Modeling extended sources of event-related potentials using anatomical and physiological constraints. Human Brain Mapping, 8, 182–193. Kislyakov, Y.Y. and Ivanov, K.P. (1986) O2 transport in cerebral microregions (mathematical simulation). Journal of Biomechanical Enginineering, 108, 28–32. Kleinschmidt, A., Merboldt, K.D., Hänicke, W., Steinmetz, H. and Frahm, J. (1994) Correlational imaging of thalamocortical coupling in the primary visual pathway of the human brain. Journal of Cerebral Blood Flow and Metabolism, 14, 952–957. Kleinschmidt, A., Büchel, C., Zeki, S. and Frackowiak, R.S.J. (1998) Human brain activity during spontaneously reversing perception of ambiguous figures. Proceedings of the Royal Society London, B, 265, 2427–2433. Knorr, U., Weder, B., Kleinschmidt, A., Wirrwar, A., Huang, Y., Herzog, H. and Seitz, R.J. (1993) Identification of task-specific rCBF changes in individual subjects: validation and application for positron emission tomography. Journal of Computer Assisted Tomography, 17, 517–528. Korvenoja, A., Huttunen, J., Salli, E., Pohjonen, H., Martinkauppi, S., Palva, J.M., Lauronen, L., Virtanen, J., Ilmoniemi, R.J. and Aronen, H.J. (1999) Activation of multiple cortical areas in response to somatosensory stimulation: combined magnetoencephalographic and functional magnetic resonance imaging. Human Brain Mapping, 8, 12–27. Kosslyn, S.M., Alpert, N.M. and Thompson, W.L. (1995) Identifying objects at different levels of hierarchy: a positron emission tomography study. Human Brain Mapping, 3, 107–132. Kosslyn, S.M., Pascual-Leone, A., Felician, O., Camposano, S., Keenan, J.P., Thompson, W.L., Ganis, G., Sukel, K.E. and Alpert, N.M. (1999) The role of area 17 in visual imagery: convergent evidence from PET and rTMS. Science (Washington), 284, 167–170. Kossut, M., Hand, P.J., Greenberg, J. and Hand, C.L. (1988) Single vibrissal cortical column in SI cortex of rat and its alterations in neonatal and adult vibrissa-deafferent animals: a quantitative 2DG study. Journal of Neurophysiology, 60, 829–852. Krakow, K., Woermann, F.G., Symms, M.R., Allen, P.J., Lemieux, L., Barker, G.J., Duncan, J.S. and Fish, D.R. (1999) EEG-triggered functional MRI of interictal epileptiform activity in patients with partial seizures. Brain, 122, 1679–1688. Kristeva, R., Cheyne, D. and Deecke, L. (1991) Neuromagnetic fields accompanying unilateral and bilateral voluntary movements: topography and analysis of cortical sources. Electroencephalography and Clinical Neurophysiology, 81, 284–298.
© 2002 Taylor & Francis
Functional Brain Mapping
103
Kuschinsky, W., Suda, S. and Sokoloff, L. (1981) Local cerebral glucose utilization and blood flow during metabolic acidosis. American Journal of Physiology, 241, H772–H777. Lemon, R. (1988) The output map of the primate motor cortex. Trends in Neuroscience, 11, 501–506. Logothetis, N.K., Pauls, J., Augath, M., Trinath, T. and Oeltermann, A. (2001) Neurophysiological investigation of the basis of the fMRI signal. Nature, 412, 150–157. Lueck, C.J., Zeki, S., Friston, K.J., Deiber, M.-P., Cope, P., Cunningham, V.J., Lammertsma, A.A., Kennard, C. and Frackowiak, R.S.J. (1989) The colour centre in the cerebral cortex of man. Nature (London), 340, 386–389. Madsen, P.L., Cruz, N.F., Sokoloff, L. and Dienel, G.A. (1999) Cerebral oxygen/glucose ratio is low during sensory stimulation and rises above normal during recovery: excess glucose consumption during stimulation is not accounted for by lactate efflux from or accumulation in brain tissue. Journal of Cerebral Blood Flow and Metabolism, 19, 393–400. Magistretti, P.J. and Pellerin, L. (1999) Cellular mechanisms of brain energy metabolism and their relevance to functional brain imaging. Philosophical Transactions of the Royal Society, London, series B, 354, 1155–1163. Malonek, D. and Grinvald, A. (1996) Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping. Science (Washington), 272, 551–554. Marin, G., Guerin, C., Baillet, S., Garnero, L. and Meunier, G. (1998) Influence of skull anisotropy for the forward and inverse problem in EEG: simulation studies using FEM on realistic head models. Human Brain Mapping, 6, 250–269. Mata, M., Fink, D.J., Gainer, H., Smith, C.B., Davidsen, L., Saviki, H., Schwartz, W.J. and Sokoloff, L. (1980) Activity-dependent energy metabolism in rat posterior pituitary primarily reflects sodium pump activity. Journal of Neurochemistry, 34, 213–215. Maynard, E.M., Hatsopoulos, N.G., Ojakangas, C.L., Acuna, B.D., Sanes, J.N., Normann, R.A. and Donoghue, J.P. (1999) Neuronal interactions improve cortical population coding of movement direction. Journal of Neuroscience, 19, 8083–8093. Mazziotta, J.C., Phelps, M.E., Plummer, D. and Kuhl, D.E. (1981) Quantitation in positron emission computed tomography: 5. physical-anatomical effects. Journal of Computer Assisted Tomography, 5, 734–743. McIntosh, A.R. and Gonzalez-Lima, F. (1994) Structural equation modeling and its application to network analysis in functional brain imaging. Human Brain Mapping, 2, 23–44. McIntosh, A.R., Grady, C.L., Ungerleider, L.G., Haxby, J.V., Rapoport, S.I. and Horwitz, B. (1994) Network analysis of cortical visual pathways mapped with PET. Journal of Neuroscience, 14, 655–666. Menon, R.S. and Kim, S.-G. (1999) Spatial and temporal limits in cognitive neuroimaging with fMRI. Trends in Cognitive Science, 3, 207–216. Mesulam, M.M. (1981) A cortical network for directed attention and unilateral neglect. Annals of Neurology, 10, 309–325. Mesulam, M.M. (1990) Large-scale neurocognitive networks and distributed processing for attention, language, and memory. Annals of Neurology, 28, 597–613. Missimer, J., Knorr, U., Maguire, R.P., Herzog, H., Seitz, R.J., Tellman, L. and Leenders, K.L. (1999) On two methods of statistical image analysis. Human Brain Mapping, 8, 245–258. Mountcastle, V.B., Talbot, W.H., Sakata, H. and Hyvärinen, J. (1969) Cortical neuronal mechanisms in fluttervibration studied in unanaesthetized monkeys: Neural periodicity and frequency discrimination. Journal of Neurophysiology, 32, 452–484. Müller, R.-A., Rothermal, R.D., Behen, M.E., Muzik, O., Mangner, Rj. and Chugani, H.T. (1998) Differential patterns of language and motor reorganization following early left hemispheric lesion. Archives of Neurology, 55, 1113–1119. Näätänen, R., Ilmoniemi, R.J. and Alho, K. (1994) Magnetoencephalography in studies of human cognitive brain function. Trends in Neuroscience, 17, 389–395. Neumann-Haefelin, T., Wittsack, H.-J., Wenserski, F., Siebler, M., Seitz, R.J., Mödder, U. and Freund, H.-J. (1999) Diffusion- and perfusion-weighted MRI. The DWI/PWI mismatch region in acute stroke. Stroke, 30, 1591–1597. Nudo, R.J., Wise, B.M., SiFurentes, F. and Milliken, G.W. (1996) Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Science (Washington), 272, 1791–1794. Ojemann, G., Ojemann, J., Lettich, E. and Berger, M. (1989) Cortical language localization in left, dominant hemisphere. An electrical stimulation mapping investigatin in 117 patients. Journal of Neurosurgery, 71, 316–326. O’Sullivan, B.T., Roland, P.E. and Kawashima, R. (1994). A PET study of somatosensory discrimination in man: Microgeometry versus macrogeometry. European Journal of Neuroscience, 6, 137–148. Pascual-Leone, A., Grafman, J. and Hallett, M. (1994) Modulation of cortical motor output maps during development of implicit and explicit knowledge. Science (Washington), 263, 1287–1289. Pascual-Leone, A., Wassermann, E.M., Sadato, N. and Hallett, M. (1995) The role of reading activity on the modulation of motor cortical outputs to the reading hand in Braille readers. Annals of Neurology, 38, 910–915. Paus, T., Marrett, S., Worsley, K. and Evans, A. (1996) Imaging motor-to-sensory discharges in the human brain: an experimental tool for the assessment of functional connectivity. Neuroimage, 4, 78–86.
© 2002 Taylor & Francis
104
Rüdiger J. Seitz
Pendlebury, S.T., Blamire, A.M., Lee, M.A., Style, P. and Matthews, P.M. (1999) Axonal injury in the internal capsule correlates with motor impairment after stroke. Stroke, 30, 956–962. Penfield, W. and Boldrey, E. (1953) Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain, 60, 389–443. Penfield, W. and Jasper, H. (1954) Epilepsy and the functional anatomy of the human brain. Little, Brown, Boston. Peters, A. and Kara, D.A. (1987) The neuronal composition of area 17 of rat visual cortex. IV. The organization of pyramidal cells. Journal of Comparative Neurology, 260, 573–590. Petersen, S.E., Fox, P.T., Posner, M.I., Mintun, M. and Raichle, M.E. (1988) Positron emission tomographic studies of the cortical anatomy of single-word processing. Nature (London), 331, 585–589. Phillips, J.R., Johnson, K.O. and Hsiao, S.S. (1988) Spatial pattern representation and transformation in monkey somatosensory cortex. Proceedings of the National Academy of Science, U.S.A., 85, 1317–1321. Poeck, K., deBleser, R. and Graf v. Keyserlingk, D. (1984) Neurolinguistic status and localization of lesion in aphasic patients with exclusively consonant-vowel recurring utterances. Brain, 107, 199–217. Poline, J.B., Vandenberghe, R., Holmes, A.P., Friston, K.J. and Frackowiak, R.S.J. (1996) Reproducibility of PET activation studies: lessons from a multi-center European experiment. EU concerted action on functional imaging. Neuroimage, 4, 34–54. Porro, C.A., Francescato, M.P., Cettolo, V., Diamond, M.E., Baraldi, P., Zuiani, C., Bazzocchi, M. and di Prampero, P.E. (1996) Primary motor and sensory cortex activation during motor performance and motor imagery: a functional magnetic resonance imaging study. Journal of Neuroscience, 16, 7688–7698. Prichard, J.W. and Brass, L.M. (1992) New anatomical and functional imaging methods. Annals of Neurology, 32, 395–400. Price, C.J., Moore, C.J. and Friston, K.J. (1997) Subtractions, conjunctions, and interactions in experimental design of activation studies. Human Brain Mapping, 5, 264–272. Rademacher, J., Caviness, V.S., Steinmetz, H. and Galaburda, A.M. (1993) Topographical variation of the human primary cortices: implications for neuroimaging and brain mapping studies. Cerebral Cortex, 3, 313–329. Raichle, M.E. (1987) Circulatory and metabolic correlates of brain function in normal humans. In: F. Plum (ed.), Handbook of Physiology—Nervous System Volume V, Higher Functions of the brain. Bethesda, M.D.: American Physiological Society, pp. 643–674. Rao, S.M., Binder, J.R., Bandettini, P.A., Hammeke, T.A., Yetkin, F.Z., Jesmanowicz, A., Lisk, L.M., Morris, G.L., Mueller, W.M., Estkowski, L.D., Wong, E.C., Haughton, V.M. and Hyde, J.S. (1993) Functional magnetic resonance imaging of complex human movements. Neurology, 43, 2311–2318. Regard, M., Cook, N.D., Wieser, H.G. and Landis, T. (1994) The dynamics of cerebral dominance during unilateral limbic seizures. Brain, 117, 91–104. Ribary, U., Ioannides, A.A., Singh, K.D., Hasson, R., Bolton, J.P., Lado, F., Mogilner, A. and Llinas, R.R. (1991) Magnetic field tomography of coherent thalamocortical 40-Hz oscillations in humans. Proceedings of the National Academy of Science, U.S.A., 88, 11037–11041. Rijntjes, M., Dettmers, C., Buchel, C., Kiebel, S., Frackowiak, R.S. and Weiller, C. (1999) A blueprint for movement: functional and anatomical representations in the human motor system. Journal of Neuroscience, 19, 8043–8048. Ringelstein, E.B., Binieck, R., Weiller, C., Ammeling, B., Nolte, P.N. and Thron, A. (1992) Type and extent of hemispheric brain infarctions and clinical outcome in early and delayed middle cerebral artery recanalization. Neurology, 42, 289–298. Rizzolatti, G., Luppino, G. and Matelli, M. (1998) The organization of the cortical motor system: new concepts. Electroencephalography and Clinical Neurophysiology, 106, 283–296. Roland, P.E., Eriksson, L., Stone-Elander, S. and Widén, L. (1987) Does mental activity change the oxidative metabolism of the brain? Journal of Neuroscience, 7, 2373–2389. Roland, P.E. (1993) Brain activation. John Wiley, New York. Roland, P.E. and Gulyas, B. (1994) Visual imagery and visual representation. Trends in Neuroscience, 17, 281–287. Roland, P.E., Geyer, S., Amunts, K., Schormann, T., Schleicher, A., Malikovic, A. and Zilles, K. (1997) Cytoarchitectural maps of the human brain in standard anatomical space. Human Brain Mapping, 5, 222–227. Romani, G.L. and Rossini, P. (1988) Neuromagnetic functional localization: principles, state of the art, and perspectives. Brain Topography, 1, 5–21. Rondot, P., DeRecondo, J. and Ribadeu Duma, J.L. (1977) Visuomotor ataxia. Brain, 100, 355–376. Roth, M., Decety, J., Raybaudi, M., Massarelli, R., Delon-Martin, C., Segebarth, C., Morand, S., Gemignani, A., Décorps, M. and Jeannerod, M. (1996) Possible involvement of primary motor cortex in mentally simulated movement: a functional magnetic resonance imaging study. NeuroReport, 7, 1280–1284. Scherg, M. (1990) Fundamentals of dipole source potential analysis. In: F. Grandori, M. Hoke and G.L. Romani (eds), Auditory evoked magnetic fields and electrical potentials. (Advances in Audiology, Vol. 6), Basel: Karger, pp. 40–69. Schieber, M.H. and Hibbard, L.S. (1993) How somatotopic is the motor cortex hand area? Science (Washington), 261, 489–492.
© 2002 Taylor & Francis
Functional Brain Mapping
105
Schlaug, G., Knorr, U. and Seitz, R.J. (1994) Inter-subject variability of cerebral activations in acquiring a motor skill. A study with positron emission tomography. Experimental Brain Research, 98, 523–534. Schlaug, G., Jäncke, L., Huang, Y. and Steinmetz, H. (1995) In vivo evidence of structural brain asymmetry in musicians. Science, Washington, 267, 699–701. Schmahmann, J.D. (2000) Cerebellum and Brainstem. In: A.W. Toga and J.C. Mazziotta (eds), Brain Mapping: the systems. San Diego: Academic Press, pp. 207–259. Schneider, R. and Gautier, J.-C. (1994) Leg weakness due to stroke. Site of lesions, weakness patterns and causes. Brain, 117, 347–354. Schnitzler, A., Witte, O.W., Cheyne, D., Haid, G., Vrba, J. and Freund, H.J. (1995). Modulation of somatosensory evoked magnetic fields by sensory and motor interferences. NeuroReport, 6, 1653–1658. Schnitzler, A., Volkmann, J., Enck, J., Frieling, T., Witte, O.W. and Freund, H.-J. (1999) Different cortical organization of visceral and somatic sensation in humans. European Journal of Neuroscience, 11, 305–315. Schormann, T. and Zilles, K. (1998) Three-dimensional linear and nonlinear transformations: an integration of light microscopical and MRI data. Human Brain Mapping, 6, 339–347. Seitz, R.J., Bohm, C., Greitz, T., Roland, P.E., Eriksson, L., Blomkvist, G., Rosenkvist, G. and Nordell, B. (1990) Accuracy and precision of the computerized brain atlas programme (CBA) for localization and quantification in positron emission tomography. Journal of Cerebral Blood Flow and Metabolism, 10, 443–457. Seitz, R.J., Roland, P.E., Bohm, C., Greitz, T. and Stone-Elander, S. (1991) Somatosensory discrimination of shape: tactile exploration and cerebral localization. European Journal of Neuroscience, 3, 481–492. Seitz, R.J. and Roland, P.E. (1992a) Vibratory stimulation increases and decreases the regional cerebral blood flow and oxidative metabolism: a positron emission tomography (PET) study. Acta Neurologica Scandinavica, 86, 60–67. Seitz, R.J. and Roland, P.E. (1992b) Learning of finger movement sequences: a combined kinematic and positron emission tomography study. European Journal of Neuroscience, 4, 154–165. Seitz, R.J., Schlaug, G., Kleinschmidt, A., Knorr, U., Nebeling, B., Wirrwar, A., Steinmetz, H., Benecke, R. and Freund, H.-J. (1994) Remote depressions of cerebral metabolism in hemiparetic stroke: topography and relation to motor and somatosensory functions. Human Brain Mapping, 1, 81–100. Seitz, R.J., Schlaug, G., Knorr, U., Steinmetz, H., Tellmann, L. and Herzog, H. (1996) Neurophysiology of the human supplementary motor area: positron emission tomography. In: H.O. Lueders (ed.), Supplementary sensorimotor area. (Advances in Neurology, Vol. 70). New York: Raven Press, pp. 167–175. Seitz, R.J., Canavan, A.G.M., Yagüez, L., Herzog, H., Tellmann, L., Knorr, U., Huang, Y. and Hömberg, V. (1997) Representations of graphomotor trajectories in the human parietal cortex: evidence for controlled processing and automatic performance. European Journal of Neuroscience, 9, 378–389. Seitz, R.J., Höflich, P., Binkofski, F., Tellmann, L., Herzog, H. and Freund, H.-J. (1998) Role of the premotor cortex for recovery from middle cerebral artery infarction. Archives of Neurology, 55, 1081–1088. Seitz, R.J. and Azari, N.P. (1999) Postlesional neuroplasticity in man. In: P. Stefan, F. Andermann, S.A. Shorvon and P. Chauvel (eds), Plasticity and epilepsy. (Advances in Neurology, Volume 81). New York: Raven Press, pp. 37–47. Seitz, R.J., Azari, N.P., Knorr, U., Binkofski, F., Herzog, H, and Freund, H.-J. (1999) The role of diaschisis in stroke recovery. Stroke, 30, 1844–1850. Seitz, R.J., Volkmann, J. and Witte, O.W. (2000) Functional Mapping of the Human Brain. The Neuroscientist, 6, 75–76. Seitz, R.J., Knorr, U., Azari, N.P. and Weder, B. (2001) Cerebral networks in sensorimotor disturbances. Brain Research Bulletin, 54, 299–305. Shulman, G.L., Fiez, J.A., Corbetta, M., Buckner, R.L., Miezin, F.M., Raichle, M.E. and Petersen, S.E. (1997) Common blood flow changes across visual tasks. II. Decreases in cerebral cortex. Journal of Cognitive Neuroscience, 9, 648–663. Sitzer, M., Knorr, U. and Seitz, R.J. (1994) Cerebral hemodynamics during sensorimotor activation in humans. Journal of Applied Physiology, 77, 2804–2811. Sokoloff, L., Reivich, M., Kennedy, C., Des Rosiers, M.H., Patlak, C.S., Pettigrew, K.D., Sakurada, O. and Shinohara, M. (1977) The [14C]deoxyglucose method for the measurement of local cerebral glucose utilization: theory, procedure, and normal values in the conscious and anesthetized albino rat. Journal of Neurochemistry, 28, 897–916. Snyder, A.Z. (1991) Dipole source localization in the study of EP generators: a critique. Electroencephalography and Clinical Neurophysiology, 80, 321–325. Stehling, M.K., Turner, R. and Mansfield, P. (1991) Echo-planar imaging: magnetic resonance imaging in a fraction of a second. Science, Washington, 254, 43–50. Steinmetz, H., Fürst, G. and Freund, H.-J. (1989) Cerebral cortical localization: application and validation of the proportional grid system in MR imaging. Journal of Computer Assisted Tomography, 13, 10–19. Steinmetz, H., Huang, Y., Seitz, R.J., Knorr, U., Herzog, H., Hackländer, T., Kahn, T. and Freund, H.-J. (1992a) Individual integration of positron emission tomography and high-resolution magnetic resonance imaging. Journal of Cerebral Blood Flow and Metabolism, 12, 919–926.
© 2002 Taylor & Francis
106
Rüdiger J. Seitz
Steinmetz, H., Jäncke, L., Kleinschmidt, A., Schlaug, G., Volkmann, J. and Huang, Y. (1992b) Sex but no hand difference in the isthmus of the corpus callosum. Neurology, 42, 749–752. Steinmetz, H., Herzog, A., Schlaug, G., Huang, Y. and Jäncke, L. (1995) Brain (a)symmetry in monogzygotic twins. Cerebral Cortex, 5, 296–300. Stephan, K.M., Fink, G.R., Passingham, R.E., Silberzweig, D., Ceballos-Baumann, A.O., Frith, C.D. and Frackowiak, R.S.J. (1995a) Functional anatomy of the mental representation of upper extremity movements in healthy subjects. Journal of Neurophysiology, 73, 373–386. Stephan, K.M., Dabringhaus, A., Fink, G.R., Geyer, S., Passingham, R.E., Schormann, T., Knorr, U., Seitz, R.J., Roland, P.E., Frith, C.D., Frackowiak, R.S.J. and Zilles, K. (1995b) Motor imagery and motor performance: PET activations compared to cytoarchitectonic data. Human Brain Mapping, Suppl 1, 301. Stephan, K.M. and Frackowiak, R.S.J. (1996) Motor imagery—anatomical representation and electrophysiological characteristics. Neurochemical Research, 21, 1105–1116 Stephan, K.M., Binkofski, F., Halsband, U., Dohle, C., Wunderlich, G., Schnitzler, A., Tass, P., Posse, S., Herzog, H., Sturm, V., Zilles, K., Seitz, R.J. and Zilles, K. (1999) The role of ventral medial wall motor areas for bimanual coordination: a combined lesion and activation study. Brain, 122, 351–368. Stepniewska, I., Preuss, T.M. and Kaas, J. (1993) Architectonics, somatotopic organization, and ipsilateral cortical connections of the primary motor area (M1) of Owl monkey. Journal of Comparative Neurology, 330, 238–271. Talairach, J., Szikla, G., Tournoux, P., Prossalentis, A., Bordas-Ferrer, M., Covello, L., Joacob, M., Mempel, A., Buser, P. and Bancaud, J. (1967) Atlas d’anatomie stéréotaxique du télencéphale. Paris: Masson. Talairach, J. and Tournoux, P. (1988) Co-planar stereotaxic atlas of the human brain. Thieme, Stuttgart, New York. Thurfjell, L., Bohm, C., Bengtsson, E. (1995) CBA—an atlas based software tool used to facilitate the interpretation of neuroimaging data. Computer Methods and Programs in Biomedicine, 47, 51–71. Toma, K., Honda, M., Hanakawa, T., Okada, T., Fukuyama, H., Ikeda, A., Nishizawa, S., Konishi, J. and Shibasaki, H. (1999) Activities of the primary and supplementary motor areas increase in preparation and execution of voluntary muscle relaxation: an event-related fMRI study. Journal of Neuroscience, 19, 3527–3534. Tong, D.C., Yenari, M.A., Albers, G.W., O’Brien, M., Marks, M.P. and Moseley, M.E. (1998) Correlation of perfusion- and diffusion-weighted MRI with NIHSS score in acute (<6.5 hour) ischemic stroke. Neurology, 50, 864–870. Tootell, R.B.H., Reppas, J.B., Kwong, K.K., Malach, R., Born, R.T., Brady, T.J., Rosen, B.R. and Belliveau, J.W. (1995) Functional analysis of human MT and related visual cortical areas using magnetic resonance imaging. Journal of Neuroscience, 15, 3215–323. Tranel, D. (1991) Dissociated verbal and nonverbal retrieval and learning following left anterior temporal damage. Brain and Cognition, 15, 187–200. Ueki, M., Linn, F. and Hossmann, K.-A. (1988) Functional activation of cerebral blood flow and metabolism before and after global ischemia of rat brain. Journal of Cerebral Blood Flow and Metabolism, 8, 486–494. Ungerleider, L.G. and Haxby, J.V. (1994) “What” and “where” in the human brain. Current Opinion in Neurobiology, 4, 157–165. Van Essen, D.C. (1985) Functional organization of primate visual cortex. In: A. Peters and E.G. Jones (eds), Cerebral Cortex, Vol 3, Visual cortex. New York: Plenum Press, pp. 259–329. Votaw, J.R., Henry, T.R., Shoup, T.M., Hoffman, J.M., Woodard, J.L. and Goodman, M.M. (1999) Butanol is superior to water for performing positron emission tomography activation studies. Journal of Cerebral Blood Flow and Metabolism, 19, 982–989. Volkmann, J., Witte, O.W., Dammers, J., Arnold, S., Seitz, R.J., Müller-Gärtner, H.W. and Freund, H.-J. (1998) Relationship between epileptogenic and symptomatogenic zone in epilepsia partialis continua: A MEG and PET study. Journal of Neuroimaging, 8, 103–106. von Giesen, H.J., Schlaug, G., Steinmetz, H., Benecke, R., Freund, H.-J. and Seitz, R.J. (1994) Cerebral network underlying unilateral motor neglect: evidence from positron emission tomography. Journal of Neurological Science, 125, 29–38. Wassermann, E.M., Wang, B., Zeffiro, T.A., Sadato, N., Pascual-Leone, A., Toro, C. and Hallett, M. (1996) Locating the motor cortex on the MRI with transcranial magnetic stimulation and PET. Neuroimage, 3, 1–9. Watson, J.D.G., Myers, R., Frackowiak, R.S.J., Hajnal, J.V., Woods, R.P., Mazziotta, J.C., Shipp, S. and Zeki, S. (1993) Area V5 of the human brain: evidence from a combined study using positron emission tomography and magnetic resonance imaging. Cerebral Cortex, 3, 79–94. Weiss, D.S. and Keller, A. (1994) Specific patterns of intrinsic connections between representations zones in the rat motor cortex. Cerebral Cortex, 4, 205–214. Welker, E., Rao, S.B., Dörfl, J., Melzer, P. and van der Loos, H. (1992) Plasticity in the barrel cortex of the adult mouse: effects of chronic stimulation upon deoxyglucose uptake in the behaving animal. Journal of Neuroscience, 12, 153–170. Winstein, C.J. and Pohl, P.S. (1995) Effects of unilateral brain damage on the control of goal-directed hand movements. Experimental Brain Research, 105, 163–174.
© 2002 Taylor & Francis
Functional Brain Mapping
107
Wood, C.C., Cohen, D., Cuffin, B.N., Yarita, M. and Allison, T. (1985) Electrical sources in human somatosensory cortex: identification by combined magnetic and potential recordings. Science (Washington), 227, 1051–1053. Worsley, K.J., Evans, A.C., Marrett, S. and Neelin, P. (1992) A three-dimensional statistical analysis for CBF activation studies in human brain. Journal of Cerebral Blood Flow and Metabolism, 12, 900–918. Wunderlich, G., Knorr, U., Kiwit, J.C.W., Herzog, H., Freund, H.-J. and Seitz, R.J. (1998) Precentral glioma location determines the displacement of cortical hand representation. Neurosurgery, 42, 18–27. Xerri, C., Merzenich, M.M., Peterson, B.E. and Jenkins, W. (1998) Plasticity of primary somatosensory cortex paralleling sensorimotor skill recovery from stroke in adult monkeys. Journal of Neurophysiology, 79, 2119–2148, Yarowsky, P., Kadekaro, M. and Sokoloff, L. (1983) Frequency-dependent activation of glucose utilization in the superior cervical ganglion by electrical stimulation of cervical sympathic trunk. Proceedings of the National Academy of Science, U.S.A., 80, 4179–4183. Young, I.R., Bailes, D.R., Bult, M., Collins, A.G., Smith, D.T., McDonnell, M.J., Orr, J.S., Banks, L.M., Bydder, G.M., Greenspan, R.H. and Steiner, R.E. (1982) Initial clinical evaluation of a whole body nuclear magnetic resonance (NMR) tomograph. Journal of Computer Assisted Tomography, 6, 1–18. Yousry, T.A., Schmid, U.D., Jassoy, A.G., Schmidt, D., Eisner, W.E., Reulen, H.J., Reiser, M.F. and Lissner, J. (1995) Topography of the cortical motor hand area: prospective study with functional MR imaging and direct motor mapping at surgery. Radiology, 195, 23–29. Zihl, J. and von Cramon, D. (1985) Visual field recovery from scotoma in patients with postgeniculate damage. A review of 55 cases. Brain, 108, 335–365. Zihl, J., von Cramon, D., Mai, N. and Schmid, C. (1991) Disturbance of movement vision after bilateral posterior brain damage. Brain, 114, 2235–2252.
© 2002 Taylor & Francis
Part II CORTICAL AREAS: CORRELATION WITH CONNECTIVITY
© 2002 Taylor & Francis
6 Regional Dendritic Variation in Primate Cortical Pyramidal Cells Bob Jacobs1 and Arnold B. Scheibel2 1
Laboratory of Quantitative Neuromorphology, Department of Psychology, The Colorado College, 14 E. Cache La Poudre, Colorado Springs, CO 80903 2 Department of Neurobiology, Brain Research Institute, University of California, Los Angeles, CA 90024-1769 Correspondence to: Bob Jacobs, Ph.D. Laboratory of Quantitative Neuromorphology, Department of Psychology, The Colorado College, 14 East Cache La Poudre, Colorado Springs, CO 80903, USA Tel: (719) 389-6594; FAX: (719) 389-6284; e-mail:
[email protected]
This chapter reviews quantitative neuromorphological investigations of primate neocortex. In particular, we explore regional variation in the basal dendritic and spine systems of pyramidal neurones. This synthesis indicates a relatively consistent, stepwise increase in dendritic extent and spine number in a caudal-rostral direction. Cortical regions involved in the early stages (e.g. primary sensory areas) of processing generally exhibit less complex dendritic/spine systems than those regions involved in the latter stages of information processing (e.g. prefrontal cortex). This dendritic progression appears to reflect significant differences in the nature of cortical processing, with spine-dense neurones at hierarchically higher association levels integrating a broader range of synaptic input than those at lower cortical levels. In concluding the chapter, we consider the characteristics of the receptive dendritic membrane of individual neuronal elements (e.g. voltage-gated channels, input resistance, voltage attenuation) and how such factors may relate to cortical computation. KEYWORDS: cerebral cortex, dendrite, quantitative, regional variation, spine
1. INTRODUCTION This chapter considers the degree to which neurones in different areas of the primate cerebral cortex vary with regard to the extent and complexity of their dendritic ensembles. We focus here on pyramidal neurones because they are the principal component of neocortical circuitry. Moreover, we limit our discussion to the basal dendrite ensembles of pyramidal cells, and thus follow the precedent set in most quantitative studies of cortical neurones (Schlaug et al., 1993). Since the horizontal components of the pyramidal cell ensemble, and notably the basal dendrites, provide the main receptive surface for axons of intracortical origin (Globus and Scheibel, 1967a,b,c), there is a robust anatomical basis for this choice. In addition, the enormous morphological variation of the stellate and nonpyramidal cell contingents has always made these fascinating elements more problematic for quantitative evaluation (see Prinz et al., 1997; Seldon, 1982). In the last century, the classic qualitative studies of Golgi (1886), Cajal (1909), and Lorente de Nó (1922), among others, provided the structural framework for a brilliant
111 © 2002 Taylor & Francis
112
Bob Jacobs and Arnold B. Scheibel
period of descriptive structuro-functional studies of the cerebral hemispheres. But as we leave the decade of the brain and enter the 21st century, a more discriminating and computationally knowledgeable group of investigators poses questions that demand information with higher levels of resolution. The intimate organization of dendrite membrane patches, the structure, placement, and dimensions of individual dendrite spines, the length and orientation of dendrite tips, and so on, become data necessary for a more profound understanding of the structuro-functional basis of neural computation. In what follows, we attempt an overview of several quantitative approaches to analysis of cortical dendrites as seen from an end-of-the-century point of view. We lean heavily upon our own Golgi-based studies of human cortices with the following justifications: (1) We are probably more aware of the shortcomings of our own work than of others’; and (2) the field of quantitative human neural morphology is still very young with relatively few published investigations. The major thrust of this overview is to provide evidence relating dendritic length and spine number to cortical area; in general, the more complex the computational functions of the area, the longer the dendrites and the more numerous the spines. This relationship may be meaningful in terms of what is known about the underlying physiology of dendrites and their spines, and may reflect significant regional differences in the nature of cortical processing.
2. QUANTITATIVE TECHNIQUES AND METHODOLOGICAL CONSIDERATIONS Comparisons across neuromorphological studies are somewhat problematic because each quantitative technique exhibits its own strengths and weaknesses (for review, see Uylings et al., 1975, 1986). Moreover, all quantitative neuromorphological investigations face formidable methodological constraints. 2.1. Quantitative Techniques With considerable histological and technological advances over the last few decades, several quantitative methodologies have emerged. At present, three morphological techniques are commonly employed for dendritic quantification. The oldest and most widely used is the semi-quantitative analysis of Sholl (1953, 1956), whereby a series of equidistant, concentric rings are superimposed over a (typically traced) neurone, allowing the number of dendritic intersections per ring to be counted (see Figure 6.1A, 6.1B). In the Eayrs’ (1955) concentric circle variation of this technique, the researcher also quantifies the number of bifurcations and terminal endings within each ring. As such, these techniques do not quantify dendritic segments in great detail, but do provide a first order approximation of the overall dendritic profile. The second technique, a metric reconstruction, involves tracing the dendritic tree, branch by branch, and measuring the length (and possibly diameter) of each segment (see Figure 6.1C, 6.1D). This can be accomplished either indirectly by tracing cells with a camera lucida and entering the coordinates on a digitizing tablet, or directly by quantifying neurones through a microscope interfaced with a computer system (e.g. the Neurolucida system, MicroBrightfield, Inc.). Although older versions of this technique were somewhat limited, most recent versions can reconstruct accurately the entire dendritic ensemble in 3-dimensional space, and can also provide estimates of spine number/density.
© 2002 Taylor & Francis
Regional Dendritic Variation
© 2002 Taylor & Francis
113
Figure 6.1. Illustrative depictions of common neuromorphological quantitative techniques. (A) Sample supragranular pyramidal cell (scale bar = 100 µm) with Sholl concentric rings superimposed. (B) Results of a Sholl analysis within one individual comparing the basal dendritic systems in the trunk and hand-finger region of the somatosensory cortex. Note the relatively more complex dendrites in the hand-finger region compared with the trunk region, as determined by the higher number of concentric ring crossings (adapted from Scheibel et al., 1990). (C) A metric reconstruction of individual basal dendritic segments, which are traced here in a somatofugal pattern. (D) Results of such a metric reconstruction illustrating a greater total dendritic length in prefrontal (BA10) compared with occipital (BA18) pyramidal neurones (adapted from Jacobs et al., 1997). (E) Sample basal dendritic system (tangential view from the pial surface) with a polygon (hull) around dendritic tips (scale bar = 100 µm). This analysis provides a rough estimate of dendritic field size. (F) A modified Sholl analysis is performed by superimposing concentric rings with 30° polar angle intervals, resulting in a polar plot (G) of dendritic tree intersections as a function of direction from the soma. This polar plot provides a measure of dendritic tree orientation. In order to quantify the degree of dendritic bias, the researchers calculate the sum of the angles (q) subtended between a circle (radius equal to the half-maximal value of the polar plot) and the polar plot. Cells with tightly clustered dendritic trees have low q values; those with no bias have q value near 360° (E, F and G: adapted from Elston et al., 1998b).
114
Bob Jacobs and Arnold B. Scheibel
Such analyses permit a fine-level, topographical depiction (e.g. length, number, and volume) of individual dendritic segments, and allow these segments to be integrated somatofugally (Van der Loos, 1959) within the 3-dimensional geometry of the dendritic array. The third technique examines the dendritic field of the neurone (see Figure 6.1E), an idea first investigated in a quantitative manner by Colonnier (1964). To examine the overall area of the dendritic field, researchers draw a polygon (hull) that joins the distal tips of the outermost dendrites, and calculate the enclosed area. This technique provides a relatively simple estimate of dendritic spread, but does not address overall complexity of branching. To estimate such complexity, along with the clustering and orientation of the dendritic tree, researchers can employ a modified Sholl analysis, which involves (1) comparing the overall number of intersections per concentric ring, and (2) examining the distribution of intersections as a function of the radial position of the dendrite relative to the soma, and depicting these in polar plots (see Figure 6.1F, 6.1G). This has been a particularly useful technique for investigating the geometry of the domains of dendritic trees in different cortical regions. 2.2. Methodological Considerations Extensive quantitative neuromorphological research has been conducted on non-human organisms (e.g. Valverde, 1976; Juraska, 1982; Murphy and Magness, 1984; Braitenberg and Schüz, 1991; Bannister and Larkman, 1995; Ishizuka et al., 1995), with appropriately cautious heterospecific comparisons providing valuable insights into the characteristics of cortical neuropil. Unfortunately, there have only been a handful of such studies in non-human primates, and even fewer in humans. Moreover, when analyzing data from human subjects, several special methodological issues constrain potential generalizations (Scheibel, 1988; Flood, 1993; Jacobs et al., 1993b, 1997). Human subjects. Practical considerations in human research are numerous, including the restrictions of retrospective analyses and the problems of relatively small sample sizes. These are significant obstacles because correlative research on human dendritic systems typically requires the broadest possible sociocultural, vocational, and avocational histories on subjects (Scheibel et al., 1990), and because large sample sizes are required to overcome the tremendous interindividual variation that characterizes human tissue (Ojemann and Whitaker, 1978; Ojemann et al., 1989; Stensaas et al., 1974; Whitaker and Selnes, 1976). Moreover, it is impossible to determine in post-mortem tissue whether topographically identical cortical areas in different individuals share the same function. Fortunately, for the purpose of regional comparisons, each subject can serve as his/her own control, insofar as all areas of the cortex have been exposed to the same historical variables. Histology. Even more limiting in human research are the autolytic consequences of post-mortem delays (Williams et al., 1978; de Ruiter, 1983) and the restricted number of histological techniques that can be employed on immersion-fixed tissue. By far the most common stain for human brain tissue is the Golgi silver impregnation technique (see Figure 6.2), the relative merits of which are well documented (Scheibel and Scheibel, 1978; Braak and Braak, 1985). There are, however, innumerable variations of Golgi impregnations, each with its own idiosyncratic characteristics (Buell, 1982; Meller and Dennis, 1990), which further complicate cross-study comparisons. Recently, modern intracellular injection techniques have focused attention on the limitations of such silver impregnation methods. Although fluorescent dyes (e.g. Lucifer Yellow) have proven effective in
© 2002 Taylor & Francis
Regional Dendritic Variation
115
Figure 6.2. Photomicrographs of human supragranular pyramidal cells (A and B) and associated basal dendritic spines (C and D) stained with a modified rapid Golgi technique. Several individuals and Brodmann’s areas (BA) are represented: (A) M32 (=32 year-old male), angular gyrus (BA39); (B) M23, prefrontal pole (BA10); (C) F15 (=15 year-old female), somatosensory cortex (BA3-1-2); and (D) M14, inferior prefrontal pole (BA11). For A and B, scale bars = 50 µm; for C and D, scale bars = 10 µm.
neuromorphological reconstructions in non-human animals (Trommald et al., 1995; Elston et al., 1996), they remain somewhat problematic in human autopsy tissue (Ohm and Diekmann, 1994; Belichenko and Dahlström, 1995) due to factors such as dye leakage. Therefore, despite the development of new histological techniques, the Golgi methods remain the stain of choice in extensive quantitative studies of immersion fixed human tissue. Quantification. Several factors determine what a particular quantitative and histological technique will reveal about cortical neuropil. Here we outline four of these factors. (1) Findings will be substantially affected if separate subpopulations of cells are quantified (e.g. layer II vs layer III pyramidal cells), even within the same region (Larkman, 1991; Matsubara et al., 1996). (2) Section thickness, which typically ranges from 50 µm to 200 µm in human Golgi preparations (e.g. Takashima et al., 1981; Koenderink et al., 1994), can also substantially affect quantitative measures by producing varying degrees of cut dendritic segments (Jacobs et al., 1997). (3) Quantitative studies conducted under dry (as opposed to oil-immersion) objectives will result in a diminution of observed length measurements (Uylings et al., 1986). (4) Spine counts, which underestimate actual numbers if they quantify only visible spines, will vary considerably with distance from the soma (Globus and Scheibel, 1967a; Marin-Padilla, 1967), and between the basal and
© 2002 Taylor & Francis
116
Bob Jacobs and Arnold B. Scheibel
apical dendrites of pyramidal neurones (Uemura, 1980). Given these constraints, among others, it should be clear that quantitative measurements typically represent relative rather than absolute values, and provide but a small window into the overall arrangement and complexity of cortical neuropil.
3. INDIRECT INDICATORS OF REGIONAL VARIATION IN CORTICAL NEUROPIL The first quantitative study of cortical dendritic structure appears to have been Bok’s (1936) examination of the relationship between the nuclear volume of the cell and the number of dendritic branches. In terms of regional cortical variation, the first quantitative documentation appears to be Sholl’s (1953) pioneering exploration of the dendritic branching pattern in cat visual and motor cortices. In addition to finding a positive relationship between overall dendritic length and segment number, Sholl observed a greater number of branches in the visual area compared with the motor area for both pyramidal and stellate cells. Since Sholl’s initial observations, however, most quantitative neuromorphological investigations have not directly addressed regional dendritic variation. Instead, they have focused primarily on one cortical area at a time, while exploring factors such as hemispheric differences, cortical development, and aging. Limited inferences related to cortical variation are nevertheless possible when findings from other types of research (e.g. neuroimaging) are integrated. 3.1. Hemispheric Differences The most general indicator of regional variation involves comparison of homologous areas in the two cerebral hemispheres. Several investigations of this nature have been conducted in humans, particularly on cortical areas involved in language. In an extensive study of the human auditory cortex, Seldon (1981a,b, 1982) found that pyramidal cell basal dendrites exhibited a larger tangential extent in the left hemisphere (LH) than in the right hemisphere (RH). It was postulated that this dendritic advantage was related to greater capacity for differential phonemic responses in the computationally specialized LH. Similarly, slightly more complex basal dendritic systems have been documented in classical Wernicke’s area over the RH homologue (Jacobs et al., 1993b). Findings in classical Broca’s area have been mixed. Scheibel et al., (1985) found that higher order dendritic segments tended to be more complex in the LH over the RH, presumably because of the complexity of LH speech processing (cf. Jacobs et al., 1993a). In a very limited study, however, Hayes and Lewis (1996) failed to observe interhemispheric differences of dendrites in magnopyramidal neurones in Broca’s area, but did suggest that LH cells may be specialized to receive a more restricted complement of afferents. Given that these quantitative morphological observations are consistent with documented interhemispheric functional differences (Walker, 1980; Seldon, 1985; Zatorre et al., 1992), one can suppose with some confidence that quantitative intrahemispheric differences should also obtain across various cortical areas. It should be emphasized, however, that these dendritic systems are extremely plastic. As such, each quantitative study provides but a synchronic “snapshot” of the cortical neuropil, a snapshot that can be diachronically enriched by observations of developmental and aging.
© 2002 Taylor & Francis
Regional Dendritic Variation
117
3.2. Development Dramatic histological changes characterize pre- and post-natal cortical development, including transient overproduction of neuropil, and selective elimination of excessive connectivity (Marin-Padilla, 1970; Rakic et al., 1986; Mrzljak et al., 1990). Several quantitative dendritic investigations have elucidated the timeline in primates for some of these changes in individual cortical areas (Schadé and Van Groenigen, 1961; Schulz et al., 1992; Koenderink et al., 1994; Koenderink and Uylings, 1995) but have not directly suggested regional variability. In their cross-sectional, developmental human study, however, Simonds and Scheibel (1989) inferred a gradual transition in dendritic primacy (i.e. relative complexity of dendritic trees) from the RH to the LH, and from the orofacial motor cortex to the motor speech region, intimating the adult pattern they had previously observed (Scheibel et al., 1985). More recently, the Jacobs laboratory (unpublished data) has compared basal dendritic systems across multiple cortical regions (Brodmann’s area, BA 3-1-2, BA4, BA18, and BA10) in human infants and adults. In infants, those regions that mature earliest (BA3-1-2 and BA4) tend to exhibit more complex dendritic trees than those that mature later (BA18, and especially BA10), a finding consistent with the fact that primary cortical areas are initially more active metabolically than association regions (Chugani et al., 1987). In the adult, the regional dendritic pattern tends to be reversed for these four regions; that is, BA10 in the adult surpasses all other regions in dendritic complexity (see below). These morphological findings, coupled with measures of quantitative synaptogenesis (Huttenlocher and Dabholkar, 1997) and with metabolic indicators (Chugani et al., 1987; Jacobs et al., 1995), indicate a heterochronous path for cortical development. This path is particularly clear in humans, where synaptic density peaks at approximately 3 months postnatally in auditory cortex, at 8–12 months in striate cortex, and after 15 months in frontal cortex (Huttenlocher et al., 1982; Huttenlocher and de Courten, 1987; Huttenlocher and Dabholkar, 1997). Coincident with this synaptic proliferation, local cerebral metabolic rates begin to increase between 1–2 years of age (Chugani et al., 1987). Dendritic growth continues for several more years, even after neuronal and synaptic density decline (Conel, 1939–67; Schadé and Van Groenigen, 1961). With some regional variation, greatest dendritic length in humans is probably reached between 8–10 years of age (Becker et al., 1984; Semenova et al., 1989; Mrzljak et al., 1990; Jacobs and Scheibel, 1993). This dramatic growth in dendritic arborization coincides with a concomitant increase in the brain’s metabolic demands (Mata et al., 1980; Nudo and Masterton, 1986). Thus, the metabolic plateau in humans (at about 50% above adult levels) is achieved between 4–9 years of age (Chugani et al., 1987). In terms of regional dendritic variability, the importance of this heterochronous cortical development lies in the finding that some cortical areas in the resting adult brain (e.g. prefrontal cortex) tend to exhibit higher rates of metabolism than other cortical areas (Roland, 1984), in turn suggesting enhanced synaptic activity and greater dendritic complexity in those more active regions.
3.3. Aging Quantitative dendritic investigations have contributed greatly to our understanding of the aging process. In one of the first (cross-sectional) studies to examine individual segment length by digitizing camera lucida tracings, Cupp and Uemura (1980) suggested that
© 2002 Taylor & Francis
118
Bob Jacobs and Arnold B. Scheibel
terminal basal segment growth may continue with age in some neurones in the frontal cortex of rhesus monkeys, although some neurones may exhibit a distoproximal type of degeneration. Neurones in different layers may be particularly susceptible to the aging process. Nakamura et al. (1985), for example, noted that layer V basal dendrites appeared to be more affected by the aging process than those in layer III. Using a Sholl analysis in the parahippocampal gyrus, Buell and Coleman (1981) postulated that the normal aging cortex appears to contain both regressing and proliferating (pyramidal) dendritic systems, suggesting continued plasticity in the adult human brain. This age-related cortical plasticity appears wide-spread, insofar as the possibility of these two co-existing populations of neurones has been reconfirmed in subsequent studies on granule cells of the dentate gyrus (Flood et al., 1985) and supragranular pyramidal neurones in Wernicke’s area (Jacobs and Scheibel, 1993). In one of the first studies to examine multiple regions of the human cerebral cortex (specifically, BA4, BA6, BA39, and BA10), Schierhorn (1981) documented age-related decreases in spine density, which appeared to be consistent for all four areas, although specific regional comparisons were not made. The only study to date that has specifically compared age-related changes in dendritic/spine systems across multiple cortical regions is Jacobs et al. (1997), which explored supragranular pyramidal neurones in secondary visual (BA18) and prefrontal (BA10) regions in 26 human brains ranging from 14 to 106 years of age. Dendritic measures, particularly for spine systems, decreased substantially from the youngest individuals to approximately 40 years of age, after which the measures remained relatively stable. These losses appeared to be somewhat greater in BA10 than in BA18, which is consistent with other research indicating that certain regions (e.g. frontal lobes, selected association regions) may be particularly susceptible to aging (Kuhl et al., 1982; Terry et al., 1987; Raz et al., 1993, 1997; Sullivan et al., 1995). More extensive quantitative neuromorphological investigations are still required to evaluate age-related regional loss in cortical neuropil (for review, see Coleman and Flood, 1987).
4. DIRECT INVESTIGATIONS OF REGIONAL VARIATION IN CORTICAL NEUROPIL: NON-HUMAN PRIMATES In recent years, an extensive series of studies by Elston and Rosa on monkey visual pathways has directly addressed the question of regional dendritic variation. Using Lucifer Yellow injection techniques, a modified Sholl analysis, and dendritic field measures, these investigations have examined basal dendritic complexity along the hierarchically arranged occipitotemporal and occipitoparietal visual pathways, and in the frontal eye fields (see Figure 6.3 for a summary). 4.1. The Occipitotemporal Visual Pathway In one of their earliest comparative studies, Elston et al. (1996) examined four areas of the adult Marmoset monkey brain (Callithrix jacchus): first (V1) and second (V2) visual areas, the dorsolateral part (DL) and the fundus of the superior temporal (FST) region. They found larger dendritic fields in the pyramidal cells of extrastriate regions, which are involved in shape/color processing (DL) and motion/spatial analyses (FST; Boussaoud et al., 1990), than in primary visual cortical areas (V1, V2) involved in the early stages of visual
© 2002 Taylor & Francis
Regional Dendritic Variation
119
Figure 6.3. Relative position of cortical tissue samples from the monkey, and representative tracings of supragranular pyramidal neurones (synthesized from Elston and Rosa, 1997, 1998a,b). Basal systems are drawn in a plane tangential to the cortical layers and represent neurones in the 60th complexity percentile (scale bar = 100 µm). These areas are arranged in a relative hierarchy, reflecting a general progression in dendritic complexity along the occipitotemporal and occipitoparietal visual pathways, and in the frontal eye fields: primary visual cortex (V1), secondary visual cortex (V2), the fourth visual region (V4), a temporal lobe subdivision (TEO), the middle temporal region (MT), the lateral intraparietal area (LIPv), the anterior bank of the superior temporal sulcus in the parietal lobe (7a), and the frontal eye fields (FEF).
processing. This caudal-rostral progression in field size suggests a more extensive input sampling by dendritic systems at higher levels, and corresponds with demonstrated size increases in intrinsic axonal clusters (Yoshioka et al., 1992; Amir et al., 1993). This is expected insofar as the area of axonal patches appears positively correlated with the basal dendritic field of superficial pyramidal cells along this caudal-rostral visual gradient (Lund et al., 1993). In a similar, but more extensive investigation of adult Macaca fascicularis, Elston and Rosa (1998b) explored the dendritic and spine characteristics of pyramidal neurones in four visual regions: V1, V2, V4, and the occipitotemporal transitional zone (TEO). Several morphological differences were observed both within and across cortical regions. Within V1, cells located in cytochrome oxidase-rich blobs had more extensive dendritic fields than those in interblob regions. Dendritic trees which were morphologically-oriented
© 2002 Taylor & Francis
120
Bob Jacobs and Arnold B. Scheibel
(i.e. dendrites clustered in two diametrically opposing directions) and directionally-biased (i.e. dendrites clustered in a particular direction) were more common in V1 and V2 than in more rostral visual areas (V4 and TEO), where non-biased cells predominated. Finally, not only did more rostral, higher visual areas (V4 and TEO) exhibit significantly larger dendritic fields than primary regions (V1 and V2), but the number of spines along the basal dendritic array roughly doubled at each successive stage in the pathway. These findings suggest a stepwise progression in dendritic complexity, with the more rostrally located, spine-dense neurones integrating a wider range of (non-visual) modulatory input than the more caudally located, sparsely-spiny dendritic trees (Moran and Desmond, 1985; Miyashita et al., 1993). 4.2. The Occipitoparietal Visual Pathway and Frontal Eye Fields By extending their investigations to macaque temporo-parietal lobe visual areas (specifically, the middle temporal area, MT; the lateral intraparietal area, LIPv; and the dorsal superior temporal sulcus, 7a), Elston and Rosa (1997) have provided additional support for a progressive hierarchy, although morphological differences were not as pronounced as those in the occipitotemporal visual pathway. They documented a serial increase in basal dendritic field territories and in branching complexity for pyramidal neurones in the early stages of the occipitoparietal pathway (V1, V2 and MT), but not in the latter stages (MT, LIPv and 7a). As in the occipitotemporal pathway, orientation and directional biases were less marked in more rostral visual areas than in the primary visual regions. Finally, by coupling (visible) spine estimates with basal (but not apical) dendritic extent, they extrapolated a clear stepwise progression in the average spine counts from V1 (799 spines/ neurone) to 7a (2572 spines/neurone). When pyramidal neurones in the two parietal regions (LIPv and 7a) were compared with those in the frontal eye fields (FEF), clear differences emerged, with FEF neurones being more complex in terms of basal dendritic field size and branching complexity (Elston and Rosa, 1998a). Moreover, spine counts in FEF neurones were approximately 30% higher than in the parietal regions. FEF neurones had the largest and most complex basal dendritic systems, with most dense spines, of any of the areas Elston and Rosa had examined, presumably because FEF cells are less functionally compartmentalized, integrating extensive polymodal input (Huerta et al., 1986). Incorporation of FEF neurones into the visual pathways thus reveals an even more general caudal-rostral complexity gradient for basal dendritic systems. Although progression along a proposed hierarchy may not be paralleled exactly by concomitant increases in dendritic field area, especially if functional or anatomical classification within that hierarchy is uncertain, other factors such as spine density and dendritic orientation may contribute significantly to the ultimate structure of hierarchically arranged neuropil.
5. DIRECT INVESTIGATIONS OF REGIONAL VARIATION IN CORTICAL NEUROPIL: HUMANS Much of the early quantitative work on regional variation in human dendritic systems was performed in the Scheibel laboratory, and was inspired by the observation that certain functional talents (e.g. eidetic imagery) are associated with particular anatomical arrangements
© 2002 Taylor & Francis
Regional Dendritic Variation
121
(Scheibel, 1988). In a pioneering undertaking to explore this potential structure-function relationship, Scheibel et al. (1990) quantified basal dendritic systems by means of a Sholl analysis in four cortical regions of the LH: somatosensory cortex (BA3-1-2—thoracic region; BA3-1-2—finger region), prefrontal cortex (BA9), and the supramarginal gyrus (BA40). They found partial support for a positive relationship between dendritic extent and functional complexity insofar as the dendritic arbors in the two association cortices (BA9 and BA40) and in the finger region of BA3-1-2 were typically more complex than those in the thoracic region of BA3-1-2 (recall Figure 6.1B). More recently, Schlaug et al. (1993) compared layer V basal dendritic systems in the anterior (BA24b) and posterior (BA23b) cingulate gyrus by means of a Sholl analysis. Consistent with the posterior-anterior gradient suggested by Elston and Rosa’s research, the anterior cingulate exhibited greater dendritic complexity than the posterior cingulate. This anterior advantage may reflect functional differences between the two regions as well as differences in interconnectivity (Vogt et al., 1979), with the anterior cingulate pyramidal cells receiving diverse synaptic input of both an affective and a cognitive nature (Devinsky et al., 1995). Importantly, the study also incorporated a quantitative measure that has seldom been examined with regard to dendritic extent: cell packing density. The anterior cingulate region was characterized by a lower cell packing density than the posterior portion, thus indicating an inverse relationship between cell packing density and dendritic arborization, at least in homotypical isocortex. Incorporating both measures in future quantitative morphological research should provide valuable insights into regional distributions of neuropil and interconnectivity patterns. At present, the most extensive work on regional dendritic variation in humans has been performed in the Jacobs laboratory, which has been exploring the morphological underpinnings of the functional cortical hierarchy proposed by Benson (1993, 1994). Benson’s hierarchy draws heavily on the sensory-fugal gradients of cortical connectivity proposed by Mesulam (1985), which have recently undergone considerable elaboration (Mesulam, 1998). In Benson’s over-simplified, but useful hierarchical schema, the cerebral cortex is roughly classified into four divisions based on clinical/anatomical correlations: primary, unimodal, heteromodal, and supramodal (see Table 6.1). These cortical types represent progressively more complex levels of neural processing. Although these divisions and their anatomical boundaries are far from absolute, they do provide an initial framework for examining dendritic/spine systems vis-à-vis a functional hierarchy. To date, two studies have been performed within this hierarchical schema to explore regional dendritic variation. Table 6.1.
Proposed functional hierarchy for human cerebral cortexa
Cortical divisions
Function
Sample areas
Primary cortex Unimodal association cortex
Transfer of sensory or motor impulses Discrimination, categorization, and integration of single modality information to form a unimodal percept Formation and processing of complex multimodal percepts Executive control of cognitive networks
BA3-1-2, BA4 BA18, BA22, BA44
Heteromodal association cortex Supramodal association cortex Note a Based on Benson (1993, 1994).
© 2002 Taylor & Francis
BA6B, BA39 BA10, BA11
122
Bob Jacobs and Arnold B. Scheibel
The first investigation, mentioned previously with regards to aging (Jacobs et al., 1997), examined dendritic/spine differences between the secondary occipital area (BA18) and the prefrontal cortex (BA10). BA18, which distributes functionally unique streams of visual information (e.g. color, form, orientation) to other extrastriate areas (Burkhalter and Van Essen, 1986; Gegenfurtner et al., 1996), represents typical unimodal cortex. BA10, which is involved in several higher level integrative functions (e.g. drive, executive control, planning; Stuss and Benson, 1984), represents the quintessential supramodal region of the brain. As predicted, the basal dendrites and associated spines of supragranular pyramidal cells in BA10 were significantly more extensive than those in BA18—by approximately 18% for total dendritic length, and by approximately 35% for spine number (recall Figure 6.1D). Reinforcing the robust nature of this finding, this regional advantage for BA10 was observed in all but a few of the 26 individuals examined. Recently, we have also documented that the pyramidal cell packing density in layer III was 24% greater in BA18 than in BA10 (unpublished data), a finding that further supports an inverse relationship between dendritic length and cell packing density in homotypical isocortex (cf. Schlaug et al., 1993). Regardless of cellular density, the more complex dendritic array in BA10 neurones appears to facilitate a broader sampling of afferent information, thereby potentially increasing their integrative capacity. In contrast, the more limited basilar dendritic systems in BA18 neurones may correspond with more discrete sampling of afferent information (i.e. smaller receptive fields; cf. Rosa and Schmid, 1995), as would be characteristic of information processing that is more unimodal than supramodal in nature. These results appear consistent with the anatomical hierarchy proposed by Pandya and Yeterian (1990), and provide initial support at the dendritic level for Benson’s functional hierarchy. The second investigation (Prather et al., 1997; Jacobs et al., 2001) is perhaps the most extensive quantitative neuromorphological study to date. It examined the basal dendritic/ spine systems of supragranular pyramidal cells (N = 800) across eight regions of human cerebral cortex. Tissue was removed from the lateral surface of the LH to represent each level of Benson’s hierarchical functional schema: primary cortex (somatosensory, BA3-1-2; motor, BA4), unimodal association cortex (Wernicke’s area, BA22; Broca’s area, BA44), heteromodal association cortex (supplementary motor area, BA6b; angular gyrus, BA39), and supramodal cortex (superior frontopolar zone, BA10; inferior frontopolar zone, BA11). Subsequently, primary and unimodal areas were grouped as “lower integrative regions;” heteromodal and supramodal areas were grouped as “higher integrative regions.” Despite the considerable interindividual variation that typifies human tissue, there were significant differences across Brodmann’s areas and between the higher and lower integrative zones for all dendritic and spine measures (see Figure 6.4). Dendritic systems in primary and unimodal regions were consistently less complex than heteromodal and supramodal areas. Nevertheless, the exact sequence of individual Brodmann’s areas depended somewhat on what aspect of the dendritic tree was examined (e.g. total dendritic length: BA3-1-2 < BA22 < BA4 < BA44 < BA11 < BA39 < BA6b < BA10; spine number: BA3-1-2 < BA22 < BA4 < BA44 < BA6b < BA11 < BA39 < BA10). The range within these rankings is substantial, with total dendritic length in BA10 being 31% greater than that in BA3-1-2, and dendritic spine number being 69% greater. In terms of individual areas, it is not surprising that BA6b, BA10, and BA39 exhibited the most elaborate dendritic systems given the vast interconnections and functional
© 2002 Taylor & Francis
Regional Dendritic Variation
123
Figure 6.4. Sample tracings of human supragranular pyramidal cells and a bar graph of the dendritic spine number (DSN) for eight Brodmann areas (BA), arranged from lowest (BA3-1-2) to highest (BA10) in terms of overall complexity. Areas have also been grouped as Low (BA3-1-2, BA22, BA4 and BA44) and High (BA6, BA11, BA39 and BA10) Integration regions, with the average DSN value for each grouping indicated by the dotted lines. Note that DSN values represent only spines that were visible on the basal dendrites. In general, DSN values in the High Integration regions are considerably higher than those in the Low Integration regions. This hierarchy is roughly reflected in the individual tracings of neurones (synthesized from Prather et al., 1997; Jacobs et al., 2001). Scale bars = 100 µm.
complexity of these cortical regions (for reviews, see Goldberg, 1985; Zilles, 1990; Roland, 1993). It is unclear why the dendritic arbors of BA22 were at the low end of the spectrum, although this may be due to this area’s close proximity to the primary auditory cortex (see Jacobs and Scheibel, 1993). Conceivably, a section more posterior along the superior temporal gyrus would be involved in synthesizing a greater proportion of polymodal information, especially given that the sensory speech region receives a wide sampling of cortical and subcortical input (Pandya et al., 1969; Jones and Powell, 1970; Seldon, 1985). Thus, in some instances, there were minor exceptions to Benson’s functional hierarchy (e.g. a primary area such as BA4 being slightly more complex than a predominantly— though probably not exclusively—unimodal area such as BA22). Such exceptions should
© 2002 Taylor & Francis
124
Bob Jacobs and Arnold B. Scheibel
be expected, given the vast interconnectivity of cortical areas, which do not readily conform to strict hierarchical boundaries. On the whole, however, the results indicate that the dendritic/spine systems of cortical areas involved in the initial stages of information processing are not as complex as those involved later in the processing stream, and further underscore that the processing demands placed on dendritic systems in various cortical regions substantially influence their ultimate expression (Cajal, 1894; Hebb, 1949; Diamond et al., 1964).
6. DENDRITIC INTEGRATION AND FUNCTIONAL IMPLICATIONS Functional localization in the cerebral cortex has become one of the conceptual foundations of neuroscience. It has traditionally been based on two streams of research activity: (1) clinical and physiological dissections (e.g. Broca, 1861; Fritsch and Hitzig, 1870; Woolsey, 1958), which established areal specificity for the various aspects of perception and behaviour at a cortical level, and (2) cytoarchitectonic studies, which began to define the anatomical extent and organization of such areas (e.g. Brodmann, 1909; Von Economo, 1929). At the same time, the beginnings of studies at higher resolution, based on visualization of dendritic ensembles and axonal patterns, became possible with the use of the Golgi silver impregnation methods (Golgi, 1886; Cajal, 1909; Lorente de Nó, 1922). However, useful anatomical comparisons of cell structure and neuropil patterns across areas have only become possible with the development of quantitative morphological techniques (e.g. Bok, 1936; Sholl, 1953). A sufficient number of such analyses now suggests that some patterns in neuropil may eventually be understood in terms of the nature of the cortical function subsumed. One such pattern appears to be the length and complexity of dendrite arrangements. We had previously suggested a positive correlation between computational complexity and dendritic extent (Scheibel et al., 1985). Recently, several studies have provided more rigorous support for this idea (Elston and Rosa, 1998b; Jacobs et al., 2001), which correlates reasonably well with hierarchical conceptions of cortical organization. Essentially, as one progresses from “first level” cortical input stages through intermediate levels of association areas (unimodal and heteromodal) to the presumed hierarchically highest levels of cortical associative activity (supramodal), there is a coincidental increment in basal dendrite length and spine number. As indicated above, the increase in dendrite length in humans over the entire sequence is of the order of one third, and that of total spine number by about two thirds. Although fairly consistent, and significant in a quantitative sense, these are by no means massive changes. On an entirely intuitive level, it might be difficult to conceive that the robust qualitative differences assumed to exist between processing mechanisms active in BA18 and BA10, for instance, can in large part be accounted for by a 30% difference in dendritic extension. Certainly, factors other than dendrite extent must be responsible for the dramatic inter-areal differences in function. Phrasing the question differently, we might wonder whether the relatively modest though consistent differences in basal dendrite dimensions among the cortical areas studied are minor variants on a standard pattern, thereby providing only processing changes of a quantitative nature, or whether these differences reflect much more fundamental computational mechanisms, thereby providing variations of a qualitative nature. Studies by Tyc-Dumont and colleagues suggest that the latter may be true (Gogan and
© 2002 Taylor & Francis
Regional Dendritic Variation
125
Tyc-Dumont, 1989). In a series of communications, these researchers report significant differences in the behaviour of individual dendrites. Each dendrite seems to have its own electrical characteristics depending on a group of parameters, including input resistance, voltage attenuation, and charge transfer effectiveness ratio (Gogan and Tyc-Dumont, 1989). These factors are computed for every dendritic site in the arborization to give the electrical image of the tree. Structural studies have emphasized the functional importance of the small caliber outermost branches of basal dendrite systems. In the enrichment paradigm, for example, it is these peripheral processes in rats which develop in response to enhanced input and which disappear when the animal is input-deprived (Connor et al., 1981). Similarly in the human brain, it is the development of these smallest caliber (fifth and sixth order) branches in Broca’s area of the left hemisphere that accompanies the development and maturation of the language faculty (Simonds and Scheibel, 1989). Some years ago, on the basis of a group of selective lesion experiments in rabbits, Globus and Scheibel (1967a,b,c) emphasized the essentially modular nature of the dendritic domains of cortical pyramids. For any one cortical area, there was a high degree of consonance in the extent of the horizontal dendritic components (basal and oblique branches and the apical arch). Depending on the location of the cell body, however, the apical shaft might be as short as 100 µm (layer II), or as long as 4000 µm (deep layer V). Fibre terminals of intracortical derivation seemed almost exclusively related to the horizontal branch systems of the cell (i.e. apical obliques and basal dendrites), whereas extracortically derived afferents appeared to terminate predominantly on apical shafts. The extent of the horizontal dendritic component of the cell could then be considered a measure of its exposure to intracortically derived information, especially since the neocortex communicates predominantly with itself (Braitenberg, 1978; Nieuwenhuys, 1994). Progressive increase in the extent of the basal dendritic skirt might therefore be expected to increase the exposure of the neurone to intracortical influences, a correlation that intuitively meets assumptions about the needs of increasingly more complex associative functions. There have been suggestions that the fine, tapering, outermost branches of the dendritic ensemble may assume a physiological importance out of proportion to the modest fraction of the neuronal dendrite that they represent: “Most distally from the soma, the extremely fine dendritic branches are practically independent subunits where nonlinear synaptic interactions operate, and only the results of these operations are transmitted to the soma with different efficiencies, depending on the cable properties of the individual dendritic channel. [Thus] each neuron is like a complex computer with many nonlinear coprocessors operating in parallel.” (Gogan and Tyc-Dumont, 1989, p. 129) It is interesting to recall that earlier physiological studies of neurones led some to the conclusion that, in the case of motoneurones at least, electrotonic considerations indicated that distal dendrites contribute little to electrical events at the soma (Eccles, 1964). On the basis of theoretical considerations, however, Rall (1974) suggested that remote dendrite terminals might exercise significant electrical effects at the cell body. A number of possible mechanisms for this have been advanced over the last thirty years. Among these possibilities, Redman (1973) suggested that more current may be injected by distal
© 2002 Taylor & Francis
Bob Jacobs and Arnold B. Scheibel
126
synapses than by proximal ones, thereby resulting in excitatory postsynaptic potentials of approximately similar shape and size at the cell body. An alternative suggestion by Rall (1974) is based on the observation that the stems of dendritic spines are generally longest and thinnest at the peripheral portions of dendrites and become increasingly short and stubby as the soma is approached (Jones and Powell, 1969). The long thin spine stem has a much higher resistance value and therefore a lower current carrying capacity. Rall suggested that this attenuated spine stem might have value as an impedance matching device vis-à-vis the underlying dendritic branch input. These most peripheral spines might therefore be particularly effective in adjusting synaptic potency: “The design principle involved here is to sacrifice maximum power in order to gain flexibility and control. Adjustability of potency means either increase or decrease relative to other synapses. Thus we think of delicate adjustments of the relative weights (potency) of many different synapses to any given neurone. We think of these changes as responsible for changes in dynamic patterns of activity in assemblies of neurones organized with convergent and divergent connective overlaps.” (Rall, 1974, p. 17) Indeed, recent research suggests that the excitability of an entire dendrite may be regulated by changes in distal spine density (Jaslove, 1992). It thus seems likely that synaptic activity in the outermost segments of the dendrite tree not only affects neuronal activity, but may, in fact, exert effects out of proportion to dendritic extent and geographic distance from the soma. Another aspect of terminal dendrite function that may enrich the role of this portion of the dendrite tree is the possibility of interaction among dendrite terminal branches. In addition to dendro-dendritic synapses such as those described in the olfactory bulb (Rall et al., 1966), a number of investigators have described the presence of dendrite bundles in many sites throughout the central nervous system including the cerebral cortex (Fleischhauer, 1974; Scheibel and Scheibel, 1970). Although the role of such structural complexes has never been fully clarified, it has been suggested that distal dendrites in close apposition to those from other neurones may in fact transmit information, thereby emphasizing again the possibly special role of this portion of the nerve cell (Bras et al., 1987; Gogan and Tyc-Dumont, 1989). Finally, complementing Rall’s theoretical speculations are more recent insights into the active characteristics of dendritic branches provided by high-speed fluorescence imaging and dendritic patch clamping. It seems relatively clear that dendrites can no longer be viewed as simply passive structures adhering to the principles of cable theory (for review, see Johnston et al., 1996). Indeed, at least some cortical dendrites appear capable of sustaining active propagation of electrical potentials through voltage-sensitive Na+ and Ca2+ channels. These active characteristics would indeed boost the effect of distal synaptic input, contributing significantly to synaptic integration locally and across the entire neuron.
7. CONCLUSION Quantitative neuromorphological techniques have substantially enhanced our understanding of the dendritic ensembles first described and categorized according to qualitative
© 2002 Taylor & Francis
Regional Dendritic Variation
127
observations (e.g. Ramón-Moliner, 1962). Indeed, as noted by Sholl (1953), the characteristic dendritic patterns across various cortical regions can only be revealed through quantitative investigations. We conclude this chapter by noting that there is a very strong likelihood that these inter-areal variations in basal dendritic dimensions revealed by quantitative neuromorphological investigations of the cerebral cortex reflect significant differences in the nature of cortical processing. Many other factors are undoubtedly involved in determining the range of computational strategems as one moves from first level sensory representations to the highest associational levels. Some of these are considered elsewhere in this volume. Nonetheless, the characteristics of the receptive dendritic membrane of individual neuronal elements and their variations along the length of the dendritic shaft are bound to represent central issues in our developing knowledge of cortical computation.
ACKNOWLEDGEMENTS We would like to thank the following individuals for their suggestions on preliminary versions of this manuscript: Jesse Jacobs, Elisa Kapler, Jennifer Ransom and Sharon Sann.
REFERENCES Amir, Y., Harel, M. and Malach, R. (1993) Cortical hierarchy reflected in the organization of intrinsic connections in macaque monkey visual cortex. Journal of Comparative Neurology, 334, 19–64. Bannister, N.J. and Larkman, A.U. (1995) Dendritic morphology of CA1 pyramidal neurons from the rat hippocampus: I. Branching patterns. Journal of Comparative Neurology, 360, 150–160. Becker, L.E., Armstrong, D.L., Chan, F. and Wood, M.M. (1984) Dendritic development in human occipital cortical neurons. Developmental Brain Research, 13, 117–124. Belichenko, P.V. and Dahlström, A. (1995) Studies of the 3-dimensional architecture of dendritic spines and varicosities in human cortex by confocal laser scanning microscopy and Lucifer Yellow microinjections. Journal of Neuroscience Methods, 57, 55–61. Benson, D.F. (1993) Progressive frontal dysfunction. Dementia, 4, 149–153. Benson, D.F. (1994) The Neurology of Thinking. New York: Oxford Press. Bok, S.T. (1936) The branching of the dendrites in the cerebral cortex. Proceedings of the Academy of Sciences (Amsterdam), 39, 1209–1218. Boussaoud, D., Ungerleider, L.G. and Desimone, R. (1990) Pathways for motion analysis: Cortical connections of the medial superior temporal and fundus of the superior temporal visual areas in the macaque. Journal of Comparative Neurology, 296, 462–495. Braak, H. and Braak, E. (1985) Golgi preparations as a tool in neuropathology with particular reference to investigations of the human telencephalic cortex. Progress in Neurobiology, 25, 93–139. Braitenberg, V. (1978) Cortical architectonics: General and areal. In: M.A.B. Brazier and H. Petsche (eds), Architectonics of the Cerebral Cortex, New York: Raven Press, pp. 443–465. Braitenberg, V. and Schüz, A. (1991) Anatomy of the Cortex: Statistics and Geometry. Berlin: Springer. Bras, H., Destombes, J., Gogan, P. and Tyc-Dumont, S. (1987) The dendrites of single brain-stem motoneurons intracellularly labelled with horseradish peroxidase in the cat. An ultrastructural analysis of the synaptic covering and the microenvironment. Neuroscience, 22, 971–981. Broca, P. (1861) Remarques sur la siège de la faculté du language articulé, suivies d’une observation d’aphemie (perte de la parole). Bulletin de Societé d’Anatomie (Paris), 36, 330–357. Brodmann, K. (1909) Vergleichende Lokalisationlehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues. Leipzig: J.A. Barth. Buell, S.J. and Coleman, P.D. (1981) Quantitative evidence for selective dendritic growth in normal human aging but not in senile dementia. Brain Research, 214, 23–41. Buell, S.J. (1982) Golgi-Cox and rapid Golgi methods as applied to autopsied human brain tissue: Widely disparate results. Journal of Neuropathology and Experimental Neurolology, 41, 500–507. Burkhalter, A. and Van Essen, D.C. (1986) Processing of color, form and disparity information in visual areas VP and V2 of ventral extrastriate cortex in the macaque monkey. Journal of Neuroscience, 6, 2327–2351.
© 2002 Taylor & Francis
128
Bob Jacobs and Arnold B. Scheibel
Cajal, S. Ramón y (1894) Les Nouvelles Ideés sur la Structure du Système Nerveux chez L’homme et chez les Vertébrés. Trans. by L. Azoulay. Paris: Reinwald. Cajal, S. Ramón y (1909, 1911) Histologie du Système Nerveux de L’homme et des Vertébrés, 2 Vols. Trans. by L. Azoulay. Paris: Maloine. Chugani, H.T., Phelps, M.E. and Mazziotta, J.C. (1987) Positron emission tomography study of human brain functional development. Annals of Neurology, 22, 487–497. Coleman, P.D. and Flood, D.G. (1987) Neuron numbers and dendritic extent in normal aging and Alzheimer’s disease. Neurobiology of Aging, 8, 521–545. Colonnier, M. (1964) The tangential organization of the visual cortex. Journal of Anatomy (London), 98, 327–344. Conel, J. (1939–67) The Postnatal Development of the Human Cerebral Cortex (Vols. 1–6). Cambridge, Massachusetts: Harvard University Press. Connor, J.R., Melone, J., Yuen, A. and Diamond, M.C. (1981) Terminal segment lengths in dendrites in aged rats: An experimentally induced response. Experimental Neurology, 73, 827–830. Cupp, C.J. and Uemura, E. (1980) Age-related changes in prefrontal cortex of Macaca mulatta: Quantitative analysis of dendritic branching patterns. Experimental Neurology, 69, 143–163. de Ruiter, J.P. (1983) The influence of post-mortem fixation delay on the reliability of the Golgi silver impregnation. Brain Research, 266, 143–147. Devinsky, O., Morrell, M.J. and Vogt, B.A. (1995) Contributions of anterior cingulate cortex to behaviour. Brain, 118, 279–306. Diamond, M.C., Krech, D. and Rosenzweig, M.R. (1964) The effects of an enriched environment on the histology of the rat cerebral cortex. Journal of Comparative Neurology, 123, 111–119. Eayrs, J.T. (1955) The cerebral cortex of normal and hypothyroid rats. Acta Anatomica, 25, 160–183. Eccles, J.C. (1964) The Physiology of Synapses. Berlin: Springer. Elston, G.N. and Rosa, M.G.P. (1997) The occipitoparietal pathway of the macaque monkey: Comparison of pyramidal cell morphology in layer III of functionally related cortical visual areas. Cerebral Cortex, 7, 432–452. Elston, G.N. and Rosa, M.G.P. (1998a) Complex dendritic fields of pyramidal cells in the frontal eye field of the macaque monkey: Comparison with parietal areas 7a and LIP. NeuroReport, 9, 127–131. Elston, G.N. and Rosa, M.G.P. (1998b) Morphological variation of layer III pyramidal neurones in the occipitotemporal pathway of the macaque monkey visual cortex. Cerebral Cortex, 8, 278–294. Elston, G.N., Rosa, M.G.P. and Calford, M.B. (1996) Comparison of dendritic fields of layer III pyramidal neurons in striate and extrastriate visual areas of the marmoset: A Lucifer Yellow intracellular injection study. Cerebral Cortex, 6, 807–813. Fleischhauer, K. (1974) On different patterns of dendrite bundling in the cerebral cortex of the rat. Zeitschrift für Anatomie und Entwicklungsgeschichte, 143, 115–126. Flood, D.G. (1993) Critical issues in the analysis of dendritic extent in aging humans, primates, and rodents. Neurobiology of Aging, 14, 649–654. Flood, D.G., Buell, S.J., Defiore, C.H., Horwitz, G.J and Coleman, P.D. (1985) Age-related dendritic growth in dentate gyrus of human brain is followed by regression in the ‘oldest old.’ Brain Research, 345, 366–368. Fritsch, G. and Hitzig, E. (1870) Über die elektrische Erregbarkeit des Grosshirns. Archiv für Anatomie und Physiologie (Leipzig), 37, 300–332. Gegenfurtner, K.R., Kiper, D.C. and Fenstemaker, S.B. (1996) Processing of color, form, and motion in macaque area V2. Visual Neuroscience, 13, 161–172. Globus, A. and Scheibel, A.B. (1967a) Pattern and field in cortical structure: The rabbit. Journal of Comparative Neurology, 131, 155–172. Globus, A. and Scheibel, A.B. (1967b) Synaptic loci on visual cortical neurons of the rabbit: The specific afferent radiation. Experimental Neurology, 18, 116–131. Globus, A. and Scheibel, A.B. (1967c) Synaptic loci on parietal cortical neurons: Termination of corpus callosum fibers. Science (Washington), 156, 1127–1129. Gogan, P. and Tyc-Dumont, S. (1989) How do dendrites process neural information? News in Physiological Sciences (American Physiological Society), 4, 127–130. Goldberg, G. (1985) Supplementary motor area structure and function: Review and hypothesis. Behavioral and Brain Sciences, 8, 567–616. Golgi, C. (1886) Studi Sulla Fina Anatomia degli Organi Centrali del Sistema Nervoso. Milano: Hoepli. Hayes, T.L. and Lewis, D.A. (1996) Magnopyramidal neurons in the anterior motor speech region. Archives of Neurology, 53, 1277–1283. Hebb, D. (1949) The Organization of Behavior. New York: Wiley. Huerta, M.F., Krubitzer, L.A. and Kaas, J.H. (1986) Frontal eye field as defined by intracortical microstimulation in squirrel monkeys, owl monkeys, and macaque monkeys: I. Subcortical connections. Journal of Comparative Neurology, 253, 415–439. Huttenlocher, P.R. and Dabholkar, A.S. (1997) Regional differences in synaptogenesis in human cerebral cortex. Journal of Comparative Neurology, 387, 167–178.
© 2002 Taylor & Francis
Regional Dendritic Variation
129
Huttenlocher, P.R. and de Courten, C. (1987) The development of synapses in the striate cortex of man. Human Neurobiology, 6, 1–9. Huttenlocher, P.R., de Courten, C., Garey, L.J. and Van Der Loos, H. (1982) Synaptogenesis in human visual cortex: Evidence for synapse elimination during normal development. Neuroscience Letters, 33, 247–252. Ishizuka, N., Maxwell Cowan, W. and Amaral, D.G. (1995) A quantitative analysis of the dendritic organization of pyramidal cells in the rat hippocampus. Journal of Comparative Neurology, 362, 17–45. Jacobs, B. and Scheibel, A.B. (1993) A quantitative dendritic analysis of Wernicke’s area in humans. I. Lifespan changes. Journal of Comparative Neurology, 327, 83–96. Jacobs, B., Batal, H.A., Lynch, B., Ojemann, G., Ojemann, L. and Scheibel, A.B. (1993a) Quantitative dendritic and spine analyses of speech cortices: A case study. Brain and Language, 44, 239–253. Jacobs, B., Schall, M. and Scheibel, A.B. (1993b) A quantitative dendritic analysis of Wernicke’s area in humans. II. Gender, Hemispheric, and Environmental Factors. Journal of Comparative Neurology, 327, 97–111. Jacobs, B., Chugani, H.T., Allada, V., Chen, S., Phelps, M.E., Pollack, D.B. and Raleigh, M.J. (1995). Developmental changes in brain metabolism in sedated rhesus macaques and vervet monkeys revealed by positron emission tomography. Cerebral Cortex, 5, 222–233. Jacobs, B., Driscoll, L. and Schall, M. (1997) Life-span dendritic and spine changes in areas 10 and 18 of human cortex: A quantitative Golgi Study. Journal of Comparative Neurology, 386, 661–680. Jacobs, B., Schall, M., Prather, M., Kapler, E., Driscoll, L., Baca, S., Jacobs, J., Ford, K., Wainwright, M. and Treml, M. (2001) Regional dendritic and spine variation in human cerebral cortex: A quantitative Golgi study. Cerebral Cortex, 11, 558–571. Jaslove, S.W. (1992) The integrative properties of spiny distal dendrites. Neuroscience, 47, 495–519. Johnston, D., Magee, J.C., Colbert, C.M. and Christie, B.R. (1996) Active properties of neuronal dendrites. Annual Review of Neuroscience, 19, 165–186. Jones, E.G. and Powell, T.P.S. (1969) Morphological variations in the dendritic spines of the neocortex. Journal of Cell Science, 5, 509–529. Jones, E.G. and Powell, T.P.S. (1970) An anatomical study of converging sensory pathways with the cerebral cortex of the monkey. Brain, 93, 793–820. Juraska, J.M. (1982) The development of pyramidal neurons after eye opening in the visual cortex of hooded rats: A quantitative study. Journal of Comparative Neurology, 212, 208–213. Koenderink, M.J.Th., Uylings, H.B.M. and Mrzljak, L. (1994) Postnatal maturation of the layer III pyramidal neurons in the human prefrontal cortex: A quantitative Golgi analysis. Brain Research, 653, 173–182. Koenderink, M.J.Th. and Uylings, H.B.M. (1995) Postnatal maturation of layer V pyramidal neurons in the human prefrontal cortex: A quantitative Golgi analysis. Brain Research, 678, 233–243. Kuhl, D.E., Metter, E.J., Riege, W.H. and Phelps, M.E. (1982) Effects of human aging on patterns of local cerebral glucose utilization determined by the [18F] fluorodeoxyglucose method. Journal of Cerebral Blood Flow and Metabolism, 2, 163–171. Larkman, A.U. (1991) Dendritic morphology of pyramidal neurones of the visual cortex of the rat: III. Spine distributions. Journal of Comparative Neurology, 306, 332–343. Lorente de Nó, R. (1922) La corteza cerebral del raton. Primera contribucion: La corteza acustica. Trabajos del Instituto Cajal de Investigaciones Biologicas (Madrid), 20, 41–78. Lund, J.S., Yoshioka, T. and Levitt, J.B. (1993) Comparison of intrinsic connectivity in different areas of macaque monkey cerebral cortex. Cerebral Cortex, 3, 148–162. Marin-Padilla, M. (1967) Number and distribution of the apical dendritic spines of the layer V pyramidal cells in man. Journal of Comparative Neurology, 131, 475–490. Marin-Padilla, M. (1970) Prenatal and early postnatal ontogenesis of the human motor cortex: A Golgi study. I. The sequential development of the cortical layers. Brain Research, 23, 167–183. Mata, M., Fink, D.J., Gainer, H., Smith, C.B., Davidsen, L., Savaki, H. et al. (1980) Activity-dependent energy metabolism in rat posterior pituitary reflects sodium pump activity. Journal of Neurochemistry, 34, 213–215. Matsubara, J.A., Chase, R. and Thejomayen, M. (1996) Comparative morphology of three types of projectionidentified pyramidal neurons in the superficial layers of cat visual cortex. Journal of Comparative Neurology, 366, 93–108. Meller, S.T. and Dennis, B.J. (1990) A multiple Golgi analysis of the periaqueductal gray in the rabbit. Journal of Comparative Neurology, 302, 66–86. Mesulam, M.-M. (1985) Principles of Behavioral Neurology. Philadelphia: F.A. Davis. Mesulam, M.-M. (1998) From sensation to cognition. Brain, 121, 1013–1052. Miyashita, Y., Okuno, H. and Hasegawa, I. (1993) Tuning and association: Neuronal memory mechanisms of complex visual forms in monkey temporal cortex. Biomedical Research, 14, 89–94. Moran, J. and Desmone, R. (1985) Selective attention gates visual processing in the extrastriate cortex. Science (Washington), 229, 782–784. Mrzljak, L., Uylings, H.B.M., van Eden, C.G. and Judás, M. (1990) Neuronal development in human prenatal and postnatal stages. Progress in Brain Research, 85, 185–222.
© 2002 Taylor & Francis
130
Bob Jacobs and Arnold B. Scheibel
Murphy, E.H. and Magness, R. (1984) Development of the rabbit visual cortex: A quantitative Golgi analysis. Experimental Brain Research, 53, 304–314. Nakamura, S., Akiguchi, I., Kameyama, M. and Mizuno, N. (1985) Age-related changes of pyramidal cell basal dendrites in layers III and V of human motor cortex: A quantitative Golgi study. Acta Neuropathologica (Berlin), 65, 281–284. Nieuwenhuys, R. (1994) The neocortex. Anatomy and Embryology, 190, 307–337. Nudo, R.J. and Masterton, R.B. (1986) Stimulation-induced (14C)2-deoxyglucose labeling of synaptic activity in the central auditory system. Journal of Comparative Neurology, 245, 553–565. Ohm, T.G. and Diekmann, S. (1994) The use of Lucifer Yellow and Mini-Ruby for intracellular staining in fixed brain tissue: Methodological considerations evaluated in rat and human autopsy brains. Journal of Neuroscience Methods, 55, 105–110. Ojemann, G.A. and Whitaker, H.A. (1978) Language localization and variability. Brain and Language, 6, 239–260. Ojemann, G.A., Ojemann, J., Lettich, E. R.E.E.G.T. and Berger, M. (1989) Cortical language localization in left, dominant hemisphere: An electrical stimulation mapping investigation in 117 patients. Journal of Neurosurgery, 71, 316–326. Pandya, D.N. and Yeterian, E.H. (1990) Prefrontal cortex in relation to other cortical areas in rhesus monkey: Architecture and connections. In: H.B.M. Uylings, C.G. Van Eden, J.P.C. De Bruin, M.A. Cornere and M.G.P. Feenstra (eds), The Prefrontal Cortex. Its Structure, function and pathology (Progress in Brain Research, Vol. 85) Amsterdam: Elsevier, pp. 63–94. Pandya, D.N., Hallett, M. and Mukherjee, S.K. (1969) Intra- and interhemispheric connections of neocortical auditory system in rhesus monkey. Brain Research, 14, 49–65. Prather, M., Treml, M., Driscoll, L., Schall, M. and Jacobs, B. (1997) Regional variation in dendritic and spine complexity: A quantitative Golgi analysis of human cerebral cortex. Society for Neuroscience Abstracts, 23, 87.13. Prinz, M., Prinz, B. and Schulz, E. (1997) The growth of non-pyramidal neurons in the primary motor cortex of man: A Golgi study. Histology and Histopathology, 12, 895–900. Rakic, P., Bourgeois, J.-P., Eckenhoff, M.F., Zecevic, N. and Goldman-Rakic, P.S. (1986) Concurrent overproduction of synapses in diverse regions of the primate cerebral cortex. Science (Washington), 232, 232–235. Rall, W., Shepherd, G.M., Reese, T.S. and Brightman, M.W. (1966). Dendrodendritic synaptic pathway for inhibition in the olfactory bulb. Experimental Neurology, 14, 44–56. Rall, W. (1974) Dendritic spines, synaptic potency and neuronal plasticity. In Cellular Mechanisms Subserving Changes in Neuronal Activity, (eds) C.D. Woody, K.A. Brown, T.J. Crow and J.D. Knispel, Brain Information Service Research Report #3: University of California, Los Angeles, pp. 13–21. Ramón-Moliner, E. (1962) An attempt at classifying nerve cells on the basis of their dendritic patterns. Journal of Comparative Neurology, 119, 211–227. Raz, N., Torres, I.J., Spencer, W.D. and Acker, J.D. (1993) Pathoclysis in aging human cerebral cortex: Evidence from in vivo MRI morphometry. Psychobiology, 21, 151–160. Raz, N., Gunning, F.M., Head, D., Dupuis, J.H., McQuain, J., Briggs, S.D., Laken, W.J., Thornton, A.E. and Acker, J.D. (1997) Selective aging of the human cerebral cortex observed in vivo: Differential vulnerability of the prefrontal gray matter. Cerebral Cortex, 7, 268–282. Redman, S.J. (1973) The attenuation of passively propogating dendritic potentials in a motoneurone cable model. Journal of Physiology (London), 234, 637–664. Roland, P.E. (1984) Metabolic measurements of the working frontal cortex in man. Trends in Neuroscience, 7, 430–435. Roland, P.E. (1993) Brain Activation. New York: John Wiley & Sons. Rosa, M.G.P. and Schmid, L.M. (1995) Visual areas in the dorsal and medial extrastriate cortices of the marmoset. Journal of Comparative Neurology, 359, 272–299. Schadé, J.P. and Van Groenigen, W.B. (1961) Structural organization of the human cerebral cortex. 1. Maturation of the middle frontal gyrus. Acta Anatomica, 47, 74–111. Scheibel, M.E. and Scheibel, A.B. (1970) Organization of spinal motoneuron dendrites in bundles. Experimental Neurology, 28, 106–112. Scheibel, M.E. and Scheibel, A.B. (1978) The methods of Golgi. In: R.T. Robinson (ed.), Neuroanatomical Research Techniques, New York: Academic Press, pp. 89–114. Scheibel, A.B., Paul, L.A., Fried, I., Forsythe, A.B., Tomiyasu, U., Wechsler, A., Kao, A. and Slotnick, J. (1985) Dendritic organization of the anterior speech area. Experimental Neurology, 87, 109–117. Scheibel, A.B. (1988) Dendritic correlates of human cortical function. Archives Italiennes de Biologie, 126, 347–357. Scheibel, A.B., Conrad, T., Perdue, S., Tomiyasu, U. and Wechsler, A. (1990) A quantitative study of dendrite complexity in selected areas of the human cerebral cortex. Brain and Cognition, 12, 85–101. Schierhorn, H. (1981) Strukturwandel neokortikaler Pyramidenneurone des Menschen während des 5. bis 9. Dezenniums. Psychiatrie, Neurologie und medizinische Psychologie, 33, 664–673. Schlaug, G., Armstrong, E., Schleicher, A. and Zilles, K. (1993) Layer V pyramidal cells in the adult human cingulate cortex: A quantitative Golgi-study. Anatomy and Embryology, 187, 515–522.
© 2002 Taylor & Francis
Regional Dendritic Variation
131
Schulz, E., Renner, J. and Meyer, U. (1992) Quantitative Analyse von Lamina III-Pyramiden-Neuronen im Parietalcortex des Neugeborenen. Journal für Hirnforschung (Berlin), 6, 661–672. Seldon, H.L. (1981a) Structure of human auditory cortex. I. Cytoarchitectonics and dendritic distributions. Brain Research, 229, 277–294. Seldon, H.L. (1981b) Structure of human auditory cortex. II. Axon distributions and morphological correlates of speech perception. Brain Research, 229, 295–310. Seldon, H.L. (1982) Structure of human auditory cortex. III. Statistical analysis of dendritic trees. Brain Research, 249, 211–221. Seldon, H.L. (1985) The anatomy of speech perception: Human auditory cortex. In: A. Peters and E.G. Jones (eds), Cerebral Cortex (Vol. 4), New York: Plenum Press, pp. 273–327. Semenova, L.K., Vasil’eva, V.A., Tsekhmistrenko, T.A. and Shumeiko, N.S. (1989) Characteristics of the ensemble organization of the human cerebral cortex from birth to 20 years of age. Arkhiv Anatomii, Gistologii I Embriologii, 97, 15–24. Sholl, D.A. (1953) Dendritic organization in the neurones of the visual and motor cortices of the cat. Journal of Anatomy, 87, 387–406. Sholl, D.A. (1956) The Organization of the Cerebral Cortex. New York: Wiley. Simonds, R.J. and Scheibel, A.B. (1989) The postnatal development of the motor speech area: A preliminary study. Brain and Language, 37, 42–58. Stensaas, S.S., Eddington, D.K. and Dobelle, W.H. (1974) The topography and variability of the primary visual cortex in man. Journal of Neurosurgery, 40, 747–755. Stuss, D.T. and Benson, D.F. (1984) Neuropsychological studies of the frontal lobes. Psychological Bulletin, 95, 3–28. Sullivan, E.V., Marsh, L., Mathalon, D.H., Lim, K.O. and Pfefferbaum, A. (1995) Age-related decline in MRI volumes of temporal lobe gray matter but not hippocampus. Neurobiology of Aging, 16, 591–606. Takashima, S., Becker, L.E., Armstrong, D.L. and Chan, F. (1981) Abnormal neuronal development in the visual cortex of the human fetus and infant with Down’s syndrome. A quantitative and qualitative Golgi study. Brain Research, 225, 1–21. Terry, R.D., DeTeresa, R. and Hansen, L.A. (1987) Neocortical cell counts in normal human adult aging. Annals of Neurology, 21, 530–539. Trommald, M., Jensen, V. and Andersen, P. (1995) Analysis of dendritic spines in rat CA1 pyramidal cells intracellularly filled with a fluorescent dye. Journal of Comparative Neurology, 353, 260–274. Uemura, E. (1980) Age-related changes in prefrontal cortex of Macaca mulatta: Synaptic density. Experimental Neurology, 69, 164–172. Uylings, H.B.M., Smit, G.J. and Veltman, W.A.M. (1975) Ordering methods in quantitative analysis of branching structures of dendritic trees. In: G.W. Kreutzberg (ed.), Advances in Neurology (Vol. 12), New York: Raven Press, pp. 247–254. Uylings, H.B.M., Ruiz-Marcos, A. and van Pelt, J. (1986) The metric analysis of three-dimensional dendritic tree patterns: A methodological review. Journal of Neuroscience Methods, 18, 127–151. Valverde, F. (1976) Aspects of cortical organization related to the geometry of neurons with intra-cortical axons. Journal of Neurocytology, 5, 509–528. Van der Loos, H. (1959) Dendro-dendritic Connections in the Cerebral Cortex. [in Dutch] Doctoral dissertation, University of Amsterdam. Haarlem: Stam. Vogt, B.A., Rosene, D.L. and Pandya, D.N. (1979) Thalamic and cortical afferents differentiate anterior from posterior cingulate cortex in the monkey. Science (Washington), 204, 205–207. Von Economo, C.F. (1929) The Cytoarchitectonics of the Human Cerebral Cortex. London: Oxford Medical Publications. Walker, S.F. (1980) Lateralization of function in the vertebrate brain: A review. British Journal of Psychology, 71, 329–367. Whitaker, H.A. and Selnes, O.A. (1976) Anatomic variations in the cortex: Individual differences and the problem of the localization of language functions. Annals of the New York Academy of the Sciences, 280, 844–854. Williams, R.S., Ferrante, R.J. and Caviness, Jr., V.S. (1978) The Golgi rapid method in clinical neuropathology: Morphological consequences of suboptimal fixation. Journal of Neuropathology and Experimental Neurology, 37, 13–33. Woolsey, C.N. (1958) Organization of somatic sensory and motor areas of the cerebral cortex. In: H.F. Harlow and C.N. Woolsey (eds), Biological and Biochemical Bases of Behavior, Madison: University of Wisconsin, pp. 63–81. Yoshioka, T., Levitt, J.B. and Lund, J.S. (1992) Intrinsic lattice connections of macaque monkey visual cortical area V4. Journal of Neuroscience, 12, 2785–2802. Zatorre, R.J., Evans, A.C., Meyer, E. and Gjedde, A. (1992) Lateralization of phonetic and pitch discrimination in speech processing. Science (Washington), 256, 846–849. Zilles, K. (1990) Cortex. In: G. Paxinos (ed.), The Human Nervous System, San Diego: Academic Press, pp. 757–802.
© 2002 Taylor & Francis
7 Intrinsic Connections in Mammalian Cerebral Cortex Jonathan B. Levitt1 and Jennifer S. Lund2 1
Corresponding author. Department of Biology, City College of the City University of New York, 138th Street & Convent Avenue, New York NY 10031, USA 2 Department of Ophthalmology, Moran Eye Center, University of Utah, 50 North Medical Drive, Salt Lake City, UT 84132, USA Tel: +1-212-650-8539; FAX: +1-212-650-8585; e-mail:
[email protected]
The superficial layers of all areas of mammalian cerebral cortex are characterised by an extensive network of axons running parallel to the cortical surface. These projections, furnished primarily by pyramidal neurones (but in certain species also by inhibitory basket neurones) span several millimeters of cortex, and make synaptic contacts in terminal fields that are generally patchy, or clustered into discrete zones of high terminal density separated by zones of low density. These are referred to as intrinsic connections, as they interconnect loci within a single cortical area. This chapter reviews the basic structure of this connectional lattice: its laminar specificity, sources and targets, spatial arrangement, and relation to functional maps. This description will centre on primary visual cortex in cat and monkey as these have been most intensively studied, but we will also discuss the cortices of other species where data are available, and will touch upon extrastriate visual areas, as well as auditory, somatosensory, motor and prefrontal cortices. It is our purpose to show which features of this connectional lattice are common to all areas of mammalian cerebral cortex, thus illustrating what makes this a fundamental architectural feature of the mammalian cerebral cortex. KEYWORDS: circuitry, horizontal connections, neuroanatomy, pyramidal neurone, visual cortex
1. INTRODUCTION The columnar structure of the cerebral cortex has now been appreciated for well over 40 years as a fundamental architectural feature of cerebral cortex organization (Mountcastle, 1957; Powell and Mountcastle, 1959; Hubel and Wiesel, 1962). Cells situated in a column, perpendicular to the cortical surface, share a number of important functional properties such as receptive field position or stimulus selectivity. While we now appreciate the limitations of this scheme (in that cells situated in different layers are in fact known to differ to some degree in their connections and receptive field properties), it is still clear that in many important respects neurones located in a single column from pia to white matter share key receptive field properties. This columnar organisation is mirrored by the anatomical organisation of cerebral cortex: in any area one chooses to investigate, there is a dense vertical focus of connections linking cells at different depths within a single column of cortex. This has been appreciated for even longer than the physiological columnar organisation: golgi studies have shown that connections running perpendicular to the cortical surface, linking 133 © 2002 Taylor & Francis
134
Jonathan B. Levitt and Jennifer S. Lund
the different layers, are more prominent than those running parallel to the cortical surface (Cajal, 1922; O’Leary, 1941; Lund and Boothe, 1975). Connections running horizontally, parallel to the cortical surface, are difficult to trace with the Golgi technique. In such studies, axons are generally traced no more than a few hundred microns from the soma. However, it was clear, even from these relatively crude techniques, that there did exist neural circuits to mediate communication among cells at different locations across the cortex (i.e. in different columns). Increasingly sophisticated techniques have been used to study these horizontal connections in the cerebral cortex: These include fibre degeneration, autoradiographic demonstration of transport of radioactive amino acids, transport of neural tracer substances, in vitro intracellular labeling and electrophysiological recording of individual neurones, and optical imaging to name but a few. In this chapter, we will review the key features of these intrinsic horizontal connections, i.e. circuits linking different columns within a single cortical area (as opposed to those circuits making vertical connections within a column—e.g. Lund, 1973; Blasdel et al., 1985; Yoshioka et al., 1994). This chapter is not intended as an exhaustive review of intrinsic connections. In the visual cortex alone there is now an enormous literature concerning the organisation of cortical connections linking cells both at different depths within a column, or in different columns (for recent reviews of this topic see Fitzpatrick, 1996; Callaway, 1998b). Despite obvious differences among different cortical areas in cytoarchitecture, functional properties, extrinsic connections, etc., it has also long been appreciated that certain neuronal components and architectural features are common to all areas of cerebral cortex (Cajal, 1909; Rockel et al., 1980; Tyler et al., 1998). Our purpose here is to review the basic features of this connectional system to discern what is common to all areas of cerebral cortex. We will show that horizontal connections intrinsic to any area are a fundamental feature of the organization of the cerebral cortex. We will emphasise primary visual cortex (V1) in cat and monkey, which has been studied most intensively, but we will also discuss differences among other species and cortical areas to elucidate common threads.
2. BASIC FEATURES OF INTRINSIC CONNECTIONS Here we review the evidence that the basic features of intrinsic connections are similar in nearly all cortical areas of all mammalian species, differing only quantitatively rather than qualitatively. Later in this chapter we will provide a rationale for both the similarities and the exceptions. 2.1. Degeneration Studies The first studies to describe in any detail, the pattern of connections within any single area of the cerebral cortex used degeneration techniques. Small lesions (surgical slits or electrolytic lesions) are placed in the cortex, and the pattern of degenerating fibres is revealed, thus indicating the extent and topography of projections from the lesion site. Studies in monkey visual (Fisken et al., 1973), motor (Gatter et al., 1978), and somatosensory cortex (Shanks et al., 1978; Vogt and Pandya, 1978), showed that degenerating fibres spread from the lesion site in a similar pattern in all these areas, suggesting that intrinsic cortical connections were essentially similar in all cortical areas. Degenerating fibres were most dense within a millimeter of the lesion, but could span several millimeters of cortex. The density of
© 2002 Taylor & Francis
Intrinsic Connections of the Cortex
135
degenerating fibres appeared to fall off with distance from the lesion, but with no obvious substructure to the label pattern. However, the total field of cortex containing degenerating fibres was not always isotropic, for example forming an ellipse elongated in the anteroposterior axis in the motor cortex. The 1975 study of the primate striate cortex by Fisken et al. was the most detailed of these, and provided a fuller characterisation of intrinsic connections using the degeneration technique. They quantified and described more carefully a number of features of intrinsic connections not previously appreciated. One important factor was the use of smaller lesions. Fibre degeneration superficial and deep to lesions occupied roughly the same width as the lesion itself, confirming the columnar vertical projection focus suggested by the earlier Golgi studies. However, these authors also reported a laminar specificity in the spread of fibre degeneration: horizontal connections were essentially absent from layer 4, were light in layers 1 and 6, and were most prominent in lower layers 2/3 (extending into the uppermost part of layer 4) and layer 5. They quantified the distribution of degenerating fibres across the cortex, and found that fibre density was greatest within 1 mm of the lesion (roughly 60% of all such fibres they observed). The distribution appeared essentially continuous, not obviously patchy or clustered, but there were suggestions that the degenerating fibres and terminals furthest from the lesion did segregate into discrete zones (see their Figure 25). They noted intense fine degeneration within 200 µm of the lesion site, and moderate terminal and fibre degeneration up to 2–3 mm from the lesion. Fisken et al. also examined the ultrastructure of degenerating profiles to determine their synaptic relationships, and noted that the vast majority (roughly 90%) of degenerating synaptic profiles were of the asymmetric type, and were found contacting spines; degenerating symmetric profiles were much rarer (although as they conceded it is more difficult to be confident that a degenerating axon terminal has a symmetric type of synapse), contacting both pyramidal and nonpyramidal cells. Intrinsic connecions thus appeared to be a connectional network primarily linking excitatory pyramidal neurones to one another. In these early papers, what was emphasised was the limited extent of degeneration following a small lesion to the cerebral cortex, but they are significant for revealing that horizontal connections within a given cortical area could indeed span several millimeters (up to 5–6 mm total in visual cortex), thus linking neurones at physically offset points across the cortex. However, certain of their conclusions can be questioned on a number of technical grounds. Firstly, the lack of any obvious fine structure to the projections might simply have reflected the uncertainty of whether it was fibres originating from the lesion site rather than simply those passing through the lesion site that were being revealed. A number of the lesions in these studies were quite large, which also might have masked specificity of intrinsic connections. Secondly, it is possible that with different survival times, the maximum extent of intrinsic connections would have been greater. Nonetheless, they are noteworthy for being the first demonstration of the phenomenon of long-range clustered projections parallel to the cortical surface. Indeed, it is remarkable, how many details of the organisation of intrinsic connections were elucidated using these relatively crude techniques. 2.2. Tracer Studies Such connections are now best revealed using sensitive neuronal tracers: The tracers of choice are the Choleratoxin B subunit (CTb), biocytin, Phaseolus vulgaris leucoagglutinin (PHA-L), biotinylated dextran amine (BDA), fluorescent latex beads, fluorescent dyes such
© 2002 Taylor & Francis
136
Jonathan B. Levitt and Jennifer S. Lund
as Nuclear Yellow or Fast Blue, or wheatgerm agglutinin-horseradish peroxidase (WGA-HRP). These offer several advantages over older techniques, namely the ability to label tissue without destroying it, the ability to confine injections to small cortical loci (of the same size as single columns), and their much superior sensitivity. The first studies to use tracer techniques to study the organisation of intrinsic connections were those of Rockland and Lund (1982, 1983) and Gilbert and Wiesel (1979, 1983). These studies in cat, tree shrew, and monkey demonstrated, in agreement with the earlier degeneration studies, that a restricted locus of cells (or even single cells) in the visual cortex furnished projections spanning several millimeters across the cortical surface, and whose axons ramified into terminal clusters at discrete locations (clusters or patches) across the cortex. They showed that these projections were most prominent in the supragranular layers, though they were to be found in layer 5 as well, and terminal clusters were typically 200–300 µm in diameter, spaced 350–600 µm apart (depending on the particular species). Furthermore, the coexistence of labeled cells and terminals in these patches suggested that such connections were reciprocal. Figures 7.1 and 7.2 illustrate the basic features of this connectional lattice. The photomicrograph of Figure 7.1 shows the result of making a small injection of the neuronal
Figure 7.1. Biocytin injection site in macaque primary visual cortex (injection core marked by arrowhead). Note strong vertical focus of projections, and fibers running laterally past area marked by open arrow before ending in a terminal cluster. Scale bar = 200 µm.
© 2002 Taylor & Francis
Intrinsic Connections of the Cortex
137
tracer biocytin into the primary visual cortex of a macaque monkey. Biocytin is primarily taken up by cell bodies at the injection site and transported in the orthograde direction to label axon terminals; in a less reliable way it also labels cells in the retrograde direction which project to the injection site. In this injection, centred in the lower part of layer 3, the dense columnar focus of projections is apparent. Interlaminar axonal projections pass through layer 4 with very little lateral spread, whereas laterally-spreading fibres are apparent in the superficial layers as well as in layer 5. Axons in the superficial layers run parallel to the cortical surface without ramifying until they reach a particular target location on the cortex where they ramify profusely, making terminal endings. A similar terminal cluster in layer 5 is found in vertical registration with that in the superficial layers. This photomicrograph shows only the terminal cluster nearest to the injection site. For a better appreciation of the overall connectivity pattern, the cortex is best viewed tangential to the pial surface. The photomicrographs of Figure 7.2 show the results of making injections of the bidirectional tracer CTb (choleratoxin B subunit). This tangential section was taken through layer 3, shown in Figure 7.1 to be the site of the most prominent lateral projections. The lowpower view shows the injection site surrounded by a number of distinct clusters of orthogradely-labeled terminals and retrogradely-labeled cell bodies. Apart from the projection field being patchy and spanning several millimeters of cortex, this figure also shows two other common features of such projections, their anisotropy and their reciprocity. Generally, connections do not spread out isotropically, instead extending further from the injection site along one axis. Furthermore, most (although not all) patches of terminals are also coincident with retrogradely labeled cells. This indicates that pyramidal cells in the superficial layers generally receive inputs from those cortical columns to which they project. The higher power view of Figure 7.2B shows clusters of labeled pyramidal neurones coincident with labeled fibres making terminal endings. Although these patches of terminal label are round or oval-shaped in most cortical areas, in certain areas the pattern can be rather different. The photomicrograph in Figure 7.3 shows the biocytin-labeled projections in macaque dorsolateral prefrontal cortex. Here it is very common to find elongated terminal fields; one such zone is indicated by the arrow below the injection core. Figure 7.4 shows complete serial reconstructions of the pattern of orthograde terminal label resulting from six separate injections into macaque cerebral cortex (four into primary visual cortex, one into extrastriate visual area V4, and one into dorsolateral prefrontal cortex). These complete reconstructions again illustrate the clustered and anisotropic spread of projections from a given cortical locus, and indicate how different the overall topography can be in different cortical regions. We now review the basic features of intrinsic circuits, and their generality to all cortical areas. 2.2.1. Laminar specificity In nearly all species and cortical areas where it has been studied, the same laminar pattern of lateral projections is observed, i.e. laterally spreading fibers are most prominent in layers 2/3, but are also to be observed in layer 5, with a more columnar focus within layer 4. This has been found to be true in primary visual cortex of cat (Gilbert and Wiesel, 1979; Martin and Whitteridge, 1984; Gabbot et al., 1987), macaque (Blasdel et al., 1985;
© 2002 Taylor & Francis
138
Jonathan B. Levitt and Jennifer S. Lund
Figure 7.2. Tangential view through layer 3 of intrinsic connections in macaque primary visual cortex surrounding a choleratoxin B subunit (CTb) injection site (indicated by dashed white circle) viewed at low (A) and high (B) magnifications. Note the clusters of labeled cells and terminals surrounding the injection site. Scale bar = 500 µm (A), 100 µm (B).
© 2002 Taylor & Francis
Intrinsic Connections of the Cortex
139
Figure 7.3. Tangential view through layer 3 of a biocytin injection site in macaque dorsolateral prefrontal cortex (area 9). The arrow indicates an elongated band of terminal label. Scale bar = 200 µm. From Levitt et al. (1993) with permission.
Yoshioka et al., 1994), tree shrew (Rockland and Lund, 1982; Rockland et al., 1982; Fitzpatrick, 1996) and human (Burkhalter and Bernardo, 1989). The same laminar pattern is found in macaque extrastriate visual areas V2 and V4 (Yoshioka et al., 1992; Kritzer et al., 1992; Levitt et al., 1994), dorsolateral prefrontal cortex (areas 9/46: Levitt et al., 1993; Kritzer and Goldman-Rakic, 1995), motor cortex (area 4: Gatter et al., 1978), and somatosensory cortex (areas 3,1,2: Shanks et al., 1978; Vogt and Pandya, 1978), as well as cat area 18 (Matsubara et al., 1985, 1987) and primary auditory cortex (Wallace et al., 1991). It may be assumed that the band of horizontal projections in layer 2/3 corresponds to the outer band of Baillarger seen in myelin preparations; however, since this tier of projections can extend down into layer 4, it may be innaccurate to simply define the outer band of Baillarger as being in a single cortical layer. We note however, that while lateral projections do appear generally more restricted within layer 4, they are by no means entirely absent. Studies in primate V1 have clearly shown projections running laterally within upper layer 4 for several hundred microns (Anderson et al., 1993; Yoshioka et al., 1994). Projections within the uppermost part of layer 4, layer 4B (the stria of Gennari), can extend several millimeters. The significance of anatomical links between columns in the deeper portion of layer 4, in the topographicallyprecise input layers, remains to be determined. 2.2.2. Total extent and anisotropy of the intrinsic connectional field A fundamental characteristic of intrinsic projections is that projections from a particular cortical locus span several millimeters of cortex, a distance much greater than the dendritic
© 2002 Taylor & Francis
140
Jonathan B. Levitt and Jennifer S. Lund
Figure 7.4. Reconstructions from serial sections of orthograde label pattern in the superficial layers resulting from biocytin injections into different areas of macaque cerebral cortex. All panels show views tangential to the cortical surface. (A) Four separate small iontophoretic injections (fringed stippled circles) into visual area V1. Terminal patches (solid black zones) are plotted in relation to cytochrome-oxidase blobs (dashed lines). (B) Large pressure injection into visual area V4. Surrounding the large injection core (central solid black region) is a dense radiation of labeled fibres (hatched area) and terminal patches (solid black zones). (C) Injection (central open circle) into prefrontal area 9. Zones of highest terminal density are black, lower density stippled. Scale bars = 1 mm. From Lund et al. (1993) with permission.
field of neurones at the source site, or of the arborisation of thalamic inputs to that site. This system therefore serves to interconnect neurones in cortical columns at different parts of the map within that cortical area. Generally, these patchy projection patterns stop at areal borders, forming a different pattern in the adjoining area. However, in certain cases an injection close to an areal border leads to a continuous pattern of labelling, which spreads into the neighbouring area, ignoring the border. This was observed in the dorsolateral prefrontal cortex, in which projections spread freely across the border between areas 9 and 46 (Levitt et al., 1993; Pucak et al., 1996). The shape of the overall projection field was not well characterised in most of the earlier studies, but it is now appreciated that projections from a given cortical locus are not
© 2002 Taylor & Francis
Intrinsic Connections of the Cortex
141
isotropic, but instead can extend further along one axis than another, not necessarily always anteroposterior. Matsubara et al. (1987) noted, in agreement with the earlier degeneration studies, that the majority of projections in cat area 18 were found within 1.4 mm of an injection site, but that isolated terminal projections could be found up to 3.4 mm away. The overall spread of label was elongated in the anteroposterior axis. Kisvárday and Eysel (1992) came to the identical conclusion in cat area 17; they showed that the overall connectional field resulting from a restricted tracer injection measured 6.5 mm in the anteroposterior axis, but only 3.5 mm mediolaterally. Similar measures have been reported in macaque V1: both Malach et al. (1993) and Yoshioka et al. (1994) reported that connections spread up to 3.7 mm from an injection site, but with the greatest spread of label running mediolaterally, parallel to the V1/V2 border and orthogonal to the ocular dominance bands. In the visual cortex, the general rule thus seems to be that the long axis parallels the area 17/18 border. The ratio of long to short axes of the connectional field ranged from 1.5–1.8, closely similar to the ratios observed in cat visual cortex, and such anisotropy is also observed in primate motor cortex (Gatter et al., 1978) and cat auditory cortex (Matsubara and Phillips, 1988; Wallace et al., 1991). It appears that this anisotropic spread of connections relates to the sensory map in each area, such that (at least in cat and monkey) a roughly circular region of the sensory periphery is monosynaptically linked—although this preferential spread of connections along one axis is also found in the prefontal cortex (Levitt et al., 1993; Kritzer and Goldman-Rakic, 1995), where there is as yet no clearly-defined “map”. Optical imaging studies are also consistent with the anatomically-measured extent and anisotropy of connections. Albowitz and Kuhnt (1993) found the spread of voltage-sensitive dye signals following focal electrical stimulation of guinea pig V1 slices to span up to 3.6 mm, while Grinvald et al. (1994) recording from macaque V1 found that visual stimuli smaller than 1 degree in diameter activated a region of cortex 2.7 × 1.5 mm in extent, with the greatest spread of activation parallel to the V1 border. Recent electrophysiological results seem to confirm the dramatic extent of these monosynaptic projections. Bringuier et al. (1999) used in vivo intracellular recording methods in cat V1 to demonstrate the spatial extent over which subthreshold depolarising inputs could be measured in response to visual stimuli. They found that this subthreshold field could span up to 20 degrees in total diameter (i.e. 10 degrees from the receptive field center). Such extensive inputs could well result from some other input spanning more space, such as cortical feedback. However, this study showed a linear relationship between response latency and stimulus distance from the receptive field; this property does not seem required of a feedback circuit, and the most parsimonious explanation is therefore that intrinsic connections mediate these responses. The increasing response latencies with distance from the receptive field reflect simply the conduction delays across the cortex. Similarly, in slices of macaque dorsolateral prefrontal cortex, Gonzalez-Burgos et al. (2000) were able to measure postsynaptic currents evoked by low-intensity electrical stimulation at sites located up to 2200 microns lateral to the recorded cell. This is surely an underestimate of the total extent over which monosynaptic activation can be activated, since the slice preparation would almost certainly cut off some more remote inputs, or would cause the investigators to miss weaker ones. But what was also noteworthy was that monosynaptic EPSCs mediated by even the most remote connections had amplitudes similar or larger than short-distance connections, suggesting that the excitatory input provided by long-distance intrinsic connections is still quite robust.
© 2002 Taylor & Francis
Jonathan B. Levitt and Jennifer S. Lund
142
2.2.3. Patchiness of the projection Perhaps the defining characteristic of intrinsic connections is their discontinuous nature. As illustrated in Figures 7.1–7.4 above, projections to or from a given cortical locus are not uniformly arranged around that point, simply decreasing in density with increasing distance from the injection. Rather, neurones providing input to one cortical location are clustered into a number of discrete patches. Similarly, projections from a given point terminate in a number of discrete zones of high terminal density interspersed with zones relatively free of such terminals. Clustered intrinsic connections have been found in every cortical area examined. A by-no-means-exhaustive list would include: •
• • • • •
V1 of cat (Gilbert and Wiesel, 1983; Gabbott et al., 1987; Kisvárday and Eysel, 1992), macaque and squirrel monkey (Rockland and Lund, 1983; Malach et al., 1993; Yoshioka et al., 1996), human (Burkhalter and Bernardo, 1989), ferret (Durack and Katz, 1996; Ruthazer and Stryker, 1996), tree shrew (Rockland et al., 1982; Rockland and Lund, 1982; Fitzpatrick, 1996), and rodent (though of a somewhat different topography: Burkhalter, 1989) extrastriate visual areas V2, V4, 7a, and MT in macaque (Rockland, 1985b; Yoshioka et al., 1992; Amir et al., 1993; Levitt et al., 1994), squirrel monkey (Malach et al., 1994), owl monkey (Malach et al., 1997), and cat (Matsubara et al., 1985, 1987) dorsolateral prefrontal cortex of the macaque (Levitt et al., 1993; Kritzer and Goldman-Rakic, 1995) somatosensory cortex of macaque (Jones et al., 1978; Juliano et al., 1990), cat (Juliano et al., 1989; Sonty and Juliano, 1997), and ferret (Juliano et al., 1996) auditory cortex of cat (Matsubara and Phillips, 1988; Ojima et al., 1991; Wallace et al., 1991) and ferret (Wallace and Bajwa, 1991) motor cortex of macaque (Jones et al., 1978; Huntley and Jones, 1991) and cat (Landry et al., 1980; Keller, 1993)
As noted above, these clusters of cells or terminals are generally round or oval-shaped, but in some cortical areas they can be distinctly elongated and stripe-like as in macaque prefrontal cortex or owl monkey middle temporal area. We have preliminary evidence that intrinsic projections within layer 4B of macaque V1 also take this form (Asi et al., 1996). Depending on the cortical area, these elongated patches average between 130–300 µm in width of the narrower axis, with a mean centre-to-centre spacing of 230–500 µm. Why are the projections patchy? Rockland and Lund (1982) suggested early on that some cells in V1 might simply not furnish local connections. Mitchison and Crick (1982) suggested instead that the patchiness or substructure of the projection might reflect constraints on connections set by the functional map, for example that cells would be linked if their receptive fields had similar orientation preferences and were linked across the map in a particular direction in visual space. Although it is still not entirely clear, why intrinsic projections take the form they do, this suggestion is probably closer to the truth, since wherever one injects a tracer into a given cortical area, a patchy projection obtains—although patch size increases somewhat, the cortex never “fills in” despite the largest tracer injections (Yoshioka et al., 1992; Amir et al., 1993). We will return to this point toward the end of this chapter. Each terminal patch appears rather stereotyped, apparently containing roughly the same number of synaptic boutons. For example, Kisvárday and Eysel (1992) showed in cat V1
© 2002 Taylor & Francis
Intrinsic Connections of the Cortex
143
that each labeled layer-3 pyramidal cell furnishes axonal projections to 4–8 (or more) patches, each patch containing 80 synaptic boutons from that cell. Furthermore, these connections are mainly but not strictly reciprocal, i.e. one can find labeled cells not surrounded by labeled terminals and vice versa (Boyd and Matsubara, 1991; Amir et al., 1993; Levitt et al., 1994). 2.2.4. Cells furnishing and targeted by intrinsic connections In both visual and prefrontal cortices, long-range intrinsic projections are furnished primarily by spine-bearing pyramidal neurones, and their main synaptic targets are the distal dendritic spines of other pyramidal neurones (Fisken et al., 1975; Rockland, 1985a; Kisvárday et al., 1986; LeVay, 1988; McGuire et al., 1991; Melchitzky et al., 1998). In agreement with the earlier degeneration study of Fisken et al., the later studies using tracers to label projections to be examined ultrastructurally, showed that cells labeled from an injection received synaptic contacts characteristic of excitatory neurones, while synaptic terminals labeled from an injection mainly made asymmetric contacts onto dendritic spines. It is clear, however, from all these studies that a significant proportion of synaptic contacts are also made onto inhibitory neurones. In fact, one study (Keller and Asanuma, 1993) analysed the synaptic contacts made by 3 cells in cat motor cortex onto other cells within a few hundred microns, and found one to have most of its outputs to a non-pyramidal (presumably inhibitory) neurone. In cat and monkey V1, roughly 20% of the supragranular cells are GABAergic (Gabbott and Somogyi, 1986; Hendry et al., 1987). As pointed out by McGuire et al. (1991), this is essentially the same proportion as intrinsic pyramidal neurone synapses made onto smooth stellate cell dendrites, while the proportion of synapses onto spines (75%) is nearly the same as the overall proportion of axospinous type I synapses in the neuropil of the supragranular layers (Beaulieu and Colonnier, 1985). Thus projections seem not to target particular cell types, but rather to end randomly on all neurones within a terminal cluster according to the proportion of neurones found there. However, the possibility still remains that synapses at different distances from the cell of origin may exhibit different patterns of specificity or topography. This remains to be resolved. Furthermore, it should not be assumed that inhibitory GABAergic neurones do not furnish such long-range projections. While their contribution may be less prominent, they do exist. In cat V1, Albus et al. (1991) retrogradely labeled cells, and then labeled the tissue for GABA, searching for cells that were GABAergic as well as lateral projectors. In layer 3, 70% of the double-labeled cells were found within 1 mm of the injection site, while 30% were found between 1–2.5 mm from the injection. Double-labeled cells were not so clearly clustered as excitatory neurones; nearly half the labeled cells were scattered and not in clusters. The relatively small numbers and different topography implied that the rule for inhibitory circuits may be different from that for excitatory ones. Kisvárday et al. (1993) used a more sensitive method to show that large GABAergic basket cells in layer 3 can project across a region of cortex spanning 2.3 × 2.2 mm, while those in layer 5 can span 3.8 × 1.7 mm. They further showed that these basket cells contact both other basket cells as well as pyramidal neurones, thus mediating direct inhibition and facilitation via disinhibition. Kritzer et al. (1992) similarly demonstrated the existence of GABAergic projections parallel to the cortical surface in macaque visual areas V1, V2, and V4. The functional relevance of these differences in proportion and spatial extent between excitatory and inhibitory projections within cortex remains enigmatic, particularly since some GABAergic
© 2002 Taylor & Francis
144
Jonathan B. Levitt and Jennifer S. Lund
neurones in cat and monkey send axons into the white matter (e.g. Lund and Wu, 1997), and in rat at least can also project to other cortical areas (McDonald and Burkhalter, 1993). 2.2.5. Strength and reliability of intrinsic synapses While a great deal is now known about the cellular biophysics of pyramid to pyramid or pyramid to interneurone interactions (for recent reviews see Thomson and Deuchars, 1994, 1997), this is once again a topic whose complete treatment is beyond the scope of this chapter. Broadly, studies on the synaptic physiology of intrinsic connections have shown that synaptic effects due to activation of intrinsic connections can be detected. In visual or prefrontal areas, electrical stimulation of intracortical pathways leads to monosynaptic EPSPs in recorded neurones, while stronger stimuli evoke disynaptic IPSPs as well (Hirsch and Gilbert, 1991; Gonzalez-Burgos et al., 2000). Generally, the EPSPs are weak, but if the cell is depolarised, these EPSPs evoked by stimulation of intrinsic pathways can elicit spike activity in the cell. Furthermore, the strength of these intrinsic pathways is not fixed, but appears to be modifiable by the parameters of the stimulation (Hirsch and Gilbert, 1993). Bringuier et al. (1999) have directly shown in cat V1 that visual stimuli beyond the classical receptive field (which translates to activation of locations lateral to the recording site) can also evoke subthreshold excitation in cortical neurones. A fundamental feature of corticocortical synapses between neurones laterally offset from one another is that they are weaker and more variable than thalamocortical synapses or corticocortical synapses between neurones vertically offset within the same column (Stratford et al., 1996; Yoshimura et al., 2000). Nonetheless it appears that intracortical inputs may well be strong enough to provide a large part of the excitation to cortical cells in vivo. Yoshimura et al. also showed that EPSPs evoked by simultaneous activation of vertical and lateral inputs summated linearly when the postsynaptic cell was at resting potential, but supralinearly when the postsynaptic cell was depolarised. Furthermore, it appears that there are important differences in how presynaptic activity in pyramidal neurones leads to activity in other pyramids versus interneurones. For example, the synaptic interaction between two pyramidal cells offset laterally from one another shows paired-pulse depression—the postsynaptic response to the second of two presynaptic spikes is usually smaller (Thomson and West, 1993; Yoshimura et al., 2000), while pyramid–pyramid interactions between cells vertically aligned sometimes showed synaptic facilitation in this paradigm. However, pyramid-interneurone connections showed pronounced paired-pulse facilitation (Thomson et al., 1993). Thus, with increasing activity levels (and greater depolarisation), pyramidal neurones seem more sensitive to inputs from within that same cortical area, and preferentially able to recruit inhibitory interneurone activity; this may serve as one form of gain-control in the cortex. 2.2.6. Conduction velocity To understand the function of these laterally-running cortical circuits, it is also of course critical to know how quickly signals can propagate along them. There have now been a number of measurements of the conduction velocity of intrinsic connections, using a number of different techniques and in different species. These studies all come to a remarkably consistent conclusion, that horizontal projections within cortex conduct impulse activity rather slowly, in the range of 0.1–0.2 m/s.
© 2002 Taylor & Francis
Intrinsic Connections of the Cortex
145
Tanifuji et al. (1994) used optical imaging techniques in slice preparations of young rat primary visual cortex. They measured the rate at which the optical signals from voltagesensitive dyes spread across the cortex following electrical stimulation of the white matter. They reported a mean value in layer 3 of 0.06 ± 0.035 m/s (whereas activity spread in the columnar axis with a mean velocity of 0.2 ± 0.051 m/s). Albowitz and Kuhnt (1993) used closely similar techniques in slice preparations of guinea pig V1; their data suggest similar conduction velocity values of 0.06–0.12 m/s. Grinvald et al. (1994) measured the rate of spread of voltage-sensitive dye signals in in vivo recordings from macaque monkey V1, using visual stimuli to activate cortical neurones; they showed activity to spread across the cortex at a rate of 0.09–0.25 m/s. Gonzalez-Burgos et al. (2000) used whole-cell patch clamp recordings in an in vitro preparation of macaque dorsolateral prefrontal cortex. They measured postsynaptic currents evoked by low-intensity electrical stimulation of the slice. Their data indicate that monosynaptic connections within cortex are conveyed across the cortex at approximately 0.14 m/s. Bringuier et al. (1999) also recorded intracellularly from single neurones in cat V1 in vivo, but using visual stimuli to drive activity. They found that the latency of subthreshold responses increased with distance of the stimulus in the visual field from the receptive field centre. They arrived at an apparent conduction velocity of 0.1–0.2 m/s (since they did not directly measure conduction velocity, but simply translated spread of activation in degrees of visual angle to millimeters of cortex, by assuming a cortical magnification factor of 1 mm/deg which is reasonable for cat V1 at the retinal eccentricities they studied). These studies thus all concur that for neuronal signals to propagate 2–3 mm laterally from a given site on the cortex requires roughly 10–20 ms. The calibre of the axons furnishing these projections has been reported as roughly 1–3 µm in diameter in cat V1 (Kisvárday and Eysel, 1992), and probably finer in the primate. Such slow conduction velocities ought to result from axons having diameters much finer than this (Miller, 1996; Swadlow, 2000), so it remains puzzling why propagation rate is so slow in these axons. 2.2.7. Specificity of connections and functional correlates A central question about intrinsic circuits concerns their functional role and what governs their detailed topography—why do projections from a given site on the cortex target particular other sites? While many questions remain, it is now clear that intrinsic connections relate to the functional map in each area, such that neurones with similar functional properties are connected. This relationship has been most thoroughly examined in the visual cortex, with respect to the arrangement of orientation columns. Rockland et al. (1982) first attempted to correlate the local transport of HRP with the 2-deoxyglucose pattern evoked in tree shrew V1 in response to a single stimulus orientation. They noted the similarities of the patterns, but reached no firm conclusion. Gilbert and Wiesel (1989) subsequently repeated the experiment in cat V1, injecting fluorescent latex beads into a site of known orientation preference. They found that retrograde label was essentially confined to regions with the same orientation preference as the injection site. Cross-correlation studies are consistent with this result: one observes peaks in cross-correlograms between single cells separated by up to 4.2 mm if they have similar orientation preferences and spatially overlapping receptive fields (Ts’o et al., 1986; Schwarz and Bolz, 1991). This relationship has been subsequently confirmed and refined by combining transport of more sensitive
© 2002 Taylor & Francis
146
Jonathan B. Levitt and Jennifer S. Lund
tracers (such as biocytin) with more sophisticated optical imaging techniques that allow the entire orientation map to be specified. In this way, it has been shown that intrinsic connections are closely related to the orientation map; regions of similar orientation preference are more likely to be interconnected. This has now been demonstrated repeatedly in V1 of cat (Kisvárday et al., 1994, 1997), macaque (Malach et al., 1993), and tree shrew (Bosking et al., 1997), as well as V2 of squirrel monkey (Malach et al., 1994) and cat (Matsubara et al., 1985, 1987) although Matsubara et al.’s studies suggested connections were predominantly between regions preferring orthogonal orientations. This relationship needs to be qualified, however. Firstly, as Mitchison and Crick (1982) originally proposed, axis in space is also relevant: neurones seem more likely to be interconnected not only if they share orientation preference, but also if the connections’ axis in visual space is parallel to the preferred orientation so as to link cells responding to an elongated contour (Fitzpatrick, 1996; Bosking et al., 1997; Schmidt et al., 1997). Indeed, electrophysiological experiments have revealed response facilitation among such axially aligned cells with similar orientation preferences (Nelson and Frost, 1985). Secondly, the correlation is not perfect. Very local connections (within about 500 µm of an injection site) are much less specific than long-range ones (Malach et al., 1993; Bosking et al., 1997; Toth et al., 1997), and even the more specific long-range projections seem to show some proportion (as much as a third) which do not obey the strict “like-to-like” connectivity rule. Of course connections within visual cortex may also relate to some other parameter such as ocular dominance (Katz et al., 1989; Yoshioka et al., 1996), or the distribution of particular afferent populations (indicated by staining for cytochrome oxidase: Livingstone and Hubel, 1984; Yoshioka et al., 1996; Yabuta and Callaway, 1998). In other cortical areas, the rule that intrinsic connections link neurones with similar properties is not so obvious. In auditory cortex, intrinsic connections appear to relate to the tonotopic map. Several studies have shown that projections in A1 spread preferentially in the dorsoventral direction (Reale et al., 1983; Matsubara and Phillips, 1988; Wallace et al., 1991; Wallace and Bajwa, 1991) such that cells with similar characteristic frequencies are interconnected. However, as in visual cortex, connections do not conform strictly to this rule, but instead seem to relate to the particular axis of isofrequency contours (patches being more numerous dorsoposterior to an injection when isofrequency contours run obliquely); many cells or terminal patches are found in regions of higher frequency. Furthermore, connections do not bear any strict relationship to the distribution of binaural properties. The rules governing connections in sensorimotor cortex are similarly equivocal. Juliano et al. (1990) made tracer injections into physiologically-characterized sites in monkey somatosensory cortex and then compared the resulting label pattern with the activity pattern in cortex elicited by that stimulus (revealed by 2DG mapping). They found orthoand retrograde label largely confined to regions with similar response properties. However, in the barrel field of mouse somatosensory cortex, cells representing one entire whisker row are more strongly interconnected than cells representing different whisker rows (Bernardo et al., 1990), and in motor cortex, connections can link neurones representing portions of the forelimb representation as far apart as the digits and the shoulder (Huntley and Jones, 1991). Despite the patchy organization, there are still no clear functional correlates of local connections in somatosensory cortex. The main problem (as noted by Juliano et al., 1990) is that there still does not seem to be a generally-agreed set of parameters to define a functional column in somatosensory cortex. Correlating anatomical circuits with functional units therefore remains problematic.
© 2002 Taylor & Francis
Intrinsic Connections of the Cortex
147
Any parameter which is mapped regularly across the cortex could potentially serve to guide intrinsic connections (for example direction preference in certain visual cortices: Swindale et al., 1987; Weliky et al., 1996). A key role of future work will be to determine which parameter, or constellation of parameters, determines the detailed spatial topography of intrinsic connections in cortex. The functional role of these connections remains unclear. In visual cortex, for example, it is now known that neurones’ orientation and direction selectivity is much more dynamic than previously appreciated: Tuning can be dramatically modified by the presence outside the classical receptive field of stimuli which themselves evoke no responses (Gilbert and Wiesel, 1990; Sillito et al., 1995; Levitt and Lund, 1997). Intrinsic circuits could serve to link neurones at different retinotopic locations, thus mediating these effects, which could be one manifestation of a response gain-control mechanism in the cortex. Theoretical studies also suggest that intrinsic cortical circuits may play an important role in generation of basic receptive field properties, either by amplifying weakly-tuned thalamic afferent signals (Somers et al., 1995) or even by generating strong tuning themselves (Adorjan et al., 1999). It has also been suggested that patchy connections may serve to maximize the amount of information available to neurones in the cortex (Malach, 1994) or to enhance signal transmission through cortex (Schüz, 1994). These issues are far from decided, but their resolution will clearly be necessary for a complete understanding of how circuitry leads to the elaboration of functional properties in cerebral cortex. 2.2.8. Development and refinement Why do intrinsic cortical circuits develop this way? Although a detailed discussion of this issue is beyond the scope of this chapter, it is now clear that, like all other neural circuits, intrinsic cortical conections are not generated with their final topography or synaptic complement, but require some period to refine. Although certain basic features of intrinsic connections may be present even prenatally (as in macaque monkey V1: Callaway, 1998a), these generally require a substantial postnatal maturation or refinement period before intrinsic circuits attain their final adult state, in terms of both inter- and intralaminar specificity (as in cat or ferret visual or somatosensory cortices: Callaway and Katz, 1990; Juliano et al., 1996; Durack and Katz, 1996; Ruthazer and Stryker, 1996; Sonty and Juliano, 1997). There is also a huge overproduction and refinement of synapses during the early postnatal period (Lund et al., 1977; Rakic et al., 1986); thus, the synapses in these connectional fields must undergo modification. It is also now known that these circuits’ final layout may be affected by experience; for example, Löwel and Singer (1992) showed that rearing cats with a strabismus modified the pattern of local connections in V1 relative to ocular dominance columns, essentially restricting intracortical projections to sites of similar ocular dominance as the injection site. Such data indicate that refinement is not merely an activity-dependent process, but that patterned activity serves to sculpt these circuits. While many details of the mechanisms by which this refinement take place remain to be elucidated, laterally-running glial processes can be found in cortex (Albus and Luebke, 1992; Juliano et al., 1996) which could serve as a scaffolding for developing lateral projections just as they seem to do for vertical interlaminar projections. While much is now known, a great deal remains to be explored regarding the relative timing of maturation of intrinsic circuits, extrinsic circuits, and receptive field properties of cortical neurones.
© 2002 Taylor & Francis
148
Jonathan B. Levitt and Jennifer S. Lund
3. SCALING OF INTRINSIC CONNECTIONS We conclude by attempting to answer what may determine the size of these patchy connectional fields in all cortical areas. One striking feature of this connectional lattice is that not only does the extent of cortex monosynaptically linked vary across areas, but the size of individual cell or terminal clusters also varies across areas. Admittedly, both of these characteristics might simply reflect differences in the maps in each area (e.g. cortical magnification factor) such that a “column” of cells necessary to process a given amount of visual field, or range of auditory frequencies, or extent on the body surface has a different dimension. Nevertheless, it has also been noted that there are differences among areas in the dimensions of the pyramidal neurones that furnish and receive these projections. However, different studies use different techniques, tracers, survival times, injection sizes, etc. to study intrinsic connections, making comparisons problematic. Both Lund et al. (1993) and Amir et al. (1993) used consistent techniques to label intrinsic connections in several different areas of macaque cerebral cortex. They both reported consistent differences among areas in mean patch size and spacing: in visual cortex, these parameters, as well as the total extent of the labeled field, increase the further one gets from primary visual cortex into higher visual areas. While this may also be true of other sensory systems, these have been almost exclusively studied in primary sensory areas. Proof awaits future experiments in non-visual cortical areas. Based on these criteria, primary motor cortex appears to resemble a higher sensory area, rather than a primary one, but this also remains equivocal. Lund et al. then compared these dimensions to those of the basal dendritic fields of pyramidal neurones in each area, labeled by Golgi impregnations. Figure 7.5 illustrates their finding, that the size and spacing of this connectional latice is highly correlated with the dimensions of basal dendritic fields, despite a two-fold range in dendritic field size. This finding is consistent with an earlier statement by Landry et al. (1980), who noted in motor cortex that “The tangential expansion of this local field corresponds to that of the basal dendritic domain of pyramidal tract neurons.” What seems to be the case is not that the dimensions are strictly commensurate in any area, but rather that they covary across areas. There is an interesting consequence of this arrangement. Rockel et al. (1980) reported that in all cortical areas (with the exception of V1), a column of fixed diameter contains the same number of cells through the depth of the cortex. Furthermore, bouton density (interbouton interval) along intrinsically-projecting axons remains essentially constant all along these processes (Amir et al., 1993; Yabuta and Callaway, 1998); axons simply ramify more in the vicinity of a terminal cluster. Thus, areas whose connectional lattice has larger terminal patches are likely to be contacting an increasing number of cells. The functional relevance of this is completely unknown. Perhaps intrinsic “modulatory” influences are greater in cortical areas with larger patch sizes for this reason. Finally, we note that patchy intrinsic connections are also found not only in the eutherian species described above, but also in V1 of the marsupial quokka (Tyler et al., 1998). Given that patchy superficial layer pyramidal neurone projections are found in species that diverged at least 135 million years ago, this further suggests that this is indeed an ancient and fundamental architectural element in the organisation of neocortex.
© 2002 Taylor & Francis
Intrinsic Connections of the Cortex
149
Figure 7.5. (A) Plot of the relationship between mean diameter of terminal patches or narrow dimension of terminal bands (measured from orthograde transport of biocytin) and the mean diameter of basal dendritic field of individual layer 2/3 pyramidal neurones in different areas of macaque cerebral cortex (measured from Golgi impregnations). Error bars indicate standard deviations. Each point summarises data from one cortical area: macaque visual (V1, V2, V4), somatosensory (3b, 1, 2), motor (4), and prefrontal (9, 46: indicated by asterisk) areas. Also included are measures from V1 of cat and tree shrew (TS). (B) Plot of the relationship between mean terminal patch centre-to-centre spacing and mean terminal patch diameter. Dashed lines are regression lines through macaque data (A: r = 0.779, p < 0.05; B: r = 0.989, p < 0.001). From Lund et al. (1993) with permission.
© 2002 Taylor & Francis
Jonathan B. Levitt and Jennifer S. Lund
150
4. SUMMARY We have reviewed a number of anatomical and physiological studies, and highlight the following as central defining characteristics of intrinsic connections in all areas of cerebral cortex: • •
•
• •
•
• •
Connections parallel to the cortical surface are most prominent in layers 2/3 and 5, less so in layers 1, 4, 6. Connections spread several millimeters from any locus, spanning a total extent up to 6–8 mm depending on the particular cortical area. The overall connectional field is generally anisotropic, forming an elongated ellipse, apparently related to the functional map in that area. These projections are patchy rather than diffuse; axons ramify and make extensive terminal branches only at particular discrete locations across the cortex (the exception being very local, within 300 µm of a projection source, where connections are dense and relatively uniform). Intervening regions are relatively free of terminals. Single neurones’ projections are also patchy. Individual terminal patches are generally round/oval, but these can appear more stripelike (for example in visual area MT, prefrontal areas 9/46, or in layer 4B of V1). The size and spacing of these terminal patches differs between areas, and seems to scale to the diameter of the basal dendritic field of pyramidal neurones in each area. These connections are furnished by excitatory, spine-bearing pyramidal neurones (though in some species like the cat, certain inhibitory cells—the basket cells—can make medium-range inhibitory connections). Intrinsic connections contact mainly other excitatory neurones, but a smaller proportion of synaptic contacts are on to inhibitory interneurones. Synapses made by horizontal intrinsic corticocortical connections evoke EPSPs and IPSPs of smaller magnitude and lower reliability than thalamocortical or vertical (columnar) corticocortical synapses. However their strength appears to be dynamically modifiable. The average conduction velocity of intrinsic connections is 0.1–0.2 m/s. Intrinsic connections relate to the functional map in each area linking mainly, but not exclusively, neurones of like kind.
REFERENCES Adorjan, P., Levitt, J.B., Lund, J.S. and Obermayer, K. (1999) A model for the intracortical origin of orientation preference and tuning in macaque striate cortex. Visual Neuroscience, 16, 303–318. Albowitz, B. and Kuhnt, U. (1993) The contribution of intracortical connections to horizontal spread of activity in the neocortex as revealed by voltage sensitive dyes and a fast optical recording method. European Journal of Neuroscience, 5, 1349–1359. Albus, K., Wahle, P., Luebke, J. and Matute, C. (1991) The contribution of GABAergic neurons to horizontal intrinsic connections in upper layers of the cat’s striate cortex. Experimental Brain Research, 85, 235–239. Albus, K. and Luebke, J. (1992) Widespread lateral processes of glial cells in the immature striate cortex of the cat. Developmental Brain Research, 68, 278–281. Amir, Y., Harel, M. and Malach, R. (1993) Cortical hierarchy reflected in the organization of intrinsic connections in macaque monkey visual cortex. Journal of Comparative Neurology, 334, 19–46. Anderson, J.C., Martin, K.A.C. and Whitteridge, D. (1993) Form, function, and intracortical projections of neurons in the striate cortex of the monkey Macacus nemestrinus. Cerebral Cortex, 3, 412–420.
© 2002 Taylor & Francis
Intrinsic Connections of the Cortex
151
Asi, H., Levitt, J.B. and Lund, J.S. (1996) In macaque V1 lateral connections in layer 4B have a different topography than in layers 2/3. Society for Neuroscience Abstracts, 22, 1608. Beaulieu, C. and Colonnier, M. (1985) A laminar analysis of the number of round-symmetrical and flatasymmetrical synapses on spines, dendritic trunks, and cell bodies in area 17 of the cat. Journal of Comparative Neurology, 231, 180–189. Bernardo, K.L., McCasland, J.S. and Woolsey, T.A. (1990) Local axonal trajectories in mouse barrel cortex. Experimental Brain Research, 82, 247–253. Blasdel, G.G., Lund, J.S. and Fitzpatrick, D. (1985) Intrinsic connections of macaque striate cortex: axonal projections of cells outside lamina 4C. Journal of Neuroscience, 5, 3350–3369. Bosking, W.H., Zhang, Y., Schofield, B. and Fitzpatrick, D. (1997) Orientation selectivity and the arrangement of horizontal connections in tree shrew striate cortex. Journal of Neuroscience, 17, 2112–2127. Boyd, J. and Matsubara, J. (1991) Intrinsic connection in cat visual cortex: a combined anterograde and retrograde tracing study. Brain Research, 560, 207–215. Bringuier, V., Chavane, F., Glaeser, L. and Fregnac, Y. (1999) Horizontal propagation of visual activity in the synaptic integration field of area 17 neurons. Science (Washington), 283, 695–699. Burkhalter, A. (1989) Intrinsic connections of rat primary visual cortex: laminar organization of axonal projections. Journal of Comparative Neurology, 279, 171–186. Burkhalter, A. and Bernardo, K.L. (1989) Organization of corticocortical connections in human visual cortex. Proceedings of the National Academy of Science U.S.A., 86, 1071–1075. Cajal, S. Ramón y (1909–1911) Histologie du système nerveux de l’homme et des vertébrés. Vols. I and II. Paris, Maloine. Reprinted Madrid, Consejo Superior de Investigaciones Cientificas, 1952. Cajal, S. Ramón y (1922) Studien über die Sehrinde der Katze. Journal of Psychology and Neurology, 29, 161–181. Callaway, E.M. (1998a) Prenatal development of layer-specific local circuits in primary visual cortex of the macaque monkey. Journal of Neuroscience, 18, 1505–1527. Callaway, E.M. (1998b) Local circuits in primary visual cortex of the macaque monkey. Annual Review of Neuroscience, 21, 47–74. Callaway, E.M. and Katz, L.C. (1990) Emergence and refinement of clustered horizontal connections in cat striate cortex. Journal of Neuroscience, 10, 1134–1153. Durack, J.C. and Katz, L.C. (1996) Development of horizontal projections in layer 2/3 of ferret visual cortex. Cerebral Cortex, 6, 178–183. Fisken, R.A., Garey, L.J. and Powell, T.P.S. (1973) Patterns of degeneration after intrinsic lesions of the visual cortex (area 17) of the monkey. Brain Research, 4, 369–374. Fisken, R.A., Garey, L.J. and Powell, T.P.S. (1975) The intrinsic, association and commissural connections of area 17 of the visual cortex. Philosophical Transactions of the Royal Society of London, Series B, 272, 487–536. Fitzpatrick, D. (1996) The functional organization of local circuits in visual cortex: insights from the study of tree shrew striate cortex. Cerebral Cortex, 6, 329–341. Gabbott, P.L.A. and Somogyi, P. (1986) Quantitative distribution of GABA-immunoreactive neurons in the visual cortex (area 17) of the cat. Experimental Brain Research, 61, 323–331. Gabbott, P.L.A., Martin, K.A.C. and Whitteridge, D. (1987) Connections between pyramidal neurons in layer 5 of the cat visual cortex (area 17). Journal of Comparative Neurology, 259, 364–381. Gatter, K.C., Sloper, J.J. and Powell, T.P.S. (1978) The intrinsic connections of the cortex of area 4 of the monkey. Brain, 101, 513–541. Gilbert, C.D. and Wiesel, T.N. (1979) Morphology and intracortical projections of functionally characterised neurones in the cat visual cortex. Nature (London), 280, 120–125. Gilbert, C.D. and Wiesel, T.N. (1983) Clustered intrinsic connections in cat visual cortex. Journal of Neuroscience, 3, 1116–1133. Gilbert, C.D. and Wiesel, T.N. (1989) Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex. Journal of Neuroscience, 9, 2432–2442. Gilbert, C.D. and Wiesel, T.N. (1990) The influence of contextual stimuli on the orientation selectivity of cells in primary visual cortex of the cat. Vision Research, 30, 1689–1701. Gonzalez-Burgos, G., Barrionuevo, G. and Lewis, D.A. (2000) Horizontal synaptic connections in monkey prefrontal cortex: an in vitro electrophysiological study. Cerebral Cortex, 10, 82–92. Grinvald, A., Lieke, E.E., Frostig, R.D. and Hildesheim, R. (1994) Cortical point-spread function and long-range lateral interactions revealed by real-time optical imaging of macaque monkey primary visual cortex. Journal of Neuroscience, 14, 2545–2568. Hendry, S.H.C., Schwark, H.D., Jones, E.G. and Jan, J. (1987) Numbers and proportions of GABAimmunoreactive neurons in different areas of monkey cerebral cortex. Journal of Neuroscience, 7, 1503–1519. Hirsch, J.A. and Gilbert, C.D. (1991) Synaptic physiology of horizontal connections in the cat’s visual cortex. Journal of Neuroscience, 11, 1800–1809. Hirsch, J.A. and Gilbert, C.D. (1993) Long-term changes in synaptic strength along specific intrinsic pathways in the cat visual cortex. Journal of Physiology (London), 461, 247–262.
© 2002 Taylor & Francis
152
Jonathan B. Levitt and Jennifer S. Lund
Hubel, D.H. and Wiesel, T.N. (1962) Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex. Journal of Physiology (London), 160, 106–154. Huntley, G.W. and Jones, E.G. (1991) Relationship of intrinsic connections to forelimb movement representations in monkey motor cortex: a correlative anatomic and physiological study. Journal of Neurophysiology, 66, 390–413. Jones, E.G., Coulter, J.D. and Hendry, S.H.C. (1978) Intracortical connections of architectonic fields in the somatic sensory, motor and parietal cortex of monkeys. Journal of Comparative Neurology, 181, 291–348. Juliano, S.L., Whitsel, B.L., Tommerdahl, M. and Cheema, S.S. (1989) Determinants of patchy metabolic labeling in the somatosensory cortex of cats: a possible role for intrinsic inhibitory circuitry. Journal of Neuroscience, 9, 1–12. Juliano, S.L., Friedman, D.P. and Eslin, D.E. (1990) Corticocortical connections predict patches of stimulusevoked metabolic activity in monkey somatosensory cortex. Journal of Comparative Neurology, 298, 23–39. Juliano, S.L., Palmer, S.L., Sonty, R.V., Noctor, S. and Hill, G.F. (1996) Development of local connections in ferret somatosensory cortex. Journal of Comparative Neurology, 374, 259–277. Katz, L.C., Gilbert, C.D. and Wiesel, T.N. (1989) Local circuits and ocular dominance columns in monkey striate cortex. Journal of Neuroscience, 9, 1389–1399. Keller, A. (1993) Patterns of intrinsic connections between motor representation zones in the cat motor cortex. NeuroReport, 4, 515–518. Keller, A. and Asanuma, H. (1993) Synaptic relationships involving local axon collaterals of pyramidal neurons in the cat motor cortex. Journal of Comparative Neurology, 336, 229–242. Kisvárday, Z.F., Martin, K.A.C., Freund, T.F., Magloczky, Z., Whitteridge, D. and Somogyi, P. (1986) Synaptic targets of HRP-filled layer III pyramidal cells in the cat striate cortex. Experimental Brain Research, 64, 541–552. Kisvárday, Z.F. and Eysel, U.T. (1992) Cellular organization of reciprocal patchy networks in layer III of cat visual cortex (area 17). Neuroscience, 46, 275–286. Kisvárday, Z.F., Beaulieu, C. and Eysel, U.T. (1993) Network of GABAergic large basket cells in cat visual corex (area 18): implications for lateral disinhibition. Journal of Comparative Neurology, 327, 398–415. Kisvárday, Z.F., Kim, D.S., Eysel, U.T. and Bonhoeffer, T. (1994) Relationship between lateral inhibitory connections and the topography of the orientation map in cat visual cortex. European Journal of Neuroscience, 6, 1619–1632. Kisvárday, Z.F., Toth, E., Rausch, M. and Eysel, U.T. (1997) Orientation-specific relationship between populations of excitatory and inhibitory lateral connections in the visual cortex of the cat. Cerebral Cortex, 7, 605–618. Kritzer, M.F., Cowey, A. and Somogyi, P. (1992) Patterns of inter- and intralaminar GABAergic connections distinguish striate (V1) and extrastriate (V2, V4) visual cortices and their functionally specialized subdivisions in the rhesus monkey. Journal of Neuroscience, 12, 4545–4564. Kritzer, M.F. and Goldman-Rakic, P.S. (1995) Intrinsic circuit organization of the major layers and sublayers of the dorsolateral prefrontal cortex in the rhesus monkey. Journal of Comparative Neurology, 359, 131–143. Landry, P., Labelle, A. and Deschenes, M. (1980) Intracortical distribution of axonal collaterals of pyramidal tract cells in the cat motor cortex. Brain Research, 191, 327–336. LeVay, S. (1988) Patchy intrinsic projections in visual cortex, area 18, of the cat: morphological and immunocytochemical evidence for an excitatory function. Journal of Comparative Neurology, 269, 265–274. Levitt, J.B., Lewis, D.A., Yoshioka, T. and Lund, J.S. (1993) Topography of pyramidal neuron intrinsic connections in macaque monkey prefrontal cortex (areas 9 and 46). Journal of Comparative Neurology, 338, 360–376. Levitt, J.B., Yoshioka, T. and Lund, J.S. (1994) Intrinsic cortical connections in macaque visual area V2: evidence for interaction between different functional streams. Journal of Comparative Neurology, 342, 551–570. Levitt, J.B. and Lund, J.S. (1997) Contrast dependence of contextual effects in primate visual cortex. Nature (London), 387, 73–76. Livingstone, M.S. and Hubel, D.H. (1984) Specificity of intrinsic connections in primate primary visual cortex. Journal of Neuroscience, 4, 2830–2835. Löwel, S. and Singer, W. (1992) Selection of intrinsic horizontal connections in the visual cortex by correlated neuronal activity. Science (Washington), 255, 209–212. Lund, J.S. (1973) Organization of neurons in the visual cortex, area 17 of the monkey (Macaca mulatta). Journal of Comparative Neurology, 147, 455–496. Lund, J.S. and Boothe, R.G. (1975) Interlaminar connections and pyramidal neuron organization in the visual cortex, area 17, of the macaque monkey. Journal of Comparative Neurology, 159, 305–334. Lund, J.S., Boothe, R.G. and Lund, R.D. (1977) Development of neurons in the visual cortex of the monkey (Macaca nemestrina): A Golgi study from fetal day 127 to postnatal maturity. Journal of Comparative Neurology, 176, 149–188. Lund, J.S., Yoshioka, T. and Levitt, J.B. (1993) Comparison of intrinsic connectivity in different areas of macaque monkey cerebral cortex. Cerebral Cortex, 3, 148–162.
© 2002 Taylor & Francis
Intrinsic Connections of the Cortex
153
Lund, J.S. and Wu, C.Q. (1997) Local circuit neurons of macaque monkey striate cortex: IV. Neurons of laminae 1–3A. Journal of Comparative Neurology, 384, 109–126. Malach, R., Amir, Y., Harel, M. and Grinvald, A. (1993) Relationship between intrinsic connections and functional architecture revealed by optical imaging and in vivo targeted biocytin injections in primate striate cortex. Proceedings of the National Academy of Science U.S.A., 90, 10469–10473. Malach, R. (1994) Cortical columns as devices for maximizing neuronal diversity. Trends in Neuroscience, 17, 101–104. Malach, R., Tootell, R.B. and Malonek, D. (1994) Relationship between orientation domains, cytochrome oxidase stripes, and intrinsic horizontal connections in squirrel monkey area V2. Cerebral Cortex, 4, 151–165. Malach, R., Schirman, T.D., Harel, M., Tootell, R.B. and Malonek, D. (1997) Organization of intrinsic connections in owl monkey area MT. Cerebral Cortex, 7, 386–393. Martin, K.A.C. and Whitteridge, D. (1984) Form, function, and intracortical projections of spiny neurones in the striate visual cortex of the cat. Journal of Physiology (London), 353, 463–504. Matsubara, J., Cynader, M., Swindale, N.V. and Stryker, M.P. (1985) Intrinsic projections within visual cortex: evidence for orientation-specific local connections. Proceedings of the National Academy of Science U.S.A., 82, 935–939. Matsubara, J.A., Cynader, M.S. and Swindale, N.V. (1987) Anatomical properties and physiological correlates of the intrinsic connections in cat area 18. Journal of Neuroscience, 7, 1428–1446. Matsubara, J.A. and Phillips, D.P. (1988) Intracortical connections and their physiological correlates in the primary auditory cortex (AI) of the cat. Journal of Comparative Neurology, 268, 38–48. McDonald, C.T. and Burkhalter, A. (1993) Organization of long-range inhibitory connections within rat visual cortex. Journal of Neuroscience, 13, 768–781. McGuire, B.A., Gilbert, C.D., Rivlin, P.K. and Wiesel, T.N. (1991) Targets of horizontal connections in macaque primary visual cortex. Journal of Comparative Neurology, 305, 370–392. Melchitzky, D.S., Sesack, S.R., Pucak, M.L. and Lewis, D.A. (1998) Synaptic targets of pyramidal neurons providing intrinsic horizontal connections in monkey prefrontal cortex. Journal of Comparative Neurology, 390, 211–224. Miller, R. (1996) Axonal conduction time and human cerebral laterality. Harwood Academic Publishers, Amsterdam. Mitchison, G. and Crick, F. (1982) Long axons within the striate cortex: their distribution, orientation, and patterns of connection. Proceedings of the National Academy of Science, U.S.A., 79, 3661–3665. Mountcastle, V.B. (1957) Modality and topographic properties of single neurons of cat’s somatic sensory cortex. Journal of Neurophysiology, 20, 408–434. Nelson, J.I. and Frost, B.J. (1985) Intracortical facilitation among co-oriented, co-axially aligned simple cells in cat striate cortex. Experimental Brain Research, 61, 54–61. Ojima, H., Honda, C.N. and Jones, E.G. (1991) Patterns of axon collateralization of identified supragranular pyramidal neurons in the cat auditory cortex. Cerebral Cortex, 1, 80–94. O’Leary, J.L. (1941) Structure of the area striata of the cat. Journal of Comparative Neurology, 75, 131–161. Powell, T.P.S. and Mountcastle, V.B. (1959) Some aspects of the functional organization of the cortex of the postcentral gyrus of the monkey: a correlation of findings obtained in a single unit analysis with cytoarchitecture. Johns Hopkins Hospital Bulletin, 105, 133–162. Pucak, M.L., Levitt, J.B., Lund, J.S. and Lewis, D.A. (1996) Patterns of intrinsic and associational circuitry in monkey prefrontal cortex. Journal of Comparative Neurology, 376, 614–630. Rakic, P., Bourgeois, J.-P., Zecevic, N. and Goldman-Rakic, P.S. (1986) Concurrent overproduction of synapses in diverse regions of the primate cerebral cortex. Science (Washington), 232, 232–235. Reale, R.A., Brugge, J.F. and Feng, J.Z. (1983) Geometry and orientation of neuronal processes in cat primary auditory cortex (AI) related to characteristic-frequency maps. Proceedings of the National Academy of Sciences U.S.A., 80, 5449–5453. Rockel, A.J., Hiorns, R.W. and Powell, T.P.S. (1980) The basic uniformity in structure of the neocortex. Brain, 103, 221–244. Rockland, K.S. and Lund, J.S. (1982) Widespread periodic intrinsic connections in the tree shrew visual cortex. Science (Washington), 215, 1532–1534. Rockland, K.S., Lund, J.S. and Humphrey, A.L. (1982) Anatomical binding of intrinsic connections in striate cortex of tree shrews (Tupaia glis). Journal of Comparative Neurology, 209, 41–58. Rockland, K.S. and Lund, J.S. (1983) Intrinsic laminar lattice connections in primate visual cortex. Journal of Comparative Neurology, 216, 303–318. Rockland, K.S. (1985a) Intrinsically projecting pyramidal neurons of monkey striate cortex: an EM-HRP study. Society for Neuroscience Abstracts, 11, 17. Rockland, K.S. (1985b) A reticular pattern of intrinsic connections in primate area V2 (area 18). Journal of Comparative Neurology, 235, 467–478. Ruthazer, E.S. and Stryker, M.P. (1996) The role of activity in the development of long-range horizontal connections in area 17 of the ferret. Journal of Neuroscience, 16, 7253–7269.
© 2002 Taylor & Francis
154
Jonathan B. Levitt and Jennifer S. Lund
Schmidt, K.E., Goebel, R., Löwel, S. and Singer, W. (1997) The perceptual grouping criterion of colinearity is reflected by anisotropies of connections in the primary visual cortex. European Journal of Neuroscience, 9, 1083–1089. Schüz, A. (1994) Patchiness as a means to get a message across. Trends in Neuroscience, 17, 365. Schwarz, C. and Bolz, J. (1991) Functional specificty of a long-range horizontal connections in cat visual cortex: a cross-correlation study. Journal of Neuroscience, 11, 2995–3007. Shanks, M.F., Pearson, R.C.A. and Powell, T.P.S. (1978) The intrinsic connections of the primary somatic sensory cortex of the monkey. Proceedings of the Royal Society of London (Biology), 200, 95–101. Sillito, A.M., Grieve, K.L., Jones, H.E., Cudeiro, J. and Davis, J. (1995) Visual cortical mechanisms detecting focal orientation discontinuities. Nature (London), 378, 492–496. Somers, D.C., Nelson, S.B. and Sur, M. (1995) An emergent model of orientation selectivity in cat visual cortical simple cells. Journal of Neuroscience, 15, 5448–5465. Sonty, R.V. and Juliano, S.L. (1997) Development of intrinsic connections in cat somatosensory cortex. Journal of Comparative Neurology, 384, 501–516. Stratford, K.J., Tarczy-Hornoch, K., Martin, K.A.C., Bannister, N.J. and Jack, J.J.B. (1996) Excitatory synaptic inputs to spiny stellate cells in cat visual cortex. Nature (London), 382, 258–261. Swadlow, H.A. (2000) Information flow along neocortical axons. In: R. Miller (ed.), Time and the brain. Conceptual Advances in Brain Research series, Harwood Academic Publishers, Amsterdam, pp. 131–150. Swindale, N.V., Matsubara, J.A. and Cynader, M.S. (1987) Surface organization of orientation and direction selectivity in cat area 18. Journal of Neuroscience, 7, 1414–1427. Tanifuji, M., Sugiyama, T. and Murase, K. (1994) Horizontal propagation of excitation in rat visual cortical slices revealed by optical imaging. Science (Washington), 266, 1057–1059. Thomson, A.M. and West, D.C. (1993) Fluctuations in pyramid-pyramid excitatory postsynaptic potentials modified by presynaptic firing pattern and postsynaptic membrane potential using paired intracellular recording in rat neocortex. Neuroscience, 54, 329–346. Thomson, A.M., Deuchars, J. and West, D.C. (1993) Single axon excitatory postsynaptic potentials in neocortical interneurons exhibit pronounced paired pulse facilitation. Neuroscience, 54, 347–360. Thomson, A.M. and Deuchars, J. (1994) Temporal and spatial properties of local circuits in neocortex. Trends in Neuroscience, 17, 119–126. Thomson, A.M. and Deuchars, J. (1997) Synaptic interactions in neocortical local circuits: dual intracellular recordings in vitro. Cerebral Cortex, 7, 510–522. Toth, L.J., Kim, D.-S., Rao, S.C. and Sur, M. (1997) Integration of local inputs in visual cortex. Cerebral Cortex, 7, 703–710. Ts’o, D.Y., Gilbert, C.D. and Wiesel, T.N. (1986) Relationships between horizontal interactions and functional architecture in cat striate cortex as revealed by cross-correlation analysis. Journal of Neuroscience, 6, 1160–1170. Tyler, C.J., Dunlop, S.A., Lund, R.D., Harman, A.M., Dann, J.F., Beazley, L.D. and Lund, J.S. (1998) Anatomical comparison of the macaque and marsupial visual cortex: common features that may reflect retention of essential cortical elements. Journal of Comparative Neurology, 400, 449–468. Vogt, B.A. and Pandya, D.N. (1978) Cortico-cortical connections of somatic sensory cortex (areas 3,1, and 2) in the rhesus monkey. Journal of Comparative Neurology, 177, 179–191. Wallace, M.N. and Bajwa, S. (1991) Patchy intrinsic connections of the ferret primary auditory cortex. Neuroreport, 2, 417–420. Wallace, M.N., Kitzes, L.M. and Jones, E.G. (1991) Intrinsic inter- and intralaminar connections and their relationship to the tonotopic map in cat primary auditory cortex. Experimental Brain Research, 86, 527–544. Weliky, M., Bosking, W.H. and Fitzpatrick, D. (1996) A systematic map of direction preference in primary visual cortex. Nature (London), 379, 725–728. Yabuta, N.H. and Callaway, E.M. (1998) Cytochrome-oxidase blobs and intrinsic horizontal connections of layer 2/3 pyramidal neurons in primate V1. Visual Neuroscience, 15, 1007–1027. Yoshimura, Y., Sato, H., Imamura, K. and Watanabe, Y. (2000) Properties of horizontal and vertical inputs to pyramidal cells in the superficial layers of the cat visual cortex. Journal of Neuroscience, 20, 1931–1940. Yoshioka, T., Levitt, J.B. and Lund, J.S. (1992) Intrinsic lattice connections of macaque monkey visual cortical area V4. Journal of Neuroscience, 12, 2785–2802. Yoshioka, T., Levitt, J.B. and Lund, J.S. (1994) Independence and merger of thalamocortical channels within macaque monkey primary visual cortex: anatomy of interlaminar projections. Visual Neuroscience, 11, 467–489. Yoshioka, T., Blasdel, G.G., Levitt, J.B. and Lund, J.S. (1996) Relation between patterns of intrinsic lateral connectivity, ocular dominance, and cytochrome oxidase-reactive regions in macaque monkey striate cortex. Cerebral Cortex, 6, 297–310.
© 2002 Taylor & Francis
8 Thalamic Systems and the Diversity of Cortical Areas Catherine G. Cusick Department of Structural and Cellular Biology and Neuroscience Program, Tulane University School of Medicine, 1430 Tulane Avenue, New Orleans Louisiana, U.S.A. 70112 e-mail:
[email protected]
The thalamus of higher primates may be organized into sets of nuclei that reflect the organization of different cortical sensory systems (visual, somatosensory, auditory) into complex distributed hierarchies. For the visual system, there appear to be three levels of thalamic nuclear organization: a first level nucleus corresponding to the lateral geniculate nucleus, a second level nucleus corresponding to the inferior pulvinar complex, and a third, high order group comprising the dorsal pulvinar nuclei. Thalamic nuclei can be defined using criteria similar to those used for cortical areas, especially chemoarchitecture, cortical interconnections, and topographic maps. First level, primary sensory thalamic nuclei share many common chemoarchitectonic features. However, other thalamocortical and corticothalamic sensory systems appear not to be so extensively developed as those for vision. For example, whereas the somatosensory and auditory thalamocortical relations support first level and high order nuclei, the suggested second level nuclei are not so easily identified nor neurochemically differentiated as the visual pulvinar. KEYWORDS: auditory system, lateral geniculate nucleus, primate, somatosensory system, thalamic nuclei, visual system
1. INTRODUCTION Research in the last several decades on the organization of the cerebral cortex into separate highly interconnected functional areas and systems has revealed principles that have parallels in thalamic organization. The focus of this chapter will be to highlight aspects of thalamic organization that provide for partially segregated, or parallel processing, within systems, and to explore some hints that thalamic nuclei respect but also may coordinate activity within the well-described cortical hierarchies. The emphasis will be on visual thalamocortical systems in macaque monkeys, as the interconnections and distributed parallel processing are best understood within this model. It should be noted that while the macaque visual thalamus has become one of the better understood models, the hierarchical organization exhibited within this system may not be typical of most others, whose organization may be more strongly parallel. It should be noted that much work on microcircuitry and physiology has been conducted on other thalamocortical systems and species (see Jones, 1985; Sherman and Koch, 1998; McCormick and Huguenard, 1992; Ramcharan et al., 2000). Given the many specializations in the organization of cerebral cortex of different mammals, reliance on a single well-studied model is attractive for extracting general rules 155 © 2002 Taylor & Francis
156
Catherine G. Cusick
of thalamocortical organization. To maintain an awareness of possible limits of this approach, other systems will be contrasted with the macaque visual thalamocortical model to illustrate the range of different organizational strategies that may exist. 1.1. Overview of Cortical Organization Cortical systems are traditionally regarded as broadly divided into sensory, motor and “high order” (supramodal) systems. Cortical systems consist of functional units termed areas that are interconnected with each other in specific patterns that are both distributed and hierarchical (for reviews see Felleman and Van Essen, 1991; Kaas and Garraghty, 1991; Casagrande, 1994). In monkeys, the first visual area (V1) receives the bulk of the projections from the primary relay nucleus, the lateral geniculate (LGN). V1 sends inputs to the immediately adjacent cortex, V2, and to several other retinotopically organized areas, e.g. the middle temporal (MT), dorsomedial (DM), and medial (M) visual areas, that may be considered to be at the same level in the cortical hierarchy. Several further processing levels exist and there are in turn multiple areas at each level (Felleman and Van Essen, 1991). Interconnections among cortical areas diverge into different dorsal and ventral streams that are directed toward the posterior parietal cortex and inferior temporal lobe and are concerned with visually guided actions and perception, respectively (Ungerleider and Mishkin, 1982; Milner and Goodale, 1993). The headwaters, as it were, of these partially segregated streams are already identifiable within separate layers and modules of V1 and different modules of V2. To an extent, the dorsal and ventral cortical streams are traceable back to the LGN (Casagrande, 1994; Van Essen and Gallant, 1994). 1.2. Changing Views of Thalamocortical Relations Views on how the thalamic inputs relate to the conceptualization of cortical organization into systems, hierarchies, areas, modules and layers have changed dramatically in recent years. The early studies on thalamocortical relations developed the concept that each cortical area received information from one thalamic nucleus (Rose and Woolsey, 1949). It soon became clear that individual thalamic nuclei might target more than one cortical area within the same functional system. This was particularly true for the cat visual system, in which the lateral geniculate nucleus was recognized to project to areas 17, 18, and 19 of visual cortex (Garey et al., 1968; Heath and Jones, 1970). In monkeys, however, V1 was thought to be the only target of projections from the LGN, and extrastriate visual cortex was thought to receive inputs only from the pulvinar, although the pulvinar was known to project to most of the individual areas of extrastriate cortex (Garey and Powell, 1971). Tracing studies utilizing more sensitive methods suggest that many if not most cortical areas in monkeys receive projections from more that one thalamic nucleus (Friedman and Murray, 1986). Thus, the extrastriate areas MT and V4 receive major projections from the pulvinar, but also minor inputs from the LGN (Cusick et al., 1993; Stepniewska et al., 1999). Taking a further example from the somatosensory system, the primary area 3b receives connections from the major relay nucleus, the ventroposterior (VP) complex, including its component divisions ventroposterior proper (VP), ventroposterior inferior (VPI) and ventroposterior superior (VPS) (see below), as well as from the adjacent anterior pulvinar nucleus (Cusick and Gould, 1990).
© 2002 Taylor & Francis
Thalamic Systems and Cortical Areas
157
Since interconnections between thalamus and cortex do not follow a “single nucleus to single area” rule, or vice versa, the question is raised about the determinants of specificity of thalamocortical relations. To gain an approximate answer to this question will require cellular and developmental approaches, and obviously also a full understanding of the degree of specificity exhibited in the adult brain.
2. CONCEPTS OF THALAMIC ORGANIZATION 2.1. Organization of Thalamic Nuclei into Systems Within the thalamus, there have been considered to be two broad sorts of cortical projection nuclei, specific (terminating in layers III/IV) and modulatory (terminating in layers I and VI; (Herkenham, 1980; Sherman and Guillery, 1998). Modulatory nuclei have topographic projections with widespread regions of the cerebral hemisphere, and in many cases target the basal ganglia, via thalamostriatal projections, as well as the cerebral cortex (Jones, 1985). The specific nuclei form groups that are analogous to cortical systems. The traditional anatomical parcellation of the thalamus into medial, lateral, and anterior groups, provided by the position of the internal medullary lamina, is meaningful in terms of the topography of thalamocortical relations, but the “thalamic system” spoken of here is the set of nuclei that interconnects with particular cortical systems. Thalamic systems are proposed to consist of primary nuclei, second order nuclei and “high order” nuclei. Although somewhat similar to the schema of Guillery (1995), the term high order is reserved here for non-modality specific (“supramodal”) nuclei, and the non-primary thalamic nucleus within a modality is termed second order. Cortical systems exhibit several hierarchically organized levels of processing, with estimates for the visual system at over 10 levels (Felleman and Van Essen, 1991). By contrast, thalamic systems can be identified at this time as having perhaps 2–3 levels within each system. For the visual system, the primary nucleus is ofcourse the LGN, the second level nucleus, the inferior pulvinar complex, and high order nuclei would include other portions of the pulvinar, especially the dorsal pulvinar complex.
2.2. What is a Thalamic Nucleus? Because the collections of neurones within the thalamus sometimes have complicated three-dimensional shapes, the definitions of nuclei have been difficult, and there are many examples in which different nomenclatures have been proposed in different species for apparently homologous structures. Similar definitions of thalamic nuclei can be developed as have long existed for cortical areas (Van Essen and Zeki, 1978; Kaas, 1990; Krubitzer, 1995). First, single cortical areas are characterized by uniform cyto- and myeloarchitecture. Incorporating recent evidence that modularity may exist within a cortical area, such modular structure would be a feature of the cortical area (e.g. V2; Rosa and Krubitzer, 1999). Second, for “lower” or “early” cortical areas, there is a topographic representation of the entire contralateral sensory receptor sheet or motor space. Third, there are topographic patterns of connections with the similar sets of cortical and subcortical structures, allowing for differences in strength of connections for different portions of the functional
© 2002 Taylor & Francis
158
Catherine G. Cusick
map. Fourth, cortical areas contain neurones which have similar physiological properties and which may be clustered into a columnar organization that has a structural correlate to architectonic modules (e.g. cytochrome oxidase domains in V2; myelin densities in area 3b of monkeys [Jain et al., 1998]). A thalamic nucleus has a parallel cytoarchitectural integrity: similar types of neurones are disposed in clusters or layers, and with particular relations to the encapsulating fibre lamellae within the thalamus. For the primary nuclei, there are orderly topographic maps of the sensory or motor space. The mapping rules for thalamic nuclei are not as straightforward as they are for cortex, onto which sensory representations are laid out as twodimensional sheets.
3. THE MACAQUE LGN AS A MODEL OF A PRIMARY RELAY NUCLEUS 3.1. Layers and Maps Representations in thalamus are best understood for the LGN, in which the visual hemifield is mapped onto separate layers that can be modelled as a series of discs folded around the hilus of the LGN (Kaas et al., 1978). In primates, these layers consist of three different types, magno-, parvo- and koniocellular (M, P, and K) layers (Casagrande, 1994). There are 2 sets of M layers, one for each eye, and the 2 sets of P layers are variably divided in different primate species into further leaflets according to response types (Kaas et al., 1978). Each LGN contains a map of the contralateral visual hemifield, with maps in the different layers in visuotopic register, such that a point in visual space is represented by a projection line that traverses all of the layers (Malpeli and Baker, 1975; Malpeli et al., 1996; Erwin et al., 1999). Each of the separate layers of the LGN then contains a complete map of the contralateral visual hemifield, and the aligned layers could be viewed in the aggregate as several stacked maps of visual space, each concerning a different type of information channel (M, P, or K) and receiving inputs from one of the two eyes. The map of visual space in the LGN can also be demonstrated by its pattern of anatomical connections with the striate cortex. A focal projection of a neuroanatomical tracer reveals retrograde labeled neurones that follow a radial path or projection line through all of the LGN laminae. Although broader in mediolateral extent, feedback axons from the cortex target the same columnar region (Shatz and Rakic, 1981; Robson, 1983, 1984), and the terminals form fine beaded projections termed “elongate” (Type I) (Robson, 1983; Rockland, 1996). A primary thalamic nucleus can thus be viewed as containing a single aggregate map that contains multiple maps sorted by physiological response types. The subdivision of thalamic nuclei into clusters or layers is analogous to the partitioning of cortical areas into separate modules. The different LGN layers exhibit different laminar projections. The principal (P and M) layers have projections to separate sublayers of IVc (Blasdel and Lund, 1983; Blasdel and Fitzpatrick, 1984), and the koniocellular (K) layers, a term including both the superficial and interlaminar cell groups, have projections that are predominantly to the CO dense blobs within the supragranular layers, as well as to layer I (Lachica and Casagrande, 1992; Hendry and Yoshioka, 1994). The principal layers have been referred to as contributing a “lemniscal” (secure, fast conducting pathway from the discrete receptive fields on the sensory periphery to the cortex) thalamocortical pathway
© 2002 Taylor & Francis
Thalamic Systems and Cortical Areas
159
and the K layers as participating in a “non-lemniscal” (slower conducting projections with broader receptive fields) pathway (Casagrande, 1994). 3.2. Layers and Neurochemical Signatures The LGN layers have neurochemical signatures that have certain commonalities with other specific thalamic nuclei (Table 8.1 and Figure 8.1). Principal layers have metabolically highly active relay neurones that stain intensely with Nissl stains, and for cytochrome oxidase. In addition to containing calcium binding proteins (e.g. Ca-calmodulin kinases) that are ubiquitously found in neurones, the principal layer neurones contain parvalbumin, a calcium binding protein whose functional significance in thalamus is not well known. The K relay neurones also contain parvalbumin, but in addition they express calbindin, a calcium binding protein not normally found in the principal layers, at least in normal
Table 8.1.
Compartments (layers/modules) and projections of selected thalamic nuclei “Compartment” Cat-301/WFA CO Parvalbumin Calbindin AChE Cortical targets
LGN
VP
M layers P layers K layers CO rich zones
CO poor zones PI complex PIm PIc
+
++ ++ + +
+(variable)
++ ++
+
++ ++ + +
++ + ++
++ ++
++ +
V1 layer IV layer IV layer I Areas 3b/1 layer IV layer I MT DLc
Notes Plus symbols indicate relative density of staining for the different neurochemical markers. Compartments indicated in bold are thought to be lemniscal; plain text indicates non-lemniscal type of chemoarchitecture and cortical projection. Not all cortical projections are listed for the individual nuclei. For further explanation see text.
Figure 8.1. Neurochemical stains demarcate the laminar organization of the macaque LGN into principal (magnocellular and parvocellular) and small-celled (koniocellular) layers. Immunocytochemistry for Cat-301 antibody and binding for the lectin Wisteria floribunda agglutinin (WFA) localize more densely to the magnocellular layers than the parvocellular layers. Calbindin immunocytochemistry stains cells in the thin koniocellular layers.
© 2002 Taylor & Francis
160
Catherine G. Cusick
adult monkeys (e.g. Gutierrez and Cusick, 1994). Thus, in calbindin-immunoreactive sections of the LGN, there are numerous calbindin-positive fibres in the nucleus, many of these apparently of retinal origin, but calbindin neurones only occupy the K layers adjacent to the optic tract and the interlaminar zones. The K neurones also express a high level of the alpha-subunit of the CaMKII-kinase complex, a subunit not found in the P and M layers (Hendry and Yoshioka, 1994). The principal layers of the LGN also show different neurochemical staining patterns from each other. The M layers stain somewhat more intensely for cytochrome oxidase and Nissl than the P layers. The M neurones are decorated with specific extracellular matrix components (ECM) that are relatively lacking in P neurones. These can be demonstrated by binding of the Cat-301 antibody to protein cores of ECM (Hockfield et al., 1983; Hendry et al., 1984), or by binding of the lectin Wisteria floribunda agglutinin (WFA) to ECM sugar residues (Preuss et al., 1998). The magnocellular layers also show stronger staining for non-phosphorylated neurofilament protein with the SMI-32 antibody (Gutierrez et al., 1995), and for acetylcholinesterase (McDonald et al., 1993), than do the parvocellular layers. Although at the present time there is not a specific marker with higher affinity for the parvocellular layers, the three layer types obviously have distinct neurochemical signatures. 3.3. Parallels in Neurochemical Organization of Primary Somatosensory, Auditory, and Motor Thalamic Nuclei Certain of the neurochemical characteristics of the LGN layers have parallels in other thalamic nuclei. In the ventroposterior complex, one compartment is cytochrome oxidasepoor and calbindin-rich, consisting of two zones termed the ventroposterior inferior nucleus (VPI) and the CO-poor matrix. The CO-poor matrix occurs in patches in the more dorsal part of the ventroposterior nucleus (VP) proper. The rest of VP proper (Figure 8.2), by contrast, is a zone with dense clusters of cytochrome oxidase stain that often correspond to important subregions of the somatosensory map, separated by thin fibre lamellae or septa (Jones et al., 1986; Cusick and Gould, 1990; Rausell et al., 1991; Krubitzer and Kaas, 1992). These CO patches are parvalbumin-rich and calbindin-poor, receive dense inputs from the medial lemniscus, and project densely to layer IV of the first somatosensory cortex area 3b. The CO-poor matrix on the other hand is the target of patchy foci of projections from the spinothalamic tract and contains many smaller neurones that project to supragranular layers of the anterior parietal cortex (Rausell and Jones, 1991; Rausell et al., 1992a,b). The “lemniscal” CO patches thus appear to be analogous to the LGN principal layers, whereas the “non-lemniscal” CO-poor matrix, including VPI, appears to be analogous to the LGN koniocellular layers. Interestingly, the positions of entry of the retinal afferents to the LGN and the somatosensory inputs to the ventroposterior complex, the S layers and VPI respectively, are both calbindin-rich compartments. A further division of the VP patch compartment into two subregions is suggested by the clustering of inputs from slowly adapting (SA) and rapidly adapting (RA) fibers (Dykes et al., 1981; Kaas et al., 1984). These somatosensory responses resemble, in part, the sustained and transient properties of the P and M layers (Sherman et al., 1976; Schiller and Malpeli, 1978), respectively. Interestingly, the pattern of binding for WFA and Cat-301 antibody (Figure 8.2) suggests that the CO dense (“patch”) compartment may contain functional subdivisions. Based on somatotopic mapping and receptor distributions, the most ventral
© 2002 Taylor & Francis
Thalamic Systems and Cortical Areas
161
Figure 8.2. Binding for the lectin WFA in squirrel monkey thalamus delimits thalamic nuclei chemoarchitectonically and shows their lamellar/modular organization as well. Rostral-to-caudal series of coronal sections. Abbreviations of thalamic nuclei: AD, anterodorsal nucleus; AM, anteromedial; AV, anteroventral; CL, central lateral; CM, centromedian; Hl, lateral habenula; Hm, medial habenula; LD, lateral dorsal; Lim, limitans; LP, lateral posterior; mc, magnocellular layers; MD, mediodorsal; MDpc, parvocellular division of mediodorsal; MDmc, magnocellular division of MD; MDmf, multiform division of MD; LGN, lateral geniculate; MGN, medial geniculate; Pa, anterior pulvinar; Pc, paracentral; pc, parvocellular layers; Pf, parafascicular; PIc, central division of the inferior pulvinar complex; PIl, lateral division of PI complex; PIm, medial division of PI complex; PIp, posterior division of PI complex PL, lateral pulvinar; PM, medial pulvinar; Ret, reticular; VAmc, magnocellular division of ventral anterior; VApc, parvocellular division of ventral anterior; VLa, anterior division of ventrolateral nucleus; VLd, dorsal division of ventrolateral nucleus; VLp, posterior division of VL; VLx, division “x” of VL; VM, ventral medial; VPI, ventroposterior inferior; VPL, ventroposterior lateral; VPM, ventroposterior medial; VPMpc, parvocellular division of VPM; VPS, ventroposterior superior nucleus. From Preuss et al., 1998.
portion of the “hand subnucleus” of VP would be expected to have a heavy representation of SA inputs from the finger tip pads (Kaas et al., 1984), and this portion of VP is relatively devoid of WFA and Cat-301 stain but has dense CO (Preuss et al., 1998). Thus, it is possible that WFA and Cat-301 binding might discriminate RA and SA cell clusters in the VP proper, in a similar manner to the pattern shown in M and P layers of the LGN. This point deserves further investigation.
© 2002 Taylor & Francis
162
Catherine G. Cusick
It should be noted, however, that other neurochemical patterns are found in VP which differ strongly from those of the LGN: for example, whereas LGN principal layers are AChE rich (McDonald et al., 1993), presumably related to the cholinergic input from the parabrachial nucleus in the brainstem (Hu et al., 1989a,b), the patch compartment of VP is AChE poor (Hirai and Jones, 1989). Parallel parvalbumin- and calbindin-rich compartments have been identified in the medial geniculate nucleus of monkeys (Hashikawa et al., 1991, 1995; Molinari et al., 1995). The ventral nucleus contains dense clusters of parvalbumin neurones that project onto layer IV of A1, which in turn has an especially dense plexus of parvalbumin fibers. Calbindin neurones concentrate in the caudal part of the posterodorsal and in the magnocellular divisions of the medial geniculate complex, and project to layer I of A1. The ventral division of the medial geniculate complex projects both to a core zone of three primarylike koniocellular fields, and adjacent belt areas receive from all divisions of the medial geniculate (Kaas and Hackett, 1998). Thus, similar to the somatosensory thalamocortical system, there is evidently more parallelism in the projections from the primary auditory nucleus than there is for the geniculocortical projections.
Figure 8.3. Chemoarchitecture of the ventroposterior complex revealed by cytochrome oxidase (CO) histochemistry and WFA binding. Section A is adjacent to B, and C to D. Note that some regions of intense CO stain correspond to zones with dense WFA binding, but the WFA binding pattern is more restricted, suggestive of further compartmentation within the ventroposterior complex. From Preuss et al., 1998.
© 2002 Taylor & Francis
Thalamic Systems and Cortical Areas
163
For motor thalamus, the pattern of neurochemical stain has recently been described in several investigations and there is general agreement that the pattern of AChE stain is highly correlated with presumably separate functional zones of cerebellar and pallidal input (Hirai and Jones, 1993; Stepniewska et al., 1994a,b; Sakai et al., 2000). Similar correlations may be provided by WFA binding, which shows marked differences between different divisions of the motor thalamus (Figure 8.3; Preuss et al., 1998). In summary, it has been suggested that lemniscal and non-lemniscal categories may extend to compartments within each of the major thalamic relay nuclei (Jones, 1998a,b).
4. THE INFERIOR PULVINAR COMPLEX: A “SECOND LEVEL” THALAMIC NUCLEUS The organization of visual thalamic cell groups in the monkey pulvinar has been extensively investigated recently by means of neurochemical markers correlated with patterns of cortical connections. In primates, the expansion of extrastriate visual cortex and differentiation into multiple areas and levels has been accompanied by the enormous enlargement of the pulvinar nucleus of the thalamus. Striate cortex and “early” extrastriate visual areas that are retinotopically organized, such as V2, V4, and MT, have retinotopically organized connections within approximately the ventral half of the pulvinar, including portions of the traditional medial, lateral, and inferior pulvinar nuclei (Figure 8.5). This retinotopically organized zone includes several distinct neurochemical units that also overlap the traditional boundaries. In order to simplify the language used to describe these architectonic-connectional units, it was proposed that the entire zone encompassed by the striate cortex connections be termed the inferior pulvinar (PI) complex (Gutierrez et al., 1995; Gutierrez and Cusick, 1997). Five neurochemically distinct regions have been distinguished within the PI complex, and similar patterns of chemoarchitecture have been identified in several species of monkeys (Gray et al., 1999), in chimpanzees (Cola et al., 2000), and in humans (Cola et al., 1999). The organization of the PI complex into different neurochemical subdivisions has been proposed to be analogous to the organization of the LGN into separate layers, with the PI complex as a whole having the status of a thalamic nucleus (Gutierrez et al., 1995) (see Figure 8.4). Thus, although the newly defined PI complex encompasses the traditional PI, a small ventromedial corner of PM, and the adjacent parts of the PL “nuclei”, the architectonic and especially the connectional patterns argue for a larger, single nucleus. The status of the separate subdivisions as “layers” rather than as separate nuclei is supported by the patterns of visuotopic organization revealed by large injections within striate cortex that show continuous or nearly continuous projection of the center of gaze representation across the different subdivisions (Gutierrez and Cusick, 1997), and evidence, from double labeling studies, that individual neurones project to topographically similar representations in different visual areas (Kennedy and Bullier, 1985; Kaske et al., 1991). This concept clearly needs further investigation, as the results of small injections do not give the impression of wedge-like bands crossing all of the subdivisions as they do for the layers of the LGN. The present evidence of differential connections of the V4 and MT regions (Adams et al., 2000; Cusick et al., 1993; Gray et al., 1999; Kaske et al., 1991) can be interpreted either as support for the subdivisions being separate nuclei or layers of a single nucleus.
© 2002 Taylor & Francis
164
Catherine G. Cusick
Figure 8.4. Summary of divisions of the pulvinar according to traditional Nissl architecture (A, A′) compared to chemoarchitecture (B, B′), in this example for AChE histochemistry. A′ and B′ are adjacent sections; the arrows point to the same blood vessel. Note the divisions drawn in A follow the positions of fibre bundles and the brachium of the superior colliculus (bsc) on the Nissl section in A′. The histochemical divisions of the inferior pulvinar complex are indicated by thick lines, and the divisions of the dorsal pulvinar by thinner lines. Bsc, brachium of the superior colliculus; L-S, lateral-shell of the inferior pulvinar (PI) complex; -P, posterior division of the PI complex; PIc, central division of the PI complex; PIm, medial division of the PI complex; PIl, lateral division of the PI complex; PLd, dorsal Lateral Pulvinar; PMl, lateral division of PM; PMm, medial division of PM; PMm-c, medial-central PM. Modified from Gutierrez et al. (2000).
4.1. Lamellae and Maps in the PI Complex The PI complex can be conceptualized as a set of nested lamellae, enclosing a central core, something in the manner of the layers of an onion. The core zone consists of the medial subdivision PIm, and the medially and laterally adjacent posterior (PIp) and central (PIc) divisions (Lin and Kaas, 1979; Cusick et al., 1993; Gutierrez et al., 1995). Based on a uniform architectonic appearance, direct continuity with each other behind PIm, and strong connections of both zones with the superior colliculus, it seems likely that PIp and PIc are in fact a single unit that envelops the caudal pole of PIm (Stepniewska and Kaas, 1997; Gray et al., 1999; Stepniewska et al., 1999). There is broad agreement about the existence and defining characteristics of PIm, PIc, and PIp (even though the latter two might be considered as one; Stepniewska and Kaas, 1997; Adams et al., 2000; Gray et al., 1999), but the organization of the remainder of the PI complex, its larger, more lateral part, has
© 2002 Taylor & Francis
Thalamic Systems and Cortical Areas
165
been an area of some disagreement. This zone has been termed the lateral division of the PI complex, or PIl, since it is a composite of the lateral part of the traditional PI and the inferior part of the traditional PL (Gutierrez et al., 1995; Gray et al., 1999). PIl contains a coherent set of topographically organized projections from the upper and lower visual quadrants, wherein the lower quadrant extends above the brachium of the superior colliculus (bsc), outside of the limits of the traditional PI. It should also be noted that this concept makes minimal assumptions about the significance of fibres of the bsc, which do not form a border for any of the agreed medial three subdivisions.
4.2. Modulatory and Driving Inputs The connections of the PI complex with V1 thus form a guide to the unifying of the different architectonic components into a single zone. The PI subdivisions or “lamellae” of the PI complex, each have connections with multiple extrastriate visual areas and vice versa (Lin and Kaas, 1979; Gutierrez and Cusick, 1997; Beck and Kaas, 1998b; Gray et al., 1999; Stepniewska et al., 1999; Adams et al., 2000). However, in the pulvinar there is a new degree of corticothalamic complexity that characterizes the non-primary nuclei in general. That is, the pulvinar receives two different types of corticothalamic fibres. The first type (termed elongated, E or Type I) is similar to the feedback axons provided by striate cortex to the LGN and consists of fine axons with a broadly distributed set of fine terminals. These appear to extend across the subdivisions of the PI complex in a manner analogous to feedback axons coursing through the projection lines of the LGN, and provide sprays of branches in specific locations (Rockland, 1994, 1996). A reasonable but currently untested hypothesis is that these axon sprays target retinotopically similar representations within individual PI subdivisions. Furthermore, it is likely that the E-type axons arise from cortical layer VI and target zones containing neurones that project to the same cortical region. Interestingly, Type I axons have been shown to contain growth associated protein 43 (GAP-43), suggesting a role in plasticity of corticothalamic circuits (Bickford, 1999). The second type of corticothalamic axon to the PI complex provides dense foci of large round terminals (R or Type II terminals) to more discrete regions of the pulvinar (Rockland, 1996, 1998). Morphologically, the terminals resemble those of the specific sensory inputs (e.g. retinal fibres, medial lemniscus fibres) to primary nuclei (Guillery and Colonnier, 1970; Mason et al., 1984; Ralston and Ralston, 1994) and it has been hypothesized that these R type cortical terminals within second level nuclei essentially substitute for the “primary” type of inputs within principal sensory relay nuclei (Guillery, 1995). Thus, these axons have been termed feedforward, since they are large, presumably glutamatergic and target proximal portions of dendrites on their targeted neurones, and are proposed to form a driving influence on the pulvinar. They appear to arise from large neurones in layer V, some of which also send a collateral branch to other targets such as the superior colliculus (Rockland, 1998). Some Type II terminal fields are found in locations that appear not to contain pulvinar neurones projecting to the same cortical site. Most evidence for this is indirect, combining, for example, the dense foci of R type terminations of V1 axons within PIm (Feig and Harting, 1998; Gutierrez and Cusick, 1997) with the absence of retrograde cell labeling from V1 within PIm (Adams et al., 2000). This segregated pattern of microcircuitry
© 2002 Taylor & Francis
166
Catherine G. Cusick
implies that forward-going pathways between sets of cortical areas can be not only direct, corticocortical, but also indirect, by way of a corticothalamocortical loop through the pulvinar (Guillery, 1995; Feig and Harting, 1998). It should be remembered that the contributions of pulvinocortical circuits to cortical hierarchies could also be either modulatory (to supragranular layers and layer I) or driving (to layer III/IV) and that individual axons do show one or both types of terminations within different cortical fields (Rockland et al., 1999). Thus, a projection can serve different functions in different areas, and since a substantial number of pulvinar neurones send collateral projections to retinotopically-matched locations in different extrastriate areas (Rockland et al., 1999), the pulvinocortical circuit can serve not only to transfer information downstream, but may also coordinate and perhaps co-activate similar topographic and functional sites between areas (Nowak et al., 1999). 4.3. The PI Complex as the Striate and Tectal Recipient “Visual” Pulvinar Previous concepts of the role of pulvinar in visual processing have emphasized, for nonprimate taxa, that the tectal recipient zone of pulvinar provides a “second visual system” route to extrastriate visual cortex (Diamond, 1976). Primates, by contrast, emphasize the role of the geniculocortical pathway in driving the pulvinocortical circuit. Thus, the striate cortex sends a strong feedforward connection to PI subdivisions, including PIl (the largest and its outermost sector, PIl-s) and PIm. Interestingly, V1-to-pulvinar projections are notably weak in PIc and have not been reported for PIp (Gutierrez and Cusick, 1997), the two divisions that receive a dense projection from the superior colliculus (Stepniewska et al., 1999). Thus, the geniculostriate and collicular inputs are predominantly segregated within different subdivisions of the PI complex, and considered together, these two projection systems outline the complex as a whole. Based on the relative sizes of the striate- and collicular-recipient zones, it appears that the PI complex is dominated by cortical inputs, and indeed removal of striate cortex inputs produces a profound decrease in visual driving in the pulvinar, while collicular ablation produces minimal effects (Bender, 1983). 4.4. M- and P-Stream Segregation in the PI Complex? The PI complex thus appears most heavily influenced by its cortical inputs. Since the “early” cortical visual areas show evidence of parcellation according to their relative influences of M and P layers of the lateral geniculate nucleus (Maunsell et al., 1990; Casagrande, 1994; Van Essen and Gallant, 1994), the question arises whether that partial segregation is retained or integrated within the PI complex. Cortical connections of PIm, especially its strong interconnections with the motion sensitive area MT (Figure 8.6) as well as with area DM (Cusick et al., 1993), suggest that PIm is predominantly concerned with the M stream. Its connections with V1 and V2 may be closely associated with M stream as well, but this has not been determined. PIm generally lacks connections with the V4 region (Adams et al., 2000; Cusick et al., 1993), whose inputs may emphasize the P and K pathways (Van Essen and Gallant, 1994) but which does show significant M contribution (Ferrera et al., 1992). Histochemically, PIm has several features in common with the magnocellular layers of the LGN: These include conspicuously dense staining for CO, AChE, parvalbumin, non-phosphorylated neurofilament protein, Cat-301, and WFA binding (Cusick et al., 1993; Adams et al., 2000; Gray et al., 1999; Gutierrez et al., 1995; Lysakowski et al., 1986; Stepniewska and Kaas, 1997). Interestingly, in macaques, all of
© 2002 Taylor & Francis
Thalamic Systems and Cortical Areas
167
Figure 8.5. Connections of V1 with the inferior pulvinar complex in a macaque. Band-like injections of two different tracers were placed near the vertical meridian representation in V1. The tracer deposit in lower field (wheat germ agglutinin conjugated to HRP) is shown in gray, and for upper field (35S-labeled methionine), in black. The upper field injections curved upward toward the representation of the horizontal meridian. Thick lines indicate the upper border of the PI complex. PI subdivisions are outlined. Very thin outlines surround the zones of label. Zones of label transported from the lower field representation are indicated by minus signs. Stars denote the estimated centre-of-gaze representation in the PI complex, where the two labels touch. Serial 25 µm thick sections are numbered from caudal to rostral. Modified from Gutierrez and Cusick (1997).
© 2002 Taylor & Francis
168
Catherine G. Cusick
these chemoarchitectonic methods show non-uniform staining, with small patches of about the same calibre as the foci of projections from V1 (Gutierrez and Cusick, 1997). PIm is characterized by very light staining for calbindin, and taking into account all of its chemoarchitectonic features described to date, appears a candidate for a metabolically very active “lemniscal” pathway through the inferior pulvinar complex (Cusick et al., 1993), albeit with its driving inputs arising from layer V in the striate cortex (Lund et al., 1975; Rockland, 1996). By contrast, evidence suggests that PIl is concerned mainly with parvocellular but possibly also with koniocellular LGN layers, considering its interconnections with V2 and with V4, and the general segregation of MT connections always from the zones of V4 and MT input into the PIl-s region (Gray et al., 1999; Adams et al., 2000). PIl has neurochemical staining characteristics more or less intermediate in density levels between PIm and PIc. From the dense neuropil staining for calbindin found within PIc (Gray et al., 1999; Stepniewska and Kaas, 1997), one might speculate not only a neurochemical but also a functional parallel with the K pathway (Behan et al., 1992; Gutierrez and Cusick, 1994). This supposition is circumstantial but attractive because of the dense inputs to PIc from the superior colliculus (Stepniewska et al., 1999), coupled with the dense inputs from the superficial layers of the superior colliculus with the koniocellular layers of the LGN (Harting et al., 1978). Interestingly, connections of PIc are dense, specifically with an area of cortex that is in the DLr region directly caudal to MT (Gray et al., 1999), and with DM, which is densely interconnected with that same cortical location (Beck and Kaas, 1998a,b). The functional contributions of these cortical areas and of PIc to visual processing, however, are not well understood. The PIc, similar to the koniocellular layers of the LGN (Hendry and Reid, 2000), may be a non-leminiscal type of pathway through the PI complex, involving the superior colliculus rather than the striate cortex as the main driving influence. 4.5. Second Level Nuclei for Other Systems? This possible further elaboration of sensory channels within a second level nucleus, the PI complex, does not have an obvious correspondence within other sectors of sensory and motor thalamus, chiefly because the pulvinar is so large and well-differentiated in primates as compared to those other systems. Some comparisons with the somatosensory system are worth noting, however, as the thalamocortical somatosensory system appears to emphasize more parallel than hierarchical structure. The primary, ventroposterior, nucleus connects with several different somatosensory areas, including areas 3a (Huffman and Krubitzer, 2001), 3b, 1 and 2 (Pons and Kaas, 1985; Cusick and Gould, 1990; see Kaas and Garraghty, 1991 for review) and has clustered neurochemical organization reminiscent of the separate LGN layers. In view of the multiple architectonic areas that are targeted by the cutaneous “core” relay nucleus, it is perhaps not surprising that the definition of the ventroposterior has been somewhat problematic. A “shell” region dorsal to the ventroposterior nucleus has been termed a separate nucleus, the ventroposterior superior or VPS (Cusick et al., 1985; Pons and Kaas, 1985; Cusick and Gould, 1990). VPS conveys information about deep body receptors predominantly to cortical areas 3a and 2, but there are minor connections from VPS to the cutaneous core areas 3b and 1 (Pons and Kaas, 1985; Cusick and Gould, 1990). Thus, other possible interpretations are that VP and VPS together constitute a single thalamic nucleus rather than being separate nuclei, or that VP
© 2002 Taylor & Francis
Thalamic Systems and Cortical Areas
169
and VPS represent an intermediate form in which different modalities are represented in thalamic structures that are only partially differentiated into separate nuclei. The hierarchical patterns of functional cortical connections between the anterior parietal fields, at least, argue in favour of the view that area 3b is the primary somatosensory area for cutaneous somatosensory processing and against the view that VPS is a primary relay nucleus targeting area 3b. The anterior pulvinar (Pa) may be considered a second order nucleus, and high order, multisensory thalamic processing would involve the LP/dorsal pulvinar complex (Gutierrez et al., 2000). The anterior pulvinar has been proposed on the basis of architecture, relative position to VP, and spinothalamic inputs (Rausell et al., 1992a) to correspond to the medial division of the posterior nucleus (Pom) described in other groups of mammals (Krubitzer and Kaas, 1992). Analogous to corticothalamic fibres within the LGN and inferior pulvinar complex, cortical fibres exhibit both type E and type R terminations within “medial pulvinar” (here termed Pa; Darian-Smith et al., 1999). Pa shows somatotopically organized connections with area 3b of anterior parietal cortex (squirrel monkey; Cusick and Gould, 1990) as well as with area 2 (macaque; Pons and Kaas, 1985), and appears to have widespread connections with most somatosensory areas. The nucleus is relatively pale in cytochrome oxidase stains and dense for calbindin, and thus has connectional and neurochemical features of non-lemniscal thalamocortical systems. It should be noted that the type of “laminar” histochemical heterogeneity characteristic of the PI complex has not been found for the anterior pulvinar, and Pa appears not to contain cell groups with lemniscal neurochemical features, such as found in PIm. In the auditory system, a candidate second level nucleus is not quite so clear, but, on the basis of cortical connections with areas outside of the koniocellular fields, it might include such diverse cell groups as the magnocellular medial geniculate nucleus (Molinari et al., 1995), a portion of the suprageniculate/limitans complex that caps the medial geniculate, and the ventral portion of the medial pulvinar nucleus PMmv (Hackett et al., 1998; Gutierrez et al., 2000). More dorsal portions of PM, e.g. PMl (Gutierrez et al., 2000) may receive the auditory contributions to thalamic multisensory processing (Hackett et al., 1998; Romanski et al., 1997).
5. A “THIRD LEVEL” THALAMIC COMPONENT OF THE VISUAL, SOMATOSENSORY AND AUDITORY SYSTEMS: THE DORSAL PULVINAR COMPLEX A “high order” or third level of thalamic nuclei for vision is represented by the dorsal pulvinar complex, recently determined to contain three broad histochemical zones. The dorsolateral corner is sharply outlined in chemoarchitectonic stains and termed PLd (Lysakowski et al., 1986; Gutierrez et al., 2000). Darkly-stained for AChE and calbindinpale, PLd occupies the dorsal sector of the traditional PL and is continuous rostrally with the lateral posterior nucleus LP. PLd has connections with “high order” visual cortex of the inferior parietal lobule, and with dorsolateral prefrontal cortex in the location of the frontal eye fields (Asanuma et al., 1985; Gutierrez et al., 2000). The traditional PM nucleus of the dorsal pulvinar has two distinct neurochemical divisions. The more lateral division, PMl, stains moderately for AChE, and the more medial division, PMm, is pale for AChE, although both divisions show heterogeneity of histochemical staining patterns
© 2002 Taylor & Francis
Catherine G. Cusick
170
Table 8.2. Neurochemical characteristics and selected cortical connections of dorsal pulvinar subdivisions Histochemical marker
PLd
PMl
PMm
PMm-c
AChE Parvalbumin Calbindin Selected Connections DPF IPL STG
++ ++ –
+, patchy +, patchy +
+, patchy +, patchy +
+ + –
++ ++ –
++ +++ +
+ ++ ++
– – ++
Notes Pluses indicate relative intensity of neurochemical stain or density of connections. Minuses indicate low levels of staining or absence of connections. DPF, dorsolateral prefrontal cortex; IPL, inferior parietal lobule; STG, superior temporal gyrus. For other abbreviations, see legend to Figure 8.4.
suggestive of a further modular organization. Similar patterns of histochemical staining in the dorsal pulvinar have been identified in humans (Cola et al., 1999). The dorsal pulvinar connects with visual cortical areas whose neurones have large receptive fields, and which as a whole show little retinotopic organization. Thus, the organization of dorsal pulvinar is more difficult to determine based on sensory topography, but the patterns of connections with different cortical areas are nevertheless informative. The traditional medial pulvinar nucleus PM has been shown to have connections with many if not most high order regions of the cerebral hemisphere, including visual regions (inferotemporal cortex, superior temporal polysensory region and inferior parietal lobule: Asanuma et al., 1985; Yeterian and Pandya, 1989; Baleydier and Morel, 1992; Baizer et al., 1993), auditory regions (superior temporal gyrus: Hackett et al., 1998; Gutierrez et al., 2000) and somatosensory regions (insula: Mufson and Mesulam, 1984). Connections also exist with cingulate and prefrontal cortex, and thus PM has associations with a hemisphere-wide system or network proposed to be concerned with directed spatial attention (see Gutierrez et al., 2000 for review). The connections of PM with functionally different cortical areas appear to follow rules that parallel those for connections between the same cortical areas (Figure 8.6). In other words, cortical areas that do not have direct corticocortical links interconnect with different parts of PM, so that there is partial segregation with somatosensory connections rostrally, auditory cortex connections at a rostro-intermediate level, and visual connections located caudally within PM (Pons and Kaas, 1985; Hackett et al., 1998; Grieve et al., 2000; Gutierrez et al., 2000). Of particular interest are the connections at the mid-rostral levels, where visual cortex (inferior parietal lobule, IPL) and auditory cortex (middle and caudal superior temporal gyrus, STG) both connect. The visual and auditory connections often lie directly adjacent to each other, in a tightly interlocking pattern, with almost no evidence of direct overlap (Gutierrez et al., 2000). This is true of both the corticocortical and corticothalamic relations of the IPL and STG (Seltzer et al., 1996; Gutierrez et al., 2000). It should be pointed out that the connection patterns of PM have not been worked out in terms of corticothalamic and thalamocortical microcircuitry. While vision-related connections from IT cortex (and others) have been reported to be of the R-type (Rockland, 1996), it is not fully known which of the many cortical areas that interconnect with PM provide R-type, driving
© 2002 Taylor & Francis
Thalamic Systems and Cortical Areas
171
projections. With respect to the model proposed by Guillery (1995; see Sherman and Koch, 1998), one might expect that PM interconnections with highest cortical levels would be particularly informative.
6. FUNCTIONAL AND COMPARATIVE IMPLICATIONS OF THALAMIC SYSTEMS Synchronization of neuronal activity within cortical networks has been observed and extensively characterized. In the visual cortex, oscillatory activity has been proposed as a possible coding mechanism for sensory processing as well as a means of achieving “binding”, the simultaneous perception of different stimulus attributes (Frien et al., 1994; Tallon-Baudry et al., 1997; Castelo-Branco et al., 1998; Frien and Eckhorn, 2000; but see Tovee and Rolls, 1992; Ghose and Freeman, 1997). Electrophysiological studies have suggested that thalamocortical and corticothalamic activity contributes to gammaoscillations, which correlate with sensory processing, as well as to delta activity, a component of slow wave sleep (Nunez et al., 1992; Bekisz and Wrobel, 1999; Blumenfeld and
Figure 8.6. Summary of selected connections of the inferior pulvinar complex of squirrel monkeys (A) and the dorsal pulvinar of macaques (B) Part A shows the strongest connections identified following injections into area MT are with PIm whereas the caudal division of the dorsolateral area (DLc) connects most strongly with PIc. Although these two cortical areas both connect with PIl and with the dorsal pulvinar, their densest connections support the concept of segregation of function between the different PI subdivisions. Based on its strong connections with area MT, PIm appears associated with M-stream. Part B shows dorsal pulvinar in macaques connecting with dorsolateral prefrontal, inferior parietal, and superior temporal gyrus cortex in macaques. Each area has separate connections with individual divisions. Overlapping connections are revealed by pairs of injections within prefrontal and posterior parietal cortex of the same hemisphere (lines going to the same arrowheads). Injections within the superior temporal gyrus and posterior parietal cortex, however, show only non-overlapping connections. A is from Gray et al., 1999 and B from Gutierrez et al., 2000.
© 2002 Taylor & Francis
172
Catherine G. Cusick
McCormick, 2000). Embedded in each specific relay nucleus there appears a set of parvalbumin neurones that project onto layer IV, and a set of “non-specific” callbindin cells that project to layer I (Jones 1998a,b). It is tempting to associate the different histochemical compartments of the thalamus with different oscillatory behaviors. One might speculate that the differential expression of calcium binding proteins in the thalamus indicates different intracellular requirements for calcium buffering and may provide molecular signatures for neurones involved in different thalamocortical oscillatory behaviors. In support of this, it should be noted that the neurochemical “signatures” present in the lateral geniculate nucleus appear to be modified under different functional states. For example, calbindin, which is usually expressed in a sparse, small-celled component of the VP complex and the LGN, is upregulated in response to peripheral deafferentation (Rausell et al., 1992b; Gutierrez and Cusick, 1994). Numerous species differences exist in localization of calcium binding proteins within the thalamus (Leuba and Saini, 1997; Fortin et al., 1998). Nevertheless, a broad pattern exists which allows the patterns of neurochemical markers to be useful in delineating functional units of thalamus (Morel et al., 1997; Munkle et al., 1999, 2000; Blomqvist et al., 2000) and in states of long term injury (Woods et al., 2000). Calcium conductances are thought to play important roles in rhythmic thalamocortical activity (Pedroarena and Llinas, 1997). Differential requirements for intracellular calcium buffering may underlie the different distributions of calcium binding proteins in discrete thalamic populations. For first and second order cortical areas, the thalamic connections are well positioned to play a role in synchronization of cortical networks. Driving influences to this early portion of the cortical network appear to emanate from the first order cortical areas (Felleman and Van Essen, 1991). There are examples of collateralized thalamocortical connections that target pairs of early areas, for example, finger tip representations of anterior parietal fields in monkeys (Cusick et al., 1985) and retinotopically matched sites in V1 and V2 (Kennedy and Bullier, 1985), and V1 and V4 (Lysakowski et al., 1988). Of particular interest are observations that certain visual responses in the superficial layers of V2 are strongly synchronized to those of V1 (Nowak et al., 1999). It has been proposed that the hierarchical patterns of cortical connectivity may be matched by patterns of feedforward and feedback connections through the thalamus (Sherman and Koch, 1998). Alternatively, the role of the thalamus may be to activate or enhance selectively the activity of matched cortical sites. In this regard, it has been suggested that the LP/pulvinar complex in the cat strengthens and propagates oscillatory synchronization of cortical networks (Molotchnikoff and Shumikhina, 1996). The microcircuitry of cortico-thalamo-cortical loops with regard to driving and modulatory inputs will lend only partial insights to their functional role; sequential or coordinated activation, whether or not by collateralization of thalamocortical axons, needs to be directly assessed. As a final point, the macaque visual system provides a well-characterized model system for addressing some of these questions, but there are some qualifications. Patterns of visual corticocortical and thalamocortical connections suggest distributed hierarchical processing (Felleman and Van Essen, 1991), but this is not the only way that a system can be organized. Generalizations to other thalamic systems, such as somatosensory and auditory (Kaas and Garraghty, 1991; Kaas and Hackett, 1998), can only be made with caution. These systems are organized with more strongly parallel aspects, i.e. a first level nucleus that connects with a primary cortical area as well as with adjacent second-level or “belt” areas. It may be that the second-level nucleus, the inferior pulvinar complex of the visual
© 2002 Taylor & Francis
Thalamic Systems and Cortical Areas
173
thalamic system, uniquely arose in evolution as a primate specialization, in concert with the differentiation of extrastriate cortex into a system with only sparse connections with the primary nucleus.
ACKNOWLEDGEMENTS I thank Drs. Jon Kaas and Todd Preuss for comments on an earlier version of the manuscript. The research from this laboratory was supported by NIH Grant EY08906.
REFERENCES Adams, M.M., Hof, P.R., Gattass, R., Webster, M.J. and Ungerleider, L.G. (2000) Visual cortical projections and chemoarchitecture of macaque monkey pulvinar. Journal of Comparative Neurology, 419, 377–393. Asanuma, C., Andersen, R.A. and Cowan, W.M. (1985) The thalamic relations of the caudal inferior parietal lobule and the lateral prefrontal cortex in monkeys: divergent cortical projections from cell clusters in the medial pulvinar nucleus. Journal of Comparative Neurology, 241, 357–381. Baizer, J.S., Desimone, R. and Ungerleider, L.G. (1993) Comparison of subcortical connections of inferior temporal and posterior parietal cortex in monkeys. Visual Neuroscience, 10, 59–72. Baleydier, C. and Morel, A. (1992) Segregated thalamocortical pathways to inferior parietal and inferotemporal cortex in macaque monkey. Visual Neuroscience, 8, 391–405. Beck, P.D. and Kaas, J.H. (1998a) Cortical connections of the dorsomedial visual area in prosimian primates. Journal of Comparative Neurology, 398, 162–178. Beck, P.D. and Kaas, J.H. (1998b) Thalamic connections of the dorsomedial visual area in primates. Journal of Comparative Neurology, 396, 381–398. Behan, M., Jourdain, A. and Bray, G.M. (1992) Calcium binding protein (calbindin D28k) immunoreactivity in the hamster superior colliculus: ultrastructure and lack of co-localization with GABA. Experimental Brain Research, 89, 115–124. Bekisz, M. and Wrobel, A. (1999) Coupling of beta and gamma activity in corticothalamic system of cats attending to visual stimuli. NeuroReport, 10, 3589–3594. Bender, D.B. (1983) Visual activation of neurons in the primate pulvinar depends on cortex but not colliculus. Brain Research, 279, 258–261. Bickford, M.E. (1999) Growth-associated protein 43 is located in type I corticothalamic terminals in the cat visual thalamus. Journal of Neuroscience (Online), 19, RC22. Blasdel, G.G. and Lund, J.S. (1983) Termination of afferent axons in macaque striate cortex. Journal of Neuroscience, 3, 1389–1413. Blasdel, G.G. and Fitzpatrick, D. (1984) Physiological organization of layer 4 in macaque striate cortex. Journal of Neuroscience, 4, 880–895. Blomqvist, A., Zhang, E.T. and Craig, A.D. (2000) Cytoarchitectonic and immunohistochemical characterization of a specific pain and temperature relay, the posterior portion of the ventral medial nucleus, in the human thalamus. Brain, 123, 601–619. Blumenfeld, H. and McCormick, D.A. (2000) Corticothalamic inputs control the pattern of activity generated in thalamocortical networks. Journal of Neuroscience, 20, 5153–5162. Casagrande, V.A. (1994) A third parallel visual pathway to primate area V1. Trends in Neuroscience, 17, 305–310. Castelo-Branco, M., Neuenschwander, S. and Singer, W. (1998) Synchronization of visual responses between the cortex, lateral geniculate nucleus, and retina in the anesthetized cat. Journal of Neuroscience, 18, 6395–6410. Cola, M.G., Gray, D.N., Seltzer, B. and Cusick, C.G. (1999) Human thalamus: neurochemical mapping of inferior pulvinar complex. NeuroReport, 10, 3733–3738. Cola, M.G., Preuss, T.M., Seltzer, B. and Cusick, C.G. (2000) Neurochemical organization of chimpanzee inferior pulvinar complex. Society for Neuroscience Abstracts, 26, 1203. Cusick, C.G., Steindler, D.A. and Kaas, J.H. (1985) Corticocortical and collateral thalamocortical connections of postcentral somatosensory cortical areas in squirrel monkeys: a double-labeling study with radiolabeled wheatgerm agglutinin and wheatgerm agglutinin conjugated to horseradish peroxidase. Somatosensory Research, 3, 1–31. Cusick, C.G. and Gould, H.J.3d. (1990) Connections between area 3b of the somatosensory cortex and subdivisions of the ventroposterior nuclear complex and the anterior pulvinar nucleus in squirrel monkeys. Journal of Comparative Neurology, 292, 83–102.
© 2002 Taylor & Francis
174
Catherine G. Cusick
Cusick, C.G., Scripter, J.L., Darensbourg, J.G. and Weber, J.T. (1993) Chemoarchitectonic subdivisions of the visual pulvinar in monkeys and their connectional relations with the middle temporal and rostral dorsolateral visual areas, MT and DLr. Journal of Comparative Neurology, 336, 1–30. Darian-Smith, C., Tan, A. and Edwards, S. (1999) Comparing thalamocortical and corticothalamic microstructure and spatial reciprocity in the macaque ventral posterolateral nucleus (VPLc) and medial pulvinar. Journal of Comparative Neurology, 410, 211–234. Diamond, I.T. (1976) Organization of the visual cortex: comparative anatomical and behavioral studies. Federation Proceedings, 35, 60–67. Dykes, R.W., Sur, M., Merzenich, M.M., Kaas, J.H. and Nelson, R.J. (1981) Regional segregation of neurons responding to quickly adapting, slowly adapting, deep and Pacinian receptors within thalamic ventroposterior lateral and ventroposterior inferior nuclei in the squirrel monkey (Saimiri sciureus). Neuroscience, 6, 1687–1692. Erwin, E., Baker, F.H., Busen, W.F. and Malpeli, J.G. (1999) Relationship between laminar topology and retinotopy in the rhesus lateral geniculate nucleus: results from a functional atlas. Journal of Comparative Neurology, 407, 92–102. Feig, S. and Harting, J.K. (1998) Corticocortical communication via the thalamus: ultrastructural studies of corticothalamic projections from area 17 to the lateral posterior nucleus of the cat and inferior pulvinar nucleus of the owl monkey. Journal of Comparative Neurology, 395, 281–295. Felleman, D.J. and Van Essen, D.C. (1991) Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1–47. Ferrera, V.P., Nealey, T.A. and Maunsell, J.H. (1992) Mixed parvocellular and magnocellular geniculate signals in visual area V4. Nature (London), 358, 756–761. Fortin, M., Asselin, M.C., Gould, P.V. and Parent, A. (1998) Calretinin-immunoreactive neurons in the human thalamus. Neuroscience, 84, 537–548. Friedman, D.P. and Murray, E.A. (1986) Thalamic connectivity of the second somatosensory area and neighboring somatosensory fields of the lateral sulcus of the macaque. Journal of Comparative Neurology, 252, 348–373. Frien, A., Eckhorn, R., Bauer, R., Woelbern, T. and Kehr, H. (1994) Stimulus-specific fast oscillations at zero phase between visual areas V1 and V2 of awake monkey. NeuroReport, 5, 2273–2277. Frien, A. and Eckhorn, R. (2000) Functional coupling shows stronger stimulus dependency for fast oscillations than for low-frequency components in striate cortex of awake monkey. European Journal of Neuroscience, 12, 1466–1478. Garey, L.J., Jones, E.G. and Powell, T.P. (1968) Interrelationships of striate and extrastriate cortex with the primary relay sites of the visual pathway. Journal of Neurology, Neurosurgery & Psychiatry, 31, 135–157. Garey, L.J. and Powell, T.P. (1971) An experimental study of the termination of the lateral geniculo-cortical pathway in the cat and monkey. Proceedings of the Royal Society of London—Series B: Biological Sciences, 179, 41–63. Ghose, G.M. and Freeman, R.D. (1997) Intracortical connections are not required for oscillatory activity in the visual cortex [corrected and republished in Vis Neurosci 1997 Nov–Dec;14(6):963R-979R]. Visual Neuroscience, 14, 963–979. Gray, D., Gutierrez, C. and Cusick, C.G. (1999) Neurochemical organization of inferior pulvinar complex in squirrel monkeys and macaques revealed by acetylcholinesterase histochemistry, calbindin and Cat-301 immunostaining, and Wisteria floribunda agglutinin binding. Journal of Comparative Neurology, 409, 452–468. Grieve, K.L., Acuña, C. and Cudeiro, J. (2000) The primate pulvinar nuclei: vision and action. Trends in Neuroscience, 23, 35–39. Guillery, R.W. and Colonnier, M. (1970) Synaptic patterns in the dorsal lateral geniculate nucleus of the monkey. Zeitschrift Für Zellforschung und Mikroskopische Anatomie, 103, 90–108. Guillery, R.W. (1995) Anatomical evidence concerning the role of the thalamus in corticocortical communication: a brief review. Journal of Anatomy, 187, 583–592. Gutierrez, C. and Cusick, C.G. (1994) Effects of chronic monocular enucleation on calcium binding proteins calbindin-D28k and parvalbumin in the lateral geniculate nucleus of adult rhesus monkeys. Brain Research, 651, 300–310. Gutierrez, C., Yaun, A. and Cusick, C.G. (1995) Neurochemical subdivisions of the inferior pulvinar in macaque monkeys. Journal of Comparative Neurology, 363, 545–562. Gutierrez, C. and Cusick, C.G. (1997) Area V1 in macaque monkeys projects to multiple histochemically defined subdivisions of the inferior pulvinar complex. Brain Research, 765, 349–356. Gutierrez, C., Cola, M.G., Seltzer, B. and Cusick, C. (2000) Neurochemical and connectional organization of the dorsal pulvinar complex in monkeys. Journal of Comparative Neurology, 419, 61–86. Hackett, T.A., Stepniewska, I. and Kaas, J.H. (1998) Thalamocortical connections of the parabelt auditory cortex in macaque monkeys. Journal of Comparative Neurology, 400, 271–286.
© 2002 Taylor & Francis
Thalamic Systems and Cortical Areas
175
Harting, J.K., Casagrande, V.A. and Weber, J.T. (1978) The projection of the primate superior colliculus upon the dorsal lateral geniculate nucleus: autoradiographic demonstration of interlaminar distribution of tectogeniculate axons. Brain Research, 150, 593–599. Hashikawa, T., Rausell, E., Molinari, M. and Jones, E.G. (1991) Parvalbumin- and calbindin-containing neurons in the monkey medial geniculate complex: differential distribution and cortical layer specific projections. Brain Research, 544, 335–341. Hashikawa, T., Molinari, M., Rausell, E. and Jones, E.G. (1995) Patchy and laminar terminations of medial geniculate axons in monkey auditory cortex. Journal of Comparative Neurology, 362, 195–208. Heath, C.J. and Jones, E.G. (1970) Connexions of area 19 and the lateral suprasylvian area of the visual cortex of the cat. Brain Research, 19, 302–305. Hendry, S.H., Hockfield, S., Jones, E.G. and McKay, R. (1984) Monoclonal antibody that identifies subsets of neurones in the central visual system of monkey and cat. Nature (London), 307, 267–269. Hendry, S.H. and Yoshioka, T. (1994) A neurochemically distinct third channel in the macaque dorsal lateral geniculate nucleus. Science (Washington), 264, 575–577. Hendry, S.H. and Reid, R.C. (2000) The koniocellular pathway in primate vision. Annual Review of Neuroscience, 23, 127–153. Herkenham, M. (1980) Laminar organization of thalamic projections to the rat neocortex. Science (Washington), 207, 532–535. Hirai, T. and Jones, E.G. (1989) A new parcellation of the human thalamus on the basis of histochemical staining. Brain Research—Brain Research Reviews, 14, 1–34. Hirai, T. and Jones, E.G. (1993) Comparative anatomical study of ventrolateral thalamic mass in humans and monkeys. Stereotactic & Functional Neurosurgery, 60, 6–16. Hockfield, S., McKay, R.D., Hendry, S.H. and Jones, E.G. (1983) A surface antigen that identifies ocular dominance columns in the visual cortex and laminar features of the lateral geniculate nucleus. Cold Spring Harbor Symposia on Quantitative Biology, 48, 877–889. Hu, B., Steriade, M. and Deschenes, M. (1989a) The cellular mechanism of thalamic pontogeniculo-occipital waves. Neuroscience, 31, 25–35. Hu, B., Steriade, M. and Deschenes, M. (1989b) The effects of brainstem peribrachial stimulation on neurons of the lateral geniculate nucleus. Neuroscience, 31, 13–24. Huffman, K.J. and Krubitzer, L.A. (2001) Thalamo-cortical connections of areas 3a and M1 in marmoset monkeys. Journal of Comparative Neurology, 435, 291–310. Jain, N., Catania, K.C. and Kaas, J.H. (1998) A histologically visible representation of the fingers and palm in primate area 3b and its immutability following long-term deafferentations. Cerebral Cortex, 8, 227–236. Jones, E.G. (1985) The Thalamus. New York: Plenum Press. Jones, E.G., Hendry, S.H. and Brandon, C. (1986) Cytochrome oxidase staining reveals functional organization of monkey somatosensory thalamus. Experimental Brain Research, 62, 438–442. Jones, E.G. (1998a) A new view of specific and nonspecific thalamocortical connections. Advances in Neurology, 77, 49–71; discussion 72–43. Jones, E.G. (1998b) Viewpoint: the core and matrix of thalamic organization. Neuroscience, 85, 331–345. Kaas, J.H., Huerta, M.F., Weber, J.T. and Harting, J.K. (1978) Patterns of retinal terminations and laminar organization of the lateral geniculate nucleus of primates. Journal of Comparative Neurology, 182, 517–553. Kaas, J.H., Nelson, R.J., Sur, M., Dykes, R.W. and Merzenich, M.M. (1984) The somatotopic organization of the ventroposterior thalamus of the squirrel monkey, Saimiri sciureus. Journal of Comparative Neurology, 226, 111–140. Kaas, J.H. (1990) How sensory cortex is subdivided in mammals: implications for studies of prefrontal cortex. In: H.B.M. Uylings, C.G. Van Eden, J.P.C. De Bruin, M.A. Corner and M.G.P. Feenstra (eds), The Prefrontal cortex: its structure, function and pathology (Progress in Brain Research, Volume 85), Elsevier Science, Amsterdam, pp. 3–10; (also discussion pp. 10–11). Kaas, J.H. and Garraghty, P.E. (1991) Hierarchical, parallel, and serial arrangements of sensory cortical areas: connection patterns and functional aspects. Current Opinion in Neurobiology, 1, 248–251. Kaas, J.H. and Hackett, T.A. (1998) Subdivisions of auditory cortex and levels of processing in primates. Audiology & Neuro-Otology, 3, 73–85. Kaske, A., Dick, A. and Creutzfeldt, O.D. (1991) The local domain for divergence of subcortical afferents to the striate and extrastriate visual cortex in the common marmoset (Callithrix jacchus): a multiple labelling study. Experimental Brain Research, 84, 254–265. Kennedy, H. and Bullier, J. (1985) A double-labeling investigation of the afferent connectivity to cortical areas V1 and V2 of the macaque monkey. Journal of Neuroscience, 5, 2815–2830. Krubitzer, L.A. and Kaas, J.H. (1992) The somatosensory thalamus of monkeys: cortical connections and a redefinition of nuclei in marmosets. Journal of Comparative Neurology, 319, 123–140. Krubitzer, L. (1995) The organization of neocortex in mammals: are species differences really so different? Trends in Neuroscience, 18, 408–417.
© 2002 Taylor & Francis
176
Catherine G. Cusick
Lachica, E.A. and Casagrande, V.A. (1992) Direct W-like geniculate projections to the cytochrome oxidase (CO) blobs in primate visual cortex: axon morphology. Journal of Comparative Neurology, 319, 141–158. Leuba, G. and Saini, K. (1997) Colocalization of parvalbumin, calretinin and calbindin D-28k in human cortical and subcortical visual structures. Journal of Chemical Neuroanatomy, 13, 41–52. Lin, C.S. and Kaas, J.H. (1979) The inferior pulvinar complex in owl monkeys: architectonic subdivisions and patterns of input from the superior colliculus and subdivisions of visual cortex. Journal of Comparative Neurology, 187, 655–678. Lund, J.S., Lund, R.D., Hendrickson, A.E., Bunt, A.H. and Fuchs, A.F. (1975) The origin of efferent pathways from the primary visual cortex, area 17, of the macaque monkey as shown by retrograde transport of horseradish peroxidase. Journal of Comparative Neurology, 164, 287–303. Lysakowski, A., Standage, G.P. and Benevento, L.A. (1986) Histochemical and architectonic differentiation of zones of pretectal and collicular inputs to the pulvinar and dorsal lateral geniculate nuclei in the macaque. Journal of Comparative Neurology, 250, 431–448. Lysakowski, A., Standage, G.P. and Benevento, L.A. (1988) An investigation of collateral projections of the dorsal lateral geniculate nucleus and other subcortical structures to cortical areas V1 and V4 in the macaque monkey: a double label retrograde tracer study. Experimental Brain Research, 69, 651–661. Malpeli, J.G. and Baker, F.H. (1975) The representation of the visual field in the lateral geniculate nucleus of Macaca mulatta. Journal of Comparative Neurology, 161, 569–594. Malpeli, J.G., Lee, D. and Baker, F.H. (1996) Laminar and retinotopic organization of the macaque lateral geniculate nucleus: magnocellular and parvocellular magnification functions. Journal of Comparative Neurology, 375, 363–377. Mason, C.A., Guillery, R.W. and Rosner, M.C. (1984) Patterns of synaptic contact upon individually labeled large cells of the dorsal lateral geniculate nucleus of the cat. Neuroscience, 11, 319–329. Maunsell, J.H., Nealey, T.A. and DePriest, D.D. (1990) Magnocellular and parvocellular contributions to responses in the middle temporal visual area (MT) of the macaque monkey. Journal of Neuroscience, 10, 3323–3334. McCormick, D.A. and Huguenard, J.R. (1992) A model of the electrophysiological properties of thalamocortical relay neurons. Journal of Neurophysiology, 68, 1384–1400. McDonald, C.T., McGuinness, E.R. and Allman, J.M. (1993) Laminar organization of acetylcholinesterase and cytochrome oxidase in the lateral geniculate nucleus of prosimians. Neuroscience, 54, 1091–1101. Milner, A.D. and Goodale, M.A. (1993) Visual pathways to perception and action. Progress in Brain Research, 95, 17–337. Molinari, M., Dell’Anna, M.E., Rausell, E., Leggio, M.G., Hashikawa, T. and Jones, E.G. (1995) Auditory thalamocortical pathways defined in monkeys by calcium-binding protein immunoreactivity. Journal of Comparative Neurology, 362, 171–194. Molotchnikoff, S. and Shumikhina, S. (1996) The lateral posterior-pulvinar complex modulation of stimulusdependent oscillations in the cat visual cortex. Vision Research, 36, 2037–2046. Morel, A., Magnin, M. and Jeanmonod, D. (1997) Multiarchitectonic and stereotactic atlas of the human thalamus [published erratum appears in J Comp Neurol 1998 Feb 22;391(4):545]. Journal of Comparative Neurology, 387, 588–630. Mufson, E.J. and Mesulam, M.M. (1984) Thalamic connections of the insula in the rhesus monkey and comments on the paralimbic connectivity of the medial pulvinar nucleus. Journal of Comparative Neurology, 227, 109–120. Munkle, M.C., Waldvogel, H.J. and Faull, R.L. (1999) Calcium-binding protein immunoreactivity delineates the intralaminar nuclei of the thalamus in the human brain. Neuroscience, 90, 485–491. Munkle, M.C., Waldvogel, H.J. and Faull, R.L. (2000) The distribution of calbindin, calretinin and parvalbumin immunoreactivity in the human thalamus. Journal of Chemical Neuroanatomy, 19, 155–173. Nowak, L.G., Munk, M.H., James, A.C., Girard, P. and Bullier, J. (1999) Cross-correlation study of the temporal interactions between areas V1 and V2 of the macaque monkey. Journal of Neurophysiology, 81, 1057–1074. Nunez, A., Amzica, F. and Steriade, M. (1992) Intrinsic and synaptically generated delta (1–4 Hz) rhythms in dorsal lateral geniculate neurons and their modulation by light-induced fast (30–70 Hz) events. Neuroscience, 51, 269–284. Pedroarena, C. and Llinas, R. (1997) Dendritic calcium conductances generate high-frequency oscillation in thalamocortical neurons. Proceedings of the National Academy of Sciences, U.S.A., 94, 724–728. Pons, T.P. and Kaas, J.H. (1985) Connections of area 2 of somatosensory cortex with the anterior pulvinar and subdivisions of the ventroposterior complex in macaque monkeys. Journal of Comparative Neurology, 240, 16–36. Preuss, T.M., Gray, D. and Cusick, C.G. (1998) Subdivisions of the motor and somatosensory thalamus of primates revealed with Wisteria floribunda agglutinin histochemistry. Somatosensory & Motor Research, 15, 211–219.
© 2002 Taylor & Francis
Thalamic Systems and Cortical Areas
177
Ralston III, H.J. and Ralston, D.D. (1994) Medial lemniscal and spinal projections to the macaque thalamus: an electron microscopic study of differing GABAergic circuitry serving thalamic somatosensory mechanisms. Journal of Neuroscience, 14, 2485–2502. Ramcharan, E.J., Gnadt, J.W. and Sherman, S.M. (2000) Burst and tonic firing in thalamic cells of unanesthetized, behaving monkeys. Visual Neuroscience, 17, 55–62. Rausell, E. and Jones, E.G. (1991) Chemically distinct compartments of the thalamic VPM nucleus in monkeys relay principal and spinal trigeminal pathways to different layers of the somatosensory cortex. Journal of Neuroscience, 11, 226–237. Rausell, E., Bae, C.S., Viñuela, A., Huntley, G.W. and Jones, E.G. (1992a) Calbindin and parvalbumin cells in monkey VPL thalamic nucleus: distribution, laminar cortical projections, and relations to spinothalamic terminations. Journal of Neuroscience, 12, 4088–4111. Rausell, E., Cusick, C.G., Taub, E. and Jones, E.G. (1992b) Chronic deafferentation in monkeys differentially affects nociceptive and nonnociceptive pathways distinguished by specific calcium-binding proteins and down-regulates gamma-aminobutyric acid type A receptors at thalamic levels. Proceedings of the National Academy of Sciences, U.S.A., 89, 2571–2575. Robson, J.A. (1983) The morphology of corticofugal axons to the dorsal lateral geniculate nucleus in the cat. Journal of Comparative Neurology, 216, 89–103. Robson, J.A. (1984) Reconstructions of corticogeniculate axons in the cat. Journal of Comparative Neurology, 225, 193–200. Rockland, K.S. (1994) Further evidence for two types of corticopulvinar neurons. NeuroReport, 5, 1865–1868. Rockland, K.S. (1996) Two types of corticopulvinar terminations: round (type 2) and elongate (type 1). Journal of Comparative Neurology, 368, 57–87. Rockland, K.S. (1998) Convergence and branching patterns of round, type 2 corticopulvinar axons. Journal of Comparative Neurology, 390, 515–536. Rockland, K.S., Andresen, J., Cowie, R.J. and Robinson, D.L. (1999) Single axon analysis of pulvinocortical connections to several visual areas in the macaque. Journal of Comparative Neurology, 406, 221–250. Romanski, L.M., Giguere, M., Bates, J.F. and Goldman-Rakic, P.S. (1997) Topographic organization of medial pulvinar connections with the prefrontal cortex in the rhesus monkey. Journal of Comparative Neurology, 379, 313–332. Rosa, M.G. and Krubitzer, L.A. (1999) The evolution of visual cortex: where is V2? Trends in Neuroscience, 22, 242–248. Rose, J.E. and Woolsey, C.N. (1949) Organization of the mammalian thalamus and its relationships to the cerebral cortex. Electroencephalography and Clinical Neurophysiology, 1, 391–403. Sakai, S.T., Stepniewska, I., Qi, H.X. and Kaas, J.H. (2000) Pallidal and cerebellar afferents to pre-supplementary motor area thalamocortical neurons in the owl monkey: a multiple labeling study. Journal of Comparative Neurology, 417, 164–180. Schiller, P.H. and Malpeli, J.G. (1978) Functional specificity of lateral geniculate nucleus laminae of the rhesus monkey. Journal of Neurophysiology, 41, 788–797. Seltzer, B., Cola, M.G., Gutierrez, C., Massee, M., Weldon, C. and Cusick, C.G. (1996) Overlapping and nonoverlapping cortical projections to cortex of the superior temporal sulcus in the rhesus monkey: double anterograde tracer studies. Journal of Comparative Neurology, 370, 173–190. Shatz, C.J. and Rakic, P. (1981) The genesis of efferent connections from the visual cortex of the fetal rhesus monkey. Journal of Comparative Neurology, 196, 287–307. Sherman, S.M., Wilson, J.R., Kaas, J.H. and Webb, S.V. (1976) X- and Y-cells in the dorsal lateral geniculate nucleus of the owl monkey (Aotus trivirgatus). Science (Washington), 192, 475–477. Sherman, S.M. and Guillery, R.W. (1998) On the actions that one nerve cell can have on another: distinguishing “drivers” from “modulators”. Proceedings of the National Academy of Science, U.S.A., 95, 7121–7126. Sherman, S.M. and Koch, C. (1998) Thalamus. In: G.M. Shepherd, (ed.), The Synaptic Organization of the Brain, 4th edition. Oxford University Press: New York, pp. 289–328. Stepniewska, I., Preuss, T.M. and Kaas, J.H. (1994a) Architectonic subdivisions of the motor thalamus of owl monkeys: Nissl, acetylcholinesterase and cytochrome oxidase patterns. Journal of Comparative Neurology, 349, 536–557. Stepniewska, I., Preuss, T.M. and Kaas, J.H. (1994b) Thalamic connections of the primary motor cortex (M1) of owl monkeys [published erratum appears in J Comp Neurol 1995 Feb 27;353(1):160]. Journal of Comparative Neurology, 349, 558–582.Stepniewska, I. and Kaas, J.H. (1997) Architectonic subdivisions of the inferior pulvinar in New World and Old World monkeys. Visual Neuroscience, 14, 1043–1060. Stepniewska, I., Qi, H.X. and Kaas, J.H. (1999) Do superior colliculus projection zones in the inferior pulvinar project to MT in primates? European Journal of Neuroscience, 11, 469–480. Tallon-Baudry, C., Bertrand, O., Delpuech, C. and Permier, J. (1997) Oscillatory gamma-band (30–70 Hz) activity induced by a visual search task in humans. Journal of Neuroscience, 17, 722–734.
© 2002 Taylor & Francis
178
Catherine G. Cusick
Tovee, M.J. and Rolls, E.T. (1992) Oscillatory activity is not evident in the primate temporal visual cortex with static stimuli. NeuroReport, 3, 369–372. Ungerleider, L.G. and Mishkin, M. (1982). Two cortical visual systems. In: D.J. Ingle, M.A. Goodale and R.J.W. Mansfield (eds), Analysis of Visual Behavior. Cambridge, Massachusetts: MIT Press, pp. 549–586. Van Essen, D.C. and Zeki, S.M. (1978) The topographic organization of rhesus monkey prestriate cortex. Journal of Physiology, 277, 193–226. Van Essen, D.C. and Gallant, J.L. (1994) Neural mechanisms of form and motion processing in the primate visual system. Neuron, 13, 1–10. Woods, T.M., Cusick, C.G., Pons, T.P., Taub, E. and Jones, E.G. (2000) Progressive transneuronal changes in the brainstem and thalamus after long-term dorsal rhizotomies in adult macaque monkeys. Journal of Neuroscience, 20, 3884–3899. Yeterian, E.H. and Pandya, D.N. (1989) Thalamic connections of the cortex of the superior temporal sulcus in the rhesus monkey. Journal of Comparative Neurology, 282, 80–97.
© 2002 Taylor & Francis
9 Cortical Areas and Patterns of Cortico-Cortical Connections Jon H. Kaas Department of Psychology, Vanderbilt University Correspondence to: Jon H. Kaas, Vanderbilt University, Department of Psychology, 111 21st Avenue South, 301 Wilson Hall, Nashville, TN 37240 Tel: 615-322-6029; FAX: 615-343-4342; e-mail:
[email protected]
Traditionally, cortical areas have been defined by differences in histological appearance, first by cytoarchitectonic features and then by myeloarchitectonic and chemoarchitectonic features. However, architectonic distinctions are often subtle, and interpretations of how the cortex is divided into areas by various investigators have varied. Other methods such as recording or evoking patterns of sensory and motor representation, evaluating response properties of neurones, and tracing patterns of connections have been used to delimit or help delimit cortical areas. Many researchers now recognize that the strengths of each method are additive, and that areas are best identified when results from several methods agree. However, relatively few areas have been established as valid by the congruence of conclusions based on different procedures, and the reality of many of the traditional areas, conceived as functionally distinct “organs of the brain”, is in doubt. Nevertheless, the corticocortical connections which have been demonstrated for some well-established areas suggest that many and perhaps all valid areas have unique, identifying patterns of connections. Even so, connection patterns such as those between the two cerebral hemispheres do not always reflect areal boundaries; and because connections can be distributed in modules within an area and can be similar in adjacent areas, identifying areas by connection patterns alone can be difficult. The relationship between architectonic characteristics and connection patterns is uncertain, but at least some of the architectonic features of areas may emerge as a result of their cortico-cortical connections. KEYWORDS: cytoarchitecture, development, neocortex, representation
1. INTRODUCTION The neocortex is a sheet of tissue that varies greatly in surface area, although only somewhat in thickness, across mammalian species (Kaas, 2000). Neurones throughout this variable sheet are similarly arranged in layers, and much of the processing is done within local circuits of neurones that are highly interconnected in vertical arrays across the thickness of cortex, wherever they are located. Thus, some aspects of neocortical structure and function are relatively uniform across this sheet. Nevertheless, we have known from the 1782 report of Gennari (see Gross, 1997) that this sheet is not completely uniform in structure, and from the time of Broca (1861) at least, that it is not completely uniform in function. Different regions of the neocortex have long been recognized as mediating different functions, and early neuroanatomists such as Brodmann (1909) laboured to identify and delimit such functional regions or “organs of the brain” by the few methods available at the time. 179 © 2002 Taylor & Francis
180
Jon H. Kaas
Brodmann, and others at that time, stained brain sections by the Nissl method for cell bodies and used regional differences in the detailed appearance of various sections of cortex to reveal the locations of functional subdivisions of the brain. Brodmann in particular studied the brains of many different species of mammals and developed elaborate and complete theories or proposals of how their cortex is subdivided into areas. In brief, Brodmann proposed that humans, with a large sheet of neocortex, have many areas, nearly 50, while small-brained mammals with little neocortex, such as hedgehogs, have fewer than 15. Even so, hedgehogs and other small-brained mammals shared some areas with humans, such as area 17 (or primary visual cortex), and other areas in such mammals were “composites”, such as 5 plus 7, that had differentiated into separate areas in humans and other large-brained mammals. Thus, Brodmann presented a broad, comprehensive theory of brain organization and evolution. According to this theory, the neocortex is divided into a number of areas that varies across mammalian taxa, thereby accounting at least partly for variations in behaviour and ability. Humans and other large-brain mammals have more areas, some areas are shared across species from a common ancestor, and some are new. New areas emerged by differentiating from old areas. The basic features of Brodmann’s proposal, as restated and simplified here, are those that most investigators would hold today. It is in the specifics of the proposal that contemporaries of Brodmann and investigators ever since have come to differ. What is at issue is not whether the cortex can be divided into areas, or if areas vary in number across species, but exactly where each area is and exactly how they vary in number. Identifying areas in such a reliable and compelling manner that nearly all investigators will agree, has proven to be an extremely difficult task. It is a task so widely recognized as difficult, that few today would attempt to replicate Brodmann’s effort to identify all the areas of neocortex, and to do so in a range of species. Such uncertainty about areas clearly complicates the present discussion of the relationship of cortico-cortical connections to cortical areas, but fortunately some cortical areas have been well defined, and some conclusions about the relationship of these areas to connection patterns are possible. First, we discuss the issue of defining cortical areas.
2. WHAT IS A CORTICAL AREA? The concept of the cortical area has been fundamental to the great progress that has been made in understanding how brains function. Instead of a large sheet of tissue with uniformly distributed functions and the equipotentiality of all sectors, the premise that neocortex is divided into a number of areas of differing functions is now universally accepted. We generally consider the cortical areas as “organs of the brain”, much as Brodmann (1909) did. While this concept has been widely accepted and has been extremely useful, there is little agreement about how to define cortical areas, their sizes and numbers, how they vary across species, emerge in development, and even if they always have sharp boundaries or sometimes gradually merge with each other. In a practical sense, we may have difficulties distinguishing areas from parts of areas, from the larger modules or columns, and from constellations of areas that function together. Architectonic methods for identifying areas are based on the longstanding premise that structure reflects function. If a cortical area is specialized for performing a function or set of functions, the functional distinctiveness should be reflected in the structural design, and
© 2002 Taylor & Francis
Cortical Areas and Cortico-Cortical Connections
181
thus in the histological appearance of the area. In principle, all cortical areas can be identified by differences in cytoarchitecture, and all of the areas distinguished by Brodmann could be valid. In practice, the delineation of cortical areas by cytoarchitecture alone has often proven to be unreliable. A given investigator may subdivide the cortex of the same brain in different ways at different times, and different investigators examining the same brain or brains of the same species come to various conclusions (see Lashley and Clark, 1946 for a classical critical review). Of course, we have benefited since the time of Brodmann by the introduction of a number of additional architectonic methods, including myeloarchitectonic and chemoarchitectonic procedures, as well as by the recent introduction of semiautomatic, quantitative methods of architectonic analysis (see Schleicher et al., 1999). These methods and procedures sometimes reveal additional boundaries that were not obvious before, and they greatly extend the usefulness of the architectonic approach, but they do not address the more basic issue of determining what the architectonic distinctions mean. Are they marking borders between areas, or borders within areas (such as between the monocular and binocular segments of the primary visual cortex) or are they artifacts produced by fissures, blood vessels, or tissue folding and processing? Architectonic differences are often not very impressive, even with newer methods, and we often do not know what they mean. However, we can address these problems by also using other methods to subdivide the cortex, and see if the conclusions agree, and obtain information about the meaning of architectonic boundaries. Without other sources of information, architectonic areas remain only hypothesized areas, and no more than that. One person’s proposal is as good as the next. Brodmann’s maps have survived over others in current textbooks, not because they have been shown to be valid, but because we have been slow to develop and verify other proposals. Perhaps we need the illusion that we understand how all parts of the neocortex are divided into areas and how this is done in many species. Only Brodmann’s maps provide this scope and range. However, more recent proposals (e.g. Felleman and Van Essen, 1991), although less extensive, are based on a range of methods, and in many respects they bear little resemblance to those of Brodmann and other early investigators. Even so, many of the newly proposed areas in these current theories are still based on too little evidence, and the proposals contain areas as questionable as many of the areas of Brodmann. Elsewhere, I have argued (e.g. Kaas, 1982, 1989, 1997), as have others (e.g. Van Essen, 1985), that cortical areas are most reliably defined by the congruence of several different types of evidence. For example, if a cortical area were to perform a distinct function or set of functions, it would seem necessary that its pattern of axonal inputs and outputs reflect those functions. Furthermore, if the computational procedures within two areas are very similar, their separate functions may depend more on differences in inputs and outputs rather than internal structure. In practice, connection patterns are commonly used to help define cortical areas. Reliable methods for revealing such connections have only emerged over the last 30 or so years, but many excellent methods are now available (for example, see Angelucci et al., 1996). In addition, the internal organizations of areas should reflect their functions. Sensory areas systematically represent sensory surfaces, and these representations can be revealed by recording from arrays of locations across areas. Adjoining areas are distinguished by having different patterns of representation. Motor areas can be similarly revealed by electrically stimulating many sites in cortex while monitoring the types of movements. Neurones within different areas should have different response properties, at least at the population level, and these can be revealed by single neurone
© 2002 Taylor & Francis
182
Jon H. Kaas
recordings or can be visualized with functional imaging methods. These methods not only help define areas, but suggest functions of areas that can be tested further by deactivating regions of cortex during behaviour, or by perturbing the functions of areas in other ways, such as by stimulating the cortex electrically or magnetically. Proposed cortical areas, and boundaries of areas, are most likely to be valid when conclusions based on a number of different approaches agree. Each method by itself has its own problems of interpretation, as well as its strengths. For instance, architectonic methods may best reveal the precise borders and full extent of areas, but they indicate little about the functional significance of borders. (Admittedly, stains for metabolic enzymes say something about sustained levels of neural activity, large pyramidal neurones suggest long projections, etc.). Thus, an architectonic border may be between functional parts of an area as well as between areas. Connections are often distributed in a patchy fashion, so that patches can be misinterpreted as projections to separate areas, especially as adjoining areas usually have similar connections (e.g. Young et al., 1995; Young, this volume). In addition, projections to adjacent areas can be interpreted as if they are to the same area. In studies of neurone response properties, it is difficult to collect a large enough sample of single neurone recordings from two or more adjacent areas to characterize their contrasting properties and identify a border between them. Functional imaging studies may confuse constellations of functionally-related areas with a single area, and maps of sensory and motor representations may include parts of adjoining areas with similar representations and matched borders. Defining cortical areas will be an even more difficult task if, as some suppose, borders are sometimes or often not sharp, and areas gradually merge with each other. Brodmann (1909) allowed for gradual borders, and “transition” zones between areas are sometimes described in current studies. As an additional complication, the broad goal is not only to define areas in a single species, but to identify those areas that are homologous, and those that are not, in different species. Without correct interpretations of homology, efforts to compare neocortex and cortical function across species lose much of their meaning. Yet, this task is extremely difficult given that homologous areas can vary in appearance, connections, neural properties and any other attribute (see Preuss and Kaas, 1999).
3. HOW DO ARCHITECTONIC AREAS RELATE TO PATTERNS OF CORTICAL CONNECTIONS? If cortical areas are most reliably identified by the agreement of different types of evidence, and two major sources of evidence are architectonic distinctiveness and connectional distinctiveness, then all correctly identified architectonic areas should be connectionally unique. This premise can best be evaluated by considering the connections of cortical areas that have been well defined. In the visual system of primates, all or nearly all investigators agree on the existence and architectonic boundaries of three visual areas (see Kaas, 1997), the classical area 17 or primary visual cortex (V1), the second visual area (V2), and the middle temporal visual area (MT). Area 17 is one of the most distinctive of all cortical areas, and almost any histological procedure will identify it. Nevertheless, misidentifications have occurred. For example, Brodmann mistook the less-developed medial monocular portion of area 17 for area 18 in squirrels, leading to the preservation of
© 2002 Taylor & Francis
Cortical Areas and Cortico-Cortical Connections
183
this error in the common portrayal of an “area 18” medial to area 17 in rats. However, the boundaries of area 17 of most mammals are not in question today. The second visual area, V2, has been more difficult to delimit (see Allman and Kaas, 1974). Its inner border with area 17, ofcourse, has been obvious, but its outer border has not. Brodmann’s (1909) area 18 in New World marmosets closely corresponds in depicted width with present-day estimates of V2, and it is tempting to refer to V2 as area 18, as many investigators do. However, Brodmann’s area 18 in Old World monkeys was more than twice as wide as present estimates of V2, so it is obvious that Brodmann did not consistently define the same region of cortex as area 18, even in primates. Today, the architectonic field corresponding to V2 in monkeys is rather easily identified by its conspicuous banding pattern. This is best seen in “surface views” of V2 in sections cut parallel to the surface of flattened cortex and processed for cytochrome oxidase or myelin (e.g. Livingstone and Hubel, 1982; Tootell et al., 1985; Krubitzer and Kaas, 1989). V2 has alternating dark and light bands across the width of the area in both preparations, and the full extent of V2 is obvious. This architectonic definition of V2 might be termed the “redefined area 18”, since V2 is commonly referred to as area 18. This V2 also can be distinguished by cytoarchitecture (see Allman and Kaas, 1974), but the distinctions in Nissl-stained brain sections are not obvious enough for reliable identification. The middle temporal visual area, MT, is a visual area first identified in New World monkeys, when microelectrode recordings revealed it as a systematic representation of the contralateral visual hemifield, that was co-extensive with a densely myelinated oval of cortex (Allman and Kaas, 1971). Again, cytoarchitectonic distinctions were also apparent, but they were too subtle to serve as a practical way of identifying MT. Subsequently, MT has been identified by its myeloarchitecture and other characteristics in a broad range of primate species, including humans (see Sereno, 1998). MT probably exists in all primates, but it has not been identified with certainty in any non-primate mammal (see Kaas, 1997). Thus, MT may be an area that emerged with the evolution of the first primates. Given the distinctiveness of MT in brain sections stained for myelin, it seems surprising that the area had not been delimited in earlier myeloarchitectonic studies of cortex. Brodmann and other early investigators proposed no area like MT in shape and location. The ease and reliability of identifying V1, V2, and MT by architectonic criteria, and their wide acceptance as valid visual areas, makes them ideal for addressing the question of how connection patterns relate to architectonic fields (although not necessarily the classical fields of Brodmann). As early as 1965 (Myers, 1965; Kuypers et al., 1965), lesion studies of connectivity using anterograde degeneration techniques revealed that V1 projects to the general region of cortex in the upper temporal lobe of macaque monkeys, that was later identified as MT (Allman and Kaas, 1971). The existence of these projections was further established in subsequent investigations, but it was not until the report of Spatz (1977) that the connections were specifically attributed to MT. Spatz (1977) demonstrated that MT is reciprocally and topographically interconnected with area 17. The relationship between V1 and MT in terms of connections is now well established (for review, see Weller and Kaas, 1983; Preuss et al., 1993). Injections of tracers in central V1 label central regions of MT, and those along the outer margin of V1 label locations along the outer margin of MT (Figure 9.1). Thus, the two representations are retinotopically matched in their overall interconnection patterns. However, projections to MT are patchy and more extensive than would conform to strictly-matched retinotopic locations, and the projections from MT back to V1 are also more broadly distributed than expected from
© 2002 Taylor & Francis
Jon H. Kaas
184
V2
MT 40˚ 20˚
V1 5˚ 5˚
LF HM
HM
20˚ LF
40˚ HM
HM
UF
UF
VM VM V2 HM Figure 9.1. The connection patterns of V1 and V2 with MT in primates. The primary visual area, V1 or area 17, the second visual area, V2, and the middle temporal visual area, MT, are visual areas that are architectonically distinct and easily delimited in primates. Both V1 and V2 interconnect with MT in topographic patterns that closely match the retinotopic organizations of the three areas. The clear relationship between connection patterns and these architectonically distinct fields suggests that most or all valid architectonic fields have their own identifying patterns of cortico-cortical connections. Dots represent injection sites of tracers in V1 and V2. Visual hemifield coordinates are indicated in V1, V2, and MT. The zero horizontal meridian (HM) bisects V1 and MT and forms the outer border of V2. The zero vertical meridian forms the outer border of V1 and MT.
precisely-matched retinotopic connections (Krubitzer and Kaas, 1990a). Nevertheless, the overall retinotopic pattern is clear. The inputs to MT respect MT boundaries, and conclusions about the location and extent of MT from connectional studies agree with those from architectonic studies. MT can also be defined by connections with other visual areas. Although not so extensively investigated, a topographic pattern of connections from V2 can be used to define and delimit MT (see Stepniewska and Kaas, 1996). Perhaps as an even more dramatic example of how connection patterns define cortical areas, injections in a visual area just ventral to MT (FSTD, the dorsal area of the fundus of the superior temporal sulcus) label large portions of MT in a patchy pattern, but none of the cortex immediately surrounding MT; injections placed just slightly more ventral in the sulcus (in FSTV, the ventral area of the fundus of the superior temporal sulcus) will label cortex in a ring-like area around MT (MTC, the so-called MT crescent) but not MT (Figure 9.2; Kaas and Morel, 1993). The specificity of these connection patterns, ofcourse, also helps to establish the two injected regions, FSTD and FSTV, as separate visual areas, and the MTC crescent-shaped ring or belt around MT as an additional visual area (see Kaas, 1997). Injections into another proposed visual area, the dorsomedial area (DM) also demonstrate interconnections that identify MT (Beck and Kaas, 1999; Krubitzer and Kaas, 1993). In summary, connection patterns with any and all of at least four visual areas (V1, V2, FSTD and DM) identify and delimit the same visual area, MT, as defined by myeloarchitecture (or the dense expression of cytochrome oxidase; Tootell et al., 1985). The connections with each of these areas reach all parts of MT, and stop or change in density and pattern sharply at the MT border. Thus, at least some long cortico-cortical connection patterns precisely and completely conform to the limits of an easily and reliably identified architectonic area. Subcortical connections do so as well. Most notably, a subdivision of
© 2002 Taylor & Francis
Cortical Areas and Cortico-Cortical Connections
185
MST MT
MTC
FSTD
FSTV
Figure 9.2. MT is also distinguished by its strong connections with adjoining visual area, FSTD, and its lack of connections with a slightly more distant visual area, FSTV. (These visual areas are named after their more ventral or more dorsal locations in the fundus of the superior temporal sulcus of macaque monkeys). MT is also distinguished by connections with the medial superior temporal area, MST. Gray circles represent injection sites of tracers in visual areas, while arrowheads mark axon terminations. The projections are broadly distributed, but they may be roughly topographic between areas. This possibility has not yet been adequately explored.
the inferior pulvinar complex is densely interconnected with MT but not with other subdivisions of visual cortex (Lin and Kaas, 1980; Stepniewska et al., 1999). Many other examples of connections conforming to architectonic boundaries exist, but evidence is often less obvious. In the visual cortex, the connection patterns between V2 and other visual areas (Stepniewska and Kaas, 1996) conform to V2 as now defined architectonically. In the somatosensory cortex of primates, the connections of area 3b with areas 3a and 1, as well as other subdivisions of somatosensory cortex, provide other examples of architectonic areas with distinct patterns of connections (e.g. Krubitzer and Kaas, 1990b). The results from these and other areas strongly suggest that any valid architectonic area conforms to a unique and systematic pattern of cortico-cortical connections.
4. DO ALL LONG CORTICO-CORTICAL CONNECTIONS RESPECT ARCHITECTONIC BOUNDARIES? There are instances when patterns of connections may appear to fail to reflect architectonic boundaries, where closer inspection shows that they do. Cortical areas often have connections that are quite similar to those of the areas adjoining them (Young et al., 1995;
© 2002 Taylor & Francis
Jon H. Kaas
186
Young, this volume). For example, area 3b of monkeys projects to both area 1 and area 2 (see Pons and Kaas, 1986). Since areas 1 and 2 adjoin each other, the two projection patterns may appear as one that fails to distinguish the two fields. However, the projection density to the two areas is quite different, with the terminations in area 2 being much less dense than terminations in area 1. In addition, if injections are placed in different locations across the width of area 3b, different topographic patterns of terminations in areas 1 and 2 are revealed. Thus, some of the connections of adjoining areas may be with the same fields, but they are likely to differ in density, topographic pattern, and even laminar distribution. In other instances, a lack of differences in connections for adjoining areas may suggest that the so-called areas are parts of a larger area. If connections of two or more adjoining areas are nearly identical, as the connections of areas 3b, 1 and 2 of somatosensory cortex of cats seem to be (see Scannell et al., 1995), then one might question the validity of the claim for separate fields. Microelectrode mapping data and architectonic results suggest that all three proposed fields, 3b, 1 and 2, of somatosensory cortex of cats (Hassler and Muhs-Clement, 1964) actually constitute a single field, S1 (Felleman et al., 1983). This S1 of cats appears to be homologous with area 3b of primates, and all of this larger field in cats deserves to be called area 3b (Kaas, 1983). All parts of S1 in cats are similar in architectonic appearance. As a single field, S1 should have a single pattern of connections with other fields, and the near identity of connections of “3b”, “1” and “2” of cats suggest that they are parts of a single field. However, in at least some instances, cortical connection patterns do not reflect architectonic borders of valid areas very well. The best example may be the callosal connections of area 17 in primates (Figure 9.3; see Cusick et al., 1984; Cusick and Kaas, 1986; Kaas, 1995). Callosal connections may be of several types (Figure 9.4), and they may be of mixed types for the same area. Area 17 of Old World monkeys is almost devoid of Callosal connections Monkeys V2
Prosimian Galagos V1
V2
V1
Figure 9.3. Callosal connections of the V1/V2 border region of primates. The connections are sharply confined to the border region of V1 and larger extents of V2 in monkeys, but they extend well into V1 of galagos. Thus, these connections do not always sharply mark the border of V1 with V2. However, connections are more dense along the border.
© 2002 Taylor & Francis
Cortical Areas and Cortico-Cortical Connections
187
1. Homotopic A
A' B
B' C'
C
2. Heterotopic but homoareal A
D
B
E F
C
3. Heteroareal, but either homotopic or heterotopic
A' B'
A B
Figure 9.4. Callosal connections are of several types, and individual areas may have more than one type. Connections between representations of visual hemifields in the two cerebral hemispheres may match precisely along the two borders corresponding to the same zero vertical meridian (1. Homotopic), or neurones within one representation may project to the border of the other (2. Heterotopic). One area may project in both patterns to another area in the opposite hemisphere (3. Heteroareal). (See Kaas, 1995).
interhemispheric connections, with only a few projection cells and terminations along the fringe of the outer border. In contrast, callosal connections are densely distributed along the inner border of V2 and well into V2 (although not fully across V2 or even uniformly within the inner half). In New World monkeys, callosal connections are also dense in V2, but they barely invade V1. In Old World monkeys, the callosal connections of V1 appear to be even more restricted to the border. In prosimian galagos and probably other prosimian primates, however, the distribution of callosal connections in V2 does not stop abruptly at the V1 border, but instead continues well into V1 where it is patchy and it gradually drops off in density. Thus, if the callosal connection pattern was used as the only criterion for subdividing cortex into areas, and the compelling architectonic evidence ignored, then one would place an approximate V1/V2 border somewhere within V1 of galagos, and wrongly conclude that the border is not sharp, but rather constitutes a broad transition zone. There are probably other instances where all parts a valid architectonic field are not connected in the same way with other regions of cortex. The portions of V1 and other visual areas that represent central vision may connect with more areas than the portions representing peripheral vision (see Casagrande and Kaas, 1994). Areas are often divided into sets of modules with different connections, and studies of connections may involve one set more than the other. The blob and non-blob regions of V1 in monkeys, for
© 2002 Taylor & Francis
188
Jon H. Kaas
example, have different connections with extrastriate cortex (see Casagrande and Kaas, 1994). A single focused injection in any cortical area typically labels a patchy distribution of terminations and projection neurones in a number of other areas, and separate patches of connections may be mistaken for separate visual areas. Nevertheless, the overwhelming conclusion should be that studies of connection patterns provide a powerful way of identifying cortical areas. When connectional and architectonic approaches identify the same subdivisions of cortex, then the evidence that the proposed cortical area is a valid area is quite compelling. Such evidence is further strengthened when results from microelectrode recordings and maps, images of brain activity patterns, and ablation-behaviour studies produce congruent results.
5. ARE ARCHITECTONIC DIFFERENCES PRODUCED BY CONNECTIONAL DIFFERENCES? Little is really known of how architectonic distinctions between fields are created. One possibility is that the connection patterns develop as the result of some sort of competitive process, and that differences in connections produce different activity patterns, gene expression, and ultimately histological structures (see O’Leary, 1989; Killackey, 1990). This is obviously true in at least trivial ways. The large Meynert cells that characterize V1 of monkeys are those that conduct impulses rapidly over long distances to MT and subcortical structures. If the target fields of such neurones are ablated, these neurones would degenerate or would not develop to such conspicuous sizes (see Cowan et al., 1984). If a major activating input into a cortical area is removed, less of the metabolic enzymes such as cytochrome oxidase may be expressed (Wong-Riley, 1994), and less of the inhibitory neurotransmitter, GABA, and receptors for that transmitter, may be present (see Jones, 1993). Thus, the cyto- and chemoarchitecture of areas may be altered during and after development by changes in their connections. Some have emphasized how little most of neocortex varies in architectonic appearance from one region to another (e.g. Zeki, 1978). The argument can be made that the local circuit computations made by groups of neurones at any location in the cortex is much the same, and that the only important difference is that each area has its unique pattern of inputs and outputs. According to this argument, areas look much alike because the neurones within them are doing basically the same things, but with different inputs and outputs and with different consequences. The variable functions of areas of cortex thus stem from differences in connections rather than from other morphological specializations. Again, this does not seem to be completely the case, if only because some areas such as V1 are so remarkably distinct in their internal structure. However, when architectonic differences between fields are minor and hard to demonstrate, they may have been created in development largely or completely by connectional differences. Furthermore, if adjoining fields are performing nearly identical transformations, but on different inputs, there may be few or no significant architectonic distinctions between the fields. Despite this, it seems extremely likely that at least some cortical areas are differentiated by the uneven distribution of chemical markers before connections form. Chemical gradients have been demonstrated in the developing neocortex well before thalamo-cortical and cortico-cortical connections form (Rubenstein et al., 1998), and in mutant mice that fail to develop thalamo-cortical connections (Nakagawa et al., 1999; Miyashita-Lin et al., 1999).
© 2002 Taylor & Francis
Cortical Areas and Cortico-Cortical Connections
189
Through thresholding mechanisms these gradients could delimit at least a few primary areas, such as V1. Although many of the architectonic features of V1 may depend on the presence of normal inputs and other connections (see Rakic, 1991), some of the identifying features of the area may emerge independently. Other cortical areas, according to this scenario, might emerge as chemically specified primary areas form projections that compete for cortical space. Thus, a few basic cortical areas might differentiate in part as a consequence of early positional effects, and other areas may differentiate later as a consequence of differences in connectivity. Another possibility is that all cortical areas are created by positional effects on gene expression, and that at least some distinctive architectonic features emerge in each of these areas as a result of differences in gene expression. Usually, this possibility is related to the concept of a “protomap” of cortex in which cortical areas are predetermined even before cortex develops within a spatially differentiated sheet of germinal cells (Rakic, 1988). Cells for each area in the protomap would be labeled in some way so that after they migrate to cortex, they would attract proper connections and contribute to the unique structure of their area. While such a possibility has its attractive elements, it would seem improbable in complex brains, such as the human brain with well over 50 areas, that all such areas depend on positional effects on gene expression in the germinal layer that gives rise to cortex. In conclusion, cortical areas are distinguished by differences in connection patterns, and connection differences have at least some structural and histological consequences. If connections are experimentally altered early in development, cortical architecture may not develop in a normal manner. This suggests that at least for those areas that are not very distinct in architecture, many or all of those features that are distinct emerge as a consequence of their connection patterns and the impact of those connections on neural activity during development. Nevertheless, many or most of the architectonic features of cortical areas, especially for the most distinct primary areas, may depend on positional effects on the regional development of cortex or on the regional distribution of germinal cells that generate cortex. More experimental studies of the development of cortical areas are needed.
ACKNOWLEDGEMENTS This review was prepared while the author was a Fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford University. Financial support at the Center was provided by the John D. and Catherine T. MacArthur Foundation. Helpful comments were provided by Todd Preuss.
REFERENCES Allman, J.M. and Kaas, J.H. (1971) A representation of the visual field in the caudal third of the middle temporal gyrus of the owl monkey (Aotus trivirgatus). Brain Research, 31, 85–105. Allman, J.M. and Kaas, J.H. (1974) The organization of the second visual area (V-II) in the owl monkey: A second-order transformation of the visual hemifield. Brain Research, 76, 247–265. Angelucci, A., Clascá, F. and Sur, M. (1996) Anterograde axonal tracing with subunit B of cholera toxin, a highly sensitive immunohistochemical protocol for revealing fine axonal morphology in adult and neonatal brains. Journal of Neuroscience Methods, 65, 101–102.
© 2002 Taylor & Francis
190
Jon H. Kaas
Beck, P.D. and Kaas, J.H. (1999) Cortical connections of the dorsomedial visual area in Old World macaque monkeys. Journal of Comparative Neurology, 407, 487–502. Broca, P. (1861) Remarks on the rest of the faculty of articulate language. In: G. von Bonin, trans. (1960), Some papers on the cerebral cortex. C.C. Thomas: Springfield, IL, pp. 99–72. Brodmann, K. (1909) Vergleichende Lokalisationslehre der Grosshirnrinde. Leipzig: Barth. Casagrande, V.A. and Kaas, J.H. (1994) The afferent, intrinsic, and efferent connections of primary visual cortex in primates. In: A. Peters and K. Rockland (eds), Cerebral Cortex, Vol. 10, Primary Visual Cortex in Primates. New York: Plenum Press, pp. 201–259. Cowan, W.M., Fawatt, J.W., O’Leary, D.D.M. and Stanfield, B.B. (1984) Regressive events in neurogenesis. Science (Washington), 225, 1258–1265. Cusick, C.G., Gould, H.J. and Kaas, J.H. (1984) Interhemispheric connections of visual cortex of owl monkeys (Aotus trivirgatus), marmosets (Callithrix jacchus), and galagos (Galago crassicaudatus). Journal of Comparative Neurology, 230, 311–336. Cusick, C.G. and Kaas, J.H. (1986) Interhemispheric connections of cortical sensory and motor maps in primates, In: F. Lepore, M. Dtito and H.H. Jasper (eds), Two Hemispheres—One Brain, New York: Alan R. Liss, Inc., pp. 83–102. Felleman, D.J., Wall, J.T., Cusick, C.G. and Kaas, J.H. (1983) The representation of the body surface in S1 of cats. Journal of Neuroscience, 3, 1648–1669. Felleman, D.J. and Van Essen, D.C. (1991) Distributed hierarchical processing in primate cerebral cortex. Cerebral Cortex, 1, 1–47. Gross, C.G. (1997) From Imhotep to Hubel and Wiesel: the story of visual cortex. In: K.S. Rockland, J.H. Kaas and A. Peters (eds), Cerebral Cortex, Volume 12, Extrastriate Cortex in Primates. New York: Plenum Press, pp. 1–58. Hassler, R. and Muhs-Clement, K. (1964) Architectonischer aufbau des sensorimotorischen and parietal en cortex der katze. Journal für Hirnforschung, 6, 377–420. Jones, E.G. (1993) GABAergic neurons and their role in cortical plasticity in primates. Cerebral Cortex, 3, 361–372. Kaas, J.H. (1982) The segregation of function in the nervous system: Why do the sensory systems have so many subdivisions? Contributions to Sensory Physiology, 7, 202–240. Kaas, J.H. (1983) What, if anything, is S-1? The organization of the “first somatosensory area” of cortex. Physiological Reviews, 63, 206–231. Kaas, J.H. (1989) Why does the brain have so many visual areas? Journal of Cognitive Neuroscience, 1, 121–135. Kaas, J.H. and Morel, A. (1993) Connections of visual areas in the upper temporal lobe of owl monkeys: The MT crescent and dorsal and ventral subdivisions of FST. Journal of Neuroscience, 13, 534–546. Kaas, J.H. (1995) The organization of callosal connections in primates. In: A.G. Reeves and D.W. Roberts (eds), Epilepsy and the corpus callosum II. New York: Plenum Press, pp. 15–27. Kaas, J.H. (1997) Theories of visual cortex organization in primates. In: K.S. Rockland, J.H. Kaas and A. Peters (eds), Cerebral Cortex, Volume 12, Extrastriate Cortex in Primates. New York: Plenum Press, pp. 91–125. Kaas, J.H. (2000) Why is brain size so important: Design problems and solutions as neocortex gets bigger or smaller. Brain and Mind, 1, 7–23. Killackey, H.P. (1990) Neocortical expansion: An attempt toward relating phylogeny and ontogeny. Journal of Cognitive Neuroscience, 2, 1–17. Krubitzer, L.A. and Kaas, J.H. (1989) Cortical integration of parallel pathways in the visual system of primates. Brain Research, 478, 161–165. Krubitzer, L.A. and Kaas, J.H. (1990a) Cortical connections of MT in four species of primates: Areal, modular, and retinotopic patterns. Visual Neuroscience, 5, 165–204. Krubitzer, L.A. and Kaas, J.H. (1990b) The organization and connections of somatosensory cortex in marmosets. Journal of Neuroscience, 10, 952–974. Krubitzer, L.A. and Kaas, J.H. (1993) The dorsomedial visual area of owl monkeys: Connections, myeloarchitecture, and homologies in other primates. Journal of Comparative Neurology, 334, 497–528. Kuypers, H.G.J.M., Szwarcbart, M.K., Mishkin, M. and Rosvald, H.E. (1965) Occipito-temporal corticocortical connections in the rhesus monkey. Experimental Neurology, 11, 245–262. Lashley, K.S. and Clark, G. (1946) The cytoarchitecture of the cerebral cortex of Ateles: A critical examination of architectonic studies. Journal of Comparative Neurology, 85, 223–306. Lin, C.-S. and Kaas, J.H. (1980) Projections from the medial nucleus of the inferior pulvinar complex to the middle temporal area of visual cortex. Neuroscience, 5, 2219–2228. Livingstone, M.S. and Hubel, D.H. (1982) Thalamic inputs to cytochrome oxidase-rich regions in monkey visual cortex. Proceedings of the National Academy of Sciences, U.S.A., 79, 6098–6101. Miyashita-Lin, E.M., Hevener, R., Wassarman, K.M., Martinez, S. and Rubenstein, J. (1999) Neocortical regionalization in the absence of thalamic innervation. Science (Washington), 285, 906–909. Myers, R.E. (1965) Organization of visual pathways. In: E.G. Ehlinger (ed.), Functions of the Corpus Callosum. London: Churchill, p. 133.
© 2002 Taylor & Francis
Cortical Areas and Cortico-Cortical Connections
191
Nakagawa, Y., Johnson, J.E. and O’Leary, D.D.M. (1999) Graded and areal expression patterns of regulatory genes and cadherins in embryonic neocortex independent of thalamocortical input. Journal of Neuroscience, 19, 10877–10885. O’Leary, D.D.M. (1989) Do cortical areas emerge from a protocortex. Trends in Neuroscience, 12, 401–406. Pons, T.P. and Kaas, J.H. (1986) Corticocortical connections of areas 2, 1, and 5 of somatosensory cortex in macaque monkeys: A correlative anatomical and electrophysiological study. Journal of Comparative Neurology, 248, 313–375. Preuss, T.M., Beck, P.D. and Kaas, J.H. (1993) Areal, modular, and connectional organization of visual cortex in a prosimian primate, the slow loris (Nycticebus coucong). Brain Behavior Evolution, 42, 237–251. Preuss, T.M. and Kaas, J.H. (1999) Human brain evolution. In: M.J. Zigmond, F.E. Bloom, S.C. Landis, J.L. Roberts and L.R. Squire (eds), Fundamental Neuroscience. San Diego: Academic Press, pp. 1283–1311. Rakic, P. (1988) Specification of cerebral cortical areas. Science (Washington), 241, 170–176. Rakic, P. (1991) Experimental manipulation of cerebral cortical areas in primates. Philosophical Transactions of the Royal Society of London, Series B, 331, 291–294. Rubenstein, J.L., Shimamura, K., Martinez, S. and Puelles, L. (1998) Regionalization of the prosencephalic neural plate. Annual Review of Neuroscience, 21, 445–477. Scannell, J.W., Blakemore, C. and Young, M.P. (1995) Analysis of connectivity in the cat cerebral cortex. Journal of Neuroscience, 15, 1463–1483. Schleicher, A., Amunts, K., Geyer, S., Morosan, P. and Zilles, K. (1999) Observer-independent method for microstructural parcellation of cerebral cortex: A quantitative approach to cytoarchitectonics. Neuroimage, 9, 165–177. Sereno, M.I. (1998) Brain mapping in animals and humans. Current Opinion in Neurobiology, 8, 188–194. Spatz, W.B. (1977) Topographically organized reciprocal connections between area 17 and MT (visual area of superior temporal sulcus) in the marmoset, Callithrix jacchus. Experimental Brain Research, 27, 559–572. Stepniewska, I. and Kaas, J.H. (1996) Topographic patterns of V2 cortical connections in macaque monkeys. Journal of Comparative Neurology, 371, 129–152. Stepniewska, I., Qi, H.W. and Kaas, J.H. (1999) Do superior colliculus projection genes in the inferior pulvinar project to MT in primates? Journal of European Neuroscience, 11, 856–866. Tootell, R.B.H., Silverman, M.S., DeValois, R.L. and Jacobs, G.H. (1982) Functional organization of the second cortical area of primates. Science (Washington), 220, 737–739. Tootell, R.B.H., Hamilton, S.L. and Silverman, M.S. (1985) Topography of cytochrome oxidase activity in owl monkey cortex. Journal of Neuroscience, 5, 2786–2800. Van Essen, D.C. (1985) Functional organization of primate visual cortex. In: A. Peters and E.G. Jones (eds), Cerebral Cortex, Volume 3, Visual Cortex. New York: Plenum Press, pp. 259–329. Weller, R.E. and Kaas, J.H. (1983) Retinotopic patterns of connections of area 17 with visual areas V-II and MT in macaque monkeys. Journal of Comparative Neurology, 220, 253–279. Wong-Riley, M.T.T. (1994) Primate visual cortex. Dynamic metabolic organization and plasticity revealed by cytochrome oxidase. In: A. Peters and K.S. Rockland (eds), Cerebral Cortex, Volume 10, Primary Visual Cortex in Primates. New York: Plenum Press, pp. 191–200. Young, M.P., Scannell, J.W. and Burns, G. (1995) The analysis of cortical connectivity. Austin: Landes. Zeki, S.M. (1978) Uniformity and diversity of structure and function in rhesus monkey prestriate cortex. Journal of Physiology (London), 277, 273–290.
© 2002 Taylor & Francis
Part III CONSTANCY AND VARIATION ACROSS SPECIES
© 2002 Taylor & Francis
10 The Cerebral Cortex of Mammals: Diversity within Unity Facundo Valverde, Juan A. De Carlos and Laura López-Mascaraque Laboratorio de Neuroanatomía Comparada, Instituto Cajal (CSIC), Avenida del Doctor Arce 37,28002 Madrid, Spain Correspondence: Prof. F. Valverde, Instituto Cajal (CSIC) Avenida del Doctor Arce 37, 28002 Madrid, Spain FAX: 91 585 47 54; e-mail:
[email protected]
In this chapter, we will review our observations on the structure of the cerebral cortex obtained with the Golgi method. A comparative approach based on the study of several mammalian species will place emphasis on differences and similarities of intrinsic neuronal connectivity. The chapter first explores fundamental characteristics differentiating neocortex from allocortex, following a description of cortical cell arrangement which has been considered as a model of neocortical organization: the module concept. We review the different types of intrinsic neocortical cells and the morphology of specific cortical afferent fibres. The cerebral cortex of mammals contains an ample variety of cells which, from a general point of view, can be classified into two principle types: pyramidal and non-pyramidal cells. From a morphological point of view, pyramidal cells are relatively homogeneous, having long axons projecting outside the cortex. Non-pyramidal cells or intrinsic neurones have been classified according to their dendritic and axonal characteristics. Their axons remain inside the cortical territory in which they are located. Cells with unspecific axonal arborizations have been found in all cortical layers, with the exception of layer I, and in all mammals thus far examined. Their name indicates the lack of specialized axonal arborizations, in contrast to other cell varieties having uniquely elaborated axonal morphologies. The chapter further explores the morphology and distribution of specific thalamo-cortical fibres and the varieties of intrinsic cells they contact. Special interest was dedicated to spiny stellate cells with recurving (ascending) axons, which represent the principal relay of thalamic afferent fibres in the visual cortex of primates. The varieties of spiny stellate cells and their proportions depend on the animal and a comparison with other cortical areas show interesting and contrasting differences. We conclude that, from a general point of view, the neocortex appears uniform at a rather fundamental level of organization, even though significant differences were found among certain varieties of cells, some of which may be unique to a given species. Some of these differences can be minimal in closely-related species, but they appear substantial when the comparison is made between distant animals. KEYWORDS: comparative neuroanatomy, Golgi method, intrinsic neurones, neocortex, visual cortex
1. INTRODUCTION The cerebral cortex of mammals is an extremely complex laminated structure on which sensory organs are mapped in specific cortical domains, known as primary sensory areas. 195 © 2002 Taylor & Francis
196
Facundo Valverde, Juan A. De Carlos and Laura López-Mascaraque
Figure 10.1. Nissl-stained sections of the auditory cortex in five representative mammals reproduced at the same magnification. (A) hedgehog, (B) rat, (C) cat, (D) monkey, (E) man. The arrangement in vertical columns is clearly visible in the cat, monkey and man. (F) shows a small sector of the auditory cortex of man showing giant pyramidal cell. Scale bar at the left of F equals 200 µm; scale bar inside F equals 100 µm.
© 2002 Taylor & Francis
The Cerebral Cortex of Mammals
197
From olfaction to vision, each sensory modality has its own territory in the cortex which remains relatively constant from lower mammals to humans. Beyond the primary cortical areas, the cerebral cortex contains multiple secondary somatic and motor areas in which afferent inputs are finally elaborated into complex behavioural responses. The organization of this dominating structure, which achieves, from a human perspective, its highest complexity in man, is the result of a slow evolutionary process that supposedly began about 100 million years ago, evolving to reach a final prototype in primitive Insectivora (Valverde, 1983). The history of cortical discoveries is a passionate story of the human endeavour to unveil the organization of this remarkable structure (Lorente de Nó, 1949; Jones, 1984a). In 1878 Bevan Lewis proposed the six-layer scheme, which has remained since then, for naming the different cortical layers (Figure 10.1). Based on this plan, the cerebral cortex was then subdivided into two separate types: isocortex and allocortex, terms originally coined by Brodmann (1909) and Cécile and Oskar Vogt (1919). The isocortex or neocortex represents that part of the cerebral mantle in which a six-layered stratification can always be recognized. The allocortex consists of the archicortex (hippocampus, including the dentate gyrus) and paleocortex (olfactory cortex proper), which exhibit simple laminar structures. Both cortical types are more or less clearly separated from each other by a number of intervening para-cortical areas, which have been a matter of interest for the study of certain evolutionary trends (Sanides, 1970). The cerebral cortex contains nerve cells and fibres arranged in parallel layers. Each layer has its own individuality, as given by its specific cell varieties and input and output connections; but the various layers do not operate in isolation. The elements which characterize them are linked to the constituents of the remaining layers. This is the philosophy underlying the concepts of module operation, envisaged as spatial modules of vertically-linked cell groups surrounding cortico-cortical and specific cortical afferents (Szentágothai, 1975, 1978, 1979; Eccles, 1984). Fundamental aspects of cortical organization are based on these anatomical entities, which will provide us a reference frame for the study of those structural features that either remain constant or vary across mammalian species. In this chapter, we will review our observations on the structure of the cerebral cortex, obtained with the Golgi method in a large collection of more than 2000 brains, from Insectivora to Primates. Due to the nature of our previous work, we will concentrate on specific aspects of cortical organization from a comparative point of view, with emphasis on study in the primary visual cortex, area 17.
2. PHYLOGENETIC APPROACH TO CORTICAL ORGANIZATION ALLOCORTEX VERSUS NEOCORTEX There are fundamental characteristics which differentiate the neocortex from the allocortex. Apart from differences in pyramidal cells, which have a typical shape in the neocortex different from that in the allocortex, in neocortex and mainly in primary sensory areas, thalamocortical afferent fibres end in middle cortical layers. In the allocortex, the major afferent fibre system is formed by a strong input extending into the plexiform, or first layer; e.g. the lateral olfactory tract in the primary olfactory cortex, or perforant bundles in the hippocampal and dentate gyri (Valverde, 1998). The dependence on afferent fibres in allocortex is so strong that, for instance, unilateral olfactory bulb ablation in the rat
© 2002 Taylor & Francis
198
Facundo Valverde, Juan A. De Carlos and Laura López-Mascaraque
Figure 10.2. Transverse section through the somato-sensory cortical area in the hedgehog. In basal Insectivores, the fundamental plan of neocortical organization can hardly be recognized. The cortex shows a thick layer I, with densely populated layer II containing large polymorphic cells (e.g. cells l–t) with dendrites richly covered by spines. An exuberant number of dendrites penetrate layer I where they receive afferent input. The granular layer IV cannot be recognized, instead other pyramidal or pyramid-like cells (e.g. cells a–c) and polymorphic cells with smooth dendrites, and various axonal morphology (e.g. cells d–k) populate the middle cortical layers (III–IV). Layers V and VI appear well developed with large pyramidal cells and long apical dendrites reaching layer I (e.g. cells u–w). When axons could be traced, they were numbered followed by the letter of their parent cell (e.g. 1a). Camera lucida drawing. Golgi method. Hedgehog 30 days old. (From Valverde, 1986. Reprinted with permission from Elsevier Science).
© 2002 Taylor & Francis
The Cerebral Cortex of Mammals
199
causes the death of almost all layer II pyramidal cells in the olfactory cortex (Heimer and Kalil, 1978; Friedman and Price, 1986; López-Mascaraque and Price, 1997). Comparative anatomical studies have shown that these structural features remain constant throughout species, and can, therefore, be considered as essential for the information processing typical of allocortical formations. On the other hand, primary sensory neocortical areas receive their afferent inputs through relays made in different thalamic nuclei, ascending vertically from the underlying white matter, which arborize mainly on middle (layers III and IV) and upper (layer I) cortical levels. In spite of these strong differences between the allocortex and isocortex, the study of primitive Insectivora reveals that both types of cortical organization might have been derived from a common prototype. In the hypothetical primordial vertebrate ancestor, the end-brain developed almost exclusively as an olfactory brain, and successive differentiations are thought to have taken place by the invasion of diverse somatic systems into this primitive anlage. The hedgehog, Erinaceus europaeus, once called a “survivor of the Paleocene”, is considered to be one of the most direct descendants of the primitive placentals from which mammals originated (Simpson, 1945; Romer, 1966). The brain of the hedgehog shows a pattern of predominantly olfactory structures with a small neocortex, resembling the architecture of surrounding allocortical formations. The main cytoarchitectonic features of this initial neocortex (Figures 10.1, 10.2) show an extremely wide layer I (300 µm) with strong thalamic input, an accentuated layer II with extraverted pyramidal cells, and an absence of layer IV, so that layers III and V have no distinct boundaries (Sanides, 1970; De Carlos, 1986; Valverde and Facal-Valverde, 1986; Valverde et al., 1986; Glezer et al., 1988).
3. THE MODEL OF NEOCORTICAL ORGANIZATION Understanding the cerebral cortex has been dominated by the concept of columnar organization. This term was first used to designate the intrinsic neuronal connectivity within a vertical cylinder, or column, of cortical tissue which has a central axis formed by specific thalamo-cortical afferent fibres (Lorente de Nó, 1949). For several years, this elementary unit was envisaged as a functional concept, because it explained earlier results obtained by neurophysiology in the somatosensory (Mountcastle, 1957), auditory (Woolsey, 1960) and visual (Hubel and Wiesel, 1962) primary areas; namely, that cells having similar functional properties appear to be arranged along the vertical axis of the cortex from the pia to the white matter. In the primary somatosensory (Mountcastle, 1979) and visual cortices (Hubel and Wiesel, 1977), functional columns are defined in terms of receptive field properties, while in the primary auditory cortex, they are interpreted in terms of best frequency responses (Abeles and Goldstein, 1970; Imig et al., 1982). From a strictly anatomical point of view, vertical arrangements in the cerebral cortex are not obvious in all the species examined. In this regard, we compared transverse sections of the primary auditory cortex stained with the Nissl method in a representative series of mammals (hedgehog, rat, cat, monkey and man). This comparison is shown in Figure 10.1, where we represent the auditory cortices of these five different species at the same magnification. For instance, the thickness of the auditory cortex of the hedgehog and rat appears to be the same, but the layer distribution is different. The hedgehog does not show a clear granular layer IV; in addition, the thickness of its layer I is almost double that of
© 2002 Taylor & Francis
200
Facundo Valverde, Juan A. De Carlos and Laura López-Mascaraque
layer I of the rat. These distinctive features also occur in the brain of the dolphin, which may constitute an interesting link between marine and terrestrial mammals (De Carlos, 1986). The first anatomical evidence of an organization that could be correlated with physiological columns was obtained by Hubel and Wiesel (1969) in the visual cortex of the macaque monkey. Using silver impregnation techniques, they showed the existence of two independent and overlapping systems of bands coincident with the system of ocular dominance bands. The presence of comparable arrangements was subsequently demonstrated in almost every cortical area. Newly developed methods for the tracing of pathways and the use of enzymatic reactions and radioactive tracers provided evidence that the cerebral cortex, as far as the sensory thalamic input is concerned, is organized into regularly spaced, periodic subdivisions readily attributable to the spatial distribution of afferent fibres in the somatosensory, auditory and visual cortices of various animal species. This demonstrates that some of these thalamic distributions have specific patterns of cortical representation, such as the barrel field in the somatosensory cortex of rodents (Welker, 1976; Woolsey and Van der Loos, 1970), the banding pattern of the ocular dominance system of the visual cortex in the monkey (Le Vay et al., 1975) and the radial arrangement of cell chains with the same characteristic frequencies in the auditory cortex (Abeles and Goldstein, 1970; Brugge and Reale, 1985). This pattern of orderly partitions is not unique to cortical primary sensory areas: there is now evidence that cortico-cortical (callosal and association) projections also branch into alternating, vertically-oriented patches segregated from thalamo-cortical afferents (Jones, 1984b; Wise and Jones, 1976; Záborsky and Wolf, 1982).
4. INTRINSIC CORTICAL CELLS—ORIGIN, AREAL AND INTER-SPECIES VARIATION The cerebral cortex of mammals contains an immense variety of cell types. However, from a general point of view, cortical neurones can be classified into two principal types: pyramidal cells and non-pyramidal cells. Pyramidal cells represent the fundamental type of cell: they have long axons projecting outside the cortex. Non-pyramidal cells, or intrinsic cells, have axons that remain inside the cortical area where they are located, and do not project extracortically. There is a general consensus indicating that pyramidal cells represent the essence of cortical architecture, while intrinsic neurones add its “flavour”. Despite considerable efforts during recent years, identification of the diverse types of intrinsic neocortical cells is not yet complete. In addition, recent studies have shown that cortical cells have a variety of neurotransmitters which can be used as selective markers for different populations of cells (see reviews in DeFelipe and Fariñas, 1992; Nieuwenhuys, 1994). Pyramidal cells, and some intrinsic cells, have different developmental origins. Cortical projection neurones (pyramidal cells) originate from the neocortical ventricular zone, ascending to the surface by following the processes of radial glial cells (Rakic, 1972, 1985; Valverde et al., 1989; Misson et al., 1991; Gould et al., 1999). In contrast, recent evidence has shown that the ventricular and subventricular zones of some transient basal structures, which develop in early embryonic stages—the ganglionic eminences—give rise to another population of cortical neurones. These cells, cross the cortico-striatal boundary to enter the cortical neuroepithelium using a tangential migration path (De Carlos et al., 1996).
© 2002 Taylor & Francis
The Cerebral Cortex of Mammals
201
They follow different routes: preplate (marginal zone or future layer I) and intermediate zone (future white matter), and have been characterized as GABAergic (Anderson et al., 1997a, 1999; Tamamaki et al., 1997; Zhu et al., 1999). These neurones are not transitory, but become diluted in the thickness of the cerebral cortex during development, contributing there to the intracortical circuitry. A new area of research is rapidly growing related especially to study of the diverse genes controlling the processes of areal specification and connectivity (Anderson et al., 1997b; Donoghue and Rakic, 1999; Rubenstein and Rakic, 1999). Study of the dependence on a number of developmental genes is rapidly evolving and will provide new advances concerning the possibility that differences in cellular origins might explain which features remain constant throughout species, or to what degree cell types or their ramification patterns differ between species (Karten, 1997). Another field of interest pertains to recent immunocytochemical studies of specific subpopulations of intrinsic cells that have been shown to be immunoreactive for different proteins. Their differences in laminar distribution and their relations with specific populations of pyramidal cells reflect diverse patterns of cortical circuitry that have been correlated with functional subdivisions of the neocortex (Kritzer et al., 1992; Fujita and Fujita, 1996; Elston et al., 1999). In spite of these new advances, the Golgi method has remained one of the most elegant procedures for studying the morphology of neurones. The fact that the various Golgi methods selectively impregnate only a small proportion of neurones, but often stain them in their entirety, has made them the methods of choice for studying individual neurones for almost a century. Successful impregnation of brain tissue with this method provides a complete picture of neuronal morphology, including all dendritic branches, axonal arbors and finest terminal ramifications. It still remains true that most classes of intrinsic neocortical cells, along with their inter-species and inter-areal varieties, have been described based on Golgi preparations. In 1979, we proposed a classification of intrinsic neurones, that takes into account dendritic, as well as axonal characteristics (Fairén and Valverde, 1979). A short review follows: (a) Cells with recurving axonal arcades. These cells either have smooth dendrites or bear a very small number of spines. They have been found in all cortical layers of the mouse area 17, with the exception of layer I (Figure 10.3, cells 16–18; Figure 10.4, b). The axon emerges from the upper pole of the cell body, or from the base of a dendrite, ascends vertically and forms thick recurving arcades of horizontal and oblique branches. These cells are entirely comparable to others present in the monkey visual (Lund, 1973) and somato-sensory (Jones, 1975) cortices. (b) Cells with ascending axons. These are multipolar cells whose dendrites range from smooth (spine-free) to sparsely spiny, even though in adult animals dendrites have been found so richly endowed with spines that their classification as sparsely spiny would seem inadequate (Figure 10.3, cells 8–15; Figure 10.4, e). The axon emerges from the upper pole of the cell body and forms a straight ascending stem reaching layer I, where it arborizes. Cells of this variety that are located in upper cortical layers II and III have long horizontal axonal trajectories into layer I, while those located in layers IV to VI have axons with thin descending collaterals forming a relatively dense local arborization, and a second group of axonal branches contributing sparsely to layer I. The more deeply located cells of this variety correspond to the type described by Cajal (1911) as Martinotti cells.
© 2002 Taylor & Francis
202
© 2002 Taylor & Francis
Facundo Valverde, Juan A. De Carlos and Laura López-Mascaraque
Figure 10.3. Axonal patterns from various intrinsic cortical cells arranged according to the depth of their cells bodies below the pial surface collected from diverse neocortical areas of the mouse. The first row (1–7) comprises axons ramifying above the cell body. The second row (8–15) shows ascending axons with a vertical principal branch emitting collaterals at different levels. The first three examples in the third row (16–18) represent examples of axons with recurving and long descending collaterals ramifying above and below their cells bodies. Examples of axonal complexes in the third row 19 to 21 represent types with diversely oriented axonal collaterals. Examples 22 and 23 correspond to axons of pyramid-like cells with developed intra-cortically and forming strong recurrent arcs. The example 24 corresponds to a typical pyramidal cell with collaterals distributed in layers IV and V. The age and depth from the surface (in microns) appears in each example. Camera lucida drawings. Golgi method. (From Valverde, 1976.)
The Cerebral Cortex of Mammals
203
Figure 10.4. Transverse section through the visual cortex of the mouse. Varieties of cells with intracortical axons (intrinsic cells b and e) and pyramidal cells (cells a, c, d and f). The figure shows the contrasting differences between one cell with recurving axonal arcades (b), and one cell with ascending axon reaching layer I (e). Typical superficial (c), middle (d) and deep (a, f) pyramidal cells were also reproduced. In the cell labelled f, the possible sites of synaptic contacts provided by the axon of cell e were labelled s. When axons could be traced, they were numbered followed by the letter of their parent cell (e.g. 1b). Camera lucida drawing. Golgi method. Mouse 21 days old. (From Fairén and Valverde, 1979).
(c) Cells with specialized axonal arborizations. Included in this category are various kinds of cells having smooth or sparsely spiny dendrites and uniquely elaborated axonal arborizations, each with a peculiar morphology that suggests the name it bears. All of them have in common that they are inhibitory interneurones. Basket cells (Figure 10.5) have been considered to be large multipolar cells with smooth dendrites and long horizontal axon collaterals forming pericellular nests (baskets) around pyramidal cell bodies in layers III and V. First described by Cajal (1911) in the human motor and visual cortices, several varieties have been described in more recent times. However, not only does their existence remain a matter of considerable debate, but strong evidence for their presence outside the sensory and motor areas, or in species other than primates and carnivores, is also still lacking (see reviews in Jones and Hendry, 1984 and Fairén et al., 1984).
© 2002 Taylor & Francis
204
Facundo Valverde, Juan A. De Carlos and Laura López-Mascaraque
Figure 10.5. Several terminal axonal branches from a large basket cell (not shown) outlining an unstained large pyramidal cell of layer V in the human motor cortex. Camera lucida drawing made by F. Valverde from a Golgi preparation made by S. Ramón y Cajal. (From the Cajal Museum.)
Recent studies using parvalbumin and GABA immunoreactivity have confirmed the convergence of multiple axonal terminals around pyramidal and non-pyramidal cells; these are considered to belong to large basket cells (Somogyi et al., 1983; Hendry et al., 1989; Kisvárday et al., 1993), providing powerful inhibitory influences upon the cells they contact. Chandelier cells, so named for their axonal morphology with terminal branches resembling the vertical “candles” of a lamp fixture (Figure 10.6, a), were first described by Szentágothai and Arbib (1974) in the gyrus cinguli of the cat. They appear similar to type 4 cells of Jones (1975). Somogyi (1977) showed for the first time, in the visual cortex of the rat, that the axon terminals composing the characteristic “candles”, form symmetric synaptic junctions with the initial axonal segments of pyramidal cells. Subsequent studies confirmed their existence in the visual cortex of the cat (Fairén and Valverde, 1980). Since then, numerous observations have shown that chandelier cells are by no means specific for the visual cortex, for they have been found not only in different neo- and paleo-cortical areas, but also in practically all mammals thus far studied (Somogyi et al., 1982; Fairén and Valverde, 1980; Valverde, 1983; Fairén et al., 1984; Peters, 1984; Kisvárday et al.,
© 2002 Taylor & Francis
The Cerebral Cortex of Mammals
205
Figure 10.6. Transverse section through the visual cortex of the cat. The figure is a composite drawing from several adjacent sections. One specific afferent fibre (entering section from right) labelled 1, ramifies in layer IV. Large stellate cells with markedly spinous dendrites were labelled b, c and d, representing examples of cells receiving direct synaptic contact from specific cortical afferents. The figure also shows one cell with smooth dendrites (e), identified as a “clewed cell”, and one example of chandelier cell (a) with characteristic axonal terminals (2a) devoted to contact with the initial axonal segments of pyramidal cells (not stained), and one descending axon (1a). Axons were numbered followed by the letter of their parent cell (e.g. 2b). Camera lucida drawing. Golgi method. Cat 1 month old. (From Fairén and Valverde, 1979.)
© 2002 Taylor & Francis
206
Facundo Valverde, Juan A. De Carlos and Laura López-Mascaraque
1986; De Carlos et al., 1985, 1986, 1987; Marín-Padilla, 1987). Since chandelier cells perform a strong inhibitory action on the axon initial segment of pyramidal cells, they have been considered to have a decisive influence on pyramidal cell output. Chandelier cells probably use GABA as a neurotransmitter, as it has been reported that chandelier axon terminals in contact with the initial axon segment of pyramidal cells are glutamic acid decarboxylase (GAD)-positive (Peters et al., 1982), and some of them have been shown to contain parvalbumin (DeFelipe et al., 1989). Clewed cells, also known as neurogliaform, spiderweb or clutch cells (Figure 10.6, e; Figure10.7, g), were first described in sublayer IVc in the primary visual cortex, area 17, of the monkey as being characteristically small cells with beaded, smooth dendrites and one axon that soon resolves into numerous, densely interwoven collaterals, forming a plexus of strictly local nature (Valverde, 1971). This cell corresponds to the type 5 cell of Jones (1975) and a comparable cell variety has also been described in the monkey area 18 (Valverde, 1978). Further studies have demonstrated that the main targets of clewed cells in layer IV of the visual cortex in the monkey are spiny stellate cells, and the presence of GABA in these cells suggests that they provide a strong inhibitory input to these stellate cells (Kisvárday et al., 1986). Cells with vertical axonal bundles, also known as “double bouquet cells” were described by Cajal (1911) as one cell variety having axonal and dendritic trees arranged in vertical bundles. Although the vertical arrangement of the dendrites is not clearly evident in some cases, the most distinctive feature is the distribution of their axons running perpendicular to the surface (Figure 10.8, a, b), extending through several cortical layers and forming synaptic contacts with dendritic spines of pyramidal and non-pyramidal cells. These cells were described in layer II and the upper part of layer III of area 18 in the monkey (Valverde, 1978). Since then, they have been found and described in several neocortical areas in man and other primates, in the cat and in rodents (Somogyi and Cowey, 1984). These cells most probably use GABA as their primary transmitter and, as some recent studies have shown, they also contain several neuropeptides (DeFelipe et al., 1990) and calciumbinding protein (DeFelipe and Jones, 1992). Using immunocytochemical techniques, double bouquet cells have been found to be particularly abundant in the monkey area 18, probably being related to different processing of visual stimuli (DeFelipe et al., 1999). (d) Non-pyramidal cells with spiny dendrites. These are multipolar cells with dendrites covered by spines, as in pyramidal cells, but lacking the typical apical dendrite. It is clear that they do not constitute a uniform group: subgroups can be defined according to cell size, dendritic orientation and axonal distribution, but in all cases their axons are considered as generalized, due to the lack of specific terminal arborizations (as found for non-pyramidal neurones with no or few spines). Some non-pyramidal cells with spiny dendrites have axons projecting to the contralateral hemisphere via the corpus callosum (Valverde, 1986). These cells are abundant in practically all cortical layers, but most notably in layers III and IV, receiving specific afferent fibres. Due to the importance of these cells in the context of cortical afferent fibres, we will consider their detailed description in the next section.
© 2002 Taylor & Francis
The Cerebral Cortex of Mammals
207
Figure 10.7. Transverse section through the visual cortex, area 17 of the monkey. The drawing is a composite illustration of varieties of cells recorded from several adjacent sections. Specific cortical afferents (geniculo-cortical fibers) ascend from the white matter forming elaborate terminal ramifications in sublayer IVc (1, 2). Medium-sized conventional pyramidal cells in layer III (a) and sublayer IVb (f) have axons (1a, 1f) descending to lower layers. Small stellate cells with spinous dendrites (h, i, j) show ascending, recurrent axons (1h, 1i, 1j) ending with localized axonal plexuses in sublayer IVa. In this sublayer, there is another variety of stellate cells with spinous dendrites (b, c) running horizontally. Layer V also contains similar varieties of small pyramidal cells (l, m) with ascending recurving axons (1l, 2m). Two stellate cells with spinous dendrites (d, e) and one “clewed cell” (g) can also be observed. Camera lucida drawing. Golgi method. Adult monkey Macaca rhesus (From Fairén and Valverde, 1979).
© 2002 Taylor & Francis
208
Facundo Valverde, Juan A. De Carlos and Laura López-Mascaraque
Figure 10.8. Transverse section through the visual cortex, area 18 of the monkey. The image shows cells with vertical axonal bundles and examples of pyramidal cells. Cells with vertical axonal bundles or “double bouquet cells” (a, b), so named by S. Ramón y Cajal, display characteristic vertical axonal trees arranged in tightly packed vertical bundles spanning several cortical layers. They appear to be typical for area 18. Layer III appears subdivided into IIIa and IIIb; together with layer IV, they contain large pyramidal cells (c, d, e and f) with descending axons and collateral branches running for long distances. Collateral branches were numbered consecutively followed by the letter of their parent cell (e.g. 3a). Inset drawing shows the region from which the drawing was obtained. Camera lucida drawing. Golgi method. Adult monkey Macaca rhesus (From Valverde, 1978. Reprinted with permission from Springer-Verlag).
© 2002 Taylor & Francis
The Cerebral Cortex of Mammals
209
5. THE TARGETS OF CORTICAL AFFERENT FIBRES Target cells for thalamo-cortical fibres appear to vary widely in different mammals and in different cortical areas, and a common pattern is far from the rule. Numerous observations, based on studies carried out with the electron microscope alone, or after appropriately placed lesions in the thalamic principal relay nuclei, indicate that practically all synapses identified from thalamic terminals are of the asymmetric (presumed excitatory) type (Peters and Feldman, 1976; Peters et al., 1979; White, 1979). Hence, it has been proposed that nearly all cell bodies and dendrites located in the domain covered by thalamic afferent fibres in the middle cortical layers, and capable of forming asymmetric synapses, can receive thalamic input (Peters and Feldman, 1977). The use of procedures combining Golgi techniques with electron microscopy, have shown that the majority of dendritic spines receiving thalamo-cortical synapses belong to dendrites of pyramidal cells whose cell bodies lie in layers III, IV and V, along with the varieties of intrinsic cells located in the middle cortical layers that constitute the population of spiny stellate cells (Somogyi, 1978; Peters et al., 1979; Davis and Sterling, 1979; White, 1979; Hornung and Garey, 1981; Freund et al., 1985). As indicated, stellate cells do not constitute a uniform population: not only are there significant differences in dendritic and axonal morphology among various animal species and in different neocortical areas, but their synaptic relations also appear different. Stellate cells have been classified as: lacking spines (smooth stellate cells); having few spines (sparsely spiny stellate cells); or having a high density of spines (spiny stellate cells). However, none of these types constitute a uniform group, and other criteria, such as the form and distribution of the dendritic tree, cell size, and, principally, the axonal pattern, have been considered to give a comprehensive classification (Valverde, 1976; Feldman and Peters, 1978; Fairén and Valverde, 1979; Fairén et al., 1984; Peters and Jones, 1984). In the mouse, somato-sensory cortex corresponding to the barrel field, spiny stellate cells are particularly abundant (Woolsey et al., 1975). They have dendrites covered by numerous spines, no trace of an apical dendrite, and the patterns of dendritic orientation depend on whether the cell body is located in the barrel wall or in the barrel hollow. Except for these highly characteristic elements in this particular neocortical area, the majority of postsynaptic elements (about 83%) are represented by dendritic spines belonging to apical and oblique dendrites of pyramidal cells located in layers V and VI, and to the basal dendrites of layer III pyramidal neurones (Peters and Feldman, 1977). There is a small proportion of postsynaptic elements (about 17%) represented by the shafts of smooth or sparsely spiny stellate cells residing in layer IV (Valverde, 1968; Peters et al., 1976). Spiny stellate cells have been a matter of considerable interest since Kelly and Van Essen (1974) showed in the visual cortex of the cat that some cells, recovered by intracellular dye injections after identifying their functional characteristics, belong to this category. These spiny stellate cells (Figure 10.6, b–d) were similar to those described in Golgi preparations in the same cortical area (Cajal, 1911, 1921; O’Leary, 1941; LeVay, 1973; Fairén and Valverde, 1979; Lund et al., 1979; Peters and Regidor, 1981; Meyer and Ferres-Torres, 1984). It was clearly established that these cells receive direct contact from thalamic terminals (Hornung and Garey, 1981; Martin and Whitteridge, 1984; Freund et al., 1985; Figure 10.6, 1) and some of them, especially those cells located at the 17/18 border, have axons projecting to the contralateral hemisphere (Sanides, 1979; Innocenti, 1979; Hornung and Garey, 1980; Meyer and Albus, 1981). The fact that these cells project to © 2002 Taylor & Francis
210
Facundo Valverde, Juan A. De Carlos and Laura López-Mascaraque
the white matter had already been mentioned by Cajal (1921), and confirmed later after intracellular injections with horseradish peroxidase (Gilbert and Wiesel, 1979; Lin et al., 1979). In primates, target cells for thalamic axons are represented by intrinsic cells which do not project outside the cortex. The visual cortex of the monkey (the striate area or area 17) has been studied intensively since the demonstration of the system of ocular and orientation columns (Hubel and Wiesel, 1969, 1972). In the visual cortex of primates, layer IV has been subdivided into three tiers, labelled as IVa, IVb and IVc (Figure 10.7). Sublayer IVc contains a population of spiny stellate cells with recurving (ascending) axons, as well as varieties of cells with smooth dendrites. These spiny stellate cells are the most distinctive element found in this sublayer, which probably has no counter-part in non-primate species (Valverde, 1971). They have been a matter of considerable interest in the analysis of the anatomical and functional organization of the visual cortex. The axons of these cells originate at the lower pole of the cell body, descend for a short distance and then turn upwards, forming single or several characteristic loops in ascending bundles that reach layer III and sub-layers IVa and IVb, where they develop into distinct elongated plexuses of terminal fibres (Figure 10.7, h, i). Their general morphology has often been considered akin to pyramidal cells with truncated or absent apical dendrites (Lund, 1984). They have even been considered as the result of evolutionary transformations of pyramidal cells (Nieuwenhuys, 1994) probably related to changes in the distribution of cortical afferents, which presumably shifted from layer I in primitive mammals to predominate in middle cortical layers. Thus, in the study of the forms of different cells with spiny dendrites, including pyramidal cells, one gets the impression that all of them may share a common phylogenetic origin and that a continuum can be traced from lower forms to the primate brain. It is possible that this shift modified the intrinsic neocortical organization. In spite of these differences, the visual cortex of the cat and monkey show strong similarities that have been considered in a detailed study, using the Golgi method, by Lund and collaborators (Lund et al., 1979). However, layer IVb in primates, corresponding to the stria of Gennari, which does not receive direct input from the thalamic lateral geniculate body, has no counterpart in cat area 17. Retrograde labelling with HRP revealed the existence of a reciprocal projection from the visual area of the superior temporal sulcus (STS) to the striate area in Callithrix, ending in layer IVb (Spatz, 1977; Rockland and Pandya, 1981). The tangential spread of some axonal collaterals in the stria of Gennari, derived from descending axons of large pyramidal and stellate cells, extends for up to several millimeters (Colonnier and Sas, 1978; Fisken et al., 1975; Valverde, 1985). There is a second and major type of target cell for thalamic axons, corresponding to neurones with smooth dendrites and beaded axons. The most abundant ones show a very limited axonal field formed by densely interwoven axonal collaterals and recurving dendrites, giving the ensemble the appearance of a ball of yarn (Figure 10.7, g). They were named “clewed cells” when observed for the first time (Valverde, 1971), and have been considered previously as having specialized axonal arborizations. These cells have been a matter of considerable interest, not only for their specific axonal patterns, but also because they form symmetrical synaptic contacts and appear, therefore, to be inhibitory interneurones. These cells are most probably identical with the small basket (clutch) cells described in the visual cortex of the cat (Kisvárday et al., 1985) and monkey (Kisvárday et al., 1986). Their synaptic relations with spiny stellate cells suggest an
© 2002 Taylor & Francis
The Cerebral Cortex of Mammals
211
interesting type of local interaction (Kisvárday et al., 1986), and they have been considered as important key pieces in conceptual models of neocortical operation (Eccles, 1981, 1984). In view of the importance of spiny stellate cells as common targets for thalamic afferent fibres, several studies have been made which show that their proportion varies depending on the animal and cortical area. In the somato-sensory area of the mouse, about one-half of the cells in layer IV have dendrites covered by spines (Woolsey et al., 1975). Spiny stellate cells in the visual cortex of the rat (Golgi method) comprise around 11% (Feldman and Peters, 1978). In the visual cortex of the cat, non-pyramidal cells account for 60–80% of the cells in layer IV, where spiny stellate cells are found to be as common as cells with smooth dendrites (Garey, 1971; Winfield and Powell, 1976). In the visual cortex of the monkey, exclusively spiny stellate cells and cells with smooth dendrites form the population of target cells in sublayer IVc, where the spiny stellate variety constitutes about 95% (Mates and Lund, 1983). Spiny stellate cells have not been found in the visual cortex of the mouse and they are virtually absent in the rabbit neocortex. Non-pyramidal spiny cells have been detected only occasionally in layers III and IV of the auditory cortex of the rabbit, where 87% of all impregnated neurones (Golgi-Cox method) are pyramidal cells (McMullen et al., 1984). In our previous studies of the neocortex in the hedgehog, we could not detect any typical spiny stellate cells (Valverde, 1983).
6. COMPARISON WITH OTHER CORTICAL AREAS We have been interested in the comparison of area 17 (Figure 10.7) with area 18 (Figure 10.8) and other areas of the temporal lobe in the monkey brain (Valverde, 1978). Spiny stellate cells are apparently absent from layer IV in area 18 (Lund et al., 1981); instead, this layer contains small pyramidal cells with recurrent axons similar to those found in the upper part of layer V of the visual cortex. Their axons form strong recurrent arcades ascending to sublayer IIIb in area 18, apparently devoted to contacting large pyramidal cells. In the cortex of the superior temporal sulcus, identical small pyramidal cells with strong recurving axons have been found. Both layers III and IV also contain small stellate cells with smooth, beaded dendrites having ascending axonal branches, as well as varieties of large multipolar and bipolar cells, which are particularly abundant in these and neighbouring auditory areas. One specific type of cell was that found by Cajal (1900) only in the human auditory cortex. This type of cell is characterized by its large soma size (40–60 µm), triangular or fusiform morphology, and presence in all cortical layers except layer I. Thus far, we have found this type of giant cell (Figure 10.1F) only in the auditory cortex of human specimens (De Carlos, 1986). There is evidence that in certain cortical areas with a well-developed layer IV, thalamic afferents are not distributed in this layer. In several of the parietal and temporal fields of the monkey (e.g. areas 5, 7, second and third temporal), thalamic terminals are mainly distributed upon pyramidal cells of layer IIIB suggesting endings upon basal dendrites and proximal portions of their apical dendrites (Jones and Burton, 1976). This points to a different organization of the intrinsic circuitry of this layer, including different chemical characteristics for synaptic input (see review in DeFelipe and Fariñas, 1992). In this case, targets for thalamic fibres are represented by those cells with smooth dendrites and by typical pyramidal cells.
© 2002 Taylor & Francis
212
Facundo Valverde, Juan A. De Carlos and Laura López-Mascaraque
7. IS THERE A BASIC PLAN OF NEOCORTICAL ORGANIZATION? CONTRASTING DIFFERENCES OF TARGET NONSPECIFICITY As one ascends the phylogenetic scale, the morphology of cortical neurones becomes less uniform; more specific neuronal types appear, and the number of non-pyramidal cells increases (Lorente de Nó, 1949). The neocortex, in general, does not show a uniform structure; instead, it contains a number of diverse cortical areas which are distinguished by different anatomical and functional characteristics. However, from a general point of view, the neocortex appears functionally uniform at a rather fundamental level of organization. As just reviewed, layers III and IV are considered the principal level of termination for specific cortical afferent fibers. From here, impulses are relayed to layers II and III, which are the source of long and short association fibres and intrinsic descending connections with layers V and VI, which contain the majority of cells projecting subcortically, and a number of cells with ascending axons. However, the study of cell varieties and the mode in which they intervene in intracortical microcircuits in different animals, clearly shows the existence of important variations, some of which may be unique for a given animal. Significant differences have been found among certain varieties: cells with ascending, recurving axons, apparently unique to the primate brain; spiny stellate cells typical of the somato-sensory cortex of rodents; spiny stellate cells with axons projecting to the white matter in the visual cortex of carnivores; and extraverted pyramidal cells in the neocortex of insectivores which have no equivalent in other animals. Important differences were found in relation to the targets for thalamic afferent fibers, confirming that spiny stellate cells are by no means a common target for thalamic inputs. At a more detailed level, the existence of differences, representing adaptations to unique cortical architectures, between the most closely-related animals have long been suspected, even though their morphological characterizations still remain elusive. However, recent studies have shown, for the first time, significant differences in the cortical architecture of the visual cortex between humans and apes, the animals most closely related to humans (Preuss et al., 1999). The stage of neocortical organization attained in primates, including man, may have been accomplished, not by replication of a basic cortical module already present in lower mammals (Glezer et al., 1988), but by reshaping dendritic and axonal arbors of various categories of neocortical cells, developing into different intracortical connectivities. These differences can be minimal in closely-related species, but they appear very substantial when the comparison is made between distant animals.
ACKNOWLEDGEMENTS Supported by Ministerio de Educación y Ciencia, Grant number PB96-0813 and Consejería de Educación y Cultura de la Comunidad de Madrid, Grant number 08.5/0037/1998.
REFERENCES Abeles, M. and Goldstein, M.H. (1970) Functional architecture in cat primary auditory cortex. Columnar organization and organization according to depth. Journal of Neurophysiology, 33, 172–187. Anderson, S.A., Eisenstat, D.D., Shi, L. and Rubenstein, J.L.R. (1997a) Interneuron migration from basal forebrain to neocortex: dependence on Dlx genes. Science (Washington), 278, 474–476.
© 2002 Taylor & Francis
The Cerebral Cortex of Mammals
213
Anderson, S.A., Qiu, M.-S., Bulfone, A., Eisenstat, D.D., Meneses, J., Pedersen, R. and Rubenstein, J.L.R. (1997b) Mutation of the homeobox genes Dlx-1 and Dlx-2 disrupt the striatal subventricular zone and differentiation of late-born striatal neurons. Neuron, 19, 27–37. Anderson, S.A., Mione, M., Yun, K. and Rubenstein, J.L.R. (1999) Differential origins of neocortical projection and local circuit neurons: role of Dlx genes in neocortical interneurogenesis. Cerebral Cortex, 9, 646–654. Brodmann, K. (1909) Vergleichende Lokalisationslehre der Grosshirnrinde. Leipzig: Barth. Brugge, J.F. and Reale, R.A. (1985) Auditory cortex. In: A. Peters and E.G. Jones (eds), Cerebral Cortex, Association and Auditory Cortices, Vol. 4, New York and London: Plenum Press, pp. 229–271. Cajal, S.R. (1900) Estudios sobre la corteza cerebral humana: III. Corteza acústica. Revista Trimestral Micrográfica, tomo V, 129–183. Cajal, S.R. (1911) Histologie du Systéme Nerveux de l’Homme et des Vertébrés. Vol. 2. Paris: Maloine (Reimpress 1955, Madrid: Instituto Cajal, CSIC). Cajal, S.R. (1921) Textura de la corteza cerebral del gato. Trabajos del Laboratorio de Investigaciones Biológicas, 19, 113–144. Colonnier, M. and Sas, E. (1978) An anterograde degeneration study of the tangential spread of axons in cortical areas 17 and 18 of the squirrel monkey (Saimiri sciureus). Journal of Comparative Neurology, 179, 245–262. Davis, T.L. and Sterling, P. (1979) Microcircuitry of cat visual cortex: Classification of neurons in layer IV of area 17, and identification of the patterns of lateral geniculate input. Journal of Comparative Neurology, 188, 599–628. De Carlos, J.A., López-Mascaraque, L. and Valverde, F. (1985) Development, morphology and topography of chandelier cells in the auditory cortex of the cat. Developmental Brain Research, 22, 293–300. De Carlos, J.A. (1986) Estructura y conexiones de la corteza auditiva en mamíferos. Doctoral Thesis. Universidad Autónoma de Madrid. De Carlos, J.A., López-Mascaraque, L., Ramón y Cajal-Agüeras, S. and Valverde, F. (1987) Chandelier cells in the auditory cortex of monkey and man: a Golgi study. Experimental Brain Research, 66, 295–392. De Carlos, J.A., López-Mascaraque, L. and Valverde, F. (1996) Dynamics of cell migration from the lateral ganglionic eminence in the rat. Journal of Neuroscience, 16, 6146–6156. DeFelipe, J., Hendry, S.H.C. and Jones, E.G. (1989) Visualization of chandelier cells axons by parvalbumin immunoreactivity in monkey cerebral cortex. Proceedings of the National Academy of Sciences, U.S.A., 86, 2093–2097. DeFelipe, J., Hendry, S.H.C., Hashikawa, T., Molinari, M. and Jones, E.G. (1990) A microcolumnar structure of monkey cerebral cortex revealed by immunocytochemical studies of double bouquet cell axons. Neuroscience, 37, 655–673. DeFelipe, J. and Fariñas, I. (1992) The pyramidal neuron of the cerebral cortex: morphological and chemical characteristics of the synaptic inputs. Progress in Neurobiology, 39, 563–607. DeFelipe, J. and Jones, E.G. (1992) High resolution light and electron microscopic immunocytochemistry of colocalized GABA and calbindin D-28k in somata and double bouquet cell axons of monkey somatosensory cortex. European Journal of Neuroscience, 4, 46–60. DeFelipe, J., González-Albo, M.C., Del Rio, M.R. and Elston, G.N. (1999) Distribution and patterns of connectivity of interneurons containing calbindin, calretinin, and parvalbumin in visual areas of the occipital and temporal lobes of the macaque monkey. Journal of Comparative Neurology, 412, 515–526. Donoghue, M.J. and Rakic, P. (1999) Molecular evidence for the early specification of presumptive functional domains in the embryonic primate cerebral cortex. Journal of Neuroscience, 19, 5967–5979. Eccles, J.C. (1981) The modular operation of the cerebral neocortex considered as the material basis of mental events. Neuroscience, 6, 1839–1856. Eccles, J.C. (1984) The cerebral cortex. A theory of its operation. In: E.G. Jones and A. Peters (eds), Cerebral Cortex. Functional Properties of Cortical Cells, Vol. 2, New York and London: Plenum Press, pp. 1–36. Elston, G.N., Tweedale, R. and Rosa, M.G.P. (1999) Cortical integration in the visual system of the macaque monkey: large scale morphological differences of pyramidal neurones in the occipital, parietal and temporal lobes. Proceedings of the Royal Society, London, series B, 266, 1367–1374. Fairén, A. and Valverde, F. (1979) Specific thalamo-cortical afferents and their presumptive targets in the visual cortex. A Golgi study. In: M. Cuénod, G.W. Kreutzberg and F.E. Bloom (eds), Development and Specificity of Neurons, (Progress in Brain Research, Vol. 51), Amsterdam: Elsevier, pp. 419–438. Fairén, A. and Valverde, F. (1980) A specialized type of neuron in the visual cortex of cat: A Golgi and electron microscope study of chandelier cells. Journal of Comparative Neurology, 194, 761–779. Fairén, A., DeFelipe, J. and Regidor, J. (1984) Nonpyramidal neurons. General account. In: A. Peters and E.G. Jones (eds), Cerebral Cortex, Cellular Components of the Cerebral Cortex, Vol. 1, New York and London: Plenum Press, pp. 201–253. Feldman, M. and Peters, A. (1978) The forms of non-pyramidal neurons in the visual cortex of the rat. Journal of Comparative Neurology, 179, 761–794. Fisken, R.A., Garey, L.J. and Powell, T.P.S. (1975) The intrinsic, association and commissural connections of area 17 of the visual cortex. Philosophical Transactions of the Royal Society, London, series B, 272, 487–536.
© 2002 Taylor & Francis
214
Facundo Valverde, Juan A. De Carlos and Laura López-Mascaraque
Freund, T.F., Martin, K.A.C. and Whitteridge, D. (1985) Innervation of cat visual areas 17 and 18 by physiologically identified X- and Y-type thalamic afferents. I. Arborization patterns and quantitative distribution of postsynaptic elements. Journal of Comparative Neurology, 242, 263–274. Friedman, B. and Price, J.L. (1986) Age-dependent cell death in the olfactory cortex: Lack of transneuronal degeneration in neonates. Journal of Comparative Neurology, 246, 20–31. Fujita, I. and Fujita, T. (1996) Intrinsic connections in the macaque inferior temporal cortex. Journal of Comparative Neurology, 368, 467–486. Garey, L.J. (1971) A light and electron microscopic study of the visual cortex of the cat and monkey. Proceedings of the Royal Society, London, series B, 179, 21–40. Gilbert, C.D. and Wiesel, T.N. (1979) Morphology of intracortical projections of functionally characterised neurons in the cat visual cortex. Nature (London), 280, 120–125. Glezer, I.I., Jacobs, M.S. and Morgane, P.J. (1988) Implications of the “initial brain” concept for brain evolution in Cetacea. Behavioral and Brain Sciences, 11, 75–116. Gould, E., Reeves, A.J., Graziano, M.S.A. and Gross, C.G. (1999) Neurogenesis in the neocortex of adult primates. Science (Washington), 286, 548–552. Heimer, L. and Kalil, R. (1978) Rapid transneuronal degeneration and death of cortical neurons following removal of olfactory bulb in adult rats. Journal of Comparative Neurology, 178, 559–610. Hendry, S.H.C., Jones, E.G., Emson, P.C., Lawson, D.E.M., Heizmann, C.W. and Streit, P. (1989) Two classes of cortical GABA neurons defined by differential calcium binding protein immunoreactivities. Experimental Brain Research, 76, 467–472. Hornung, J.P. and Garey, L.J. (1980) A direct pathway from thalamus to visual callosal neurons in the cat. Experimental Brain Research, 38, 121–123. Hornung, J.P. and Garey, L.J. (1981) The thalamic projection to cat visual cortex: Ultrastructure of neurons identified by Golgi impregnation or retrograde horseradish peroxidase transport. Neuroscience, 6, 1053–1068. Hubel, D.H. and Wiesel, T.N. (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Physiology (London), 160, 106–154. Hubel, D.H. and Wiesel, T.N. (1969) Anatomical demonstration of columns in the monkey striate cortex. Nature (London), 221, 747–750. Hubel, D.H. and Wiesel, T.N. (1972) Laminar and columnar distribution of geniculo-cortical fibers in the macaque monkey. Journal of Comparative Neurology, 146, 421–450. Hubel, D.H. and Wiesel, T.N. (1977) Functional architecture of macaque monkey visual cortex. Proceedings of the Royal Society, London, series B, 198, 1–59. Imig, T.J., Reale, R.A. and Brugge, J.F. (1982) The auditory cortex. Patterns of corticocortical projections related to physiological maps in the cat. In: C.N. Woolsey (ed.), Cortical Sensory Organization, Vol. 3, Multiple Auditory Areas, New Jersey: Humana Press, pp. 1–41. Innocenti, G.M. (1979) Adult and neonatal characteristics of the callosal zone at the boundary between areas 17 and 18 in the cat. In: I.S. Russell, M.W. van Hof and G. Berlucchi (eds), Structure and Function of Cerebral Commissures, London: MacMillan Press, pp. 244–258. Jones, E.G. (1975) Varieties and distribution of non-pyramidal cells in the somatic sensory cortex of the squirrel monkey. Journal of Comparative Neurology, 160, 205–268. Jones, E.G. and Burton, H. (1976) Areal differences in the laminar distribution of thalamic afferents in cortical fields of the insular, parietal and temporal regions of primates. Journal of Comparative Neurology, 168, 197–248. Jones, E.G. and Hendry, S.H.C. (1984) Basket cells. In: A. Peters and E.G. Jones (eds), Cerebral Cortex, Vol. 1. Cellular Components of the Cerebral Cortex, New York and London: Plenum Press, pp. 309–336. Jones, E.G. (1984a) History of Cortical Cytology. In: A. Peters and E.G. Jones (eds), Cerebral Cortex, Vol. 1. Cellular Components of the Cerebral Cortex, New York and London: Plenum Press, pp. 1–32. Jones, E.G. (1984b) Laminar distribution of cortical efferent cells. In: A. Peters and E.G. Jones (eds), Cerebral Cortex, Vol. 1. Cellular Components of the Cerebral Cortex, New York and London: Plenum Press, pp. 521–553. Karten, H.J. (1997) Evolutionary developmental biology meets the brain: The origins of mammalian cortex. Proceeding of the National Academy of Sciences, U.S.A., 94, 2800–2804. Kelly, J.P. and Van Essen, D.C. (1974) Cell structure and function in the visual cortex of the cat. Journal of Physiology (London), 238, 515–547. Kisvárday, Z.F., Martin, K.A.C., Whitteridge, D. and Somogyi, P. (1985) Synaptic connections of intracellularly filled clutch cells: A type of small basket cell in the visual cortex of the cat. Journal of Comparative Neurology, 241, 111–137. Kisvárday, Z.F., Cowey, A. and Somogyi, P. (1986) Synaptic relationships of a type of GABA-immunoreactive neuron (clutch cell), spiny stellate cells and lateral geniculate nucleus afferents in layer IVc of the monkey striate cortex. Neuroscience, 19, 741–761. Kisvárday, Z.F., Beaulieu, C. and Eysel, U.T. (1993) Network of GABAergic large basket cells in cat visual cortex (area 18): implication for lateral inhibition. Journal of Comparative Neurology, 327, 398–415.
© 2002 Taylor & Francis
The Cerebral Cortex of Mammals
215
Kritzer, M., Cowey, A. and Somogyi, P. (1992) Patterns of inter- and intralaminar GABAergic connections distinguish striate (V1) and extrastriate (V2, V4) visual cortices and their functionally specialized subdivisions in the rhesus monkey. Journal of Neuroscience, 12, 4545–4564. Le Vay, S. (1973) Synaptic patterns in the visual cortex of the cat and monkey: Electronmicroscoscopy of Golgi preparations. Journal of Comparative Neurology, 150, 53–86. Le Vay, S., Hubel, D.H. and Wiesel, T.N. (1975) The pattern of ocular dominance columns in macaque visual cortex revealed by a reduced silver stain. Journal of Comparative Neurology, 159, 559–576. Lewis, W.B. (1878) On the comparative structure of the cortex cerebri. Brain, 1, 79–86. Lin, C.S., Friedlander, M.J. and Sherman, S.M. (1979) Morphology of physiologically identified neurons in the visual cortex of the cat. Brain Research, 172, 344–348. López-Mascaraque, L. and Price, J.L. (1997) Protein synthesis inhibitors delay transneuronal death in the piriform cortex of young adult rats. Neuroscience, 79, 463–475. Lorente de Nó, R. (1949) Cerebral cortex: Architecture, intracortical connections, motor projections. In Fulton’s Physiology of the Nervous System, London: Oxford University Press, pp. 288–313. Lund, J.S. (1973) Organization of neurons in the visual cortex, area 17, of the monkey (Macaca mulatta). Journal of Comparative Neurology, 147, 455–496. Lund, J.S., Henry, G.H., Macqueen, C.L. and Harvey, A.R. (1979) Anatomical organization of the primary visual cortex (area 17) of the cat. A comparison with area 17 of the macaque monkey. Journal of Comparative Neurology, 184, 599–618. Lund, J.S., Hendrickson, A.E., Ogren, M.P. and Tobin, E.A. (1981) Anatomical organization of primate visual cortex area VII. Journal of Comparative Neurology, 202, 19–45. Lund, J.S. (1984) Spiny stellate neurons. In A. Peters and E.G. Jones (eds), Cerebral Cortex. Cellular Components of the Cerebral Cortex, Vol. 1, New York and London: Plenum Press, pp. 255–308. Marín-Padilla, M. (1987) The chandelier cell of the human visual cortex: A Golgi study. Journal of Comparative Neurology, 256, 61–70. Martin, K.A.C. and Whitteridge, D. (1984) Form, function and intracortical projections of spiny neurons in the striate visual cortex of the cat. Journal of Physiology (London), 353, 463–504. Mates, S.L. and Lund, J.S. (1983) Developmental changes in the relationship between type 2 synapses and spiny neurons in the monkey visual cortex. Journal of Comparative Neurology, 221, 98–105. McMullen, T.N., Glaser, E.M. and Tagamets, M. (1984) Morphometry of spine-free nonpyramidal neurons in rabbit auditory cortex. Journal of Comparative Neurology, 222, 383–395. Meyer, G. and Albus, K. (1981) Spiny stellates as cells of origin of association fibers from area 17 to area 18 in the cat’s neocortex. Brain Research, 210, 335–341. Meyer, G. and Ferres-Torres, R. (1984) Postnatal maturation of nonpyramidal neurons in the visual cortex of the cat. Journal of Comparative Neurology, 228, 226–244. Misson, J.P., Austin, C.P., Takahashi, T., Cepko, C.L. and Caviness, Jr., V.S. (1991) The alignment of migrating neural cells in relation to the murine neopallial radial glial fiber system. Cerebral Cortex, 1, 221–229. Mountcastle, V. (1957) Modality and topographic properties of single neurons of cat’s somatic sensory cortex. Journal of Neurophysiology, 20, 408–434. Mountcastle, V. (1979) An organizing principle for cerebral function: the unit module and the distributed system. In: F.O. Schmidt and F.G. Worden (eds), The Neurosciences. Fourth Study Program, Cambridge, Massachusetts: MIT Press, pp. 21–42. Nieuwenhuys, R. (1994) The neocortex. An overview of its evolutionary development, structural organization and synaptology. Anatomy and Embryology, 190, 307–337. O’Leary, J.L. (1941) Structure of the area striata of the cat. Journal of Comparative Neurology, 75, 131–164. Peters, A. and Feldman, M.L. (1976) The projection of the lateral geniculate nucleus to area 17 of the rat cerebral cortex. I. General description. Journal of Neurocytology, 5, 63–84. Peters, A., Feldman, M.L. and Saldanha, J. (1976) The projection of the lateral geniculate nucleus to area 17 of the rat cerebral cortex. II. Terminations upon neuronal perikarya and dendritic shafts. Journal of Neurocytology, 5, 85–107. Peters, A. and Feldman, M.L. (1977) The projection of the lateral geniculate nucleus to area 17 of the rat cerebral cortex. IV. Termination upon spiny dendrites. Journal of Neurocytology, 6, 669–689. Peters, A., Proskauer, C.C., Feldman, M.L. and Kimerer, L. (1979) The projection of the lateral geniculate nucleus to area 17 of the rat cerebral cortex. V. Degenerating axon terminals synapsing with Golgi impregnated neurons. Journal of Neurocytology, 8, 331–357. Peters, A. and Regidor, J. (1981) A reassessment of the forms of nonpyramidal neurons in area 17 of cat visual cortex. Journal of Comparative Neurology, 203, 685–716. Peters, A., Proskauer, C.C. and Ribak, C.E. (1982) Chandelier cells in rat visual cortex. Journal of Comparative Neurology, 206, 397–416. Peters, A. (1984) Chandelier cells. In: A. Peters and E.G. Jones (eds), Cerebral Cortex, Cellular Components of the Cerebral Cortex, Vol. 1, New York and London: Plenum Press, pp. 361–380.
© 2002 Taylor & Francis
216
Facundo Valverde, Juan A. De Carlos and Laura López-Mascaraque
Peters, A. and Jones, E.G. (1984) Classification of cortical neurons. In: A. Peters and E.G. Jones (eds), Cerebral Cortex. Cellular Components of the Cerebral Cortex, Vol. 1, New York and London: Plenum Press, pp. 107–121. Preuss, T.M., Qi, H. and Kaas, J.H. (1999) Distinctive compartmental organization of human primary visual cortex. Proceedings of the National Academy of Science, U.S.A., 96, 11601–11606. Rakic, P. (1972) Mode of cell migration to the superficial layers of fetal monkey neocortex. Journal of Comparative Neurology, 145, 61–83. Rakic, P. (1985) Contact regulation of neuronal migration. In: G.M. Edelman and J.P. Thiery (eds), The Cell in Contact, New York: Wiley, pp. 67–91. Rockland, K.S. and Pandya, D.N. (1981) Cortical connections of the occipital lobe in the rhesus monkey: interconnections between areas 17, 18, 19 and the superior temporal sulcus. Brain Research, 212, 249–270. Romer, A.S. (1966) Vertebrate Paleontology. Chicago: The University of Chicago Press. Rubenstein, J.L.R. and Rakic, P. (1999) Genetic control of cortical development. Cerebral cortex, 9, 521–523. Sanides, D. (1979) Commissural connections of the visual cortex of the cat. In: I.S. Russell, M.W. van Hof and G. Berlucchi (eds), Structure and Function of Cerebral Commissures, London: MacMillan Press, pp. 236–243. Sanides, F. (1970) Functional architecture of motor and sensory cortices in primates in the light of a new concept of neocortex evolution. In: C.R. Noback and W. Montagna (eds), The Primate Brain, New York: Appleton Century Crofts, pp. 137–208. Simpson, G.G. (1945) The principles of classification and a classification of mammals. Bulletin of the American Museum of Natural History, 85, 1–350. Somogyi, P. (1977) A specific ‘axo-axonal’ interneuron in the visual cortex of the rat. Brain Research, 136, 345–350. Somogyi, P. (1978) The study of Golgi stained cells and of experimental degeneration under the electron microscope: A direct method for the identification in the visual cortex of three successive links in a neuron chain. Neuroscience, 3, 167–180. Somogyi, P., Freund, T. F. and Cowey, A. (1982) The axo-axonic interneuron in the cerebral cortex of the rat, cat and monkey. Neuroscience, 7, 2577–2609. Somogyi, P., Kisvárday, Z.F., Martin, K.A.C. and Whitteridge, D. (1983) Synaptic connections of morphologically identified and physiologically characterized large basket cells in the striate cortex of cat. Neuroscience, 10, 261–294. Somogyi, P. and Cowey, A. (1984) Double bouquet cells. In: A. Peters and E.G. Jones (eds), Cerebral Cortex, Cellular Components of the Cerebral Cortex, Vol. 1, New York and London: Plenum Press, pp. 337–360. Spatz, W.B. (1977) Topographically organized reciprocal connections between areas 17 and MT (visual area of superior temporal sulcus) in the marmoset Callithrix jacchus. Experimental Brain Research, 27, 559–572. Szentágothai, J. (1975) The ‘module concept’ in cerebral cortex architecture. Brain Research, 95, 475–496. Szentágothai, J. (1978) The neuron network of the cerebral cortex: A functional interpretation. Proceedings of the Royal Society of London, series B, 201, 219–248. Szentágothai, J. (1979) Local neuron circuits of the neocortex. In: F.O. Schmitt and F.G. Worden (eds), The Neurosciences. Fourth Study Program, Cambridge, Massachusetts: MIT Press, pp. 399–415. Szentágothai, J. and Arbib, M.A. (1974) Conceptual models of neural organization. Neuroscience Research Program Bulletin, 12, 307–510. Tamamaki, N., Fujimori, E. and Takauji, R. (1997) Origin and route of tangentially migrating neurons in the developing neocortical intermediate zone. Journal of Neuroscience, 17, 8313–8323. Valverde, F. (1968) Structural changes in the area striata of the mouse after enucleation. Experimental Brain Research, 5, 274–292. Valverde, F. (1971) Short axon neuronal subsystems in the visual cortex of the monkey. International Journal of Neuroscience, 1, 181–197. Valverde, F. (1976) Aspects of cortical organization related to the geometry of neurons with intra-cortical axons. Journal of Neurocytology, 5, 509–529. Valverde, F. (1978) The organization of area 18 in the monkey. A Golgi study. Anatomy and Embryology, 154, 305–334. Valverde, F. (1983) A comparative approach to neocortical organization based on the study of the brain of the hedgehog (Erinaceus europaeus). In: S. Grisolía, C. Guerri, F. Samson, S. Norton and F. Reinoso-Suárez (eds), Ramón y Cajal’s Contribution to the Neurosciences, Amsterdam: Elsevier, pp. 149–170. Valverde, F. (1985) The organizing principles of the primary visual cortex in the monkey. In: A. Peters and E.G. Jones (eds), Cerebral Cortex, Vol. 3. Visual Cortex, New York and London: Plenum Press, pp. 207–257. Valverde, F. (1986) Intrinsic neocortical organization: some comparative aspects. Neuroscience, 18, 1–23. Valverde, F. and Facal-Valverde, M.V. (1986) Neocortical layers I and II of the hedgehog (Erinaceus europaeus). I. Intrinsic organization. Anatomy and Embryology, 173, 413–430. Valverde, F., De Carlos, J.A., López-Mascaraque, L. and Doñate-Oliver, F. (1986) Neocortical layers I and II of the hedgehog (Erinaceus europaeus). II. Thalamo-cortical connections. Anatomy and Embryology, 175, 167–179.
© 2002 Taylor & Francis
The Cerebral Cortex of Mammals
217
Valverde, F., Facal-Valverde, M.V., Santacana, M. and Heredia, M. (1989) Development and differentiation of early generated cells of sublayer VIb in the somatosensory cortex of the rat. A combined Golgi and autoradiographic study. Journal of Comparative Neurology, 290, 118–140. Valverde, F. (1998) Golgi Atlas of the Postnatal Mouse Brain. Wien-New York: Springer. Vogt, C. and Vogt, O. (1919) Allgemeinere Ergebnisse unserer Hirnforschung. Journal für Psychologie und Neurologie, 25, 279–462. Welker, C. (1976) Receptive fields of barrels in the somatosensory neocortex of the rat. Journal of Comparative Neurology, 166, 173–190. White, E.L. (1979) Thalamocortical synaptic relations: A review with emphasis on the projections of specific thalamic nuclei to the primary sensory areas of the neocortex. Brain Research Reviews, 1, 275–311. Winfield, D.A. and Powell, T.P.S. (1976) The termination of thalamo-cortical fibers in the visual cortex of the cat. Journal of Neurocytology, 5, 269–281. Wise, S.P. and Jones, E.G. (1976) The organization and postnatal development of the commissural projections in the rat somatic sensory cortex. Journal of Comparative Neurology, 168, 313–344. Woolsey, C.N. (1960) Organization of cortical auditory system: a review and a synthesis. In: G.L. Rasmussen and W.F. Windle (eds), Neural Mechanisms of the Auditory and Vestibular Systems, Springfield, Illinois: Ch. Thomas, pp. 165–180. Woolsey, T.A. and Van der Loos, H. (1970) The structural organization of layer IV in the somatosensory region (SI) of mouse cerebral cortex. Brain Research, 17, 205–242. Woolsey, T.A., Dierker, M.L. and Wann, D.F. (1975) Mouse SmI cortex: qualitative and quantitative classification of Golgi-impregnated barrel neurons. Proceedings of the National Academy of Sciences, U.S.A., 72, 2165–2169. Záborsky, L. and Wolff, J.R. (1982) Distribution patterns and individual variations of callosal connections in the albino rat. Anatomy and Embryology, 165, 213–232. Zhu, Y., Li, H.-S., Zhou, L., Wu, J.Y. and Rao, Y. (1999) Cellular and molecular guidance of GABAergic neuronal migration from an extracortical origin to the neocortex. Neuron, 23, 473–485.
© 2002 Taylor & Francis
11 Laminar Continuity between Neo- and Meso-Cortex: The Hypothesis of the Added Laminae in the Neocortex Robert Miller1 and Rupa Maitra2 1
Otago Centre for Theoretical Studies in Psychiatry and Neuroscience, c/o Department of Anatomy and Structural Biology, School of Medical Science, University of Otago, P.O. Box 913, Dunedin, New Zealand Tel: 0064-3-4797357; FAX: 0064-3-479-7254; e-mail:
[email protected] 2 Department of Anatomic Pathology, Wellington Hospital, Wellington, New Zealand
A major subdivision of the mammalian cerebral cortex is between those regions lying dorsal to the rhinal fissure (dorsal and lateral in primate) and those parts lying ventral to it (ventromedial in primates). With the exception of the hippocampal formation, both parts are described conventionally as six-layered structures. However, in this chapter, the question is raised whether the laminar pattern above and below the rhinal fissure is essentially the same. Evidence relating to this question includes that from comparative anatomy of the cortex, its development, and especially that showing the extent to which different chemical markers are continuous across the rhinal fissure. The conclusion is reached that the middle laminae of the neocortex (laminae III and/or IV) are missing in the region below the rhinal fissure, where the remaining laminae “fill out” the thickness of the cortex. The slice of cortical tissue “added” in the neocortex may have biophysical and connectional properties which enable cell assemblies to form in the neocortex. This appears to be a unique endowment of the mammalian cortex, permitting cortical tissue to acquire more detailed spatiotemporal representations than in the cortical rudiment of submammalian species. KEYWORDS: brain development, comparative anatomy, cerebral cortex, laminae, neurochemical markers
1. INTRODUCTION The cerebral neocortex is a laminated structure usually described in terms of six cellular layers, which, in the main, are continuous from one cytoarchitectural area to another. There may be differences in thickness and cell density of laminae between areas, and laminae are missing in some areas (e.g. lamina IV is missing in the primary motor area). Moreover, for descriptive purposes, laminae may be subdivided in some areas (e.g. lamina IV in the primate visual cortex). Nevertheless the basic six-layered arrangment is a scheme which has general application across areas and across all mammalian species. The neocortex (sometimes referred to as “isocortex”) forming most of the convexity of the hemispheres is distinguished from the more medial parts of cortex, referred to in Broca’s terminology as the “limbic lobe”. However, the word “limbic” has a variety of definitions. As a morphological entity the limbic lobe includes the hippocampal formation (Ammon’s horn, the dentate gyrus and subiculum) which is of far simpler cellular arrangement than the neocortex. The limbic lobe also includes a variety of other areas of cortex, 219 © 2002 Taylor & Francis
220
Robert Miller and Rupa Maitra
such as the cingulate, entorhinal and piriform cortex, whose immediate appearance is more like that of the neocortex. The neocortex varies between species, from a small unfolded structure in the smaller mammals to a large and much-folded surface layer with complex patterns of sulci and gyri, the latter occurring especially in the animals with large forebrains, such as primates and cetaceae. Despite this great variation across species, there is one sulcus which is constant across all mammalian species, the rhinal fissure. This is visible early in development, though its position in the adult varies across species. It separates the neocortex, which lies dorsal and medial to the rhinal fissure (dorsal and lateral in primates), from the “limbic” regions just referred to, which lie ventrally and/or medially to this fissure. In smallbrained mammals, the rhinal sulcus is located on the ventro-lateral aspects of the cortex, running roughly antero-posteriorly. In larger-brained mammals, such as primates, the neocortex is relatively expanded compared with the limbic cortex, and, as a result, the rhinal fissure lies on the ventral aspect of the hemispheres, but, as in animals with smaller brains, runs roughly antero-posteriorly. The regions of the “limbic” cortex which have grossly similar appearance to the neocortex are sometimes referred to as the “mesocortex” (Sarnat and Netsky, 1974), to indicate that they are transitional between typical neocortex and the cortex of the hippocampal formation. Other terms are sometimes used to indicate these subdivisions, the term “allocortex” referring to the hippocampal formation, and the term “periallocortex” refering to the transitional regions, entorhinal, cingulate and piriform cortex (Chronister and White, 1977)2. In terms of laminar architecture the transitional mesocortex (or periallocortex), lying ventral or medial to the rhinal fissure has a striking feature which distinguishes it from the neocortex. This is very easily discernible, and applies universally across mammalian species. This distinction is found in lamina II, the most superficial of the cell-rich layers: in the neocortex this contains many small pyramidal cells, somewhat more denselypacked than in the underlying lamina III, but nevertheless not outstandingly prominent. In contrast, ventral to the rhinal fissure, lamina II is outstanding, as a very dense layer of neurones (Braitenberg and Schüz, 1991). This can be seen easily, with the naked eye, in Nissl stained sections, as shown in the atlases from mouse, rat (see Figure 11.1), rabbit, cat, dog, monkey, human and other species. In atlases of the rat brain, Nissl-stained sections reveal an interesting pattern of transformation across the rhinal fissure from the pattern typical of the neocortex to that typical of the mesocortex. This is depicted in Figure 11.1. Here it can be seen that the cell-dense lamina II of the mesocortex is in continuity not only with a corresponding lamina II of the neocortex, but also with a prominent layer of small cells, densely packed in the neocortex much deeper than lamina II, approximately in the position of lamina IV. This layer gradually “slopes” superficially as it approaches the rhinal fissure from the neocortical side, becoming continuous with lamina II in the mesocortex. It can also be seen in Figure 11.1 that the deeper layer of the neocortex slopes to the surface in the dorso-medial extent of the neocortex, in the region of transition with the cingulate cortex. In the rabbit (Urban and Richard, 1972; Figure 11.2A) the transition between neocortex and mesocortex ventral to the rhinal fissure is more abrupt, but the convergence of the small-celled layers 2
The term “allocortex” is sometimes used collectively to refer to the hippocampus and cortical regions such as entorhinal, cingulate and piriform cortices (the latter otherwise known as “periallocortex” (See Stephan and Andy, 1970)).
© 2002 Taylor & Francis
Laminar Continuity Across the Rhinal Fissure
221
Figure 11.1. Section through rat brain, showing the continuity of layer IV of neocortex with layer II of the cingulate cortex dorsomedially and of the entorhinal cortex ventrolaterally. (The border between the smallcelled, densely-packed layer IV and the underlying layer V in the neocortex is indicated by a black arrow.) Layer III of neocortex thus appears as an addition to the cortical architecture of cingulate and entorhinal cortex (reproduced with permission from Paxinos and Watson [1986] The rat brain in stereotaxic coordinates, 2nd ed, Figure 26R, courtesy of Academic Press).
upon layer II of the mesocortex is still evident. Exactly the same “sloping” cell layer can be seen in the cortex of other species, such as the cat (Figure 11.2B), the marmoset (Stephan et al., 1980; Figure 11.2C) and the human (Braak and Braak, 1992; Figure 11.2D), in the region of the rhinal fissure.
© 2002 Taylor & Francis
222
Robert Miller and Rupa Maitra
A
B
C
D
Figure 11.2. Details of laminar arrangements in the region of the rhinal fissure in rabbit (A: reproduced with permission from Urban, I. and Richard, P. (1972) A stereotaxic atlas of the New Zealand rabbit’s brain., p. 64; courtesy of C.C. Thomas, Springfield, Illinois); cat (B: kindly provided by C.J. Heath), marmoset (C: reproduced with permission from Stephan, H., Baron, G. and Schwertfeger, W.K. (1980), The brain of the common marmoset. A stereotaxic atlas. Springer Verlag, Figure A8.5) and from human (D: reprinted from Neuroscience Research, Vol. 15, H. Braak and E. Braak, The human entorhinal cortex: normal morphology and laminaspecific pathology in various diseases, pp. 6–31, Figure 6A. Copyright [1992], with permission of Elsevier Science). In A and B the mesocortex is located in the lower part of the cortical tissue; in C and D it is found in the right hand side of the Figure. (In D, the transition to the entorhinal cortex in humans is shown. The layers cannot be fully equated with those in the other three Nissl-stained sections, since D uses a different stain [lipofuscin]. The convergence of neocortical layers—to the left—upon the superficial cell layer of the entorhinal cortex—to the right—is clearly seen).
This simple observation gives rise to a broad hypothesis which is investigated in this paper, namely that there is a fairly regular pattern of transformation of the cell layers in the neocortex to give those in the mesocortex. In particular, it is argued that some of the middle laminae in the neocortex are not represented in the mesocortex (either ventrally in the entorhinal cortex, or dorsomedially in the cingulate cortex). They are, as it were, “additions” to convert a more basic cortical laminar architecture found in the mesocortex into that typical of the neocortex. The continuity of mesocortical lamina II to neocortical (approximately) lamina IV is then one out of a number of consequences of the laminar reorganization between the two types of cortex.
© 2002 Taylor & Francis
Laminar Continuity Across the Rhinal Fissure
223
This broad hypothesis can be evaluated using a variety of types of evidence: comparative, developmental, cytological, connectional, but most of all in terms of the layering of numerous chemical markers, which have been described in many publications in the last 20 years. As will be seen such evidence does give striking evidence of a regular transformation of laminae between the neocortex and mesocortex, such that the deeper and most superficial laminae of the neocortex are continuous into the mesocortex, and the neocortical layers between these two appear to be missing in the mesocortex. Amongst the laminar differences which could be studied are a variety of specializations which are local to particular cortical areas. For instance, in visual cortex of primate there are many very specialized laminar features, and a characteristic pattern of cytochrome oxidase staining. In lamina III, this forms an arrangement of cytochrome oxidase-rich “blobs”. Such an arrangement of cytochrome oxidase-rich “blobs” is also found in the entorhinal cortex, but in lamina II rather than lamina III (Hevner and Wong-Riley, 1992). Such features are not general characteristics of the cortex, and are taken here to represent local specializations. Details such as these, interesting though they are, are not the main focus of the present paper. The real focus is data about laminae which can be generalized across the cortical mantle, and thus allow comparison between neocortical and mesocortical regions in their entireties. The main part of this paper explores the above hypothesis in a strictly morphological context. Differences between the neo- and mesocortical regions can thus be considered as probable consequences of differences in the program of brain development between cortical regions. However, the largest body of data assimilated to test the hypothesis is concerned with the laminar distribution of chemical markers, especially those associated with various neurotransmitter systems. Some of these chemical markers may be related to developmental processes (Berger-Sweeney and Hohmann, 1997), and have little functional role in the adult. However, the markers of major neurotransmitter systems are likely to have important functional roles in the adult. Thus, if a major distinction between neocortex and mesocortex can be drawn, based on different laminar distribution of such chemical markers, it is also likely to have important large-scale implications for the differences in cybernetic operation of neocortex vs mesocortex. This topic is not explored in depth here, although a few suggestions will be offered at the end of the paper.
2. COMPARATIVE ASPECTS OF LAMINAR ARRANGEMENT Comparison of the laminar arrangement of the mammalian cortex with that of reptiles (presumably related to the evolutionary antecedents of mammals) is a useful point of departure. Reiner (1993) has discussed this topic in relation to the primitive cortex of the turtle. This cortex has a trilaminar arrangement not unlike that of Ammon’s horn (see Figure 11.3). Thus it has a single cell layer containing pyramidal cells (whose projections descend to the diencephalon and brainstem), a very thick molecular layer superficial to it, containing the apical dendrites of the pyramidal cells, and, deep to the pyramidal cell layer, a basal dendritic zone. In the turtle (according to Smith et al., 1980), thalamic inputs terminate exclusively in the outer third of the molecular layer, that is, on the distal parts of the pyramidal cell apical dendrites, where they show their distal arborization. This is a notable contrast with the mammalian neocortex, in which the cell-rich lamina IV is the main destination of projections from sensory thalamo-cortical nuclei, while projections
© 2002 Taylor & Francis
224
Robert Miller and Rupa Maitra
Figure 11.3. Laminar transformation from ancestral reptilian cortex to typical mammalian neocortex (from Reiner, 1993; Figure 8).
from other thalamic nuclei can be distributed in a variety of complex patterns amongst various laminae, mainly those rich in cells (Steriade et al., 1990), but not focussed on the most superficial layers of the cortex. The arrangement of thalamic projections to the cortex in turtles is however, very similar to their distribution to the mammalian hippocampus (Herkenham, 1978; Wouterlood et al., 1990) where thalamic afferents also terminate in a cell-sparse layer, amongst the distal dendritic arborizations of pyramidal cells. In the mammalian mesocortex, the pattern of termination of thalamic afferents is complex, but in some ways intermediate between those of the hippocampal formation and the neocortex: thalamic afferents terminate in a variety of laminae, amongst which lamina I (the most superficial) is more prominent than for thalamic afferents to the neocortex (entorhinal cortex: Robertson and Kaitz, 1981; Room and Groenewegen, 1986; Yanigahara et al., 1987; retrosplenial cortex: Van Groen and Wyss, 1992; cingulate cortex: Shibata, 1993). Reiner (1991) also points out that the pyramidal cell layer in the turtle cortex largely lacks the interregional and interhemispheric projection neurones so abundant in the mammalian neocortex (Voneida and Ebbesson, 1969; Hall, 1971; Lohman and Mentink, 1972; Ebner, 1976). In mammals, laminae II and III of the neocortex are those from whose neurones such long-distance connections arise in greatest profusion. Taking together these differences between mammalian and turtle cortex, in both thalamocortical and cortico-cortical connections, the suggestion arises that the pyramidal cell layer of the turtle cortex corresponds to lamina V of the mammalian neocortex (both of which have projections descending below the forebrain); and cortical layers II, III and perhaps IV in the mammalian neocortex are in some sense an addition to the simpler cortex found in reptiles, providing the cellular substrate for a massive associative network of cortico-cortical connections, typical of the mammalian neocortex. Viewed from this perspective, the status of the mammalian mesocortex becomes somewhat uncertain. Some areas of the mesocortex (e.g. cingulate) have lamina V pyramidal cells whose projections descend to the brainstem. For other mesocortical regions (e.g. the entorhinal cortex), there is negligible evidence for such descending connections, and probably they do not exist. Mesocortical regions do have prolific long corticocortical connections, but their pattern is quite complex, and it cannot be maintained
© 2002 Taylor & Francis
Laminar Continuity Across the Rhinal Fissure
225
that the superficial laminae are of special importance as origin or termination of such connections. Reiner (1991) adds a third argument in support of his conclusions: he describes the distribution of chemically specified neurones in the reptile cortex and compares them with the mammalian neocortex. He suggests that chemically-specified cell types found only in laminae II-IV of mammalian neocortex (Cholecystokinin-8-, Vasoactive Intestinal Polypeptide-, and Choline Acetyl Transferase-positive neurones) are absent in the equivalent cortical region of turtles, whereas those prominent in the mammalian neocortical laminae I, V and VI (e.g. Substance P-, Neuropeptide Y- and Somatostatin-positive neurones) are found in the reptile cortex. Such evidence is used to suggest that laminae II-IV are an added component in the mammal. However, this argument is less convincing than the strictly morphological ones: for instance other evidence suggests that CCK or its fragments are found in all cortical laminae, though admittedly more abundantly in laminae II and III than in the deeper laminae (Peters et al., 1983; Cho et al., 1993). Neurones which can be labelled for somatostatin mRNA are common in both the supra- and infragranular layers in all regions of rat neocortex (Garrett et al., 1994). Substance P immunoreactive neurones can be found in all layers of rat neocortex, except lamina I, with preference for II/III, according to Kaneko et al. (1994). These distributions disagree with those given by Reiner (1991). Nevertheless, the first two of Reiner’s arguments give an interesting indication of the evolutionary processes leading to the distinctively mammalian neocortex.
3. DEVELOPMENTAL RELATION BETWEEN LAMINAE OF THE MESO- AND NEOCORTEX In neurodevelopmental terms, the deeper layers of the neocortex mature earlier than the superficial layers, and the mesocortex matures before the neocortex. Thus deep neocortical laminae can be expected to develop at the same time as more superficial mesocortical laminae. This could constitute a developmental basis for the apparent continuity between lamina IV of the neocortex and lamina II of the mesocortex: once the latter superficial cell-rich mesocortical lamina has formed no further layers of cells are formed superficial to it, while the neocortex accumulates further cell layers superficial to lamina IV. A few papers give detailed support for such a developmental pattern: Smart and Smart (1982) exposed mouse embryos to tritiated thymidine at various stages of pregnancy. Labelling on the eleventh day of pregnancy labelled lamina IV of the neocortex, and lamina II of the mesocortex. Similar experiments were reported by Sanderson and Weller (1990) and Sanderson and Aitken (1990) in marsupial possums, which are born at a much earlier developmental stage, making the experiment easier to perform. The results were similar: injection at one stage of development labels a continuous band of neurones extending from lamina IV in the medial neocortex, sloping up to lamina III as the rhinal fissure is approached and constituting lamina II in the mesocortex. A cytological feature of the adult cortex allows one to offer a more specific hypothesis about correspondance of laminae between neo- and mesocortex. In the neocortex, lamina IV contains a variety of neurone called the “spiny stellate” cell, similar to pyramidal cells, but lacking prominent apical dendrites. Such cells are very densely packed in primary sensory areas, but are also found in lower density in lamina IV of most other neocortical areas. Similar cells are also characteristic of lamina II of the neocortex, although not
© 2002 Taylor & Francis
226
Robert Miller and Rupa Maitra
generally distinguished as “spiny stellate” because, in a superficial position such as lamina II, it is not remarkable that long apical dendrites are absent. In the mesocortex, lamina II also has dense congregations of spiny stellate cells, but there is no corresponding congregation of such cells in the middle layers of the mesocortex. Thus, one may suggest that the laminae “added on” in the neocortex (especially lamina III) are “inserted” within the original lamina II, so that, in the adult, neocortical layers II and IV are both derived from the original lamina II, and have cytological features in common (abundance of spiny stellate cells). This does not happen in the mesocortex, which may account for the special prominence of lamina II in the mesocortex, rich in densely-packed spiny stellate cells.
4. NEUROCHEMICAL LAMINAR MARKERS 4.1. Methodological Issues In the last fifteen years, histo- and immunochemical methods have become available for labelling a wide variety of chemical markers in brain tissue. Some of these are transmitters, others are their receptors, or the messenger RNA (or fragments of mRNA) involved in synthesis of receptor protein, while yet others are a miscellaneous collection of other neurochemical molecules. As a result of the development of these methods, a large number of papers has been published, giving textual descriptions, or photographic illustrations of the distribution of markers across the laminae of the cerebral cortex. Most of such data comes from rodents, especially rats. This serves the purposes of the present paper well: in a single coronal section of the forebrain it is possible to illustrate the laminar distribution in the neocortex, in the parts of the mesocortex ventral to the rhinal fissure, and in the dorso-medial parts of the mesocortex such as cingulate cortex. In addition, because of the simplicity of the cortex in such animals, it is easier to grasp the overall laminar patterns than in animals with highly folded cortices. Some data are available from carnivores and primates, but in such animals it is only occasionally possible to piece together a coherent picture from which to compare mesocortex and neocortex. In the Tables presented below, most data are from rats or other rodents, but a few are included from carnivores and primates. The studies included in the tables below represent only a selection of those which have been published. A primary basis for the selection of chemical markers to be included is that data are available on cortical laminar distribution in both the neocortex, and at least one mesocortical region. The latter is usually the entorhinal cortex, seen ventral to the rhinal fissure in coronal sections. For many chemical markers, clear evidence is also available for the cingulate cortex, and in a number of cases, where serial sections through the cortex are illustrated, it is clear that the laminar arrangement of the marker is different in the anterior cingulate cortex from the posterior cingulate regions. The latter details are sometimes included in the tables, where the conclusions are relatively clear. Data presented in the tables below are derived from both the illustrations shown in the specific reports, and from the corresponding statements made in the text of these papers. The tabulated data correspond (with one exception) to bands of heavy labelling relative to the surrounding deeper or more superficial laminae. When such a prominent band is tabulated, it is not meant to imply that there is no labelling in adjacent or unmentioned laminae, but rather that they are more weakly labelled than the laminae mentioned in the
© 2002 Taylor & Francis
Laminar Continuity Across the Rhinal Fissure
227
tables. The broad hypothesis under examination is thus evaluated by the continuity (or lack of it) of these oustanding bands between neo- and mesocortex, rather than by presence or absence (in an absolute sense) of label in particular bands. The exception is the banding of the Muscarinic-2 receptor, which is found in all laminae of the neocortex, with the exception of laminae IV. This “gap” is not continuous into the entorhinal cortex, so the tabulated data refers to the “gap” of lower density labelling rather than to the presence of a band of higher density. Many technical details determine the contrast and definition of the laminar distributions revealed in the papers cited. Most important is the particular ligand (tritiated or immunological) used to label a particular receptor or other marker: different ligands may differ in the specificity for the receptor of interest, even when measures are taken to occlude labelling to irrelevant receptors, with an excess of non-tritiated ligands. Another technical matter which affects the clarity of the banding is the concentration of ligand used. A few papers (e.g. Xia and Haddad, 1992) present illustrations of adjacent brain sections, exposed to a series of different concentrations of the ligand of a receptor. This shows that the contrast in the banding across the cortical laminae varies substantially according to this variable. The definition of the laminar binding in published illustrations undoubtedly also reflects such things as exposure time when autoradiography is used, as well as other details of photographic processing. The conclusion presented in the table below reflects the consensus results between different papers which sometimes use a variety of ligands. It is assumed that suboptimal techniques (with respect to details such as those just mentioned) can reduce the contrast of labelled bands in the cortex, but are not likely to produce discrete bands when none exists. Therefore, the findings represented in the Tables give greater emphasis to those illustrations in which laminar patterns are revealed with greatest clarity. Occasionally there is clear disagreement between different studies of the same marker, and this is indicated in footnotes in the tables. Only rarely do the studies cited identify laminae of distribution of a neurochemical marker by rigorous comparison with Nissl-stained sections. There may thus be minor inaccuracies in defining laminae, especially for the middle laminae of the cortex, and when contrast between labeling of adjacent bands is poor. In addition, in the mesocortex, the definition of laminae is less well established than in the neocortex. Thus, in the mesocortex laminar detail is sometimes referred to in broad terms as “superficial”, “middle” or “deep”. 4.2. Classification of Patterns of Banding In tabulating the data, banding patterns are classified into five different groups, shown in separate tables. Four of these make up patterns which are clearly those predictable from the broad hypothesis described above. The fifth constitutes miscellaneous exceptions which do not fit this hypothesis. When, as sometimes happens, a particular ligand is distributed in more than one band, these being separated in different laminae, the bands for that ligand in different laminae may have appropriate places in more than one of the tables. In such cases, the labelling for such a ligand appears more than once in the tables, with each of its bands of labelling identified in the corresponding tables as “deep” “middle” or “superficial”. It is recognized that there may be significant differences between species in laminar distribution of a ligand, and separate entries are therefore made for different
© 2002 Taylor & Francis
Robert Miller and Rupa Maitra
228
species, including humans. For a particular combination of ligand and species, entries are made in the tables only when there is data on laminar distribution from at least one paper for both the neocortex and one of the areas of mesocortex. 4.2.1. Banding Pattern I: Bands in deep laminae of neocortex continuous into deep laminae of the mesocortex The broad hypothesis particularly concerns lamina IV and laminae superficial to it. Therefore, the laminar transformation between neocortex and mesocortex, if it exists, might have little effect on the deepest laminae. Hence one might expect that ligands which label a band in lamina V and/or VI of the neocortex would label a band in a similar position of the mesocortex. Table 11.1 and Figure 11.4 shows, this to be the case: many ligands (cholinergic, monoaminergic, peptidergic, as well as an adenosine receptor) which label lamina V or VI of the neocortex also label deep layers of the entorhinal cortex. The data on the cingulate cortex is less complete, and there may be differences between anterior and posterior cingulate cortex, and inconsistencies in the relative distribution between the two when different ligands are compared. There are a few exceptions relating to neocortical laminae V and VI, which are included in Table V: Dopamine D1 receptors, and dopamine fibres constitute deep bands in the neocortex, which extend to fill all laminae in the entorhinal cortex. The dopamine- and adenosine 3′5′-monophosphate-regulated phosphoprotein
Table 11.1. PATTERN I: Ligands which label only lamina VI or VI/V of neocortex, and only deep layers of mesocortex Chemical marker
Sp. Neoctx Entorhinal Cingulate Refs
N (deep)
R
V
deep
N-mRNA, alpha-2 submit Bungarotoxin receptors (deep) Musc-2 (deep) Musc-3 mRNA (deep) D-1 (deep)
R
V, VI
deep
R
VI
deep
R R
VI VI
R
D-1 mRNA (deep) R D-2 R
ant: deep 15, 62, 72 post: ?A NR 80 presynaptic location on ACh fibres (71)
deep deep
ant: deep 16, 26 post: A NR 70 NR 12, 77
V-VI
VI
ant: VI
14, 60, 81
located extra-synaptically and in dendritic spines (69)
VI V(VI)
V-VI deep
NR ant: deep post: sup A ant: deep post: deep NR
22 9
5HT-2 (deep) CCK (deep)
R V-VI GP VI
deep deep
CCK (deep)
R
VI
NR
CCK mRNA
R
VI, V
deep
Ad-1 (deep)
R
V-VI
deep
Notes, and key to authors: see Table 11.5.
© 2002 Taylor & Francis
Comments (ref)
28, 51 87 67
ant: deep 63, 65 post: A deep 19, 24, 35, 83
located in cortico-thalamic neurones neuronal labelling
Laminar Continuity Across the Rhinal Fissure 1
2
3
4
229
Figure 11.4. Ligands whose binding to the cortex shows continuity between deep layers of neocortex and deep layers of mesocortex. 1. Bungarotoxin (reproduced with permission from Clarke, P.B.S. (1985), Nicotinic acid binding in rat brain: autoradiographic comparison of [3H]acetylcholine, [3H]nicotine and [3H]-alphabungarotoxin. Journal of Neuroscience, 5, 1307–1315, Figure 3b, right). 2. Tachikinin (reproduced with permission of Blackwells Science, Ltd., from Bergstrom, L., Torrens, Y., Saffroy, M., Beaujouan, J.C., Lavielle, S., Chassaing, G., Morgat, J.L., Glowinski, J. and Marquet, A. (1987) (3H) Neurokinin B and I251-boulton hunter eledoisin label identical tachykinin binding sites in the rat brain. Journal of Neurochemistry, 48(1), 125–133. Figure 11.7 upper right). 3. Muscarinic-3 mRNA (reproduced with permission from Buckley, N.J., Bonner, T.I. and Brann, M.R. 1988, Localization of a family of muscarinic receptor mRNAs in rat brain. Journal of Neuroscience 8, 4646–4652; Figure 2 col 3, B3). 4. CCK mRNA (reproduced with permission, from Schiffman, S.N. and Vanderhaeghen, J.J. (1991), Distribution of cells containing mRNA encoding cholecystokinin in the rat central nervous system. Journal of Comparative Neurology, 304, 219–233, Figure 1f).
(known as “DARPP-32”) labels a band in lamina VI of the neocortex, which does not continue into the entorhinal cortex. Nevertheless, there is substantial support for the broad hypothesis in so far as it has implications for laminae V and VI. 4.2.2. Banding Pattern 2: Bands in the deeper half of the neocortex which come to fill all cell-rich laminae of the mesocortex According to the hypothesis of this paper, the neocortex is envisaged to have laminae added which are not represented in the mesocortex. However, the mesocortex (particularly
© 2002 Taylor & Francis
Robert Miller and Rupa Maitra
230
Table 11.2. PATTERN II: Ligands which label laminae V & VI or IV, V & VI of neocortex, and come to fill all cell-rich laminae of mesocortex Chemical marker
Sp.
Neoctx
Entorhinal
Cingulate
Refs
AChE (deep, intermediate) AChE
Ms
IV-VI or IV
II-VI
II-VI
29
R
IV(V-VI)
all
13
5HT-1a Kainate (deep)
R R
V, VI V-VI
all all
ant: all post: II, V-VI III-VI III-VI
Comments (ref)
colocalized in GABA neurones (27A)
66, 76 41, 73
Notes, and key to authors: see Table 11.5.
the entorhinal cortex) is not significantly reduced in overall thickness compared with the neocortex. Therefore the neocortical laminae on either side of this added component would be expected to “fill out” the thickness of the cortex, where they extend into the mesocortex. It is therefore predicted that bands of labelling in the deeper half of the neocortex would come to fill out layers of mesocortex which are thicker than in the neocortex. A few markers (Table 11.2 and Figure 11.5, parts 1 and 2) clearly show this pattern, including acetylcholinesterase, serotonin 1a receptors, and the deep band of kainate receptors. It should however be noted that in monkeys, acetylcholinesterase has a heterogeneous distribution in both entorhinal and cingulate cortex, which does not clearly fit the predicted pattern (Table 11.5). Another exception (Table 11.5) is the somatostatin receptor in rats, which is found in laminae IV to VI of neocortex, but only in the deep laminae of the entorhinal cortex.
4.2.3. Banding Pattern 3: Bands in lamina IV and/or III of neocortex which terminate abruptly on approach to the rhinal fissure or the cingulate cortex The most important prediction from the broad hypothesis investigated here is that chemical markers which label bands in the middle laminae of the neocortex (IV and/or III), should show an abrupt termination on approach to either the rhinal fissure or the cingulate cortex. A wide variety of markers show this pattern, and the published pictures are sometimes of striking clarity. Markers whose bands in laminae III or IV terminate at the rhinal fissure include cholinergic receptors (both nicotinic and muscarinic), aminergic receptors (noradrenergic, serotoninergic, histaminergic), GABA and benzodiazepine receptors, neurotensin and adenosine receptors. The pattern at the border between neocortex and cingulate cortex is more complex, with a difference between anterior and posterior cingulate cortex which is repeated for several markers: the neocortical middle-depth band continues at a superficial level in the anterior cingulate cortex, but is missing in the posterior cingulate cortex. Table 11.3 shows more detail of the ligands whose banding fits this pattern, and Figure 11.6 gives illustrative examples. Apparent exceptions to the prediction are found in primates (Table 11.5)—the DARPP-32 band in lamina III of monkeys, and the 5HT-2 band in lamina III of humans. Both of these bands continue, in the same or different laminar positions, into the entorhinal or cingulate cortex.
© 2002 Taylor & Francis
1
2
3
DLG
ZI
4
5
© 2002 Taylor & Francis
231
Figure 11.5. Parts 1 and 2. Ligands binding in the deep half of the neocortex which come to fill out a greater thickness of the mesocortex. 1. Acetylcholine-esterase (reproduced with permission from Bogdanovic, N., Islam, A., Nilson, L., Bergstrom, L., Winblad, B. and Adem, A. (1993), Effects of nucleus basalis lesion on muscarinic receptor subtypes. Experimental Brain Research, 97, 225–232, Figure 3, left side) 2. 5HT1a receptors, (reproduced with permission from Pompeiano, M., Palacios, J.M. and Mengod, G. (1992), Distribution and cellular localization of mRNA coding for 5HT1a receptor in the rat brain: correlation with receptor binding. Journal of Neuroscience, 12, 440–453; Figure 2D’). Parts 3, 4 and 5. Ligands binding in laminae I and II (or I-III) of neocortex, and continuing into superficial laminae of the mesocortex. 3. NMDA receptors (reproduced with permission from Maragos, W.F., Penney, J.B. and Young, A.B. (1988) Anatomic correlation of NMDA and 3H-TCP-labeled receptors in rat brain. Journal of Neuroscience, 8, 493–501; Figure 1F; note also detail of narrow unlabelled band in entorhinal cortex). 4. DARPP-32 (reprinted from Molecular Brain Research, M. Schalling et al., Distribution and cellular localization of DARPP-32 mRNA in rat brain, pp. 139–149, Figure 1i, Copyright [1990] with permission of Elsevier Science). 5. Muscarinic-3 receptors (reprinted from Neuroscience, Vol. 40, M.T. Vilaro et al., Muscarinic cholinergic receptors in the rat caudate-putamen and olfactory tubercle belong predominantly to the M4 class: in situ hybridization and receptor autoradiography evidence, pp. 159–167, Figure 2D. Copyright [1991], with permission from Elsevier Science).
Laminar Continuity Across the Rhinal Fissure
LH
Robert Miller and Rupa Maitra
232
Table 11.3. PATTERN III: Ligands in lamina IV and/or III of neocortex, which terminate abruptly on approach to the rhinal fissure or cingulate cortex Chemical marker Sp.
Neoctx
Entorhinal
Cingulate
Refs
Comments (ref)
N (middle)
R
III-IV
A
ant: sup post: A
15, 16, 62, 72
located in both TC axons (57) and cell bodies (61)
N mRNA (alpha-3 submit) Musc-2
R
IV
A
A
80
R
IV(gap) no gap
no gap
3, 37, 61, 70*
NE-alpha1b mRNA NE-beta2
R
IV-V
A
39
R
IV
A
5HT-2 (middle)
R
IV
A
Hist-1 GABAa
R R
IV III-IV
A A
BZ
R
IV
A
NT (middle) Ad-1 (middle)
R R
IV IV
A A
ant: sup post: A ant: NR post: A ant: sup post: A A ant: NR post: A ant: sup post: A NR NR
located on thalamic axons and cortical cell dendrites (78) localized in cell bodies
32, 46 2, 7, 21, 28 43, 52, 68 50 49, 74+
in 5HT axons in S1, M1 lam V (7)
23, 74, 75 42 19, 20, 24, 35, 66, 83
Notes, and key to authors: see Table 11.5. * But see (82) for conflicting results + But see (49) for conflicting results
4.2.4. Banding Pattern 4: Bands in lamina I and/or II of neocortex (whether or not lamina III is also labelled), narrowing to superficial bands in the mesocortex It was noted above that, if the broad hypothesis about laminar reorganization between neoand meso-cortex is correct, neocortical laminae on either side of this added component would be expected to “fill out” the thickness of the cortex, where they extend into the mesocortex. This appeared to be true be for deep laminae. It might also then be expected that there be some form of continuity between the most superficial laminae of the neocortex, and superficial laminae of the mescortex. The details of this cannot be predicted from the broad hypothesis. However, available data show a tendency for several markers, which form a band in laminae I and II (or I-III) of the neocortex to continue as a much narrower band within the most superficial layer of the mesocortex. Ligands which show such a pattern in the entorhinal cortex include a subunit of the nicotinic receptor mRNA, mRNA for muscarinic receptors, DARPP-32 in rats, two types of glutamate receptor, and the mRNA for the CCK receptor. Data for the cingulate cortex is less complete, and, where available, fits the pattern in the entorhinal cortex in only some cases. Table 11.4 gives details of the ligands whose banding fits this pattern, and Figure 11.5 (3–5) illustrates some examples (see also Figure 11.4(4)). There are also some apparent exceptions: Muscarinic M-5 receptors, which form a band in superfical layers of the neocortex appear to form no band in the entorhinal
© 2002 Taylor & Francis
Laminar Continuity Across the Rhinal Fissure 1
2
3
4
233
Figure 11.6. Ligands binding in lamina III and or IV of neocortex, in a band which terminates on approaching cingulate or entorhinal cortices. 1. Nicotinic receptors (reproduced with permission from Clarke, P.B.S. (1985), Nicotinic acid binding in rat brain: autoradiographic comparison of [3H]acetylcholine, [3H]nicotine and [3H]alpha-bungarotoxin. Journal of Neuroscience, 5, 1307–1315, Figure 3b). 2. 5HT-2 receptors (reprinted from Brain Research, Vol. 453, M.E. Blue et al., Correspondance between 5HT2 receptors and serotonergic axons in rat cortex. pp. 315–328, Figure 1A. Copyright [1988], with permission of Elsevier Science). 3. Noradrenergic beta-2 receptors (from Ordway et al., 1988, Journal of Pharmacology and Experimental Therapeutics, Vol. 247, pp. 379–389, Figure 1; reproduced with permission). 4. Benzodiazepine receptors (reproduced with permission of Blackwell Science, Ltd., from Niddam, R., Dubois, A., Scatton, B., Arbilla, S. and Langer, S.Z. (1987), Autoradiographic localization of [3H]zolpidem binding sites in the rat CNS: comparison with the distribution of [3H]nitrazepam binding sites. Journal of Neurochemistry, 49(3), 890–899, Figure 1b).
Table 11.4. PATTERN IV: Ligands with bands in neocortex lamina I and/or II (whether or not lamina III is also labelled), narrowing to quite superficial band in the mesocortex Chemical marker
Sp.
Neoctx
Entorhinal
Cingulate
Refs
N mRNA alpha-2 subunit (sup) Musc-3 mRNA Musc-4 mRNA DARPP-32 NMDA
R
I-III
I
NR
80
R R R R
I-II III I-III I-III
I I I-II I, III*
12, 77 12, 77 47, 64 31, 38, 41
Kainate CCK mRNA (sup)
R R
I-II I-II
I I
I NR A ant: I-II post: A I-II ant: I post: A
Comments (ref)
41 65
Notes, and key to authors: see Table 11.5 * In entorhinal cortex there is a sharply defined lamina between the NMDA-labelled laminae I and III which is not labelled (see Figure 11.5(3)).
© 2002 Taylor & Francis
Robert Miller and Rupa Maitra
234 Table 11.5.
EXCEPTIONS
Chemical marker
Sp. Neoctx
Entorhinal Cingulate
Refs
Musc-2 (sup) AChE fibre D-1 (deep) Dopamine fibres Dopamine fibres DARPP-32 (deep)
R Mk C R Mk R
A H/C all all H/C A
37, 70 27, 40 14, 59, 60 17, 18, 54, 86 1, 4, 36 64
DARPP-32 neurones NE-alpha-1
Mk II-III + V-VI
II-III
sup H/C all all H/C ant: deep post: deep V-VI
R
IV, V(2)
middle
sup
33, 48
NE-beta-1 5HT-2 QUIS SS receptor
R H R R
I-III III, V I-II IV-V, VI
A I, II-III, V I-II(III) deep
A III or III, V NR ant: deep post: deep
32, 46, 57 30, 53* 45 58
I-II/III IV or IV-VI VI VI H/C VI
Comments (ref)
5 develops with NE fibres (34)
* But see (86) for differing results Notes Tables 11.1–11.5 Where labelling in adjacent laminae consists of two separate bands they are separated by a comma (e.g. “V, VI”) When labelling within a single lamina is split into two separate bands it is indicated as “[2]”. When labelling is continuous across laminae this is indicated with a “-” (e.g “V-VI”). References cited are those in which the tabulated labelling is most clearly seen. Abbreviations (Tables 11.1–11.5) C = cat; GP = guinea pig; H = human; Mk = monkey; Ms = mouse; R = rat; NR = Not reported or pictures not clear; Ad = adenosine; BZ = benzodiazepine receptors; CCK = cholecystokinin; D = dopamine; Musc = muscarinic; Hist = histamine; N = nicotinic; NT = neurotensin; QUIS = quisqualate receptors; S1,M1= primary somatosensory and motor cortical areas; SS = somatostatin; A = ligand absent, or band of labelling not present; H/C = complex and heterogeneous distribution. Key to authors (Tables 11.1–11.5) 1. Akil and Lewis (1993) 2. Appel et al. (1990) 3. Aubert et al. (1992) 4. Berger et al. (1988) 5. Berger et al. (1990) 6. Bergstrom et al. (1987) 7. Blue et al. (1988) 8. Bogdanovic et al. (1993) 9. Bouthenet et al. (1987) 10. Bravo and Karten (1992) 11. Broide et al. (1995) 12. Buckley et al. (1988) 13. Butcher and Woolf (1984) 14. Camps et al. (1990) 15. Clarke et al. (1984) 16. Clarke et al. (1985) 17. Descarries et al. (1987) 18. Doucet et al. (1988) 19. Fastbom and Fredholm (1987) 20. Fastbom et al. (1987) 21. Fischette et al. (1987) 22. Fremeau et al. (1991) 23. Giradino et al. (1993) 24. Goodman and Synder (1982)
© 2002 Taylor & Francis
Laminar Continuity Across the Rhinal Fissure 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87.
Hallanger et al. (1986) Harfstrand et al. (1988) Hedreen et al. (1984) Hoffman et al. (1987) Hohmann and Ebner (1985) Hoyer et al. (1986) Jacobson and Cottrell (1993) Johnson et al. (1989) Jones et al. (1985a) Jones (1985b) Lee and Reddington (1986) Lewis et al. (1988) Mash and Potter (1986) Maragos et al. (1988) McCune et al. (1993) Mesulam et al. (1984) Monoghan and Cotman (1982) Moyse et al. (1987) Nakada et al. (1984) Niddam et al. (1987) Olsen et al. (1987) Ordway et al. (1988) Ouimet et al. (1989) Palacios et al. (1987) Palacios et al. (1981a) Palacios et al. (1981b) Pazos and Palacios (1985) Pazos et al. (1985) Pazos et al. (1987) Phillipson et al. (1987) Pieribone et al. (1994) Pompeiano et al. (1992) Rainbow et al. (1984) Reubi and Maurer (1985) Richfield et al. (1987) Richfield et al. (1989) Rossner et al. (1995) Sahin et al. (1992) Savasta et al. (1988) Schalling et al. (1990) Schiffman and Vanderhaegen (1991) Schroeder et al. (1989) Senatorov et al. (1995) Slater and Patel (1983) Smiley et al. (1994) Spencer et al. (1986) Sugaya et al. (1991) Swanson et al. (1987) Unnerstall and Wamsley (1983) Unnerstall et al. (1981) Unnerstall et al. (1982) Verge et al. (1986) Vilaro et al. (1991) Vogt and Burns (1988) Vogt et al. (1990) Wada et al. (1989) Wamsley et al. (1991) Wang et al. (1989) Weber et al. (1988) Weber et al. (1990) Xia and Haddad (1992) Yoshida et al. (1988) Zarbin et al. (1983)
© 2002 Taylor & Francis
235
236
Robert Miller and Rupa Maitra
cortex. Noradrenergic beta-1 receptors are exceptional in a similar way. Quisqualate receptors show continuity between neo- and entorhinal cortex, but the band of labelling appears not to narrow in the entorhinal cortex as do other ligands included in Table 11.4.
5. SUMMARY AND COMMENT Despite a number of exceptional cases, the broad hypothesis tested in this article receives striking support. The detailed version of the hypothesis supported by the data in Table 11.1–11.4 is shown in Figure 11.7. According to this summary, lamina III and/or IV of the neocortex are missing in the entorhinal cortex and other areas of mesocortex, while deeper laminae of the neocortex appear to fill out the missing layers of the mesocortex. Layers superficial to lamina III (and sometimes including lamina III) of the neocortex tend to be reduced to a narrow band (laminae I and/or II) of the entorhinal cortex, and possibly other regions of mesocortex. The slab of cortical tissue which is added in the neocortex appears to be inserted within the original lamina II as it is found in the mesocortex3. It is interesting also to note that the laminar transformation between neo- and mesocortex documented here in mammals is not the same as that advocated by Reiner et al. (1993) between the rudimentary cortex of reptiles and the more advanced cortex of mammals. Specifically, there is no superficial layer of cells (and associated chemical markers) in common between reptiles and mammals (see Figure 11.3), whereas there is good evidence of continuity of superficial cell layers and markers between neo- and mesocortex (Table 11.4, and Figure 11.5 [parts 3–5]). Most of the data tabulated in Tables 11.1–11.5 refer to the rat. It may be asked how far the laminar transformation between neo- and mesocortex summarised in Figure 11.7 applies in other species. Figure 11.2 shows that, as far as adult cellular arrangement goes, there is considerable similarity between widely different mammalian species in the laminar transformation in the neighbourhood of the rhinal fissure. There is also similarity in the developmental sequences between different species (mouse, marsupial opossum). However, it is not clear whether this similarity across species extends to the chemical markers of different laminae compiled in Tables 11.1–11.5. In the few instances where sufficent data are available for species with larger brains, the laminar pattern appears to be more complex and more heterogeneous than in rats. In animals, with larger brains it may not be possible to summarise the banding pattern of chemical markers in terms of simple rules for laminar transformation between neocortex and mesocortex. Nevertheless, the data compiled here referring mainly to animals with smooth brains, together with the other data mentioned, suggests a systematic reorganization of laminae on either side of the rhinal fissure. Since many of the ligands whose banding is summarised in Figure 11.7 are major neurotransmitters, with potent biophysical effects on cortical neurones, the laminar
3
Sanides (1970) comes close to this formulation of the laminar transformation between neo- and meso-cortex (periallocortex in his terminology). He notes that the periallocortex receives thalamic and olfactory input to superficial cell layers compared with lamina IV in the neocortex, and emphasises another description of cellular arrangement of the periallocortex—as two cell layers, separated by a cell-sparse layer, the “lamina dissecans”. He regards neocortical growth in mammals as the accumulation of concentric rings of growth superimposed on the original limbic ring; and in this scheme the periallocortex is the first growth ring.
© 2002 Taylor & Francis
Laminar Continuity Across the Rhinal Fissure Neocortexx Neocorte
237
mesocortex
I II
III (IV)
IV
V/VI rhinal fissure Figure 11.7. Schematic summary of laminar transformation between neo- and mesocortex.
transformation shown is likely to have important implications for large scale cybernetic differences between neo- and mesocortex. It is premature to explore these differences in detail. However, there is great scope for further investigation of the cybernetic significance of the different markers. The connectional data referred to in the early part of this paper imply that the laminae added in the neocortex, as part of the distinctively mammalian cortex, have a much richer endowment of cortico-cortical connections than the mesocortex. This being so, it is likely that the mammalian cortex has developed specifically to allow representation by complex combinations of linked co-active neurones—that is by cell assemblies (to use Hebb’s terminology). Two sorts of information are needed before the cybernetic differences between neo- and mesocortex can be unravelled in more detail: (i) The cellular elements upon which each ligand is located—be they postsynaptic neuronal cell bodies or dendrites, presynaptic terminals of various kinds, or glial cells—need to be identified. (ii) The biophysical role which each receptor, neurotransmitter or neuromodulator plays for the structure on which it is located needs to be made clear. For most of the chemical markers listed in the tables above, the detail on either of these questions is scanty or non-existant. In addition, the assimilation of such data into a coherent scheme of the cybernetic differences between different regions of cortex depends on the development of hypotheses about the dynamics of laminar interaction and their role in information processing. A start was made to the latter enterprise in two recent papers (Miller, 1996a,b; see also Miller, this volume). This included one example of how laminar differences in distribution of a neurochemical marker may have cybernetic significance: the NMDA receptors are believed to be of special importance in strengthening of excitatory synapses, according to the Hebbian paradigm. They are particuarly abundant in laminae II and III of the neocortex. This is one of the arguments for suggesting that these laminae are central in the formation of neural assemblies, whose formation is envisaged to rely on strengthening of excitatory synapses. One further point of cybernetic significance is offered before finishing this paper: in origin, the mesocortex has links with the olfactory system, while the mammalian neocortex has links mainly with visual, auditory and somatosensory systems. The former of these
© 2002 Taylor & Francis
238
Robert Miller and Rupa Maitra
has, in principle, little ability to represent exact temporal patterns, since olfactory stimuli do not give information which is fine-grained in the time dimension. Moreover, olfactory information accesses the cortex directly without the relay through the thalamus. However, the latter three sensory systems do have the ability to represent precise temporal patterns, and are relayed to the cortex via the thalamus. It has recently been suggested (Miller, 1996b; Miller, this volume) that the interplay between thalamus and neocortex serves to enhance the capacity of the neocortex to represent temporal information within cell assemblies. Thus, the emergence, during evolution, of the mammalian neocortex can be regarded as a development which allows representation of exact temporal structure, as well as the “spatial” information content of cell assemblies.
REFERENCES Akil, M. and Lewis, D.A. (1993) The dopaminergic innervation of monkey entorhinal cortex. Cerebral Cortex, 3, 533–550. Appel, N.M., Mitchell, W.M., Garlick, R.K., Glennon, R.A., Teitler, M. and De Souza, E.B. (1990) Autoradiographic characterization of (+/–)-1-(2,5-dimethoxy-4-[125I]iodophenyl)-2-aminopropane ([125I]DOI) binding to 5HT2 and 5HT1c receptors in rat brain. Journal of Pharmacology and Experimental Therapeutics, 255, 843–857. Aubert, I., Cecyre, D., Gauthier, S. and Quirion, R. (1992) Characterization and autoradiographic distribution of [3H]AF-DX384 binding to putative muscarinic M2 receptors in the rat brain. European Journal of Pharmacology, 217, 173–184. Berger, B., Trottier, S., Verney, C., Gaspar, P. and Alvarez, C. (1988) Regional and laminar distribution of the dopamine and serotonin innervation in the macaque cerebral cortex: a radioautographic study. Journal of Comparative Neurology, 273, 99–119. Berger, B., Febvret, A., Greengard, P. and Goldman-Rakic, P.S. (1990) DARPP-32, a phosphoprotein enriched in dopaminoceptive neurons bearing dopamine D1 receptors: distribution in the cerebral cortex of the newborn and adult rhesus monkey. Journal of Comparative Neurology, 299, 327–348. Berger-Sweeney, J. and Hohmann, C.F. (1997) Behavioural consequences of abnormal cortical development: insights into developmental disabilities. Behavioral Brain Research, 86, 121–142. Bergstrom, L., Torrens, Y., Saffroy, M., Beaujouan, J.C., Lavielle, S., Chassaing, G., Morgat, J.L., Glowinski, J. and Marquet, A. (1987) (3H)neurokinin B and I251-boulton hunter eledoisin label identical tachykinin binding sites in the rat brain. Journal of Neurochemistry, 48, 125–133. Blue, M.E., Yagaloff, K.A., Mamounas, L.A., Hartig, P.R. and Molliver, M.E. (1988) Correspondance between 5HT2 receptors and serotonergic axons in rat cortex. Brain Research, 453, 315–328. Bogdanovic, N., Islam, A., Nilson, L., Bergstrom, L., Winblad, B. and Adem, A. (1993) Effects of nucleus basalis lesion on muscarinic receptor subtypes. Experimental Brain Research, 97, 225–232. Bouthenet, M.-L., Martres, M.-P., Sales, N. and Schwartz, J.-C. (1987) A detailed mapping of dopamine D-2 receptors in rat central nervous system by autoradiography with [125I]iodosulpride. Neuroscience, 20, 117–155. Braak, H. and Braak, E. (1992) The human entorhinal cortex: normal morphology, and lamina-specific pathology in various diseases. Neuroscience Research, 15, 6–31. Braitenberg, V. and Schüz, A. (1991) Anatomy of the cortex statistics and geometry. Springer-Verlag: Berlin Heidelberg. Bravo, H. and Karten, H.J. (1992) Pyramidal neurons of the rat cerebral cortex, immunoreactive to nicotinic acetylcholine receptors, project mainly to subcortical targets. Journal of Comparative Neurology, 320, 62–68. Broide, R.S., O’Connor, L.T., Smityh, M.A., Smith, J.A.M. and Leslie, F.M. (1995) Developmental expression of alpha-7 neuronal nicotinic receptor messenger mRNA in rat sensory cortex and thalamus. Neuroscience, 67, 83–94. Buckley, N.J., Bonner, T.I. and Brann, M.R. (1988) Localization of a family of muscarinic receptor mRNAs in rat brain. Journal of Neuroscience, 8, 4646–4652. Butcher, L.L. and Woolf, N.J. (1984) Histochemical distribution of acetylcholinesterase in the central nervous system: clues to the localization of cholinergic neurons. In: A. Bjorklund, T. Hokfelt and M.J. Kuhar (eds), Handbook of chemical neuroanatomy, Vol. 3, Classical transmitters and transmitter receptors in the CNS. Amsterdam: Elsevier, pp. 1–50. Camps, M., Kelly, P.H. and Palacios, J.M. (1990) Autoradiographic localization of dopamine D1 and D2 receptors in the brain of several mammalian species. Journal of Neural Transmission, 80, 105–127.
© 2002 Taylor & Francis
Laminar Continuity Across the Rhinal Fissure
239
Cho, H.J. and Takagi, H. (1993) Cholecystokinin (CCK)-8-immunoreactivity in the piriform cortex of the rat with special reference to its fine structures. Osaka City Medical Journal, 39, 75–92. Chronister, R.B. and White, Jr., L.E. (1977) Fiber architecture of the hippocampal formation: Anatomy, projections and structural significance. In: R.L. Isaacson and K.H. Pribram (eds), The Hippocamus, Vol. 1, Stucture and development. New York and London: Plenum Press, pp. 9–39. Clarke, P.B.S., Pert, C.B. and Pert, A. (1984) Autoradiographic distribution of nicotine receptors in rat brain. Brain Research, 323, 390–395. Clarke, P.B.S. (1985) Nicotinic acid binding in rat brain: autoradiographic comparison of [3H]acetylcholine, [3H]nicotine and [3H]-alpha-bungarotoxin. Journal of Neuroscience, 5, 1307–1315. Descarries, L., Lemay, B., Doucet, G. and Berger, B. (1987) Regional and laminar density of the dopamine innervation in adult rat cerebral cortex. Neuroscience, 21, 807–824. Doucet, G., Descarries, L., Audet, M.A., Garcia, S. and Berger, B. (1988) Radioautographic method for quantifying regional monoamine innervations in the rat brain. Application to the cerebral cortex. Brain Research, 441, 233–259. Ebner, F.F. (1976) The forebrain of reptiles and mammals. In: R.B. Masterton, M.E. Bitterman, C.B.G. Campbell and N. Hutton (eds), Evolution of brain and behaviour in vertebrates. Hillsdale, N.J.: Erlbaum, L. Associates, pp. 147–167. Fastbom, J. and Fredholm, B.B. (1987) Regional differences in the GTP-dependence of adenosine receptor binding in rat brain. Acta Physiologica Scandinavica, 131, 467–469. Fastbom, J., Pazos, A. and Palacios, J.M. (1987) The distribution of adenosine A1 receptors and 5′-nucleotidase in the brain of some commonly used experimental animals. Neuroscience, 22, 813–826. Fischette, C.T., Nock, B. and Renner, K. (1987) Effects of 5,7-dihydroxytryptamine on serotonin1 and serotonin 2 receptors throughout the rat central nervous system using quantitative autoradiography. Brain Research, 421, 263–279. Fremeau, R.T., Duncan, G.E., Fornaretto, M.-G., Dearry, A., Gingrich, J.A., Breese, G.R. and Caron, M.G. (1991) Localization of D1 dopamine receptor mRNA in brain supports a role in cognitive, affective and neuroendocrine aspects of dopaminergic neurotransmission. Proceedings of the National Academy of Sciences, U.S.A., 88, 3772–3776. Garrett, B., Finsen, B. and Wree, A. (1994) Parcellation of cortical areas by in situ hybridization for somatostatin mRNA in the frontal, parietal, occipital and temporal regions. Anatomy and Embryology, 190, 389–398. Giradino, L., Zanni, M., Velardo, A., Amato, G. and Calza, L. (1993) Effect of sertraline treatment on benzodiazepine receptors in the rat brain. Journal of Neural Transmission, General Section, 94, 31–41. Goodman, R.R. and Snyder, S.H. (1982) Autoradiographic localization of adenosine receptors in rat brain using [3H]cyclohexyladenosine. Journal of Neuroscience, 2, 1230–1241. Hall, J.A. (1971) Efferent projections from the general cortex in the turtle (Pseudemys scripta). Masters Thesis, Brown University, U.S.A. Hallanger, A.E., Wainer, B.H. and Rye, D.B. (1986) Colocalization of gamma-aminobutyric acid and acetylcholinesterose in rodent cortical neurons. Neuroscience, 19, 763–769. Harfstrand, A., Adem, A., Fuxe, K., Agnati, L., Andersson, K. and Nordberg, A. (1988) Distribution of nicotinic cholinergic receptors in the rat tel- and diencephalon: a quantitative receptor autoradiographical study using [3H]-acetylcholine, [alpha-125]bungarotoxin and [3H]nicotine. Acta Physiologica Scandinavica, 132, 1–14. Hedreen, J.C., Uhl, G.R., Bacon, S.J., Famborough, D.M. and Price, D.L. (1984) Acetycholinesterase-immunoreactive axonal network in monkey visual cortex. Journal of Comparative Neurology, 226, 246–254. Herkenham, M. (1978) The connections of the nucleus reunions thalami: Evidence for a direct thalamohippocampal pathway in the rat. Journal of Comparative Neurology, 177, 589–610. Hevner, R.F. and Wong-Riley, M.T.T. (1992) Entorhinal cortex of the human, monkey and rat: metabolic map as revealed by cytochrome oxidase. Journal of Comparative Neurology, 326, 451–469. Hoffman, B.J., Scheffel, U., Lver, J.R., Karpa, M.D. and Hartig, P.R. (1987) N1-methyl-2–125I-Lysergic acid diethylamide, a preferred ligand for in vitro and in vivo characterization of serotonin receptors. Journal of Neurochemistry, 48, 115–124. Hohmann, C.F. and Ebner, F.F. (1985) Development of cholinergic markers in mouse forebrain. I. Choline acetyltransferase enzyme activity and actylcholinesterase histochemistry. Developmental Brain Research, 23, 225–241. Hoyer, D., Pazos, A., Probst, A. and Palacios, J.M. (1986) Serotonin receptors in the human brain. II. Characterization and autoradiographic localization of 5HT1c and 5HT2 recognition sites. Brain Research, 376, 97–107. Jacobsen, W. and Cottrell, G.A. (1993) Rapid visualization of NMDA receptors in the brain: characterization of (+)-3-[125I]-iodo-MK-801 binding to thin sections of rat brain. Journal of Neuroscience Methods, 46, 17–27. Johnson, E.W., Wolfe, B.B. and Molinoff, P.B. (1989) Regulation of subtypes of beta-adrenergic receptors in rat brain following treatment with 6-hydroxydopamine. Journal of Neuroscience, 9, 2297–2305. Jones, L.S., Gauger, L.L. and Davis, J.N. (1985a) Anatomy of brain alpha1-adrenergic receptors: In vitro autoradiography with [125I]-HEAT. Journal of Comparative Neurology, 231, 190–208.
© 2002 Taylor & Francis
240
Robert Miller and Rupa Maitra
Jones, L.S., Gauger, L.L., Davis, J.N., Slotkin, T.A. and Bartolome, J.V. (1985b) Postnatal development of brain alpha-1-adrenergic receptors: in vitro autoradiography with [125]HEAT in normal rats and rats treated with alpha-difluoromethylornithine, a specific irreversible inhibitor of ornithine decarboxylase. Neuroscience, 15, 1195–1202. Kaneko, T., Shigemoto, R., Nakanishi, S. and Mizuno, N. (1994) Morphological and chemical characteristics of substance P receptor-immunoreactive neurons in the rat neocortex. Neuroscience, 60, 199–211. Lee, K.S. and Reddington, M. (1986) Autoradiographic evidence for multiple CNS binding sites for adenosine derivatives. Neuroscience, 19, 535–549. Lewis, D.A., Foote, S.L., Goldstein, M. and Morrison, J.H. (1988) The dopaminergic innervation of monkey prefrontal cortex: a tyrosine hydroxylase immunohistochemical study. Brain Research, 449, 225–243. Lohman, A.H. and Mentink, G.M. (1972) Some cortical connections of the tegu lizard (tupinambis teguixin). Brain Research, 45, 325–344. Maragos, W.F., Penney, J.B. and Young, A.B. (1988) Anatomic correlation of NMDA and 3H-TCP-labeled receptors in rat brain. Journal of Neuroscience, 8, 493–501. Mash, D.C. and Potter, L.T. (1986) Autoradiographic localization of M1 and M2 muscarine receptors in the rat brain. Neuroscience, 19, 551–564. McCune, S.K., Voigt, M.M. and Hill, J.M. (1993) Expression of multiple alpha adrenergic receptor subtype messenger mRNAs in the adult rat brain. Neuroscience, 57, 143–151. Mesulam, M.-M., Rosen, A.D. and Mufson, E.J. (1984) Regional variations in cortical cholinergic innervation: chemoarchitectonics of acetylcholinestarase-containing fibers in the macaque brain. Brain Research, 311, 245–258. Miller, R. (1996a) Neural assemblies and laminar interactions in the cerebral cortex. Biological Cybernetics, 75, 253–261. Miller, R. (1996b) Cortico-thalamic interplay and the security of operation of neural assemblies and temporal chains in the cerebral cortex. Biological Cybernetics, 75, 263–275. Monoghan, D.T. and Cotman, C.W. (1982) The distribution of [3H]kainic acid binding sites in rat CNS as determined by autoradiography. Brain Research, 252, 91–100. Moyse, E., Rostene, W., Vial, M., Leonard, K., Mazella, J., Kitabgi, P., Vincent, J.-P. and Beaudet, A. (1987) Distribution of neurotensin binding sites in rat brain: a light microscopic radioautographic study using monoiodo[125I]tyr3-neurotensin. Neuroscience, 22, 525–536. Nakada, M.T., Wieczorek, C.M. and Rainbow, T.C. (1984) Localization and characterization by quantitative autoradiography of [125I]LSD binding sites in rat brain. Neuroscience Letters, 49, 13–18. Niddam, R., Dubois, A., Scatton, B., Arbilla, S. and Langer, S.Z. (1987) Autoradiographic localization of [3H]zolpidem binding sites in the rat CNS: comparison with the distribution of [3H]nitrazepam binding sites. Journal of Neurochemistry, 49, 890–899. Olsen, R.W., Szamraj, O. and Houser, C.R. (1987) [3H]AMPA binding to glutamate receptor subpopulations in rat brain. Brain Research, 402, 243–254. Ordway, G.A., Gambarana, C. and Frazer, A. (1988) Quantitative autoradiography of central beta adrenoceptor subtypes: comparison of the effects of chronic treatment with desipramine or central administered i-isoproterenol. Journal of Pharmacology and Experimental Therapapeutics, 247, 379–389. Ouimet, C.C., Hemmings, H.C. and Greengard, P. (1989) DARPP-32, a cyclic AMP-regulated phosphoprotein enriched in dopamine-innervated brain regions. II. Immunocytochemical localization in rat brain. Journal of Neuroscience, 9, 865–875. Palacios, J.M., Wamsley, J.K. and Kuhar, M.J. (1981a) High affinity GABA receptors-autoradiographic localization. Brain Research, 222, 285–307. Palacios, J.M., Wamsley, J.K. and Kuhar, M.J. (1981b) The distribution of histamine H1 receptors in the rat brain: an autoradiographic study. Neuroscience, 6, 15–37. Palacios, J.M., Hoyer, D. and Cortes, R. (1987) Alpha-1 adrenoceptors in the mammailian brain: similar pharmacology but different distribution in rodents and primates. Brain Research, 419, 65–75. Pazos, A. and Palacios, J.M. (1985) Quantitative autoradiographic mapping of serotonin receptors in the rat brain. I. Serotonin-1 receptors. Brain Research, 346, 205–230. Pazos, A., Cortes, R. and Palacios, J.M. (1985) Quantitative autoradiographic mapping of serotonin receptors in the rat brain. II. Serotonin-2 receptors. Brain Research, 346, 231–249. Pazos, A., Probst, A. and Palacios, J.M. (1987) Serotonin receptors in the human brain-IV. Autoradiographic mapping of serotonin-2 receptors. Neuroscience, 21, 123–139. Peters, A., Miller, M. and Kimerer, L.M. (1983) Cholecystokinin-like immunoreactive neurons in rat cerebral cortex. Neuroscience 8, 431–448. Phillipson, O.T., Kilpatrick, I.C. and Jones, M.W. (1987) Dopaminergic innervation of the primary visual cortex in the rat, and some correlations with human cortex. Brain Research Bulletin, 18, 621–633. Pieribone, V.A., Nicholas, A.P., Dagerlind, A. and Hokfelt, T. (1994) Distribution of alpha-1 adrenoceptors in rat brain revealed by in situ hybridization experiments utilizing subtype-specific probes. Journal of Neuroscience, 14, 4252–4268.
© 2002 Taylor & Francis
Laminar Continuity Across the Rhinal Fissure
241
Pompeiano, M., Palacios, J.M. and Mengod, G. (1992) Distribution and cellular localization of mRNA coding for 5HT1a receptor in the rat brain: correlation with receptor binding. Journal of Neuroscience, 12, 440–453. Rainbow, T.C., Parsons, B. and Wolfe, B.B.(1984) Quantitative autoradiography of beta-1 and beta-2 adrenergic receptors in rat brain. Proceedings of the National Academy of Science, U.S.A., 81, 1585–1589. Reiner, A. (1991) A comparison of neurotransmitter-specific and neuropeptide-specific neuronal cell types present in the dorsal cortex in turtles with those present in the isocortex in mammals: Implications for the evolution of Isocortex. Brain Behavior and Evolution, 38, 53–91. Reiner, A. (1993) Neurotransmitter organisation and connections of turtle cortex: Implications for the evolution of mammalian isocortex. Comparative Biochemistry and Physiology, 104A, 735–748. Reubi, J.C. and Maurer, R. (1985) Autoradiographic mapping of somatostatin receptors in the rat central nervous system and pituitary. Neuroscience, 15, 1183–1193. Richfield, E.K., Debowey, D.L., Penney, J.B. and Young, A.B. (1987) Basal ganglia and cerebral cortex distribution of dopamine D1 and D2 receptors in neonatal and adult cat brain. Neuroscience Letters, 73, 203–208. Richfield, E.K., Young, A.B. and Penney, J.B. (1989) Comparative Distributions of dopamine D-1 and D-2 receptors in the cerebral cortex of rats, cats and monkeys. Journal of Comparative Neurology, 286, 409–426. Robertson, R.T. and Kaitz, S. (1981) Thalamic connections with limbic cortex.I. Thalamocortical projections. Journal of Comparative Neurology, 195, 501–525. Room, P. and Groenewegen, H.J. (1986) Connections of the parahippocampal cortex in the cat. II. Subcortical Afferents. Journal of Comparative Neurology, 251, 451–473. Rossner, S., Schliebs, R., Perez-Polo, J.R., Wiley, R.G. and Bigl, V. (1995) Differential changes in cholinergic markers from selected brain regions after specific immunolesion of rat cholinergic basal forebrain system. Journal of Neuroscience Research, 40, 31–43. Sahin, M., Bowen, W.D. and Donghue, J.P. (1992) Location of nicotinic and muscarinic cholinergic and u-opiate receptors in rat cerebral neocortex: evidence from thalamic and cortical lesions. Brain Research, 579, 135–147. Sanderson, K.J. and Aitken, L.M. (1990) Neurogenesis in a marsupial: The brush-tailed possum (trichosurus vulpecula) I. Visual and auditory pathways. Brain Behavior and Evolution, 35, 325–338. Sanderson, K.J. and Weller, W.L. (1990) Gradients of neurogenesis in possum neocortex. Developmental Brain Research, 55, 269–274. Sanides, F. (1970) Functional architecture of motor and sensory cortices in primates in the light of a new concept of neocortex evolution. In: C.R. Noback and W. Montagna (eds), The Primate Brain. New York: AppletonCentury-Crofts, pp. 137–208. Sarnat, H.B. and Netsky, M.G. (1974) Evolution of the Nervous System. Oxford University Press, Oxford. Savasta, M., Palacios, J.M. and Mengod, G. (1988) Regional localization of the mRNA coding for the neuropeptide cholecystokinin in the rat brain studied by in situ hybridization. Neuroscience Letters, 93, 132–138. Schalling, M., Djurfeldt, M., Hokfelt, T., Ehrlich, M., Kurihara, T. and Greengard, P. (1990) Distribution and cellular localization of DARPP-32 mRNA in rat brain. Molecular Brain Research, 7, 139–149. Schiffman, S.N. and Vanderhaegen, J.J. (1991) Distribution of cells containing mRNA encoding cholecystokinin in the rat central nervous system. Journal of Comparative Neurology, 304, 219–233. Schroeder, H., Zilles, K., Maelicke, A. and Hajos, F. (1989) Immunohisto- and cytochemical localization of cortical nicotinic cholinoceptors in rat and man. Brain Research, 502, 287–295. Senatorov, V.V., Trudeau, V.L. and Bin, H. (1995) Expression of cholecystokinin mRNA in combined fluorescence in situ hybridization and retrograde tracing study in the ventrolateral thalamus of the rat. Brain Research, Molecular Brain Research, 30, 87–95. Shibata, H. (1993) Efferent projections from the anterior thalamic nuclei to the cingulate cortex in the rat. Journal of Comparative Neurology, 330, 533–542. Slater, P. and Patel, S. (1983) Autoradiographic distribution of serotonin receptors in rat brain. European Journal of Pharmacology, 92, 297–298. Smart, I.H.M. and Smart, M. (1982) Growth Patterns in the lateral wall of the mouse telencephalon. I. Autoradiographic studies of the histogenesis of the isocortex and adjacent areas. Journal of Anatomy, 134, 273–298. Smiley, J.F., Levey, A.I., Ciliax, B.J. and Goldman-Rakic, P.S. (1994) D1 dopamine receptor immunoreactivity in human and monkey cerebral cortex: Predominant and extrasynaptic localisation in dendrite spines. Proceedings of the National Academy of Sciences, U.S.A., 91, 5720–5724. Smith, L.M., Ebner, F.F. and Colonnier, M. (1980) The thalamocortical projection in pseudemys turtles: A qualitative electron microscopic study. Journal of Comparative Neurology, 190, 445–461. Spencer, D.G., Horvath, E. and Traber, J. (1986) Direct autoradiographic determination of M1 and M2 muscarinic acetylcholine receptor distribution in the rat brain: relation to cholinergic nuclei and projections. Brain Research, 380, 59–68. Stephan, H. and Andy, O.J. (1970) The allocortex in primates. In: C.R. Noback and W. Montagna (eds), The Primate Brain. New York: Appleton-Century-Crofts, pp. 109–136. Stephan, H., Baron, G. and Schwertfeger, W.K. (1980) The brain of the common marmoset. A stereotaxic atlas. Springer Verlag, Berlin, Heidelberg.
© 2002 Taylor & Francis
242
Robert Miller and Rupa Maitra
Steriade, M., Jones, E.G. and Llinas, R.R. (1990) Thalamic oscillations and signalling. New York: Wiley Interscience. Sugaya, K., Downen, M. and Giacobini, E. (1991) Nucleus basalis lesions decraease alpha- and kappa-bungarotoxins binding in rat cortex. NeuroReport, 2, 177–180. Swanson, L.W., Simmons, D.M., Whiting, P.J. and Lindstrom, J. (1987) Immunohistochemical localization of neuronal nicotinic receptors in the rodent central nervous system. Journal of Neuroscience, 7, 3334–3342. Unnerstall, J.R., Kuhar, M.J., Niehoff, D.L. and Palacios, J.M. (1981) Benzodiazepine receptors are coupled to a subpopulation of gamma-aminobutyric acid (GABA) receptors. evidence from a quantitative autoradiographic study. Journal of Pharmacology and Experimental Therapapeutics, 218, 797–804. Unnerstall, J.R., Niehoff, D.L., Kuhar, M.J. and Palacios, J.M. (1982) Quantiative autoradiography using [3H]ultrofilm: application to multiple benzodiazepine receptors. Journal of Neuroscience Methods, 6, 59–73. Unnerstall, J.R. and Wamsley, J.K. (1983) Autoradiographic localization of high-affinity [3H]kainic acid binding sites in the rat forebrain. European Journal of Pharmacology, 86, 361–371. Urban, I. and Richard, P. (1972) A stereotaxic atlas of the New Zealand rabbit’s brain. C.C. Thomas, Springfield, Illinois. Van Groen, T. and Wyss, J.M. (1992) Projections from the laterodorsal nucleus of the thalamus to the limbic and visual cortices in the rat. Journal of Comparative Neurology, 324, 427–448. Verge, D., Daval, G., Marcinkiewicz, M., Patey, A., El Mestimawy, S., Gozlan, H. and Hamon, M. (1986) Quantitative autoradiography of multiple 5-HT1 receptor subtypers in the brain of control or 5,7-dihydroxytryptamine-treated rats. Journal of Neuroscience, 8, 3474–3482. Vilaro, M.T., Wiederhold, K.-H., Palacios, J.M. and Mengod, G. (1991) Muscarinic cholinergic receptors in the rat caudate-putamen and olfactory tubercle belong predominantly to the M4 class: in situ hybridization and receptor autoradiography evidence. Neuroscience, 40, 159–167. Vogt, B.A. and Burns, D.L. (1988) Experimental localization of muscarinic receptor subtypes to cingulate cortical afferents and neurons. Journal of Neuroscience, 8, 643–652. Vogt, B.A., Plager, M.D., Crino, P.B. and Bird, E.D. (1990) Laminar distribution of muscarinic acetylcholine, serotonin, GABA and opioid receptors in human posterior cingulate cortex. Neuroscience, 36, 165–174. Voneida, T.J. and Ebbesson, S.O.E. (1969) On the origin and distribution of axons in the pallial commissures in the Tegu Lizard (Tupinambis nigropunctatus). Brain Behavior and Evolution, 2, 467–481. Wada, E., Wada, K., Boulter, J., Deneris, E., Heinemann, S., Patrick, J. and Swanson, L.W. (1989) Distribution of alpha2, alpha3, alpha4 and beta2 neuronal nicotinic receptor subunit mRNAs in the central nervous system: A hybridization histochemical study in the rat. Journal of Comparative Neurology, 284, 314–335. Wamsley, J.K., Hunt, M.E., McQuade, R.D. and Alburges, M.E. (1991) [3H]SCH39166, a D1 dopamine receptor antagonist: binding characteristics and localization. Experimental Neurology, 111, 145–151. Wang, J.-X., Roeske, W.R., Hawkins, K.N., Gehlert, D.R. and Yamamura, H.I. (1989) Quantitative autoradiography of M2 muscarinic receptors in the rat brain identified by using a selective radioligand [3H]AF-DX 116. Brain Research, 477, 322–326. Weber, R.G., Jones, C.R., Palacios, J.M. and Lohse, M.J. (1988) Autoradiographic visualization of A1-adenosine receptors in brain and peripheral tissues of rat and guinea pig using 125I-HPIA. Neuroscience Letters, 87, 215–220. Weber, R.G., Jones, C.R., Lohse, M.J. and Palacios, J.M. (1990) Autoradiographic visualization of A1 adenosine receptors in rat brain with [3H]8-cyclopentyl-1,3-dipropylxanthine. Journal of Neurochemistry, 54, 1344–1353. Wouterlood, F.G., Saldana, E. and Witter, M.P. (1990) Projection from the nucleus reuniens thalami to the hippocampal region: Light and electron microscopic tracing study in the rat with the anterograde tracer phaseolus vulgaris-leucoagglutinin. Journal of Comparative Neurology, 296, 179–203. Xia, Y. and Haddad, G.G. (1992) Ontogeny and distribution of GABAa receptors in rat brainstem and rostral brain regions. Neuroscience, 49, 973–989. Yanigahara, M., Niimi, K. and Ono, K. (1987) Thalamic projection to the hippocampal and entorhinal areas in the cat. Journal of Comparative Neurology, 266, 122–141. Yoshida, M., Sakai, M., Kani, K., Nagatsu, I. and Tanaka, M. (1988) The dopaminergic innervation as observed by immunohistochemistry using anti-dopamine serum in the rat cerebral cortex. Experientia, 44, 700–702. Zarbin, M.A., Innis, R.B., Wamsley, J.K., Snyder, S.H. and Kuhar, M.J. (1983) Autoradiographic localization of cholecystokinin receptors in rodent brain. Journal of Neuroscience, 3, 877–906.
© 2002 Taylor & Francis
Part IV FUNCTIONAL EQUIVALENCE BETWEEN AREAS
© 2002 Taylor & Francis
12 Cross-Modal Plasticity as a Tool for Understanding the Ontogeny and Phylogeny of Cerebral Cortex Sarah L. Pallas Department of Biology, Georgia State University, P.O. Box 4010, Atlanta, GA 30302 Correspondence: S.L. Pallas, Tel: 404-651-1551; FAX: 404-651-2509; e-mail:
[email protected]
There has been a continuing debate on the relative roles of nature and nurture in specifying cortical areal identity, and it shows no signs of abatement. The cross-modal plasticity paradigm provides a unique and direct way to address the controversy, and has provided important insights. This approach has shown that diverting visual afferents to auditory thalamus from birth results in an auditory cortex with reorganized circuitry, with visual responses typical of those seen in visual cortex, and with the ability to mediate visual behaviour. This work provides strong evidence that sensory afferents have the ability to organize cortical circuits for the purpose of their own optimum processing. The ability of sensory inputs to drive the organization of cortical circuitry may also facilitate recovery from perinatal brain damage, and compensation by sensory substitution in deaf or blind individuals. Furthermore, the cross-modal approach provides insights into how new cortical areas may have arisen, or how changes in peripheral sensory systems could have been accommodated by existing cortical areas during mammalian evolution. KEYWORDS: areal specification, auditory cortex, cortical development, cross-modal plasticity, ferret, sensory cortex, visual cortex
1. INTRODUCTION Philosopher-scientists as far back as Galen (see Changeux, 1986, p. 8) understood that one of the most fundamental organizing principles of the mammalian neocortex is its parcellation into anatomically and functionally distinct areas. Neocortical areas are similar in many ways, including types of cellular constituents, the presence of six cell layers, and the laminar organization of inputs and outputs. Neocortical areas differ from one another as well, along several structural and functional dimensions, including connectivity, topography, and response properties (Seitz, this volume). During evolution, neocortical parcellation produces new areas where they did not previously exist. Although major advances in understanding the brain have certainly been made in the last two centuries, it remains unclear what factors drive the appearance of distinct neocortical areas during either development or evolution (Pallas, 2001). Why has this issue remained so refractory to resolution? With regard to evolutionary mechanisms, descriptive studies of fossil brain-cases (Jerison, 1990) and comparative studies of the brains of extant species have provided a great deal of insight into the evolution of new cortical areas. One reasonable future goal for work in this field would be to develop an experimental system in which a novel 245 © 2002 Taylor & Francis
246
Sarah L. Pallas
cortical area can be induced in the laboratory on a more manageable time scale than that allowed by evolutionary studies. Developmental approaches may allow this feat to be accomplished. The areal specification process is evident during development, in that areas are progressively carved out of a fairly uniform sheet of embryonic precursor cells. New cortical areas appear in temporal sequence during development (Luskin and Shatz, 1985; Bayer and Altman, 1991) as they do spatially during evolution. However, unlike evolving systems, developing systems have the advantage that they can be manipulated experimentally. Evolution can operate through developmental mechanisms, with development providing substrates for change and setting constraints on the degree of change over time. The “evo-devo” approach of using developmental studies to inform evolutionary studies, and vice versa, has been very fruitful in this regard (Goodman and Coughlin, 2000). Thus, understanding how areal specification occurs during development may provide considerable insight into how new areas arose from simpler ancestral brains. In this review I will argue that the cross-modal plasticity paradigm (Schneider, 1973; Frost, 1981; Sur et al., 1988) is a uniquely valuable approach to this area of research.
2. NATURE OR NURTURE? Investigators studying cortical development have more or less divided themselves into two camps which fall along the classic nature-nurture fault line. That is, regionalization of the cortical sheet could be due to intrinsic, preprogrammed differences (nature), or to extrinsic factors such as thalamic inputs or the sensory information which they carry from the periphery (nurture). Such dichotomous views have definite heuristic value, but they tend to outlive their usefulness. We are now in a position to evaluate and synthesize the contributions and shortcomings of both views. The idea that intrinsic and extrinsic patterning information act synergistically to specify an area is now gaining sway. Clearly, early events prior to the formation of connections between the brain and the sensory organs are necessarily controlled by intrinsic factors, but later events may be directed by extrinsic factors, intrinsic factors, or both. Intrinsic and extrinsic factors are likely to interact in as yet unknown ways in late development. Although activity-dependent processes have classically been thought of as being driven extrinsically, it is becoming clear that most brain regions exhibit spontaneous patterned activity (Wong et al., 1993; Gu and Spitzer, 1997; O’Donovan, 1999; Weliky and Katz, 1999; see Katz and Shatz, 1996, for review), and thus both extrinsic and intrinsic control can be exerted electrically through neuronal activity. Furthermore, specification events that are initially under intrinsic control may be reversible at later stages by extrinsic information such as patterned sensory input (see below). 2.1. Source of the Debate The swings in the number of adherents to the extrinsic- vs the intrinsic-control hypotheses of cortical regionalization have seemed almost comparable in amplitude to naturedriven versus nurture-driven trends in child rearing practices (Bruer, 1999; Gopnik et al., 1999). The most recent debate perhaps began with a review article by Pasko Rakic (1988) in which he proposed that a fate map, or “protomap” of the cortical sheet exists in the nascent ventricular zone. Subsequent reviews by Dennis O’Leary (1989) and my colleagues
© 2002 Taylor & Francis
Cross-Modal Plasticity Studies of Cortical Development
247
and I (Pallas, 1990; Sur et al., 1990) argued for what O’Leary called the “protocortex” hypothesis—that the type of thalamic input or sensory activity that any given cortical region receives is a primary determinant of its identity. Subsequent reviews have oscillated primarily around these two views (Shatz, 1992; Walsh, 1993; Levitt et al., 1997; Butler and Molnar, 1998), although a refreshingly synergistic review has appeared recently (Kingsbury and Finlay, 2001). 2.2. What do we mean by Cortical Identity? Areal specification, or parcellation, is a process that may be distinct from what some have referred to as regionalization—the process whereby large regions of cortex come to have different features from other regions, usually with respect to gene expression patterns (e.g. Pimenta et al., 1996; Bishop et al., 2000). When we refer to specification of cortical identity, do we mean regionalization or parcellation, and at what point do we consider that an identity has been attained irreversibly? One confound has been in viewing cortical identity as a unitary concept (Levitt et al., 1997). There are undoubtedly numerous factors contributing to the establishment of the multitudinous characteristics that make up each unique cortical area. Another issue is that what is defined as an extrinsic or intrinsic factor has changed over time. Extrinsic can be (and has been) defined relative to the cortex, or relative to the animal, and it is important for investigators to be clear about their frame of reference. Events that occur prior to ingrowth of thalamocortical afferents are necessarily independent of sensory experience, but the importance of early, patterned, spontaneous activity is becoming apparent (Wong et al., 1993; Ruthazer and Stryker, 1996; Kandler and Katz, 1998; Cook et al., 1999; Crair, 1999), and, depending on its source, could be considered an intrinsic or extrinsic factor. These early events are thus under intrinsic control, whereas those that occur later could be under extrinsic control, intrinsic control, or both. There could be, and certainly are, cortical specification events that are intrinsically specified initially but that are later altered by experience. If agreement can be reached on the definition of cortical identity, the remaining problem would be to determine the specific and differential roles of intrinsic and extrinsic factors, and their reversibility, for each specification event. To accomplish this, the research must be conducted in a species in which the thalamocortical pathway is formed postnatally, or in which the contribution of prenatal, patterned spontaneous activity in thalamocortical afferents can be defined.
3. EVIDENCE FAVOURING INTRINSIC OR EXTRINSIC SPECIFICATION 3.1. Evidence for Specification by Intrinsic Factors To provide the most convincing evidence that cortical parcellation is controlled by intrinsic factors, it would be desirable to identify different molecular markers or labels in each cortical area, prior to the ingrowth of axons carrying information from extrinsic sources. Several molecular markers are found only in certain cortical regions (e.g. Arimatsu et al., 1992; Cohen-Tannoudji et al., 1994; Paysan et al., 1994, 1997; Pimenta et al., 1996; Reinoso et al., 1996; Nakagawa et al., 1999; Rubenstein et al., 1999), and in some cases
© 2002 Taylor & Francis
248
Sarah L. Pallas
these markers are expressed at a time when they would be in a position to direct regional fate independently of extrinsic information. However, few of these markers have been found to be restricted to the confines of a single functionally defined cortical area (but see Cohen-Tannoudji et al., 1994; Nothias et al., 1998). Areal restrictions on markers are not a necessary condition for specification however, because sharp boundaries between adjacent cortical areas could be achieved through differential, threshold-type responses of the tissue to a gradient of a morphogen, or to several nested morphogens, as occurs elsewhere along the neuraxis (Lumsden and Krumlauf, 1996; Tanabe and Jessell, 1996). Recently, several factors have been found in the forebrain which are expressed in gradients, boundary zones, or compartments (Donoghue and Rakic, 1999; Rubenstein et al., 1999), including Emx genes ( Nakagawa et al., 1999; Bishop et al., 2000), Otx2 (Nothias et al., 1998), ephrins (Mackarehtschian et al., 1999; Miyashita-Lin et al., 1999), Pax6 (Bishop et al., 2000), cadherins (Nakagawa et al., 1999), and several classes of transcription factors (Miyashita-Lin et al., 1999; Nakagawa et al., 1999). In addition to the descriptive studies mentioned here, two recent papers using an experimental approach (Bishop et al., 2000; Mallamaci et al., 2000) strongly support the idea that morphogenic gradients can specify the cortex. They argue that knockout of the regulatory genes Emx2 or Pax6 result, respectively, in a respecification and/or prenatal loss of the caudal or rostral portions of the cortical epithelium where the respective genes are normally highly expressed, and in a disproportionate expansion of opposite areas of cortex, where the genes exhibit low expression. The results suggest that the genes are conferring regional identity on portions of cortex. Whether such a regional identity translates into an identity for discrete cortical areas remains an open question. Knockout experiments suggest that at least some of these marker patterns can be estabished independent of thalamocortical input (Miyashita-Lin et al., 1999; Nakagawa et al., 1999; Bishop et al., 2000), but some apparently require thalamic input for upregulation and maintenance of the marker (Nothias et al., 1998; Gitton et al., 1999). Unfortunately, many knockout mice die perinatally (Guillemot et al., 1993; Miyashita-Lin et al., 1999; Bishop et al., 2000), preventing determination of the role of intrinsic factors in specification of cortical features which appear postnatally, such as cytoarchitecture, local connectivity, and circuit formation—the very features most typically thought of as defining cortical identity. Hopefully the use of conditional knockouts will provide more conclusive information in the near future. 3.2. Evidence for Specification by Extrinsic Factors For the purposes of this section, “extrinsic” refers to information carried by thalamocortical afferents, whether or not that information is based on patterns of electrical activity, and whether or not activity is driven by sensory experience. Given that intrinsic molecular markers are capable of establishing at least some aspects of regional and possibly areal identity of the neocortex on their own (Miyashita-Lin et al., 1999), what is the role of extrinsic information in areal specification? There is substantial evidence that certain areal features can be altered by manipulating extrinsic information, such as sensory experience. It is not clear whether this means that an initial intrinsically-specified areal identity is altered by manipulations of thalamic input, whether the thalamic input establishes the original identity, or whether only certain aspects of cortical identity are affected, leaving earlier-established aspects intact. In at least one case, expression of a molecular marker
© 2002 Taylor & Francis
Cross-Modal Plasticity Studies of Cortical Development
249
seems to be triggered by thalamic input (Levitt et al., 1997). However a causal relationship between thalamocortical innervation, marker expression, and areal specification is difficult to establish. 3.2.1. Specificity of thalamocortical targeting It is clear that there are detailed recognition mechanisms guiding thalamic axons to the proper cortical region (Ghosh et al., 1990; Suzuki et al., 1997; Castellani et al., 1998; Inoue et al., 1998; Mann et al., 1998), because they, unlike most other central sensory pathways, exhibit considerable projection specificity and tend not to make ectopic projections even if other cortical areas are deafferented and thus available as targets (Crandall and Caviness, 1984; Miller et al., 1991). This projection specificity could allow the thalamocortical pathway to act as a framework for neocortical parcellation during development or evolution. Whether thalamocortical axons are instructed by or are instructing areal identity is unknown, however. 3.2.2. Thalamocortical activity plays an important role Although it is possible that the primary role of thalamus in pathfinding is to carry patterning information to its cortical targets independent of activity, it appears that neural activity is required to obtain correct thalamocortical targeting (Catalano and Shatz, 1998), pointing out the necessity for both activity-independent and activity-dependent processes. However, thalamocortical activity-dependent processes are essential for normal development. Coordinated, spontaneous activity in the input pathway prior to sensory organ function (Wong et al., 1993; Weliky and Katz, 1999) can drive the formation of crude circuits, but these are later refined by external sensory cues (see Katz and Shatz, 1996, for review; Ruthazer and Stryker, 1996; Sengpiel et al., 1998). The potential ability of thalamic axons to specify cortex could thus depend on patterning information at their source, on matching markers in the axons and their corresponding target, on their activity pattern, or a combination of factors. In practice, it has been difficult to demonstrate a definitive role for patterned sensory activity in neocortical parcellation. Previous approaches have employed primarily sensory deprivation or heterotopic transplantation. Sensory deprivation can markedly affect the cytoarchitecture of visual cortex (Dehay et al., 1991; Rakic et al., 1991), and heterotopic transplantation of one embryonic cortical region into the place of another can cause the donor tissue to develop host-specific cytoarchitectonic and subcortical connectivity patterns (O’Leary and Stanfield, 1989; Schlaggar and O’Leary, 1991). Although thalamic afferents may be the causal factor in the cortical changes observed in the deprivation and transplantation paradigms, there are alternative interpretations: the bilateral enucleation paradigm causes massively increased cell death in striate cortex (Rakic, 1988), and this alone would cause marked changes in cytoarchitecture, especially that of layer 4 (Finlay and Pallas, 1989). The cortical transplantation results are also difficult to interpret: although the switch from donor to host characteristics could well result from the change in the source of thalamic input, there are many other potential organizing factors that change with a change in location, such as corticocortical inputs, developmental timing, and distribution of morphogens, to name a few. Any or all of these could contribute to the altered appearance of the donor tissue, and thus all that can be rigorously concluded is that the
© 2002 Taylor & Francis
250
Sarah L. Pallas
presence of different thalamocortical afferents is correlated with the appearance of hostspecific features in the donor tissue. Whether there remain some donor-specific characteristics in the transplant that has not been thoroughly investigated. Experience may alter only later-developing characteristics of cortex, but then it should be asked at what stage one can consider that cortical “identity” has been respecified by extrinsic factors? At some point the argument becomes a semantic one, although one could argue sensibly that early choices made by molecular markers bias later ones made by extrinsic factors. The importance of extrinsic factors in cortical areal specification would be strongly supported by a demonstration that they could specify cortex independently, or could respecify cortex that had previously been specified by intrinsic factors. An insightful experiment in the marsupial Monodelphis domestica, in which large portions of caudal neocortex were ablated prior to thalamocortical invasion, showed that the remaining cortical sheet contained the same cortical areas present in normal animals (Huffman et al., 1999). The cortical areas were arranged in the same spatial pattern with respect to each other on this smaller cortical surface, and were innervated by the same thalamic nuclei as usual, although the thalamocortical axons had to shift their projection paths significantly to reach the shifted location of their target. These results may mean that the cortical tissue can compress a predetermined “fate map”, and/or that areal fate can be specified or respecified by the thalamocortical innervation. This result is in some respects reminiscent of the results from Emx2 and Pax6 knockout mice (Bishop et al., 2000; Mallamacci et al., 2000). A similar result has been obtained in the retinocollicular system of hamsters, where it has been observed that a complete but compressed visual map forms in superior colliculi partially-ablated postnatally (Finlay et al., 1979; Pallas and Finlay, 1989). Our recent evidence shows that NMDA receptor blockade does not interfere with compression of retinocollicular maps, although it does block conservation of single-cell receptive field properties (Huang and Pallas, 1999, 2000, 2001). These results raise the possibility that a prenatally-determined molecular “fate map” can be altered postnatally, and/or that fate can be specified or respecified by afferent innervation. In these cases, as in those mentioned above, it is difficult to distinguish the relative roles of intrinsic and extrinsic factors in the compression, although they are consistent with a critical role for thalamocortical afferent inputs in areal specification. 3.2.3. The cross-modal plasticity paradigm Our work on cross-modal plasticity in ferrets, and related work in hamsters, provides another approach with important advantages for investigating the role of thalamocortical afferents in areal specification. The cross-modal “rewiring” of retinal axons into the auditory thalamus (medial geniculate nucleus or MGN) provides patterned spontaneous and visually-driven activity from the retina to primary auditory cortex during its development. The manipulation is performed prior to thalamocortical ingrowth, without manipulating the thalamocortical projection. With this approach, one can address whether patterned activity can specify neocortex independent of the source and identity of the thalamocortical axons, and so can gain insight into whether any existing morphogens can be overridden. Evolution has provided a compelling illustration of the thalamic influence on areal specification. Mole rats (Spalax ehrenberghi) are burrowing animals whose fossorial habit is associated with a reduction in the visual pathway, and a dependence on communication using seismic signals (Rado et al., 1989). This species is blind to form and motion due to
© 2002 Taylor & Francis
Cross-Modal Plasticity Studies of Cortical Development
251
the vestigial form of their eye (which are subcutaneous), although they can detect light for the entrainment of circadian rhythms (Cooper et al., 1993). Visual responses cannot be detected in their geniculocortical pathway, but instead the lateral geniculate nucleus (LGN) and visual cortex respond to auditory stimulation (Bronchti et al., 1989, 1991; Heil et al., 1991). Anatomical studies of these animals have revealed that the “unused” LGN is invaded by the auditory modality via the inferior colliculus (Doron and Wollberg, 1994), although the visual cortex retains normal cytoarchitectural characteristics (Wollberg, personal communication). The behavioural role of the auditory-to-visual pathway is not yet known. The visual-to-auditory cross-modal rewiring procedure is essentially a developmental situation which is the inverse of the mole rat system. The advantage of a developmental approach is that one can view not just the end point but the entire process of parcellation on a manageable time scale, and manipulate it experimentally. In the discussion below I will summarize the results of the cross-modal plasticity work and its clinical implications as well as the insights it provides into the development and evolution of neocortex.
4. HOW DIFFERENT ARE DIFFERENT SENSORY CORTICAL AREAS? Primary sensory cortical areas, such as primary visual (V1), somatosensory (S1), and auditory (A1) cortices, have several characteristics in common, as addressed by others in this volume. The presence of 6 layers and a similar complement of cell types and laminar pattern of connectivity are examples. Visual and somatosensory cortex are similar in many ways due to their role in representing a two-dimensional spatial surface (viz, the retina and the skin surface). However, the auditory cortex is arranged in a quite different way as a result of representing the cochleae, which in itself is ignorant of the spatial location of stimuli. 4.1. The Topographic Map in A1 is Different from the Map in V1 or S1 The visual cortex maps the retina in two dimensions, azimuth and elevation (Sherman and Spear, 1982), and somatosensory cortex maps the two-dimensional skin surface (Mountcastle, 1957). In contrast, the auditory cortex maps the cochlea in one dimension, that of frequency; the orthogonal axis contains an isofrequency representation (Merzenich and Knight, 1975; Aitkin et al., 1984). Although other stimulus features are mapped in AI (Schreiner and Mendelson, 1990; Schreiner et al., 1992; Mendelson et al., 1993; Langner et al., 1997; Recanzone et al., 1999), many are the result of a computational mapping and are thus different in character from the tonotopic map, which is a direct reflection of the sensory epithelium. 4.2. Connectivity Patterns in A1 and V1 are Clustered along Different Dimensions Beyond the fact that different cortices make connections with different brain structures, there are basic differences in the organization of connections. Connections throughout the auditory pathway reflect its one-dimensional tonotopic organization, so that thalamocortical (Andersen et al., 1980; Morel and Imig, 1987; McMullen and de Venecia, 1993), callosal (Imig and Brugge, 1978) and horizontal intracortical connections (Matsubara and Phillips, 1988; Wallace and Bajwa, 1991; Gao and Pallas, 1999) form slabs or strips which
© 2002 Taylor & Francis
252
Sarah L. Pallas
interconnect neurons with similar frequency tuning or binaural organization across the tonotopic map in A1. In contrast, connectivity patterns in V1 are point-to-point (Hubel and Wiesel, 1962; Wiesel and Hubel, 1963), reflecting the two-dimensional organization of the visual pathway (Innocenti, 1986; Callaway and Katz, 1990; Ruthazer and Stryker, 1994). Somatosensory cortex has widespread, radially-arranged clusters of intrinsic projections (Juliano et al., 1996; Sonty and Juliano, 1997) similar to that seen in visual cortex and distinct from the auditory pattern. These specific connections are created from an initially diffuse pattern under the influence of spontaneous cortical activity and sensory cues (Feng and Brugge, 1983; Callaway and Katz, 1990, 1991; Ruthazer and Stryker, 1996). 4.3 There are Similarities and Differences in Receptive Field Properties between V1, S1, and A1 Primary visual cortex (Hubel and Wiesel, 1962), primary somatosensory cortex (Hyvärinen and Poränen, 1978; Essick and Whitsel, 1985), and primary auditory cortex (Mendelson and Cynader, 1985; de Charms et al., 1998; Nelken and Versnel, 2000) contain cells which are tuned to the direction and velocity of movement of a stimulus across the sensory epithelium (movement of touch or light across the receptive field, frequency modulated sweeps across the cochlea), and all contain cells which can be inhibited if the stimulus activates more than a restricted portion of the sensory organ (end-stopped cells in visual cortex, two-tone inhibition or sharpened frequency tuning in auditory cortex). However, although both visual cortex (Hubel and Wiesel, 1962) and somatosensory cortex (Phillips and Johnson, 1981) contain cells tuned to the orientation of an elongated stimulus, and visual cortex contains simple and complex cells, whose responses depend on the two-dimensional substructure of the receptive field, the auditory cortex is necessarily lacking in such two-dimensional receptive field properties because its sensory epithelium is not two-dimensional, nor is the thalamocortical projection between MGN and primary auditory cortex. The presence of such response properties in a crossmodal A1 would suggest that a visually-driven change in circuitry had occurred.
5. CROSS-MODAL PLASTICITY RESULTS IN STRUCTURAL AND FUNCTIONAL ALTERATION OF SENSORY CORTEX In cross-modal plasticity experiments, afferents of one sensory modality are induced to innervate the thalamic nucleus of a different modality, which in turn carries the crossmodal information to the sensory cortex (Figure 12.1). The affected cortical area then responds to the new modality of sensory stimulation. These experiments were first performed by Schneider (1973) and then Frost (1981, 1999) in hamsters. Combining removal of visual targets with deafferentation of the somatosensory pathway in hamsters induces retinal axons to project to nucleus VB, the somatosensory division of thalamus, whereas combined visual target ablation and auditory system deafferentation produces retinato-MGN projections in hamsters (Frost, 1981). We have utilized the procedure in ferrets, which are carnivores with sensory physiology similar to that of the cat, except that they are born at an earlier stage of brain development, prior to ingrowth of thalamic afferents and migration of layer 4 (Luskin and Shatz, 1985; Jackson et al., 1989). The lesions are
© 2002 Taylor & Francis
Cross-Modal Plasticity Studies of Cortical Development
253
made before sensory information has gained access to cortex. Patterned visual input is not available to ferrets until their eyes open and the optics clear, at one month of age. Their auditory pathway becomes functional at the same age (Moore and Hine, 1992). As a control, we also included in each experiment a group of ferrets deafened by cochlear ablation at two weeks of age. With normal ferrets as an additional control, we compared the structure and function of A1 with auditory inputs, A1 with visual inputs, and A1 with no auditory input. The cross-modal approach represents a unique and powerful way to investigate cortical parcellation. Unlike experiments which manipulate the number of inputs or the overall amount of activity reaching the cortex, the cross-modal rewiring procedure addresses the role of the spatiotemporal activity pattern of the inputs in parcellation. We can thus investigate whether thalamic inputs have a permissive or instructive role in cortical differentiation, in other words, whether thalamic activity simply allows differentiation to proceed by triggering other factors, or whether the pattern of activity guides cortical differentiation in a particular direction (Crair, 1999). Unlike transplant experiments, we can change the activity pattern without changing the local environment of the cortical cells. Our results suggest that afferent input can organize cortical circuitry in a directed fashion that allows recovery of function. 5.1. Anatomy 5.1.1. Retinofugal projections The cross-modal “rewiring” in ferrets is done on postnatal day (P)1. Unilateral superior colliculus (SC) ablation removes a main target of the retina, whereas inferior colliculus (IC) ablation and severing of the brachium of the inferior colliculus deafferents MGN (Figure 12.1). As a result, W-type retinal ganglion cells from both eyes invade MGN (Pallas et al., 1994b; Pallas and Sur, 1994; Angelucci et al., 1996) and form eye-specific clusters there (Roe et al., 1993; Angelucci et al., 1997). In ferrets, W-cells are found in SC and the C-laminae of the LGN, and these project in turn to extrastriate cortex but not V1. Thus one might not expect to see V1-type responses in the cross-modal A1. However, the differences in response properties of X, Y and W cells are minor with respect to the Retina
Retina
LGN
LGN
A1
A1 MGN V1
SC
SC
IC
IC
NORMAL
X-MODAL
MGN V1
Figure 12.1. Neonatal cross-modal surgery. Neonatal lesion of midbrain SC and IC converts the normal retinal projection (Left) to the cross-modal projection (Right).
© 2002 Taylor & Francis
254
Sarah L. Pallas
parameters we investigated (Sherman and Spear, 1982; Mulliken et al., 1984; Ferster, 1992). In hamsters, all three ganglion cell types invade the MGN (Métin et al., 1995; Bhide and Frost, 1999). Also, although there is an early exuberant projection from the retina to the MGN in hamsters, the retino-MGN axons present in the adult come from fibres which sprout in reaction to the lesions, and not from the exuberant projection. In young normal animals, we found a few retino-MGN axons at P20, 30 and 40. This was also true of early-deafened control animals. At P60 and in adults, however, retinal axons were only found in the MGN of the early-deafened and the cross-modal animals (Pallas and Moore, 1997; and in preparation). No retinal axons were found in the MGN of 57 normal adult control animals. We suggest that the anomalous projections are lost during development in normal adult ferrets but are stabilized and expanded in cross-modal and early-deafened adult ferrets. Our observations in the deafened animals show a remarkable potential for plasticity in retinal axon growth. Although the cochlea is several synapses away from the MGN, and normal retinal targets were intact, retinal growth cones can detect and react to a lack of auditory input to the MGN. However, the number of retino-MGN axons in deaf ferrets is at least two orders of magnitude less than that seen in cross-modal ferrets, providing strong support for the idea that the cross-modal surgery causes massive sprouting of retinal axons into MGN. The data further support the theory that sensory inputs can take advantage of available target space in the thalamus, and by inference in the cortex as well (see below), providing a developmental substrate for accomodation of neocortical expansion during evolution (Finlay and Darlington, 1995; Finlay et al., 1998; Darlington et al., 1999). 5.1.2. Thalamocortical projections In normal animals, the thalamocortical projection from the visual thalamic nucleus LGN projects in a point-to-point fashion to V1, maintaining the two-dimensional topography of the retina, whereas MGN projects in a slab-to-slab fashion to A1, maintaining the onedimensional topography of the cochlea (Figure 12.2A, 12.2B). Although the rewiring surgery reroutes retinofugal projections into MGN, the thalamocortical projection from MGN projects as it normally does to A1 even though it is carrying visual information. That is, it remains a one-dimensional projection (Pallas et al., 1990). It is thus hard to imagine how the two-dimensional retinal topography could be transferred to A1 from the cross-modal projection via MGN. Thus one would not expect to find a retinotopic map or receptive field properties such as orientation tuning or simple and complex cells that depend on a two-dimensional input in A1. 5.2. Physiology 5.2.1. Topography Surprisingly, the cross-modal A1 does contain a two-dimensional map of visual space, with visual azimuth represented on the tonotopic axis and visual elevation represented in place of the isofrequency axis (Roe et al., 1990). This is true despite the lack of change in the form of the thalamocortical pathway mentioned above (Pallas et al., 1990), suggesting that the visual map present in the retina and the MGN must be recreated along the
© 2002 Taylor & Francis
Cross-Modal Plasticity Studies of Cortical Development
255
A
Retina
LGN
Area 17
Cochlea
MGN
A1
Retina
MGN
A1
B
C
Figure 12.2. Schematic of thalamocortical projections in the (A) normal visual system, (B) normal auditory system, and (C) cross-modal auditory system. Note the roughly point-to-point thalamocortical projection in the visual system, in contrast to the highly overlapped slab-to-slab projection in the auditory system. In the crossmodal A1, there is a map of visual space in MGN but the thalamocortical projection remains overlapped. We hypothesize that the visual map is recreated in A1 by lateral inhibition within isofrequency laminae as shown.
isofrequency axis. This result is made even more surprising by the observation that a 2-D retinotopic map exists in the MGN in the cross-modal ferrets (Roe et al., 1991). Thus the visual map is imposed on the MGN by the manipulation, then one axis of the map (the elevational axis) is discarded as the information travels to A1 in a thalamocortical projection that is highly overlapped along the isofrequency axis (Pallas et al., 1990). In primary auditory cortex, the second dimension of the visual map is recreated, presumably by circuitry intrinsic to cortex (Figure 12.2C). Interestingly, the elevational axis is less orderly than the azimuthal axis, and is inconsistent in its polarity, as one might expect for a dynamically-created array as opposed to one that arises directly from thalamocortical connectivity. We propose that the second dimension of the map is based on correlated patterns of retinal activity which stabilize neighbouring neurones through Hebbian mechanisms (Hebb, 1949; Katz and Shatz, 1996). Cross-modal rewiring of retinal axons to VB allows interaction of two sensory structures whose topography is based on two-dimensional spatial information. Nonetheless, the visual map that is made in S1 is one-dimensional from a surface view, mapping from lower to upper visual field, excluding the nasotemporal axis (Metin and Frost, 1989). The second dimension was not observed from surface mapping, but contributes in some way to stimulus processing.
© 2002 Taylor & Francis
256
Sarah L. Pallas
5.2.2. Receptive field properties In cross-modal hamsters, it was found that the somatosensory cortex could process visual input. Direction- and orientation-tuned neurones were also found in proportions and tuning strength similar to the normal S1 (Metin and Frost, 1989). Interestingly, some multiunit recording sites showed both visual and somatosensory responsiveness. Single-unit extracellular recordings from cross-modal ferrets (Roe et al., 1992) revealed that neurones in AI responded sluggishly to visual stimulation and had large and diffuse receptive fields, as might be expected for cells with W-type input. Lateral geniculate and visual cortical neurones receiving input from W-cells are described as sluggish, with large receptive fields, lower spatial frequency tuning and tuning to slower velocity of movement than X or Y cells (Sherman and Spear, 1982; Mulliken et al., 1984; Vitek et al., 1985; Price and Morgan, 1987; Roe et al., 1990; Ferster, 1992; Baker et al., 1998). However, with respect to stimulus selectivity, cross-modal A1 neurones responded in much the same way as normal visual cortical neurones (Roe et al., 1992; Law et al., 1988), suggesting that these properties are organized primarily intracortically and not via input type. As might have been expected based on pre-existing properties in normal A1 (see above), many neurones were tuned to the direction of movement of a light stimulus, and were inhibited by long edges. However, we also saw response properties that were never found in normal A1. Specifically, neurones could be classified as simple or complex, based on the two-dimensional substructure of their receptive fields. Many neurones were found to be tuned to the orientation of a bar of light. These cells were not rare but rather were found in approximately the same proportions as in normal ferret V1, and in both cortices all orientation vectors were represented in equivalent distributions (this was also true for the distribution of direction tuning vectors). Although this result in itself is quite surprising, we also found that the strength of tuning to orientation in A1 was comparable to that in V1. One possible explanation for the presence of visual responses in cross-modal AI would be potential rerouting of visual inputs to AI as a byproduct of the rewiring surgery, which could then confer their response properties on the cross-modal auditory structures. To address this, we examined thalamocortical and corticocortical connections of AI in normal and cross-modal ferrets (Pallas et al., 1990; Pallas and Sur, 1993). With the exception of the visual inputs arising from the induced retino-MGN projection, AI did not have monosynaptic connections with visual thalamus, except for a few cells in the multimodal region of the lateral posterior thalamus. Furthermore, AI did not send to or receive connections from visual cortical areas, but rather was interconnected only with other auditory cortical regions as in normal animals. Thus the only pathway likely to account for the visual responses in cross-modal AI is the pathway induced from retina to MGN to AI. This interpretation was further supported by our behavioural experiments (see below).
5.3. Two Alternative Hypotheses These results demonstrated that some of the transformations in stimulus representation that normally occur in V1 can also occur in AI of rewired ferrets, and they show that it is not the case that AI and V1 are different by their inherent nature. Further experiments were needed in order to determine whether the early visual inputs drive changes in A1’s circuitry which allow it to process visual inputs de novo, or whether visual and auditory cortex are fundamentally similar enough that they can process each other’s inputs without
© 2002 Taylor & Francis
Cross-Modal Plasticity Studies of Cortical Development
257
alterations in circuitry. For example, perhaps the job of sensory cortex or cortex in general is simply to enhance contrast in whatever input is received, and the lateral inhibitory circuits, ubiquitous to sensory pathways, could accomplish this goal regardless of the type of input, and without any change in cortical circuitry. In this case the output of the normal auditory cortex would be different from the output of normal visual cortex only because the input is different and not due to any difference in input processing. Providing the same input would thus result in the same output. It should be noted that the two hypotheses are not mutually exclusive; that is, some aspects of cortical processing are likely to be held in common between the two cortical areas, and other aspects are likely to show differences. The presence of a two-dimensional visual map and orientation tuning certainly suggested that cortical circuitry was altered by the visual inputs. Our more recent data provides more definitive evidence in this regard, and support the hypothesis that sensory inputs can direct the construction of cortical circuits for their own purposes. 5.4. Functional Connectivity Decades of study of the functional anatomy of sensory cortex have revealed an underlying modular organization. In the normal visual cortex, a number of different modules have been described, such as ocular dominance columns, blobs of colour-sensitive cells, stripes representing form, colour and motion pathways, and pinwheels representing orientation tuning (LeVay et al., 1978; Livingstone and Hubel, 1988; Bartfeld and Grinvald, 1992; Daw, 1998). In auditory cortex, well-described modules include isofrequency slabs (Reale and Imig, 1980; Kelly et al., 1986) and binaural bands, which are arranged orthogonal to the isofrequency axis (Middlebrooks et al., 1980; Kelly and Judge, 1994). The intercortical and intracortical connectivity patterns of sensory cortices reflect their modular organization. For example, neurones which are matched in orientation tuning are interconnected and form clusters of similarly-tuned neurones across the retinotopic map. In the auditory cortex, neurones which are matched in frequency tuning and/or in binaural characteristics are interconnected (Imig et al., 1986). We reasoned that in order that AI can support visual function, the modular connectivity patterns in A1 would have to be reorganized to the “visual pattern”, reflecting the retinotopic organization of the visual system and not the tonotopic organization of the auditory system. We thus undertook a study of the callosal and horizontal connectivity patterns in normal animals (auditory input to auditory cortex), cross-modal animals (visual input to auditory cortex), and deafened animals (deafferentation of auditory cortex). 5.4.1. Callosal connectivity Callosal connections unite functionally and/or topographically-related cells in the two cortical hemispheres, and are specific and different in each sensory cortical area. In rat and cat visual cortex, callosal neurones connect areas representing the vertical meridian of the visual field (Innocenti, 1986; Lewis and Olavarria, 1995; Olavarria and Van Sluyters, 1995; Bourdet et al., 1996). In the auditory cortex, neurones in the E-E binaural bands (strips of neurones facilitated by binaural input) project across the callosum, but the neurones inhibited by binaural stimulation (EI suppression cells) project primarily ipsilaterally (Feng and Brugge, 1983). Callosal projections are exuberant early in postnatal development
© 2002 Taylor & Francis
Sarah L. Pallas
258
A
B
C
Figure 12.3. Tangential pattern of retrograde and anterograde callosal label resulting from HRP injections throughout the contralateral A1. (A) Normal animal. (B) Cross-modal animal. (C) Deaf animal. Scale bar = 2.5 mm.
(Innocenti et al., 1977; Feng and Brugge, 1983), and in visual cortex become progressively more restricted under the influence of sensory experience (Innocenti and Frost, 1979). To examine the effect of the early visual inputs on connections between AI and its contralateral homolog, we injected a tracer (wheat germ-agglutinated HRP, which travels in both retrograde and anterograde directions) throughout A1 on one side of the brain (the left side in normal and deafened ferrets, the unlesioned side in cross-modal ferrets) (Pallas et al., 1999). We found that callosal connectivity in normal ferret AI is much like that in cats, with 2–3 bands of neurones with callosal projections oriented along the tonotopic axis (Figure 12.3A). In the cross-modal AI, a different pattern was observed (Figure 12.3B). Rather than bands of callosal terminals as in normal AI, significantly smaller, rounder patches were found in a widespread arrangement, although they were absent from medial AI. In contrast, deafened animals had callosal projections which were uniformly and diffusely arranged (Figure 12.3C) These results show that sensory input is required for refinement of the callosal pathway, and represent the first evidence that changing only the modality and pattern of activity of sensory inputs, without changing the identity of the thalamocortical fibres carrying the information, can direct the pattern of cortical interconnectivity. The callosal patches are likely to connect neurones with similar visual or auditory response properties, as in normal animals. Our pilot data (S. Pallas, D. Depireaux, J. Simon and S. Shamma, unpublished) suggest that there is residual auditory input reaching the cross-modal AI from the callosal pathway, the thalamocortical pathway, or both. We propose that the pattern of callosal connections reflects a compromise in representing visual and auditory information on one cortical surface. The absence of callosal projections from the medial AI suggests that AI contains segregated visual and auditory representations, and thus a “new” cortical area, in order to prevent perceptual confusion. Such a scenario would provide a valuable model for the early stages of cortical parcellation driven by new inputs or by a modified pattern of activity during evolution. 5.4.2. Horizontal connectivity In visual cortex, layer 2/3 pyramidal neurones interconnect with neurones that have similar orientation tuning (Callaway and Katz, 1990). The regular spacing of orientation columns produces a somewhat radial pattern of clustered intrinsic horizontal projections to and from any one site (Gilbert and Wiesel, 1983; Matsubara et al., 1985, 1987; Rockland and Lund, 1982; Callaway and Katz, 1990; Weliky and Katz, 1994; Ruthazer and Stryker, 1996) that represent mainly excitatory connections (Matsubara and Boyd, 1992).
© 2002 Taylor & Francis
Cross-Modal Plasticity Studies of Cortical Development
259
Figure 12.4. Distribution of labelled boutons following a small injection of BDA in AI show its horizontal connectivity patterns in (A) normal (B) cross-modal and (C) deaf animals.
In contrast, horizontal connections in the auditory cortex are arranged in elongated strips along the isofrequency lines (Reale et al., 1983; Imig et al., 1986; Matsubara and Phillips, 1988; Ojima et al., 1991; Wallace and Bajwa, 1991). In visual cortex (Callaway and Katz, 1991), the pattern is sculpted from an initially diffuse pattern under the influence of visual activity. We hypothesized that the visual input would prevent the formation of elongated horizontal connections in cross-modal A1. Because of the role of intrinsic connections in generating response properties, we proposed that alterations in these connections induced by visual activity might be a substrate for the complex visual response properties which we observed in cross-modal AI. Small pressure injections of anterograde tracer (biotinylated dextran amine, BDA) in AI (Gao and Pallas, 1999) revealed that the horizontal connections in normal ferrets were organized as clusters of boutons along the isofrequency axis (Figure 12.4A), as in cats (e.g. Matsubara and Phillips, 1988). In the cross-modal ferrets, bouton clusters in AI were significantly increased in number, in scatter (both with respect to distance and angulation away from the anteroposterior axis), and in the zone of influence (bouton coverage area) after exposure of AI to the early anomalous visual inputs (Figure 12.4B). The bouton clusters were arranged not as anteroposteriorly-elongated strips, but more radially around the injection site, and into medial AI, an area devoid of boutons in normal A1. These results suggest that the spatiotemporal activity pattern of sensory inputs, and not their molecular identity or the identity of the cortical target, controls the organization of intracortical circuitry. It is unlikely that the pattern results from a blockade of the developmental refinement of horizontal connections, because of the increase in number of clusters and their degree of refinement. Our studies of horizontal connections in deafened animals support this interpretation (Figure 12.4C) (Gao et al., 1999a, and in preparation). In animals deafened at P14 by bilateral cochlear ablation, horizontal connectivity is diffuse and not well-organized into clusters. This diffuse state represents sprouting beyond an earlier developmental state (Moershel and Pallas, 2001). Our results are supported and confirmed by data from Sur and colleagues obtained by using tracers in concert with optical recording (Sharma et al., 2000). Their results using retrograde tracers were similar in some respects to ours using anterograde tracers; clusters of cells retrogradely labeled by a tracer injection in A1 were very different in organization to those seen in normal A1, and extended into medial AI rather than along the isofrequency axis. They reported further that clusters of labeled cells were aligned with regions exhibiting similar orientation tuning, much as they are in normal V1.
© 2002 Taylor & Francis
260
Sarah L. Pallas
5.4.3. A new cortical area? Comparison of the pattern of callosal connectivity compared to the pattern of horizontal connectivity in cross-modal A1 suggests a mirror-image relationship. Callosal connections are excluded from medial A1, whereas horizontal connections extend preferentially into medial A1. Given that the lesions in these animals were largely unilateral, it seems likely that A1 receives not only visual input from ipsilateral MGN, but also auditory input from its contralateral homolog via callosal connections. As mentioned above, we have some preliminary evidence that the cross-modal A1 is responsive to auditory stimuli (S. Pallas, D. Depireaux, J. Simon and S. Shamma, unpublished). If this is the case, then what may have occurred in these animals is the creation of a new cortical area in the medial portion of the ectosylvian gyrus which represents visual information in two dimensions, and a more lateral portion which contains a compressed version of the one-dimensional tonotopic map. If this is indeed occurring, our paradigm mimics in developmental time the addition of a new cortical pathway driven by the periphery. The ability to generate a new cortical area during development would provide a powerful model system for the cortical parcellation that occurred during mammalian brain evolution. 5.5. Activity Instructs Cortical Circuitry The anatomical results showing visually-induced alterations of callosal and horizontal connectivity, taken together with the physiological data showing visual response properties in AI, strongly suggest that patterned input activity plays an instructive and not merely a permissive role in the development of modular organization in sensory cortex (Crair, 1999; Gao and Pallas, 1999). Does this explain how A1 is able to process visual information? We suggest that changes in the modular organization of A1 are more likely to be a reflection of and not the driving force behind the visual response properties in cross-modal A1. The process of mapping stimulus attributes is likely to be independent of the process of constructing the circuits underlying specific response properties, in that stimulus tuning can be disrupted without affecting map topography (e.g. Weliky and Katz, 1997; Huang and Pallas, 1999, 2000, 2001). How then do visual response properties get constructed? 5.6. Chemoarchitecture To investigate possible alterations in cross-modal A1’s circuitry that might be the source of the 2-D visual response properties, we returned to our model (Figure 12.2), which suggested that there must be a means of selectively enhancing or suppressing some of the overlapped inputs from MGN along the isofrequency axis. With the goal of testing the hypothesis that visually-driven alteration of inhibitory circuits provided the substrate for visual response properties and topography, we examined the number, distribution, and morphology of inhibitory neurones in A1 of normal, cross-modal, and deafened ferrets. GABAergic neurones are involved in the sculpting of receptive field properties in visual cortex (Sillito, 1975a,b; Berman et al., 1992; Crook and Eysel, 1992; Sato et al., 1995, 1996; Allison et al., 1996; Crook et al., 1996, 1997, 1998; Das and Gilbert, 1999), and in the auditory pathway (Le Beau et al., 1996; Wang et al., 2000), although the auditory cortex has not been well-studied in this respect. GABA may also subserve
© 2002 Taylor & Francis
Cross-Modal Plasticity Studies of Cortical Development
261
critical functions during early (Lauder, 1993; Berninger et al., 1995; Behar et al., 1996; Antonopoulos et al., 1997) and late (Reiter and Stryker, 1988; Hendry and Carder, 1992; Rutherford et al., 1997; Hensch et al., 1998; Zheng and Knudsen, 1999) cortical development. Other studies suggest GABA regulation is of major importance during activity-dependent postnatal development (Rutherford et al., 1997; Hensch et al., 1998; Zheng and Knudsen, 1999), suggesting a role for GABA in cortical circuit construction and experience-dependent plasticity during late stages of development. It therefore seemed a likely candidate as a driving force for changes in cortical circuits in the crossmodal animals. To determine whether changes in inhibitory circuitry were involved in the creation of visual response properties in cross-modal A1, we examined the distribution of GABAergic neurones and GABAA receptors in A1 and V1 of normal, cross-modal and deaf ferrets during postnatal development. In addition, because identified subpopulations of GABAergic neurones contain different calcium-binding proteins, we also examined the distribution of parvalbumin (PV), which is found in basket and chandelier cells in layers 2–5 of cortex, and calbindin (CB), which is found in double-bouquet and bipolar cells in supra- and infragranular layers but not the granular layer of primary sensory cortex (Peters and Jones, 1984; Hendry et al., 1989; Hendry and Jones, 1991).
5.6.1. Normal animals We described the development of GABA-immunoreactive (-ir) neurones in visual and auditory cortex of normal ferrets to determine the time course of GABA expression, particularly whether it coincided with important events in cortical development. Morphology of these neurones was similar to that in cats, and not demonstrably different in normal V1 vs. AI. Quantitative analysis revealed that the percentage of GABA-ir, PV-ir and CB-ir neurones peaked late in postnatal development (Gao et al., 1999b, 2000), just prior to the end of the critical period (Issa et al., 1999). Using quantitative receptor-binding autoradiography, we found a parallel increase in GABAA receptors at P60 in both visual and auditory cortex (Pallas et al., 1994; and in preparation). These data suggest that inhibitory circuitry is stabilized relatively late in cortical development, and thus that GABAergic neurones could be malleable later in postnatal development and serve as a substrate for plasticity.
5.6.2. Deaf animals We examined the distribution of GABAA receptors and GABA, PV, and CB-ir neurones in AI of animals that had been deafened at P14 by bilateral cochlear ablation (two weeks prior to the onset of hearing: see Moore and Hine, 1992). Deafferentation of the auditory cortex during development caused a marked decline in the proportion of GABA, PV and CB-ir neurones (Gao et al., 1999a, and in preparation), and a decrease in GABAA receptor binding (Pallas et al., 1994a, and in preparation). This is similar to what occurs in visual cortex with deafferentation (Hendry and Jones, 1986, 1988; Blümcke et al., 1994; Hendry et al., 1994; Huntsman et al., 1994; Rosier et al., 1995; Arckens et al., 1998) and may relate to the tendency of neurones to regulate their overall level of excitablility (Rutherford et al., 1997).
© 2002 Taylor & Francis
Sarah L. Pallas
262
Normal
Cross-Modal
Figure 12.5. The morphology of calbindin-ir neurones is altered in cross-modal AI. Rather than arborizing within a column as is typical of calbindin-containing double-bouquet neurones, the dendrites extend laterally across columns.
5.6.3. Cross-modal animals Contrary to the results in deaf ferrets, there was no measurable decline in GABAA receptor binding in AI in cross-modal ferrets. Also, the proportion of GABAergic neurones was not significantly different from normal. However, CB neurones increased in density and were more widely distributed across all layers of cross-modal cortex, including layer 4, and many CB neurones had an atypical morphology (Figure 12.5): Rather than arborizing vertically within a cortical column, their dendrites extended horizontally across columns, where they would be in a position to suppress information gathered from across the isofrequency axis. These results are intriguing in relation to our hypothesis that visually-driven activity from early in development changes inhibitory circuitry in AI. To suppress the overlapped thalamocortical inputs along the isofrequency axis, increased lateral inhibition along that axis and within layer 4 would be expected. We hypothesize that such visuallydriven changes in inhibitory circuitry could be responsible for the creation of visual response properties in AI. If this is true, it would further support the idea that visual inputs can cause active changes in target circuitry to arrange for their own effective processing. 5.7. Behaviour Our psychophysical studies showed that the novel pathway from the retina to the auditory system is capable of subserving visual function (Merzenich, 2000; von Melchner et al., 2000). Unilaterally-lesioned cross-modal animals were trained through their unlesioned hemisphere (the monocular segment of the left eye) to distinguish auditory from visual stimuli and report accordingly in a forced-choice paradigm. When tested through the cross-modal pathway (monocular segment of the right eye), they immediately and consistently defined visual stimuli as visual, not auditory. Lesion of any visual structures remaining from the early rewiring surgery (superior colliculus, lateral posterior nucleus, LGN, visual cortex) did not affect the behavioural choice, showing that it was not dependent upon visual pathways other than the one induced through MGN. In contrast, post-testing lesion of auditory cortex eliminated the perceptual ability on the lesion side
© 2002 Taylor & Francis
Cross-Modal Plasticity Studies of Cortical Development
263
only, and the animals reported visual or auditory percepts randomly in their right monocular field. These results contradict centuries of philosophical and scientific thought on the localization of function in the brain and the development of perception. It was expected that any neuronal activity occurring in the auditory pathway would be interpreted by the ferret as auditory, not visual, according to Müller’s labelled line theory. In our experiments, because the percept depended on the input source, and not the labelled line, the animals could “see” with their auditory pathway. Although the acuity of this pathway is much lower than that of the normal visual pathway, the animals can still distinguish different grating frequencies, indicating that not only can they detect light, but that the new pathway subserves some degree of pattern vision.
6. AREAL SPECIFICATION: PREPROGRAMMED VERSUS POTENTIAL FATE Our results and those of others employing the cross-modal plasticity paradigm strongly support the notion that sensory inputs and patterned activity play an important role in the areal specification of the neocortex. But how are we to reconcile this with the demonstrated role of molecular factors intrinsic to the brain, or the several studies that have revealed the presence of area-specific modules prior to or in the absence of activity? The answer may lie in the timing and the degree of specification at each developmental stage. Early events, prior to connections between the brain and the sensory organs, are necessarily controlled by intrinsic factors. Differential expression of molecular markers may specify the dorsoventral and rostrocaudal axes as in spinal cord (Tanabe and Jessell, 1996), and construct a preliminary scaffold for the cortex, setting up regional (but perhaps not areal) markers that guide thalamic afferents to their approximate termination site (Chenn et al., 1997; Mann et al., 1998; Bishop et al., 2000). These factors may bias the brain toward its usual final state and restrict its potential somewhat. Later events may be directed by extrinsic information, intrinsic factors, or both. Furthermore, specification events that are initially under intrinsic control may be modifiable or reversible at later stages by extrinsic information. The dependence of late stages of cortical development on correlated activity patterns suggests that morphogenic markers are no longer present at that time, that the brain is no longer sensitive to them, or that they can be overridden. A study of the temporal and spatial overlap of markers and their receptors could begin to address this question. In any case, this malleability allows the brain to be exquisitely sensitive to environmental influences, a critical factor in cortical development and plasticity as well as in neocortical evolution.
7. SENSORY SUBSTITUTION STUDIES: CLINICAL RELEVANCE What is the relevance of cross-modal plasticity beyond our unique experimental situation? The paradigm is also a model for perinatal brain damage and can reveal how the brain compensates for a loss of (or a change in) sensory input. It is well-known that sensory systems even in adults can reorganize in response to altered sensory stimulation
© 2002 Taylor & Francis
264
Sarah L. Pallas
(e.g. Merzenich et al., 1996), but it is also possible for compensatory reorganization to occur across different cortical areas into other sensory representations. Clinical evidence relevant to this issue includes the observation that in blind and deaf patients, the “unused” cortical area can be taken over and used by a different sensory modality (Kujala et al., 1997; see Kujala et al., 2000, for review), leading to increased ability in the other modality compared to normal (Muchnik, 1991; Lessard et al., 1998; Roder et al., 1999). Studies in humans with early-onset deafness showed that event-related potentials could be recorded in putative auditory areas in response to visual stimuli (Neville et al., 1983; Neville and Lawson, 1987; Neville, 1990). Additionally, Braille reading in the early-blind activates visual cortex (Sadato et al., 1996; Cohen et al., 1997, 1999) and auditory localization tasks activate the occipital cortex in the blind (Weeks et al., 2000). Sensory substitution depends on attentional mechanisms, and can occur to some extent in individuals with late-onset deficits as well (Kujala et al., 1997). Cross-modal plasticity has also been demonstrated in other animal models. Rauschecker, Wollberg and their colleagues have shown that sensory substitution can occur in visuallycompromised animals (Rauschecker, 1997, 1999; Yaka et al., 1999), and in some cases performance in the intact modality is enhanced (King and Parsons, 1999). Loss of vision through deprivation or enucleation resulted in auditory activation of visual cortex and improved auditory localization ability. The converse manipulation has also been performed. When the cochlea was damaged early in life, visual responses were seen in auditory cortex (Rebillard et al., 1977; Rauschecker and Korte, 1993). It has not been clear in each case, however, whether the novel inputs reorganize cortical circuitry, or whether they simply take advantage of existing circuits. Furthermore, it is not known whether the invasion and reorganization process is dependent on the pattern of activity or the identity of the thalamocortical afferents. Our results suggest that activity-dependent control over cortical circuit formation by thalamocortical afferents, based on correlated activity within neighbouring regions of the sensory epithelium, may explain a number of these observations. Furthermore, it may provide a general mechanism for cortical regionalization at the circuit level of organization. Such an afferent-driven organization rule would have broad implications for both the development and the evolution of mammalian sensory cortex.
8. CONCLUSION Cortical areas may indeed be interchangeable to a significant extent, but only if appropriate sensory experience is provided before the end of the critical period. Results from experiments using the cross-modal plasticity paradigm reveal that the modality and pattern of activity in the inputs to cortex can play an instructive as well as a permissive role in the organization of cortical circuitry during development. Cross-modal plasticity may also provide a substrate for neocortical evolution, allowing the adaptive incorporation of changes in peripheral or central sensory structures. If the basis of the critical period in brain tissue and the limitations on axon pathfinding and synapse formation are understood, then we may eventually be in a position to offer clinical therapy for brain injuries that occur later in development, teaching the remaining cortical tissue how to substitute for the damaged regions.
© 2002 Taylor & Francis
Cross-Modal Plasticity Studies of Cortical Development
265
ACKNOWLEDGEMENTS We thank Barbara Finlay and the editors for critical reading of the manuscript. This work was supported by grants to S.L.P. from the National Institutes of Health, the National Science Foundation, the Whitehall Foundation, the Fight for Sight Inc. Research Division of Prevent Blindness America, and the Georgia Research Alliance.
REFERENCES Aitkin, L., Irvine, D.R.F. and Webster, W.R. (1984) Central neural mechanisms of hearing, Handbook of Physiology: The Nervous System III. Bethesda: American Physiological Society, pp. 675–737. Allison, J.D., Kabara, J.F., Snider, R.K., Casagrande, V.A. and Bonds, A.B. (1996) GABAB-receptor-mediated inhibition reduces the orientation selectivity of the sustained response of striate cortical neurons in cats. Visual Neuroscience, 13, 559–566. Andersen, R.A., Knight, P.L. and Merzenich, M.M. (1980) The thalamocortical and corticothalamic connections of AI, AII and the anterior auditory field (aaf) in the cat: Evidence for two largely segregated systems of connections. Journal of Comparative Neurology, 194, 663–701. Angelucci, A., Clascá, F. and Sur, M. (1996) Anterograde axonal tracing with the subunit B of cholera toxin: A highly sensitive immunohistochemical protocol for revealing fine axonal morphology in adult and neonatal brains. Journal of Neuroscience Methods, 65, 101–112. Angelucci, A., Clascá, F., Bricolo, E., Cramer, K.S. and Sur, M. (1997) Experimentally induced retinal projections to the ferret auditory thalamus: Development of clustered eye-specific patterns in a novel target. Journal of Neuroscience, 17, 2040–2055. Antonopoulos, J., Pappas, I.S. and Parnavelas, J.G. (1997) Activation of the GABAA receptor inhibits the proliferative effects of BFGF in cortical progenitor cells. European Journal of Neuroscience, 9, 291–298. Arckens, L., Eysel, U.T., Vanderhaegen, J.J., Orban, G.A. and Vandesande, F. (1998) Effect of sensory deafferentation on the GABAergic circuitry of the adult cat visual system. Neuroscience, 83, 381–391. Arimatsu, Y., Miyamoto, M., Nihonmatsu, I., Hirata, K., Uratani, Y., Hatanaka, Y. and Takiguchi-Hayashi, K. (1992) Early regional specification for a molecular neuronal phenotype in the rat neocortex. Proceedings of the National Academy of Science, U.S.A., 89, 8879–8883. Baker, G.E., Thompson, I.D., Krug, K., Smyth, D. and Tolhurst, D.J. (1998) Spatial-frequency tuning and geniculocortical projections in the visual cortex (areas 17 and 18) of the pigmented ferret. European Journal of Neuroscience, 10, 2657–2668. Bayer, S.A. and Altman, J. (1991) Neocortical development. New York: Raven Press. Bartfeld, E. and Grinvald, A. (1992) Relationships between orientation-preference pinwheels, cytochrome oxidase blobs and ocular dominance columns in primate striate cortex. Proceedings of the National Academy of Science, U.S.A., 89, 11905–11909. Behar, T.N., Li, Y.X., Tran, H.T., Ma, W., Dunlap, V., Scott, C. et al. (1996) GABA stimulates chemotaxis and chemokinesis of embryonic cortical neurons via calcium-dependent mechanisms. Journal of Neuroscience, 16, 1808–1818. Berman, N.J., Douglas, R.J. and Martin, K.A.C. (1992) GABA-mediated inhibition in the neural networks of visual cortex. In: R.R. Mize, R.E. Marc and A.M. Sillito (eds), GABA in the Retina and Central Visual System (Progress in Brain Research, Vol. 90), New York: Elsevier, pp. 443–476. Berninger, B., Marty, S., Zafra, F., Da Penha Berzaghi, M., Thoenen, H. and Lindholm, D. (1995) GABAergic stimulation switches from enhancing to repressing bdnf expression in rat hippocampal neurons during maturation in vitro. Development, 121, 2327–2335. Bhide, P.G. and Frost, D.O. (1999) Intrinsic determinants of retinal axon collateralization and arborization patterns. Journal of Comparative Neurology, 411, 119–129. Bishop, K.M., Goudreau, G. and O’Leary, D.D.M. (2000) Regulation of area identity on the mammalian neocortex by Emx2 and Pax6. Science, Washington, 288, 344–349. Blümcke, I., Weruaga, E., Kasas, S., Hendrickson, A.E. and Celio, M.R. (1994) Discrete reduction patterns of parvalbumin and calbindin D-28k immunoreactivity in the dorsal lateral geniculate nucleus and the striate cortex of adult macaque monkeys after monocular enucleation. Visual Neuroscience, 11, 1–11. Bourdet, C., Olavarria, J.F. and Van Sluyters, R.C. (1996) Distribution of visual callosal neurons in normal and strabismic cats. Journal of Comparative Neurology, 366, 259–269. Bronchti, G., Heil, P., Scheich, H. and Wollberg, Z. (1989) Auditory pathway and auditory activation of primary visual targets in the blind mole rat (Spalax ehrenbergi): I. 2-deoxyglucose study of subcortical centers. Journal of Comparative Neurology, 284, 253–274.
© 2002 Taylor & Francis
266
Sarah L. Pallas
Bronchti, G., Rado, R., Terkel, J. and Wollberg, Z. (1991) Retinal projections in the blind mole rat: A WGA-HRP tracing study of a natural degeneration. Developmental Brain Research, 58, 159–170. Bruer, J.T. (1999) The Myth of the First Three Years: A New Understanding of Early Brain Development and Lifelong Learning. San Francisco: Free Press. Butler, A.B. and Molnar, Z. (1998) Development and evolution in nervous systems: Development and evolution of ideas. Trends in Neuroscience, 21, 177. Callaway, E.M. and Katz, L.C. (1990) Emergence and refinement of clustered horizontal connections in cat striate cortex. Journal of Neuroscience, 10, 1134–1153. Callaway, E.M. and Katz, L.C. (1991) Effects of binocular deprivation on the development of clustered horizontal connections in cat striate cortex. Proceedings of the National Academy of Science, U.S.A., 88, 745–749. Castellani, V., Yue, Y. and Bolz, J. (1998) Dual action of a ligand for eph receptor tyrosine kinases on specific populations of axons during the development of cortical circuits. Journal of Neuroscience, 18, 4663–4672. Catalano, S.M. and Shatz, C.J. (1998) Activity-dependent cortical target selection by thalamic axons. Science, Washington, 281, 599–562. Changeaux, J.-P. (1986) Neuronal Man. New York: Oxford University Press. Chenn, A., Braisted, J., McConnell, S.K. and O’Leary, D.D.M. (1997) Development of the cerebral cortex: Mechanisms controlling cell fate, laminar and areal patterning and axonal connectivity. In: W.M. Cowan, T.M. Jessell and S.L. Sipursky (eds), Molecular and Cellular Approaches to Neural Development. New York: Oxford University Press, pp. 440–473. Cohen, L.G., Celnik, P., Pascual-Leone, A., Cornwell, B., Faiz, L., Dambrosias, J. et al. (1997) Functional relevance of cross-modal plasticity in blind humans. Nature, London, 389, 180–186. Cohen, L.G., Weeks, R.A. and Hallett, M. (1999) Period of susceptibility for cross-modal plasticity in the blind. Annals of Neurology, 45, 451–460. Cohen-Tannoudji, M., Babinet, C. and Wassef, M. (1994) Early determination of a mouse somatosensory cortex marker. Nature, London, 368, 460–463. Cook, P.M., Prusky, G. and Ramoa, A.S. (1999) The role of spontaneous retinal activity before eye opening in the maturation of form and function in the retinogeniculate pathway of the ferret. Visual Neuroscience, 16, 491–501. Cooper, H.M., Herbin, M. and Nevo, E. (1993) Ocular regression conceals adaptive progression of the visual system in a blind subterranean mammal. Nature, London, 361, 156–159. Crair, M.C. (1999) Neuronal activity during development: Permissive or instructive? Current. Opinion in Neurobiology, 9, 88–99. Crandall, J.E. and Caviness, V.S. (1984) Thalamocortical connections in newborn mice. Journal of Comparative Neurology, 228, 542–556. Crook, J.M. and Eysel, U.T. (1992) GABA-induced inactivation of functionally characterized sites in cat visual cortex (area 18): Effects on orientation tuning. Journal of Neuroscience, 12, 1816–1825. Crook, J.M., Kisvárday, Z.F. and Eysel, U.T. (1996) GABA-induced inactivation of functionally characterized sites in cat visual cortex (area 18): Effects on direction selectivity. Journal of Neurophysiology, 75, 2071–2088. Crook, J.M., Kisvárday, Z.F. and Eysel, U.T. (1997) GABA-induced inactivation of functionally characterized sites in cat striate cortex: Effects on orientation tuning and direction selectivity. Visual Neuroscience, 14, 141–158. Crook, J.M., Kisvárday, Z.F. and Eysel, U.T. (1998) Evidence for a contribution of lateral inhibition to orientation tuning and direction selectivity in cat visual cortex: Reversible inactivation of functionally characterized sites combined with neuroanatomical tracing techniques. European Journal of Neuroscience, 10, 2056–2075. Darlington, R.B., Dunlop, S.A. and Finlay, B.L. (1999) Neural development in metatherian and eutherian mammals: Variation and constraint. Journal of Comparative Neurology, 411, 359–368. Das, A. and Gilbert, C.D. (1999) Topography of contextual modulations mediated by short-range interactions in primary visual cortex. Nature, London, 399, 655–661. Daw, N.W. (1998) Neurobiology: Columns, slabs and pinwheels. Nature, London, 395, 20. deCharms, R.C., Blake, D.T. and Merzenich, M.M. (1998) Optimizing sound features for cortical neurons. Science, Washington, 280, 1439. Dehay, C., Horsburgh, G., Berland, M., Killackey, H. and Kennedy, H. (1991) The effects of bilateral enucleation in the primate fetus on the parcellation of visual cortex. Developmental Brain Research, 62, 137–141. Donoghue, M.J. and Rakic, P. (1999) Molecular evidence for the early specification of presumptive functional domains in the embryonic primate cerebral cortex. Journal of Neuroscience, 19, 5967–5979. Doron, N. and Wollberg, Z. (1994) Cross-modal neuroplasticity in the blind mole rat Spalax ehrenbergi: A WGA-HRP tracing study. NeuroReport, 5, 2697–2701. Essick, G.K. and Whitsel, B.L. (1985) Factors influencing cutaneous direction sensitivity: A correlative psychophysical and neurophysiological investigation. Brain Research Reviews, 10, 213–230.
© 2002 Taylor & Francis
Cross-Modal Plasticity Studies of Cortical Development
267
Feng, J.Z. and Brugge, J.F. (1983) Postnatal development of auditory callosal connections in the kitten. Journal of Comparative Neurology, 214, 416–426. Ferster, D. (1992) The synaptic inputs to simple cells of the cat visual cortex. In: R.R. Mize, R. Marc and A. Sillito (eds), GABA in the Retina and Central Visual System (Progress in Brain Research, Vol. 90). Amsterdam: Elsevier, pp. 423–441. Finlay, B.L., Schneps, S.E. and Schneider, G.E. (1979) Orderly compression of the retinotectal projection following partial tectal ablation in the newborn hamster. Nature, London, 280, 153–154. Finlay, B.L. and Pallas, S.L. (1989) Control of cell number in the developing mammalian visual system. Progress in Neurobiology, 32, 207–234. Finlay, B.L. and Darlington, R.B. (1995) Linked regularities in the development and evolution of mammalian brains. Science, Washington, 268, 1578–1584. Finlay, B.L., Hersman, M.N. and Darlington, R.B. (1998) Patterns of vertebrate neurogenesis and the paths of vertebrate evolution. Brain Behavior and Evolution, 52, 232–242. Frost, D.O. (1981) Ordered anomalous retinal projections to the medial geniculate, ventrobasal and lateral posterior nuclei. Journal of Comparative Neurology, 203, 227–256. Frost, D.O. (1999) Functional organization of surgically created visual circuits. Restorative Neurology and Neuroscience, 15, 107–113. Gao, W.-J., Moore, D.R. and Pallas, S.L. (1999a) Bilateral cochlear ablation in neonatal ferrets prevents refinement of horizontal connectivity in primary auditory cortex. Society for Neuroscience Abstracts, 25, 1771. Gao, W.-J., Newman, D.E., Wormington, A.B. and Pallas, S.L. (1999b) Development of inhibitory circuitry in visual and auditory cortex of postnatal ferrets: Immunocytochemical localization of GABAergic neurons. Journal of Comparative Neurology, 409, 261–273. Gao, W.-J. and Pallas, S.L. (1999) Cross-modal reorganization of horizontal connectivity in auditory cortex without altering thalamocortical projections. Journal of Neuroscience, 19, 7940–7950. Gao, W.-J., Wormington, A.B., Newman, D.E. and Pallas, S.L. (2000) Development of inhibitory circuitry in visual and auditory cortex of postnatal ferrets: Immunocytochemical localization of calbindin and parvalbumin-containing neurons. Journal of Comparative Neurology, 422, 140–157. Ghosh, A., Antonini, A., McConnell, S.K. and Shatz, C.J. (1990) Requirement for subplate neurons in the formation of thalamocortical connections. Nature, London, 347, 179–181. Gilbert, C.D. and Wiesel, T.N. (1983) Clustered intrinsic connections in cat visual cortex. Journal of Neuroscience, 3, 1116–1133. Gitton, Y., Cohen-Tannoudji, M. and Wassef, M. (1999) Role of thalamic axons in the expression of H-2Z1, a mouse somatosensory cortex specific marker. Cerebral Cortex, 9, 611–620. Goodman, C.S. and Coughlin, B.C. (2000) Special feature: The evolution of evo-devo biology. Proceedings of the National Academy of Science, U.S.A., 97, 4424–4425. Gopnik, A., Meltzoff, A.N. and Kuhl, P.K. (1999) The Scientist in the Crib: Minds, Brains and how Children Learn. New York: William Morrow and Co. Gu, X.N. and Spitzer, N.C. (1997) Breaking the code: Regulation of neuronal differentiation by spontaneous calcium transients. Developmental Neuroscience, 19, 33–41. Guillemot, F., Lo, L.C., Johnson, J.E., Auerbach, A., Anderson, D.J. and Joyner, A.L. (1993) Mammalian achaete scute homolog 1 is required for the early development of olfactory and autonomic neurons. Cell, 75, 463–476. Hebb, D.O. (1949) The Organization of Behavior. New York: John Wiley. Heil, P., Bronchti, G., Wollberg, Z. and Scheich, H. (1991) Invasion of visual cortex by the auditory system in the naturally blind mole rat. NeuroReport, 2, 735–738. Hendry, S.H.C. and Jones, E.G. (1986) Reduction in number of immunostained GABAergic neurones in deprived-eye dominance columns of monkey area 17. Nature, London, 320, 750–753. Hendry, S.H.C. and Jones, E.G. (1988) Activity-dependent regulation of GABA expression in the visual cortex of adult monkeys. Neuron, 1, 701–712. Hendry, S.H.C., Jones, E.G., Emson, O.C., Lawson, D.E.M., Heizmann, C.W. and Streit, P. (1989) Two classes of cortical GABA neurons defined by differential calcium binding protein immunoreactivities. Experimental Brain Research, 76, 467–472. Hendry, S.H.C. and Jones, E.G. (1991) GABA neuronal subpopulations in cat primary auditory cortex: Co-localization with calcium binding proteins. Brain Research, 543, 45–55. Hendry, S.H.C. and Carder, R. (1992) Organization and plasticity of GABA neurons and receptors in monkey visual cortex. In: R.R. Mize, R. Marc and A. Sillito (eds), GABA in the Retina and Central Visual System (Progress in Brain Research, Vol. 90). Amsterdam: Elsevier, pp. 477–502. Hendry, S.H.C., Huntsman, M.-M., Viñuela, A., Möhler, H., De Blas, A.L. and Jones, E.G. (1994) GABAA receptor subunit immunoreactivity in primate visual cortex: Distribution in macaques and humans and regulation by visual input in adulthood. Journal of Neuroscience, 14, 2383–2401. Hensch, T., Fagiolini, M., Mataga, N., Stryker, M., Baekkeskov, S. and Kash, S. (1998) Local GABA circuit control of experience-dependent plasticity in developing visual cortex. Science, Washington, 282, 1504–1508.
© 2002 Taylor & Francis
268
Sarah L. Pallas
Huang, L. and Pallas, S.L. (1999) NMDA receptor blockade in developing superior colliculus prevents map plasticity without blocking retinotectal transmission. Society for Neuroscience Abstracts, 25, 1005. Huang, L. and Pallas, S.L. (2000) Visual response properties in the superior colliculus after NMDA receptor blockade during early development. Society for Neuroscience Abstracts, 26, 119.11 Huang, L. and Pallas, S.L. (2001) NMDA antagonists in the superior colliculus prevent developmental plasticity but not visual transmission or map compression. Journal of Neurophysiology, 86, 1179–1194. Hubel, D.H. and Wiesel, T.N. (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Physiology (London), 160, 106–154. Huffman, K.J., Molnar, Z. and Krubitzer, L. (1999) Formation of cortical fields on a reduced cortical sheet. Journal of Neuroscience, 19, 9939–9952. Huntsman, M.M., Isackson, P.J. and Jones, E.G. (1994) Lamina-specific expression and activity-dependent regulation of seven GABAA receptor subunit mRNAs in monkey visual cortex. Journal of Neuroscience, 14, 2236–2259. Hyvärinen, J.L. and Poränen, A. (1978) Movement-sensitive and direction and orientation-selective cutaneous receptive fields in the hand area of the post-central gyrus in monkeys. Journal of Physiology (London), 283, 523–537. Imig, T.J. and Brugge, J.F. (1978) Sources and terminations of callosal axons related to binaural and frequency maps in primary auditory cortex of the cat. Journal of Comparative Neurology, 182, 637–660. Imig, T.J., Reale, R.A., Brugge, J.F., Morel, A. and Adrian, H.O. (1986) Topography of cortico-cortical connections related to tonotopic and binaural maps of cat auditory cortex. In: F. Lepore, M. Ptito and H.H. Jasper (eds), Two Hemispheres-One Brain: Functions of the Corpus Callosum. New York: Alan R. Liss, Inc., pp. 103–115. Innocenti, G.M., Fiore, L. and Caminiti, R. (1977) Exuberant projection into the corpus callosum from the visual cortex of newborn cats. Neuroscience Letters, 4, 237–242. Innocenti, G.M. and Frost, D.O. (1979) Effects of visual experience on the maturation of the efferent system to the corpus callosum. Nature, London, 280, 231–234. Innocenti, G.M. (1986) General organization of callosal connections in the cerebral cortex, In: A. Peters and E.G. Jones (eds), Sensory-Motor Areas and Aspects of Cortical Connectivity. Cerebral Cortex, Volume 5. New York: Plenum Press, pp. 291–353. Inoue, T., Tanaka, T., Suzuki, S.C. and Takeichi, M. (1998) Cadherin-6 in the developing mouse brain: Expression along restricted connection systems and synaptic localization suggest a potential role in neuronal circuitry. Developmental Dynamics, 211, 338–351. Issa, N.P., Trachtenberg, J.T., Chapman, B., Zahs, K.R. and Stryker, M.P. (1999) The critical period for ocular dominance plasticity in the ferret’s visual cortex. Journal of Neuroscience, 19, 6965–6978. Jackson, C.A., Peduzzi, J.D. and Hickey, T.L. (1989) Visual cortex development in the ferret. I. Genesis and migration of visual cortical neurons. Journal of Neuroscience, 9, 1242–1253. Jerison, H.J. (1990) Fossil brains and the evolution of the neocortex. In: B.L. Finlay, G. Innocenti and H. Scheich (eds), The Neocortex: Ontogeny and Phylogeny. NATO Advanced Research Workshop. New York: Plenum. Juliano, S.L., Palmer, S.L., Sonty, R.V., Noctor, S. and Hill II, G.F. (1996) Development of local connections in ferret somatosensory cortex. Journal of Comparative Neurology, 374, 259–277. Kandler, K. and Katz, L.C. (1998) Relationship between dye coupling and spontaneous activity in developing ferret visual cortex. Developmental Neuroscience, 20, 59–64. Katz, L.C. and Shatz, C.J. (1996) Synaptic activity and the construction of cortical circuits. Science, Washington, 274, 1133–1138. Kelly, J.B., Judge, P.W. and Phillips, D.P. (1986) Representation of the cochlea in primary auditory cortex of the ferret (Mustela putorius). Hearing Research, 24, 111–115. Kelly, J.B. and Judge, P.W. (1994) Binaural organization of primary auditory cortex in the ferret (Mustela putorius). Journal of Neurophysiology, 71, 904–913. King, A.J. and Parsons, C.H. (1999) Improved auditory spatial acuity in visually deprived ferrets. European Journal of Neuroscience, 11, 3945–3956. Kingsbury, M.A. and Finlay, B.L. (2001) The cortex in multidimensional space: Where do cortical areas come from? Developmental Science, 4, 125–142. Kujala, T., Alho, K., Huotilainen, M., Ilmoniemi, R.J., Lehtokoski, A., Leinonen, A., Rinne, T., Salonen, O., Sinkkonen, J., Standertskjold Nordenstam, C.G. and Naatanen, R. (1997) Electrophysiological evidence for cross-modal plasticity in humans with early- and late-onset blindness. Psychophysiology, 34, 213–216. Kujala, T., Alho, K. and Näätänen, R. (2000) Cross-modal reorganization of human cortical functions. Trends in Neuroscience, 23, 115–120. Langner, G., Sams, M. and Schulze, H. (1997) Frequency and periodicity are represented in orthogonal maps in the human auditory cortex: Evidence from magnetoencephalography. Journal of Comparative Physiology, A, 181, 665. Lauder, J.M. (1993) Neurotransmitters as growth regulatory signals: Role of receptors and second messengers. Trends in Neuroscience, 16, 233–240.
© 2002 Taylor & Francis
Cross-Modal Plasticity Studies of Cortical Development
269
Law, M.I., Zahs, K.R. and Stryker, M.P. (1988) Organization of primary visual cortex (area 17) in the ferret. Journal of Comparative Neurology, 278, 157–180. Le Beau, F.E.N., Rees, A. and Malmierca, M.S. (1996) Contribution of GABA- and glycine-mediated inhibition to the monaural temporal response properties of neurons in the inferior colliculus. Journal of Neurophysiology, 75, 902–919. Lessard, N., Pare, M. and Lassonde, M. (1998) Early-blind human subjects localize sound sources better than sighted subjects. Nature, London, 395, 278–280. LeVay, S., Stryker, M.P. and Shatz, C.J. (1978) Ocular dominance columns and their development in layer IV of the cat’s visual cortex: A quantitative study. Journal of Comparative Neurology, 179, 223–244. Levitt, P., Barbe, M.F. and Eagleson, K.L. (1997) Patterning and specification of the cerebral cortex. Annual Review of Neuroscience, 20, 1–24. Lewis, J.W. and Olavarria, J.F. (1995) Two rules for callosal connectivity in striate cortex of the rat. Journal of Comparative Neurology, 361, 119–137. Livingstone, M. and Hubel, D.H. (1988) Segregation of form, color, movement and depth: Anatomy, physiology and perception. Science, Washington, 240, 740–749. Lumsden, A. and Krumlauf, R. (1996) Patterning the vertebrate neuraxis. Science, Washington, 274, 1109–1115. Luskin, M.B. and Shatz, C.J. (1985) Neurogenesis of the cat’s primary visual cortex. Journal of Comparative Neurology, 242, 611–631. Mackarehtschian, K., Lau, C.K., Caras, I. and McConnell, S.K. (1999) Regional differences in the developing cerebral cortex revealed by ephrin-a5 expression. Cerebral Cortex, 9, 601–610. Mallamaci, A., Muzio, L., Chan, C.-H., Parnavelas, J. and Boncinelli, E. (2000) Area identity shifts in the early cerebral cortex of Emx–/– mutant mice. Nature Neuroscience, 3, 679–686. Mann, F., Zhukareva, V., Pimenta, A., Levitt, P. and Bolz, J. (1998) Membrane-associated molecules guide limbic and nonlimbic thalamocortical projections. Journal of Neuroscience, 18, 9409–9419. Matsubara, J.A., Cynader, M., Swindale, N.V. and Stryker, M. P. (1985) Intrinsic projections within visual cortex: Evidence for orientation-specific local connections. Proceedings of the National Academy of Science, U.S.A., 82, 935–939. Matsubara, J.A., Cynader, M.S. and Swindale, N.V. (1987) Anatomical properties and physiological correlates of the intrinsic connections in cat area 18. Journal of Neuroscience, 7, 1428–1446. Matsubara, J.A. and Phillips, D.P. (1988) Intracortical connections and their physiological correlates in the primary auditory cortex (AI) of the cat. Journal of Comparative Neurology, 268, 38–48. Matsubara, J.A. and Boyd, J.D. (1992) Presence of GABA-immunoreactive neurons within intracortical patches in area 18 of the cat. Brain Research, 583, 161–170. McMullen, N.T. and de Venecia, R.K. (1993) Thalamocortical patches in auditory neocortex. Brain Research, 620, 317–322. Mendelson, J.R. and Cynader, M.S. (1985) Sensitivity of cat primary auditory cortex (al) neurons to the direction and rate of frequency modulation. Brain Research, 327, 331–335. Mendelson, J.R., Schreiner, C.E., Sutter, M.L. and Grasse, K.L. (1993) Functional topography of cat primary auditory cortex: Responses to frequency-modulated sweeps. Experimental Brain Research, 94, 65–87. Merzenich, M.M. and Knight, P.L. (1975) Representation of cochlea within primary auditory cortex in the cat. Journal of Neurophysiology, 38, 231–249. Merzenich, M., Wright, B., Jenkins, W., Xerri, C., Byl, N., Miller, S. and Tallal, P. (1996) Cortical plasticity underlying perceptual, motor and cognitive skill development: Implications for neurorehabilitation. Cold Spring Harbor Symposium on Quantitative Biology, 61, 1–8. Merzenich, M.M. (2000) Seeing in the sound zone. Nature, London, 404, 820–821. Métin, C. and Frost, D.O. (1989) Visual responses of neurons in somatosensory cortex of hamsters with experimentally induced retinal projections to somatosensory thalamus. Proceedings of the National Academy of Science, U.S.A., 86, 357–361. Métin, C., Irons, W.A. and Frost, D.O. (1995) Retinal ganglion cells in normal hamsters and hamsters with novel retinal projections. I. Number, distribution and size. Journal of Comparative Neurology, 353, 179–199. Middlebrooks, J.C., Dykes, R.W. and Merzenich, M.M. (1980) Binaural response-specific bands in primary auditory cortex (AI) of the cat: Topographical organization orthogonal to isofrequency contours. Brain Research, 181, 31–48. Miller, B.M., Windrem, M.S. and Finlay, B.L. (1991) Thalamic ablations and neocortical development: Alterations in thalamic and callosal connectivity. Cerebral Cortex, 1, 241–261. Miyashita-Lin, E.M., Hevner, R., Montzka Wasserman, K., Martinez, S. and Rubenstein, J.L.R. (1999) Early neocortical regionalization in the absence of thalamic innervation. Science, Washington, 285, 906–909. Moerschel, D.J. and Pallas, S.L. (2001) Early postnatal formation of clustered horizontal connections in ferret auditory cortex. Society for Neuroscience Abstracts, 27, 903–908. Moore, D.R. and Hine, J.E. (1992) Rapid development of the auditory brainstem response threshold in individual ferrets. Developmental Brain Research, 66, 229–235.
© 2002 Taylor & Francis
270
Sarah L. Pallas
Morel, A. and Imig, T.J. (1987) Thalamic projections to fields A, AI, P and VP in the cat auditory cortex. Journal of Comparative Neurology, 265, 119–144. Mountcastle, V.B. (1957) Modality and topographic properties of single neurons of cat’s somatic sensory cortex. Journal of Neurophysiology, 20, 408–434. Muchnik, C. (1991) Central auditory skills in blind and sighted subjects. Scandinavian Journal of Audiology, 20, 19–23. Mulliken, W.H., Jones, J. P. and Palmer, L.A. (1984) Receptive field properties and laminar distribution of x-like and y-like simple cells in cat area 17. Journal of Neurophysiology, 52, 350–371. Nakagawa, Y., Johnson, J.E. and O’Leary, D.D.M. (1999) Graded and areal expression patterns of regulatory genes and cadherins in embryonic neocortex independent of thalamocortical input. Journal of Neuroscience, 19, 10877. Nelken, I. and Versnel, H. (2000) Responses to linear and logarithmic frequency-modulated sweeps in ferret primary auditory cortex. European Journal of Neuroscience, 12, 549–562. Neville, H.J., Schmidt, A. and Kutas, M. (1983) Altered visual-evoked potentials in congenitally deaf adults. Brain Research, 266, 127–132. Neville, H. and Lawson, D. (1987) Attention to central and peripheral visual space in a movement detection task: An event-related potential and behavioral study. I. Congenitally deaf adults. Brain Research, 405, 268–283. Neville, H.J. (1990) Intermodal competition and compensation in development. Evidence from studies of the visual system in congenitally deaf adults. Annals of the New York Academy of Science, 608, 71–87. Nothias, F., Fishell, G. and Ruiz i Altaba, A. (1998) Cooperation of intrinsic and extrinsic signals in the elaboration of regional identity in the posterior cerebral cortex. Current Biology, 8, 459–462. O’Donovan, M.J. (1999) The origin of spontaneous activity in developing networks of the vertebrate nervous system. Current Opinion in Neurobiology, 9, 94–104. Ojima, H., Honda, C. and Jones, E.G. (1991) Patterns of axon collateralization of identified supragranular pyramidal neurons in the cat auditory cortex. Cerebral Cortex, 1, 80–94. Olavarria, J.F. and Van Sluyters, R.C. (1995) Overall pattern of callosal connections in visual cortex of normal and enucleated cats. Journal of Comparative Neurology, 363, 161–176. O’Leary, D.D.M. (1989) Do cortical areas emerge from a protocortex? Trends in Neuroscience, 12, 400–406. O’Leary, D.D.M. and Stanfield, B.B. (1989) Selective elimination of axons extended by developing cortical neurons is dependent on regional locale: Experiments utilizing fetal cortical transplants. Journal of Neuroscience, 9, 2230–2246. Pallas, S.L. and Finlay, B.L. (1989) Conservation of receptive-field properties of superior colliculus cells after developmental rearrangements of retinal input. Visual Neuroscience, 2, 121–135. Pallas, S.L. (1990) Cross-modal plasticity in sensory cortex: Visual responses in primary auditory cortex in ferrets with induced retinal projections to the medial geniculate nucleus. In: B.L. Finlay, G. Innocenti and H. Scheich (eds), The Neocortex: Ontogeny and Phylogeny. NATO Advanced Research Workshop. New York: Plenum, pp. 205–218. Pallas, S.L., Roe, A.W. and Sur, M. (1990) Visual projections induced into the auditory pathway of ferrets. I. Novel inputs to primary auditory cortex (AI) from the LP/pulvinar complex and the topography of the MGN-AI projection. Journal of Comparative Neurology, 298, 50–68. Pallas, S.L. and Sur, M. (1993) Visual projections induced into the auditory pathway of ferrets. II. Corticocortical connections of primary auditory cortex with visual input. Journal of Comparative Neurology, 337, 317–333. Pallas, S.L. and Sur, M. (1994) Morphology of retinal axon arbors induced to arborize in a novel target, the medial geniculate nucleus. II. Comparison with axons from the inferior colliculus. Journal of Comparative Neurology, 349, 363–376. Pallas, S.L., Booth, V. and Cynader, M. (1994a) Development and plasticity of GABA circuitry in primary visual and primary auditory cortex in ferrets. Society for Neuroscience Abstracts, 20, 875. Pallas, S.L., Hahm, J. and Sur, M. (1994b) Morphology of retinal axons induced to arborize in a novel target, the medial geniculate nucleus. I. Comparison with arbors in normal targets. Journal of Comparative Neurology, 349, 343–362. Pallas, S.L. and Moore, D.R. (1997) Retinal axons arborize in the medial geniculate nucleus of neonatallydeafened ferrets. Society for Neuroscience Abstracts, 23, 1994. Pallas, S.L., Littman, T. and Moore, D.R. (1999) Cross-modal reorganization of callosal connectivity in auditory cortex without altering thalamocortical projections. Proceedings of the National Academy of Science, U.S.A., 96, 8751–8756. Pallas, S.L. (2001) Intrinsic and extrinsic factors that shape neocortical specification. Trends in Neuroscience, 24, 417–423. Paysan, J., Bolz, J., Mohler, H. and Fritschy, J.-M. (1994) GABAA receptor a1 subunit, an early marker for area specification in developing rat cerebral cortex. Journal of Comparative Neurology, 350, 133–149. Paysan, J., Kossel, A. and Fritschy, J.-M. (1997) Area-specific regulation of gamma-aminobutyric acid type a receptor subtypes. Proceedings of the National Academy of Science, U.S.A., 94, 6995–7000.
© 2002 Taylor & Francis
Cross-Modal Plasticity Studies of Cortical Development
271
Peinado, A. (2000) Traveling slow waves of neural activity: a novel form of network activity in developing neocortex. Journal of Neuroscience, 200, RC54. Peters, A. and Jones, E.G. (1984) Cerebral Cortex Volume 1: Cellular Components of the Cerebral Cortex. New York: Plenum Press. Phillips, J.R. and Johnson, K.O. (1981) Tactile spatial resolution. II. Neural representation of bars, edges and gratings in monkey afferents. Journal of Neurophysiology, 46, 1192–1203. Pimenta, A.F., Reinoso, B.S. and Levitt, P. (1996) Expression of the mRNAs encoding the limbic systemassociated membrane protein (LAMP) 2. Fetal rat brain. Journal of Comparative Neurology, 375, 289–302. Price, D.J. and Morgan, J.E. (1987) Spatial properties of neurones in the lateral geniculate nucleus of the pigmented ferret. Experimental Brain Research, 68, 28–36. Rado, R., Himelfarb, M., Arensburg, B., Terkel, J. and Wollberg, Z. (1989) Are seismic communication signals transmitted by bone conduction in the blind mole rat? Hearing Research, 41, 23–29. Rakic, P. (1988) Specification of cerebral cortical areas. Science, Washington, 241, 170–176. Rakic, P., Suner, I. and Williams, R.W. (1991) A novel cytoarchitectonic area induced experimentally within the primate visual cortex. Proceedings of the National Academy of Science, U.S.A., 88, 2083–2087. Rauschecker, J.P. and Korte, M. (1993) Auditory compensation for early blindness in cat cerebral cortex. Journal of Neuroscience, 13, 4538–4548. Rauschecker, J.P. (1997) Mechanisms of compensatory plasticity in the cerebral cortex. In: H.-J. Freund, B.A. Sabel and O.W. Witte (eds), Brain Plasticity. Philadelphia: Lippincott-Raven Publishers, pp. 137–146. Rauschecker, J.P. (1999) Auditory cortical plasticity and sensory substitution. In: J. Grafman and Y. Christen (eds), Neuronal plasticity: Building a Bridge from the Laboratory to the Clinic. Berlin: Springer-Verlag, pp. 53–63. Reale, R.A., Brugge, J.F. and Feng, J.Z. (1983) Geometry and orientation of neuronal processes in cat primary auditory cortex (AI) related to characteristic-frequency maps. Proceedings of the National Academy of Science, U.S.A., 80, 5449–5453. Reale, R.A. and Imig, T.J. (1980) Tonotopic organization in auditory cortex of the cat. Journal of Comparative Neurology, 192, 265–291. Rebillard, G., Carlier, E., Rebillard, M. and Pujol, R. (1977) Enchancement of visual responses in the primary auditory cortex of the cat after an early destruction of cochlear receptors. Brain Research, 129, 162–164. Recanzone, G.H., Schreiner, C.E., Sutter, M.L., Beitel, R.E. and Merzenich, M.M. (1999) Functional organization of spectral receptive fields in the primary auditory cortex of the owl monkey. Journal of Comparative Neurology, 415, 460–481. Reinoso, B.A., Pimenta, A.F. and Levitt, P. (1996) Expression of the mRNAs encoding the limbic systemassociated membrane protein (LAMP) 1. Adult rat brain. Journal of Comparative Neurology, 375, 274–288. Reiter, H.O. and Stryker, M.P. (1988) Neural plasticity without postsynaptic action potentials: Less-active inputs become dominant when kitten visual cortical cells are pharmacologically inhibited. Proceedings of the National Academy of Science, U.S.A., 85, 3623–3627. Rockland, K.S. and Lund, J.S. (1982) Widespread intrinsic connections in the tree shrew visual cortex. Science, Washington, 215, 1532–1534. Roder, B., Teder-Salejarvi, W. and Neville, H.J. (1999) Improved auditory spatial tuning in blind humans. Nature, London, 400, 162. Roe, A.W., Garraghty, P.E. and Sur, M. (1990) Terminal arbors of single on-center and off-center retinal ganglion cell axons within the ferret’s lateral geniculate nucleus. Journal of Comparative Neurology, 288, 208–242. Roe, A.W., Pallas, S.L., Hahm, J. and Sur, M. (1990) A map of visual space induced in primary auditory cortex. Science, Washington, 250, 818–820. Roe, A.W., Hahm, J. and Sur, M. (1991) Experimentally induced establishment of visual topography in auditory thalamus. Society for Neuroscience Abstracts, 17, 898. Roe, A.W., Pallas, S.L., Kwon, Y. and Sur, M. (1992) Visual projections routed to the auditory pathway in ferrets: Receptive fields of visual neurons in primary auditory cortex. Journal of Neuroscience, 12, 3651–3664. Roe, A.W., Garraghty, P.E., Esguerra, M. and Sur, M. (1993) Experimentally induced visual projections to the auditory thalamus in ferrets: Evidence for a W cell pathway. Journal of Comparative Neurology, 334, 263–280. Rosier, A.M., Arckens, L., Demeulemeester, H., Orban, G.A., Eysel, U.T., Wu, Y.J. et al. (1995) Effect of sensory deafferentation on immunoreactivity of GABAergic cells and on GABA receptors in the adult cat visual cortex. Journal of Comparative Neurology, 359, 476–489. Rubenstein, J.L.R., Anderson, S., Shi, L., Miyashita-Lin, E., Bulfone, A. and Hevner, R. (1999) Genetic control of cortical regionalization and connectivity. Cerebral Cortex, 9, 524–532. Ruthazer, E.S. and Stryker, M.P. (1996) The role of activity in the development of long-range horizontal connections in area 17 of the ferret. Journal of Neuroscience, 16, 7253–7269. Rutherford, L.C., DeWan, A., Lauer, H.M. and Turrigiano, G.G. (1997) Brain-derived neurotrophic factor mediates the activity-dependent regulation of inhibition in neocortical cultures. Journal of Neuroscience, 17, 4527–4535. Sadato, N., Pascual-Leone, A., Grafman, J., Ibañez, V., Deiber, M.P., Dold, G., Hallett, M. (1996) Activation of the primary visual cortex by Braille reading in blind subjects. Nature, London, 380, 526–528.
© 2002 Taylor & Francis
272
Sarah L. Pallas
Sato, H., Katsuyama, N., Tamura, H., Hata, Y. and Tsumoto, T. (1995) Mechanisms underlying direction selectivity of neurons in the primary visual cortex of the macaque. Journal of Neurophysiology, 74, 1382–1394. Sato, H., Katsuyama, N., Tamura, H., Hata, Y. and Tsumoto, T. (1996) Mechanisms underlying orientation selectivity of neurons in the primary visual cortex of the macaque. Journal of Physiology (London), 494, 757–771. Schlaggar, B.L. and O’Leary, D.D.M. (1991) Potential of visual cortex to develop an array of functional units unique to somatosensory cortex. Science, Washington, 252, 1556–1560. Schneider, G.E. (1973) Early lesions of the superior colliculus: Factors affecting the formation of abnormal retinal projections. Brain, Behavior and Evolution, 8, 73–109. Schreiner, C.E. and Mendelson, J.R. (1990) Functional topography of cat primary auditory cortex: Distribution of integrated bandwidth. Journal of Neurophysiology, 64, 1442–1459. Schreiner, C.E., Mendelson, J.R. and Sutter, M.L. (1992) Functional topography of cat primary auditory cortex: Representation of tone intensity. Experimental Brain Research, 92, 105–122. Sengpiel, F., Gödecke, I., Stawinski, P., Hübener, M., Löwel, S. and Bonhoeffer, T. (1998) Intrinsic and environmental factors in the development of functional maps in cat visual cortex. Neuropharmacology, 37, 607–621. Sharma, J., Angelucci, A. and Sur, M. (2000) Induction of visual orientation modules in auditory cortex. Nature, London, 404, 841–847. Shatz, C.J. (1992) Dividing up the neocortex. Science, Washington, 258, 237–238. Sherman, S.M. and Spear, P.D. (1982) Organization of visual pathways in normal and visually-deprived cats. Physiology Reviews, 62, 738–855. Sillito, A.M. (1975a) The contribution of inhibitory mechanisms to the receptive field properties of neurones in the striate cortex of the cat. Journal of Physiology (London), 250, 305–329. Sillito, A.M. (1975b) The effectiveness of bicuculline as an antagonist of GABA and visually evoked inhibition in the cat’s striate cortex. Journal of Physiology (London), 250, 287–304. Sonty, R.V. and Juliano, S.L. (1997) Development of intrinsic connections in cat somatosensory cortex. Journal of Comparative Neurology, 384, 501–516. Sur, M., Garraghty, P.E. and Roe, A.W. (1988) Experimentally induced visual projections into auditory thalamus and cortex. Science, Washington, 242, 1437–1441. Sur, M., Pallas, S.L. and Roe, A.W. (1990) Cross-modal plasticity in cortical development: Differentiation and specification of sensory neocortex. Trends in Neuroscience, 13, 227–233. Suzuki, S.C., Inoue, T., Kimura, Y., Tanaka, T. and Takeichi, M. (1997) Neuronal circuits are subdivided by differential expression of type-II classic cadherins in postnatal mouse brains. Molecular and Cellular Neuroscience, 9, 433–447. Tanabe, Y. and Jessell, T.M. (1996) Diversity and pattern in the developing spinal cord. Science, Washington, 274, 1115–1123. Vitek, D.J., Schall, J.D. and Leventhal, A.G. (1985) Morphology, central projections and dendritic field orientation of retinal ganglion cells in the ferret. Journal of Comparative Neurology, 241, 1–11. von Melchner, L.S., Pallas, S.L. and Sur, M. (2000) Visual behavior induced by retinal projections directed to the auditory pathway. Nature, London, 404, 871–875. Wallace, M.N. and Bajwa, S. (1991) Patchy intrinsic connections of the ferret primary auditory cortex. NeuroReport, 2, 417–420. Walsh, C. (1993) Cell lineage and regional specification in the mammalian neocortex. Perspectives in Developmental Neurobiology, 1, 75–80. Wang, J., Caspary, P. and Salvi, R.J. (2000) GABA-A antagonist causes dramatic expansion of tuning in primary auditory cortex. NeuroReport, 11, 1137–1140. Weeks, R., Horwitz, B., Aziz-Sultan, A., Tian, B., Wessinger, C.M., Cohen, L.G., Hallett, M. and Rauschecker, J.P. (2000) A positron emission tomographic study of auditory localization in the congenitally blind. Journal of Neuroscience, 20, 2664–2672. Weliky, M. and Katz, L.C. (1994) Functional mapping of horizontal connections in developing ferret visual cortex: Experiments and modeling. Journal of Neuroscience, 14, 7291–7305. Weliky, M. and Katz, L.C. (1997) Disruption of orientation tuning in visual cortex by artificially correlated neuronal activity. Nature, London, 386, 680–685. Weliky, M. and Katz, L.C. (1999) Correlational structure of spontaneous neuronal activity in the developing lateral geniculate nucleus in vivo. Science, Washington, 285, 599–604. Wiesel, T.N. and Hubel, D.H. (1963) Single cell responses in striate cortex of kittens deprived of vision in one eye. Journal of Neurophysiology, 26, 1003–1017. Wong, R.O.L., Meister, M. and Shatz, C.J. (1993) Transient period of correlated bursting activity during development of the mammalian retina. Neuron, 11, 923–938. Yaka, R., Yinon, U. and Wollberg, Z. (1999) Auditory activation of cortical visual areas in cats after early visual deprivation. European Journal of Neuroscience, 11, 1301. Zheng, W. and Knudsen, E. (1999) Functional selection of adaptive auditory space map by GABAA-mediated inhibition. Science, Washington, 284, 962–965.
© 2002 Taylor & Francis
13 Do Primary Sensory Areas Play Analogous Roles in Different Sensory Modalities? Hubert R. Dinse1 and Christoph E. Schreiner2 1
Institute for Neuroinformatics, Dept. of Theoretical Biology, Group for Experimental Neurobiology, Ruhr-University Bochum, Germany 2 Coleman Laboratory, W.M. Keck Center for Integrative Neuroscience, Sloan Center for Theoretical Neurobiology, University of California San Francisco, San Francisco, USA Correspondance: Hubert R. Dinse, PhD, Institute for Neuroinformatics, Dept. of Theoretical Biology, Group Experimental Neurobiology, Ruhr-University Bochum, ND 04, D-44780 Bochum, Germany Tel: +49-234-32 25565; FAX: +49-234-32-14209 e-mail:
[email protected]
We compare the properties of primary auditory, visual and somatosensory cortex in order to discuss whether the extraction and mapping of stimulus features shows equivalencies across sensory modalities. We propose that modality-specific differences in “early” cortical properties are largely a consequence of differences in receptor properties, and that early cortical processes and their implementation are similar across sensory modalities. This view is based largely upon the striking similarities of receptive field organization found in visual, auditory and somatosensory areas with respect to the spatio-temporal distribution of excitation and inhibition. By the same token, inspection of the shape and properties of cortical point-spread functions suggest substantial equivalencies across areas. Analysis of temporal aspects of sensory processing indicates that differences can be attributed to differences in subcortical and peripheral processing, resulting mainly from anatomical constraints and particularities. On the other hand, as exemplified by spontaneous activity and intrinsic oscillatory activity, differences found between cortical layers or those found for state-dependencies, such as wakefulness, attention or sleep, outweigh possible modality and area-specific differences. The common feature of multiple parametric maps observed in early sensory areas might reflect a rather general principle of cortical organization that allows the combination of local operations with a continuous representation of elemental parameters of the environmental scene, maintaining local neighbourhood relationships. KEYWORDS: auditory, cortical maps, distributed processing, latencies, oscillations, parametric maps, primary sensory areas, receptive fields, receptors, somatosensory, spontaneous activity, temporal aspects of processing, temporal integration, thalamus, vision
1. INTRODUCTION 1.1. Role of Primary Sensory Cortices What is the role of the representation and processing of the sensory environment in primary cortical fields? How do the principles underlying sensory processing differ among cortical areas? These questions are significant, since the current dominant model of cortex often uses the visual system as a frame of reference, thus, potentially biasing the general view. 273 © 2002 Taylor & Francis
274
Hubert R. Dinse and Christoph E. Schreiner
To address these questions, we made a comparison between different sensory modalities. The structural framework in different sensory cortices is similar with respect to many aspects (Rockel et al., 1980) such as the global arrangement of synapses, axons, excitatory and inhibitory cell types, lamination, as well as vertical and horizontal connections (as discussed in some detail in other chapters). This could lead to the conclusion that the common features of cortical machinery implement algorithms, or a set of universal operations, rather than unique, modality-specific operations. On the other hand, cortical subregions have specific anatomical and physiological distinctions, and critical differences in their functional attributes. The clearest distinctions between subregions of sensory cortex relate to the modality-specificity of receptive field properties and the resulting functional organizations. In fact, visual, auditory, and somatosensory cortex have functional characteristics and parameter representations so different that it may seem unlikely that common cortical processes and mechanisms can subserve such diversity (e.g. see Luria, 1973). However, analysis of the functional properties of cortical neurones, in conjunction with the modalityspecific subcortical processes reveals that many sensory cortical attributes reflect analogous, if not identical, local and distributed processing mechanisms. Each modality imposes some specific constraints on these processes and on their functional cortical representation. From a global perspective, we propose that primary sensory cortex in different modalities performs many of the same tasks. The underlying processes are based on analogous operations in different modalities, namely, activity- and correlation-based integration of convergent information by means of well-coordinated excitatory and inhibitory interactions.
1.2. Hypothesis Different sensory cortices perform similar functions based on common rules in processing elements and mechanisms. Substantive modality-specific differences in peripheral signal properties are transformed subcortically to provide functional equivalency at the cortex. In the cortex, homogeneity of processing is evident in the spatio-temporal arrangement of excitatory and inhibitory interactions. Receptor-specific characteristics that remain at the cortical level are implemented by local arrangements, and not by the basic principles of the processing which is applied.
1.3. Synopsis We discuss first the parallels and differences between vision, touch, and audition for signal properties of the environmental scene, receptors and receptor organs, and principles of subcortical processing. Here we will emphasize some properties of the auditory system, in order to highlight modality-specific differences in subcortical processes. We, then, compare properties of cortical processing with respect to modality-specific aspects of peripheral processing. We next assess receptive field properties, cortical maps and temporal processing in three primary areas—AI, VI, and SI—to test the notion of a unifying principle of early cortical processing. Next, we summarize experimental findings of crossmodal plasticity that support the idea of general, rather than modality-specific, cortical operational principles. We conclude by discussing briefly some potential consequences for future studies of sensory processing.
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
275
2. MODALITY CHARACTERISTICS OF SIGNALS AND SUBCORTICAL SYSTEMS 2.1. The Environmental Scene Sensory organs scan the surrounding environmental scene in different physical realms, such as electromagnetic waves, air-pressure waves, and mechanical forces. Our interaction with the environment depends critically on the identification of objects within it. Delineation of objects, and formation of frame-independent representations of objects enables us to learn their effects on us, to attach behavioural significance to these objects, and to interact with them (Koffka, 1935; Gibson, 1966). The non-chemical senses— vision, touch, and audition—all have to solve the same problem: analysis of the environmental scene, or identification of objects in complex backgrounds. Characteristic and contingent features in the scene need to be integrated so that object recognition and background perception can occur. For example, features defining a picture of a cat, a sculpture of a cat, or the word “cat”, for vision, touch and hearing, respectively, each need to be deconvoluted, coded, analyzed, identified and unified. “High level” processes that deal with the actual analysis of the scene, rely on higher-order correlations in some featurespace and on the comparison with previously stored information about the properties of objects, backgrounds, and events. “Low level” processes, in contrast, extract the information from the receptors, to construct the appropriate feature-space in which the separation of object and background can occur. We argue that the former processes are largely accomplished subcortically, and in the primary sensory cortices. They rely on lower-order correlations among spatial and temporal domains within and across different receptor types and sensory surfaces. The rest of this section refers only to these low-level processes.
2.2. Signal Statistics Visual, tactile, and auditory objects have a number of properties in common such as edges (definable in space or time), and attributes of their internal features such as colour, texture, and coherent or incoherent frequency and/or amplitude modulations. Object motion relative to the environment, and across the receptor surface, provides data optimal for the extraction of relationships between different object properties. Different physical stimulus dimensions are represented by the nature of receptor organs. Each modality represents the lower-order statistical nature of the signals. Acoustic, somatosensory and visual ecologies share statistical similarities, and embody important differences. Thus, objects that reflect light may also generate and reflect sound, so that the spatial statistics of acoustic and visual environments may have similar scaling behaviour. On the other hand, differences between the visual and acoustic worlds exist: Objects that are visually opaque can be acoustically transparent. In combination with the different generation mechanisms for light and sound, acoustic and visual ecologies may have some different statistical properties that, consequently, entail distinct differences in sensory processing requirements. However, the resulting representational differences of an object by several modalities have to be brought into alignment at some level of processing, in order to allow multi-sensory integration which ensures perceptual unity and consistency in decision-making and behaviour (Stein and Meredith, 1990).
© 2002 Taylor & Francis
276
Hubert R. Dinse and Christoph E. Schreiner
The statistical properties of the modality-specific environments can influence two major aspects of neuronal coding: what is represented and how is it encoded (e.g. deCharms and Zador, 2000). The what-aspect is reflected in the nature of the representational dimensions in feature space, as discussed above. Receptive field analysis (see section 3, below) reveals which features a neurone responds to. Each neurone represents specific values along several feature dimensions, depending on the probability of the occurrence of that stimulus aspect, as well as on the behavioural relevance of that range of values. An important influence of the stimulus statistics on receptive fields is demonstrated in the effect of temporal correlation among independent stimuli. An illuminating example is the effect of the artifical fusion of neighbouring digits of a hand (Clark et al., 1988) on its cortical representation. Normally, each finger is mapped in primary somatosensory cortex by non-overlapping receptive fields, that is, the neurones respond to stimulation of one finger alone. After surgical fusion of the skin of two adjacent fingers and extended use of the fused fingers by the monkey, the cortical receptive field boundaries cover both fingers, i.e. the normal discontinuity in the representation of the fingers is altered. This change in receptive field property relates to the high degree of spatio-temporal, correlated input to the fused fingers compared to the uncorrelated input to the fingers when they are used independently. In general, the extraction of spatio-temporal correlations and coherences across the multi-dimensional stimulus space is a critical principle, and underlies many aspects of the generation and representation of feature dimensions related directly to object properties. Stimulus-response analysis can determine how a cell responds to a stimulus and helps one to understand how stimulus information is coded by the spike train. Approaches from information theory allow one to estimate the precision of the neuronal code, and the quality of the decoding scheme used by neurones and their networks (Nicolelis, 1996; Zhang and Sejnowski, 1999; Doetsch, 2000). This defines how environmental information is coded. Several studies (reviewed in Bialek et al., 1991; Atick, 1992) have shown that early visual stations, such as the retina and the lateral geniculate nucleus, are optimized—in an information-theoretic sense—for transmitting information about scenes that have natural spatio-temporal statistics. Psychophysical image-discrimination experiments in humans find that performance is best when the pictures have natural second-order spatial statistics (Parraga et al., 2000). Similar conclusions have been drawn for the auditory system using a mutual information metric for stimulus and spike trains. Attias and Schreiner (1998) found that neurones in the inferior colliculus of the cat code naturalistic stimuli more efficiently than stimuli with non-naturalistic distributions. Similar results have been found in the auditory systems of frog (Rieke et al., 1995) and song-bird (Theunissen and Doupe, 1989). In the somatosensory system problems in evaluating the statistical properties of tactile stimuli have slowed progress. In combination, these considerations illuminate the idea that many aspects of the natural statistics of the environmental scene are imposed upon, and constrain sensory information processing. Some of these statistical constraints can be quite similar for optical, acoustical, and mechanical aspects of objects, and it is not unreasonable to assume that the neuronal processes underlying vision, audition, and somatic-sensation have developed similar mechanisms to exploit these properties for central nervous representation and coding. Conversely, a number of stimulus characteristics remain very much modality-specific and require special processing strategies to utilize and integrate that information.
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
277
Table 13.1. Parameter
Audition (A)
Touch (S)
Vision (V)
Dimensionality Laterality Receptor Types Latency
1 + 1 short
2 – >4 short
2 + 2 long
2.3. The Receptors All receptors have in common the fact that they encode intensity, duration and location of external stimuli. The most obvious distinction between the different sensory modalities are the different designs and spatial arrangements of the receptors that underlie responsiveness to a specific form of physical energy and its transformation into neuronal activity. The auditory system uses essentially a single receptor type, the inner hair cell, to convey information to the central nervous system about air pressure changes. The hair cells are arranged in a single line along the cochlea, that is, the receptor sheet is one-dimensional. Since the ear is a paired organ it provides laterality-specific information to assess correlated activity between the two sides. The eyes are also paired organs. The retina is a two-dimensional receptor sheet with two receptor types (rods and cones). The somatosensory system has a two-dimensional receptor sheet, and uses many more receptor types than vision and audition. More than ten receptor-types serve its function, with four receptors for mechano-sensation alone. While each modality is highly specific, each modality also shares some properties with the other receptors. The main correspondences are in the dimensionality of the receptor sheet (S = V); in the laterality (A = V); and in the latency (A = S) domain. These parallels imply that each modality has a unique feature that distinguishes it from the others. The receptor-level based differences between the modalities create distinct processing regimes that exploit these properties to optimize feature dimensions for object-oriented scene analysis. The differences in these optimized processing schemes are reflected in the organization and function of the subsequent processing stages. However, the resulting feature maps, despite their many transformations, conserve the plan of the primary receptor sheet, at least up to the primary cortical level. 2.4. Sub-Thalamocortical Processing Structural design, cell types, local circuits and function of the sub-thalamic processing stages, as well as the number of synapses between receptor and cortex differ, between the three modalities: Vision: Touch: Audition:
Retina –> Thalamus –> Cortex Spinal cord –> Brain stem –> Thalamus –> Cortex Spiral Ganglion –> Medulla/Pons –> Midbrain –> Thalamus –> Cortex
In the visual system, several transformations take place before the information arrives in the primary visual cortex. Within the retina, the two receptor types interact with several
© 2002 Taylor & Francis
Hubert R. Dinse and Christoph E. Schreiner
278
local-circuit cells, and give rise to at least two (and likely more) anatomically and functionally distinct pathways. The local circuit cells are a common feature in all modalities, where they modify sensory processing and recombine information internally. The preferred stimulus information conveyed by the two main visual streams differs in various ways, including colour information, receptive field size, stimulus contrast, and temporal aspects (Ungerleider and Mishkin, 1982; Livingstone and Hubel, 1988; Maunsell, 1992; Merigan and Maunsell, 1993; Ungerleider and Haxby, 1994; Hendry and Reid, 2000). In higher mammals, the stream from the retinal ganglion cells reaches the thalamus directly, which then projects to the primary visual cortex. Thus, the main sub-thalamic station which introduces functional varieties and distinctions is at the retinal level, without further brainstem or midbrain processing. In the somatosensory system, many receptor types encode tactile information. Their pathways remain parallel (that is, they do not converge), as they relay discriminable sensations, including fine touch, vibration, pain and temperature. The slowly adapting system, for example, is essential for tactile form recognition. This division of labour establishes, from the outset, a multidimensional representation of the tactile environment. Overall, the pathways from the receptors to the primary sensory cortical areas—via one brainstem station and the thalamus—provides, through considerable computational effort, a stepwise transformation from an isomorphic representation to a distributed one, in primary somatosensory cortex. In the auditory system, an even larger amount of task-oriented processing is accomplished below the thalamo-cortical level. Many brainstem stations and one obligatory midbrain nucleus are involved before the information reaches the thalamus and cortex. In addition to frequency coding and information about timing, which originate directly from the activity patterns of the receptor sheet (the Organ of Corti in the cochlea), at least three other basic features have been computed by the time the information reaches the thalamus: Sound localization: This dimensions uses differences in timing, and in spatial distribution of response strength between the receptor surfaces of the two ears to code spatial position. Unlike the visual and somatosensory system, a direct correspondence between activation of the receptor surface and object position in the external world is not possible. However, the position of an auditory object relative to the position of the two ears, in combination with the physics of sound propagation around the head, evokes unique activity patterns in the two cochleae. Differences in response timing and intensity between the two sides are used to compute the location of a sound source. This computation is capable of locating external sound sources in coordinates that are compatible with those of the other sensory modalities. Thus, in the superior colliculus, auditory space and visual space maps are superimposed (Middlebrooks and Knudsen, 1984). Spectral integration: In hearing, the extent of spectral integration, or, equivalently, spatial integration in relation to the receptor surface, varies from narrow to broad. However, subsets of midbrain and cortical neurones display properties that are similar to the psychophysical properties of “critical bands” including an integration bandwidth that is insensitive to intensity changes (Ehret and Merzenich, 1988; Ehret and Schreiner, 1997). This property underlies phenomena, such as loudness perception and stimulus discrimination. While it is not understood how this property is extracted, it is not only a consequence of receptor and receptor-surface interactions, but must be generated in
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
279
subsequent stations. By the midbrain, this process is essentially completed (Ehret and Merzenich, 1988). Periodicity: The analysis of coherent temporal modulations across the receptor surface underlies the identification of sound sources (e.g. a particular speaker), the distinction between simultaneously active sound sources (“cocktail party effect”), and it contributes to the perceptual grouping of acoustic objects (ranging, for example, from isolated vowels to words). While the neuronal basis of periodicity analysis is not understood fully, it involves several steps and is largely accomplished subcortically (Langner, 1997). The many stations of the auditory brainstem probably reflect the number and computational complexity of feature dimension required before reaching the cortex. In contrast, the many receptor types in the somatosensory system, and the complex local retinal processing schemes in vision seem to accomplish the task of generating sufficient feature dimensions without equally extensive brainstem processing. Overall then, the sub-thalamocortical stations compute several aspects from the receptor surface activity, that are optimized for representing and analyzing the environment in a framework that allows further refinement and alignment of the various representations of the external world. 2.5. Thalamus The thalamus is an obligatory nucleus for all three modalities. It has subdivisions in each modality that segregate sensory information prior to cortical processing, and it is a hub for descending (cortico-thalamic) control. The thalamic nuclei have relatively few cell types, mainly one type of output neurone and one or two types of local interneurone. It often is referred to as a relay nucleus, though this is an oversimplification since each nucleus modulates or transforms the signal passing through it. Indeed, the projections from subthalamic stations can remain anatomically and physiologically distinct. Examples of maintained segregation and pathway-specificity include magnocellular (fast-conducting) and parvocellular (slow-conducting) divisions in vision and audition (Hendry and Reid, 2000); tonotopic vs non-tonotopic pathways in audition (Winer, 1992); information for eye and ear laterality; cutaneous representation of face and body; slowly and rapidly adapting responses to cutaneous stimulation; and processing of discriminative sensation (high resolution) versus epicritic sensation (low resolution but with urgent behavioural qualities), such as pain and temperature sense. A possible reason for such segregation is that the different “channels” need to be optimized spatially and temporally at the cortical input level, for instance for binocular fusion, or higher spectral integration. From this point of view, the sensory thalamic nuclei may serve as staging areas for cortical processing. An important functional property of thalamic neurones is their high synaptic security, which enables reliable transfer at high temporal precision. Thus, thalamic neurones follow stimulus repetition rates up to >100 Hz with time-locked responses, in somatic sensation and audition. Thalamic function may be less related to local receptive field modification and feature extraction then to state-dependent modulation and gating of activity forwarded to the cortex (for reviews see: Casagrande and Norton, 1991; Winer et al., 1999). Strong modulatory influences from cortical feedback, and many extra-cortical sources have been described (e.g. Steriade and Llinas, 1988). Functionally, such complex modulatory systems may provide state-dependent synchronization or de-synchronization among different
© 2002 Taylor & Francis
280
Hubert R. Dinse and Christoph E. Schreiner
cortical inputs (Traub et al., 1999), attentional amplification of receptive field properties (Zhang et al., 1997; Yan and Suga, 1999), and, overall, an affective colouring of the sensory experience. These modulatory effects are similar across modalities, suggesting a common operating principle at this gateway to cortex. This also suggests that the content and configuration of the modality-specific information in the thalamus is at an equivalent level of abstraction and integration. Thus, consequences of the modulatory processes imposed on the sensory stream at the thalamus operate on the same level of processing for the three modalities. For an excellent survey of thalamic function, see the review by Sherman and Guillery (2001).
3. MODALITY CHARACTERISTICS OF PRIMARY CORTICAL AREAS 3.1. Receptive Fields Since its introduction more than 60 years ago, the concept of receptive fields (RFs) has constituted a powerful tool for the analysis of sensory systems. Hartline (1938) wrote: “The region of the retina which must be illuminated to produce a response in a particular nerve cell is termed the receptive field of that cell”. Although we perceive the world as unitary, the neuronal elements can analyze only small portions of the environment. Knowing structure, organization and properties of RFs is indispensable for understanding their specific contribution to signal processing and their role in brain function. Consequently, receptive fields are widely used to define and map cortical neural activity into a parametric feature-space derived from either stimulus or computed variables. This analysis allows the characterization of the processing capacities of single cells. Historically, RF properties have been described in terms of “feature-detectors” and filters operating hierarchically at increasing levels of complexity and specificity. Below, some extensions and revisions of this original scheme are discussed. Figure 13.1, depicts characteristics of single cell receptive fields in visual, auditory, and somatosensory cortex. These differ greatly in shape, dimension, and dimensionality. In all of these examples, activity is represented systematically in the parametric space of the respective modality, such as the visual field, the “frequency space”, and the skin location. More distinct and refined RF forms emerge in so-called “functional” spaces embedded in the respective modality. Here, neural activity is plotted as a function of a graded variation of a selected stimulus parameter, and the ensuing RFs are often referred to as “tuning curves”. This approach allows both quantitative and qualitative analyses of an example of neuronal sensitivity to a given stimulus. Wellknown examples include orientation tuning in vision, intensity tuning in audition, and grating resolution in somatic sensation. Is there a common principle of RF organization despite these dissimilarities? By definition, RFs exist in the parametric space of the respective modality. Thus, the “coordinates” must be different and depend on the parameters under investigation. Inevitably there are substantial and substantive differences, since parameter spaces differ across modalities. Closer inspection of RFs and tuning curves across modalities reveals at least one common aspect of RF organization: integration of excitatory and inhibitory inputs. Accordingly, the question of a possible common type of RF organization can be resolved by analysis of the spatial distribution and interaction of excitation and inhibition. Figures 13.2 and 13.3 show examples from visual, somatosensory, and auditory cortex. Although the RF structures
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
281
cortical representation of skin surface
cortical representation of visual hemifield
SI
VI
receptive fields receptive fields
skin surface
visual hemifield
cortical representation of cochlea AI
receptive fields
cochlea Figure 13.1. Examples of receptive fields (RFs) depicted in parametric space recorded in visual (top left), somatosensory (top right) and auditory cortex (bottom). As a rule, RFs in all modalities cover a certain circumscribed area in visual field, skin surface or frequencies, respectively, thereby providing a “window” to the outside world. Receptive fields are mapped by inserting microelectrodes, into the cortex (usually the middle layers), to record action potentials from single cells or multiple unit activity from small clusters of neurones. The receptive field is defined as that region on the sensory surface where stimulation evokes action potentials. This procedure maps activity recorded in the cortex into the stimulus space, which allows an easy and systematic way of parametric analysis. When moving the electrode to an adjacent location in the cortex, a systematic shift in the corresponding receptive field location will be encountered. A complete topographic map can be obtained when a large number of electrode penetrations is combined in such a way that the penetration coordinates are related to the corresponding receptive field coordinates. The inverse approach is taken when cortical activity distributions are measured. In contrast to the above, a fixed stimulus, ideally a small, “point-like” stimulus, is applied, and the entire activity in the cortex evoked by that stimulus is measured. This type of activity distribution is often referred to as “point spread function—PSF”. Technologies often employed for this kind of analysis are optical imaging and fMRI. However, it should be noted that the PSF can be obtained using microelectrodes. In this case, single or multiple neurone activity evoked by the “point-like” stimulus is recorded, and its spatial distribution is derived from a systematic mapping at different locations. In theory, RFs and point-spread functions are the corresponding counterparts of a mapping rule that describes how input is represented in a topographic map. In practice, however, due to differences in threshold and due to particularities in methodological constraints, the two descriptors of cortical representations may yield different results.
© 2002 Taylor & Francis
Hubert R. Dinse and Christoph E. Schreiner
282
A
B
C
D
E
F
G
H
Figure 13.2. Two-dimensional RF profiles recorded in somatosensory cortex (top) and visual cortex (bottom). Top: RF structures observed in the hand representation of monkey area 3b. Black indicates excitation, white suppression. The sample RFs are meant to illustrate the wide range of combinations of excitatory and inhibitory areas. A–D and G, H: single inhibitory regions located on the trailing side (i.e. towards the distal aspects of the hand) of the excitatory RF region. E and F: two regions of inhibition on opposite sides of the excitatory region. The line through each RF passes through the excitatory and inhibitory peaks (modified according to diCarlo et al., 1998). Bottom: Two-dimensional response profiles of 2 typical simple RFs to illustrate variability in sizes, shapes, and placements of individual subregions (modified according to Jones and Palmer, 1987). The letter above each RF corresponds to the one-dimensional profiles shown in Figure 13.3. Reproduced with permission.
vary widely across modalities, they typically have a central region of excitation flanked by surrounding, or offset inhibitory regions (Figure 13.2). The two-dimensional nature of both retina and skin surface makes it challenging to distinguish visual from somatosensory RFs, because the structure of excitatory and inhibitory subregions in RFs of VI and SI
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
A
A
B
C
D
E
F
G
H
B
A 1
C
283
D
2
4
BF
16
32 KHz
B 0.5
1
BF
8 KHz
Figure 13.3. One-dimensional RF profiles recorded in somatosensory (top), visual (bottom left) and auditory (bottom right) cortices. The plots display the relative intensities of the excitatory and inhibitory components. Scale bars in bottom left panel indicate 1 degree. BF: best frequency. The letter above each RF in the top and bottom left panels corresponds to the two-dimensional profiles shown in Figure 13.2. Modified according DiCarlo et al. (1998) (top), Jones and Palmer (1987) (bottom left), and Shamma and Versnel (1995) (bottom right). Reproduced with permission.
neurones is quite similar (Jones and Palmer, 1987; DiCarlo et al., 1998). An analogous sub-field structure has been described for auditory cortical neurones (Shamma and Versnel, 1987). However, due to the one-dimensional nature of the receptor surface of the cochlea, the RF structure found in AI is also one-dimensional, with inhibitory ‘sidebands’. A striking resemblance between RF structures of all three modalities emerges in one-dimensional RF-profiles (Figure 13.3). This demonstrates a common principle of sensory processing, with a complex spatial arrangement of excitatory and inhibitory processes that, together, appear crucial for processing performed in each area. A next logical step in discerning similarities in RF properties is to study RF organization directly in cortical coordinates, i.e. in the spatial distribution of dendritic activation. Unfortunately, because of technical difficulties, there is very little information available. It has been hypothesized that the often-dramatic forms of functional selectivity might arise from asymmetries and anisotropies at a cellular level. However, in spite of the improvement of anatomical staining techniques, this issue has not been sufficiently resolved. Early papers reported a substantial correlation between cell shape and functional cell types (i.e. “simple” and “complex”) in the visual cortex (van Essen and Kelly, 1973; Lin et al., 1979).
© 2002 Taylor & Francis
284
Hubert R. Dinse and Christoph E. Schreiner
In a subsequent study, the dendritic arborizations of horseradish peroxidase-filled cells were reconstructed in three dimensions. There was no consistent relationship between orientation selectivity and the tangential bias of the dendritic tree. The width of the receptive fields was compared to the equivalent “width” of the tangential extent of the dendrites, and there was no significant relationship between the two widths. Accordingly, the tangential arrangement of the dendritic field does not appear to be important in determining the orientation selectivity, or the size of the receptive fields of neurones in the cat visual cortex (Martin and Whitteridge, 1984). Because of its peculiar morphology, the Meynert cell type has been suspected to mediate “direction” selectivity (Livingstone, 1998). In her model, excitatory synapses are activated sequentially along the asymmetric basal dendrites of the large pyramidal cells of Meynert. However, recent modeling studies indicated that even when the electrotonic asymmetries in the dendrites were extreme, as in cortical Meynert cells, the biophysical properties of single neurones could contribute only partially to the directionality of cortical neurones (Anderson et al., 1999). Accordingly, the authors suggested that most of the computation of direction of motion, over the range of velocities observed, must rely on network mechanisms, most probably using the local recurrent circuits of cortex. In a series of studies, extracellular and intracellular recordings were made from neurones in the cat visual cortex, in order to compare the subthreshold membrane potentials, reflecting the input to the neurone, with the output from the neurone seen as action potentials (Douglas et al., 1988, 1991; Martin, 1988). The authors failed to find direct experimental evidence for the hypothesis that the selectivity of visual cortical neurones depends on shunting inhibition. More generally, they concluded that the intracellular recordings do not support models of directionality and orientation that rely solely on strong inhibitory mechanisms to produce stimulus selectivity. Taken together, most of the selectivity observed in cortical sensory areas may be functional rather than structural. Other aspects of cortical processing may be shared by all modalities. These aspects relate to the crucial role of context and nonlinearities. Neurones in primary visual cortex have been characterized with respect to key physical features such as visual field location, orientation, motion direction, ocular dominance, and spatial frequency. These approaches allowed the analysis of neural representations within parameter spaces that are explicitly defined by physical stimulus attributes. However, many visual illusions, such as the perception of illusory contours (Kanizsa, 1976; von der Heydt et al., 1984; Ramachandran et al., 1994; Sheth et al., 1996; Mendola et al., 1999), indicate that the visual system must contain representations within parameter spaces without a physical counterpart. This agrees with the observation that single neurones exhibit complex, non-predictable behaviour, dependent on stimulus context (for review see Gilbert et al., 2000; Kapadia et al., 2000). The complex spatio-temporal response properties are plastic, and can be altered by stimuli outside the receptive field centre, or even outside the classical receptive field (Allman et al., 1985; Dinse, 1986; Gilbert and Wiesel, 1990; Sillito et al., 1995). Horizontal circuits within the cortex contribute significantly to local cortical processing (Bolz and Gilbert, 1989). Thus, cortical representations of peripheral activity deviate significantly from a simple feedforward re-mapping of sensory space (Ferster and Miller, 2000). Particularly for the visual system, complex subsystems dedicated either to form or motion processing have been identified (Ungerleider and Mishkin, 1982; Livingstone and Hubel, 1988; Maunsell, 1992; Merigan and Maunsell, 1993; Ungerleider and Haxby, 1994). These pathways originate in the retina and can be traced serially in the visual pathway
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
285
up to functionally specific high-level areas. Similar anatomical substrates may exist for the other modalities. However, the actual degree of functional segregation or cross-talk between such subsystems is unknown (Romanski et al., 1999; Recanzone et al., 1999). In any event, the concept of two basic elements of sensory processing—form and motion—holds across modalities. Form is derived from two-dimensional representation of objects, and is best understood in the visual and somatosensory systems. In the auditory system, form contains the one-dimensional frequency space, as well as the time axis, in order to accommodate the spectral-temporal extent and structure of events such as vocalizations. Objects move relative to the receiver and the background. Movement is represented as a systematic spatio-temporal displacement. Many lines of evidence suggest that the analysis of such displacements is crucial for the processing of objects (Metzger, 1932; MacKay, 1958; Reichardt, 1961; Burr, 1980). In both the visual and somatosensory modalities, perceptual illusions of apparent motion exist (Ramachandran and Anstis, 1983, 1986; Geldard and Sherrick, 1972; Kirman, 1974; Evans and Craig, 1991). In the auditory system, two types of movement can be distinguished: movement across the receptor surface and movement in external space. Frequency sweeps are directly analogous to visual and somatosensory motion with reference to the receptor surface, and result in similar perceptual illusions (e.g. Warren, 1970; Bregman, 1990). However, frequency sweeps that are part of an auditory object (e.g. in formant transitions of stop-consonants, or in diphthongs) are not necessarily equivalent to motion of external objects. Analysis of the motion of an external object in the auditory sense involves a complex interplay of spectral and temporal changes across the receptor surfaces of both ears. Perhaps, the neural machinery for the specialized processing of motion shares similar algorithms across modalities such as directional selectivity and temporal correlation/sequencing of activity across long distances of the receptor surfaces. From these considerations of response profiles, it follows that RF characteristics, as viewed in a modality-specific framework can obscure the underlying mechanisms, which can be expressed in more general terms, spanning across modalities, such as spatial and temporal interactions of the distributions of excitatory and inhibitory responses.
3.2. Spatial Processing 3.2.1. Cortical maps Early investigators of sensory cortical areas agreed that these areas re-map certain aspects of the external world, thereby preserving the local neighbourhood relationships in the environment. These representations are known as retinotopic (visual), somatotopic (touch) and cochleotopic (auditory) maps. All constitute systematic parametric representations across cortical space. Given the differences in the respective receptor arrays of the retina, skin and cochlea, these maps differ accordingly in design, and are not a direct, one-to-one representation of the world. This is particularly obvious on a fine spatial scale, where the considerable scatter of RF position is larger than, or in the same range as the required systematic shifts due to a topographic gradient in the map (Hubel and Wiesel, 1962; Albus, 1975; Sutter and Schreiner, 1991). On a larger scale, a clear topographic gradient is present, though distorted. The retinotopic gradient of the cortical map of the visual field is overlaid by other “functional maps”. Such maps contain an orderly arrangement
© 2002 Taylor & Francis
286
Hubert R. Dinse and Christoph E. Schreiner
of stimulus attributes for portions of the respective retinal locations (Hübener et al., 1997; Kim et al., 1999) but also exhibit discontinuities that result from the discrete organization of several of such attributes (Das and Gilbert, 1997). Visual functional maps for orientation of moving gratings (Blasdel and Salama, 1986; Swindale et al., 1987; Bonhoeffer and Grinvald, 1991), direction of motion (Weliky et al., 1996), and the spatial frequency of a moving grating (Shoham et al., 1997; Kim et al., 1999; Issa et al., 2000) are now available and undoubtedly yet more such examples will be discovered as our knowledge of cortical representation deepens. In the auditory cortex, there is also evidence for multiple functional maps, that transcend simple frequency representations (Schreiner, 1998; Schreiner et al., 2000). Thus, maps of sharpness of tuning, preferred intensity, direction of FM sweeps, and onset latencies have been suggested. A comparable system for the somatosensory cortex is the spatial segregation of slowly and rapidly adapting mechanoreceptors which form an “overlay” to the general somatotopic cortical map. However, evidence is still scant for other parameter maps analogous to those in audition and vision. Besides these feature maps, in modalities with a paired arrangement of receptor organs (vision and hearing), there are maps for the inputs from the two organs: ocular dominance maps (Wiesel et al., 1974; LeVay et al., 1978) and binaural bands (Imig and Adrian, 1977; Middlebrooks et al., 1980). No such lateralization maps are known for the somatosensory system. Interestingly, “disparity maps” have been described as an example of higher-order functional maps, that combine the information from the lateralization maps to form an additional parameter space (Burkitt et al., 1998). A similar situation may exist for the auditory cortex where laterality information is readily expressed in binaural bands, whereas explicit maps of spatial information are less obvious (Furukawa et al., 2000). As noted above, parametric representations must reflect differences of the stimulus spaces. Thus, all maps known in the different modalities differ decisively from one another. A conventional way to obtain insight into the cortical coordinates of these maps is to study the spatial distribution of activation patterns in cortex. The activity pattern can be used to derive the so-called “cortical point spread function” (Fischer, 1972; Capuano and McIlwain, 1981; McIlwain, 1986). In contrast to functional maps, cortical activity is not represented in a parametric space, either by topography or in the stimulus space, but is directly recorded in cortical dimensions (cf. Figure 13.1). Due to technical restrictions, single electrode penetration maps can only reveal an approximate or incomplete picture of the point spread function (PSF). As new imaging techniques—such as optical imaging or the recent development of non-invasive imaging technology for humans such as PET or fMRI—emerge we can derive spatially continuous forms of the PSF, although the measurement itself is necessarily indirect because of the still-uncertain correlation between neural activation and cortical metabolism. PSFs obtained from activity patterns recorded in VI, SI and AI under comparable imaging conditions with simple stimuli (small squares of light, small indentations of the digit skin surface, and tone bursts, each stimulus applied at moderate intensity) share some properties (see Figure 13.4). As a rule, the simple stimuli used are meant to be an experimentally-feasible equivalent of a “point” on the receptor surface. Although the different stimuli used are not qualitatively scalable, they activate only a small portion of the receptor surfaces. While a direct comparison of absolute size of the PSFs is not possible due to the incomensurate nature of the different stimuli, several basic characteristics of PSFs are shared across modalities:
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
AI
SI
287
VI
Figure 13.4. Point-spread functions, recorded by means of optical imaging in auditory (left), somatosensory (middle), and visual (right) cortices. AI: tone burst stimulation at 6 kHz delivered at 40 dB SPL, cat primary auditory cortex, scale bar 1 mm. SI: cutaneous stimulation of digit 3 of rat hindpaw, probe diameter, 1 mm, indentation, 250 to 500 µm, rat primary somatosensory cortex, scale bar 1 mm. VI: square of light (0.4 deg visual angle), flashed for 25 ms, luminance 0.9 cd/m2 against background of 0.002 cd/m2, cat primary visual cortex, scale bar 0.5 mm. AI modified according to Dinse et al. (2000), SI and VI unpublished data of B. Godde, T. Hilger and H.R. Dinse. (see Color Plate 5)
1.
2. 3.
size: PSFs recorded in primary cortices involve a great portion of the total primary area, irrespective of simple, spatially or spectrally restricted stimuli (Grinvald et al., 1994; Das and Gilbert, 1995 (VI); Godde et al., 1995; Chen-Bee and Frostig, 1996 (SI); Bakin et al., 1996; Dinse et al., 1997, 2000 (AI)). symmetry: PSFs usually are not simply concentric distributions of activity around the core of the stimulation. Rather, they are spatially highly asymmetric. compactness: PSFs are patchy, indicating a non-monotonic decay of activity with distance from the center of stimulation.
While the PSFs recorded in VI and SI are rather similar, the PSFs recorded in AI differ from the others in terms of their shape (cf. Figure 13.4). PSFs for VI and SI are circular, in line with the two-dimensional receptor array arrangement. In contrast, PSFs recorded in AI have an elliptic shape. This might reflect the constraints of the one-dimensional cochlear receptor array, the specific central projection pattern of auditory nerve fibres (Brown and Ledwith, 1990), and specific central circuits. As in the RF comparisons, PSF analyses reveal many similarities once the differences in receptor sheet dimensionality are considered. 3.2.2. Topography and distributed activity Functional and structural segregation into subregions (patchiness) represents another common property of sensory cortical fields. Many thalamo-cortical and cortico-cortical projections target several, non-contiguous regions. Correspondingly, neurones representing similar functional parameters also tend to cluster in small subregions rather then exhibiting smooth spatial gradients. Such patchiness has been used as evidence against the existence of strict parameter gradients in the various sensory cortical fields. However, considerations of self-organizing models (e.g. Kohonen and Hari, 1999; Swindale, 2000) suggest that both topographical gradients and local patches are necessary consequences of self-organizing algorithms optimized for representing several behaviourally relevant dimensions of environmental scenes. These functional maps are overlaid so as to ensure that many, if not all,
© 2002 Taylor & Francis
288
Hubert R. Dinse and Christoph E. Schreiner
combinations of the different parameters are represented in cortex. Recent theoretical studies show that geometrical factors do not constrain the ability of the cortex to represent combinations of parameters in spatially superimposed maps of similar periodicity. Considerations of uniform coverage suggest an upper limit of six or seven maps. A higher limit, of about nine or ten, may be imposed by the numbers of neurones or minicolumns available to represent each feature within a given cortical microdomain (Swindale, 2000; Swindale et al., 2000). Thus, several feature dimensions can be expected to be represented across VI, AI, and SI. The set of all values in a given dimension is not equally represented; ranges that are especially important for behaviour are expanded (Suga, 1984; Recanzone et al., 1993). To provide the optimal range of combinations between different information-bearing parameters, complex spatial relationships between the various parameter maps are necessary (Obermayer et al., 1990; Swindale, 1991). This general principle is evident in the gross similarity of maps in the different sensory cortical fields. From this point of view, each neurone, and each location in VI, AI, and SI can be understood as representing a specific set of many independent variables in the sensory environment. Topographically, each location on the cortical surface corresponds to a specific intersection of several systematic maps. Mathematically this forms a response vector, with specific direction and length in a multidimensional parameter space (e.g. Lennie, 1998). Based on anatomical studies (Lund et al., 1993), similar conclusions have been drawn: the size-match between axonal patches and dendritic arbors should result in a maximal diversity of dendritic sampling (Malach, 1994). There is agreement that physical attributes of sensory stimuli are encoded as activity levels in populations of neurones. Reconstruction or decoding describes the inverse problem, in which the physical attributes are estimated from neural activity. Reconstruction methods have been regarded useful, first in quantifying how much information about the physical attributes is present in a neural population, and second, in providing insight into how the brain might use activity arising from many neurones (Nicolelis, 1996; Zhang and Sejnowski, 1999; Doetsch, 2000). Most of what we know in the visual cortex about functional maps, beyond orientation preference, comes from optical imaging techniques. This method allows the simultaneous assessment of entire maps, covering many square millimeters of cortex. Functional maps are calculated by finding, for each cortical location, the preferred parameter value, viz., that causing the largest change in light absorption. This approach employs a particular reconstruction scheme to decode information from the averaged activity distribution. It implicitly assumes that thresholded activity reveals pertinent aspects in brain functioning. However, this “winner-takes-all” approach is only one possible functional interpretation of distributed neural activity. Are there alternatives? Recently, it became evident that a critical step for the investigation of how distributed cell assemblies process behaviourally-relevant information is the introduction of data analysis methods that could identify functional neuronal interactions within high-dimensional data sets (cf. Nicolelis, 1999). In fact, the introduction of the multineurone/multi-site recording technique made it possible to record from a large number of neurons simultaneously, even in awake and behaving animals (Abeles, 1991; Nicolelis et al., 1997). This approach allows the exploration of dynamically-maintained distributions of activity, including aspects of cooperativity between neurones, on a single trial basis. For example, in a study devoted to exploring the representation of tactile information in three areas of the primate somatosensory cortex (areas 2, 3b and SII), small neural ensembles (30–40 neurones) of broadly-tuned somatosensory neurones were sufficient to identify correctly the
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
289
location of a single tactile stimulus on a single trial (Nicolelis et al., 1998). Interestingly, each of these cortical areas could use different combinations of encoding strategies, such as mean firing rate, or temporal patterns of ensemble firing, to represent the location of a tactile stimulus. Thus, several distinct ensembles of broadly tuned neurones, in different regions of the somatosensory cortex contain information about the location of a tactile stimulus. In contrast to functional maps, distributed representations are characterized by poor selectivity of their constituents, and accordingly, little topographic specificity. Instead, it is assumed that each neurone contributes, in a weighted form, to each possible stimulus configuration. Another (albeit related) approach was recently introduced in order to account for population activity at the level of spikes, recorded in early areas of sensory cortex. The goal was to visualize and to analyze cortical activity distributions, in the coordinates of the respective stimulus space, in order to explore cooperative processes (Dinse et al., 1996; Jancke et al., 1996; Kalt et al., 1996; Erlhagen et al., 1999; Jancke et al., 1999; Jancke, 2000). Instead of asking how accurately the parameter of, for example, stimulus location can be reconstructed or decoded, the main interest was on analyzing interaction-based deviations of population representations, dependent on defined variations of stimulus configurations. Despite the fact that very different types of stimuli were employed, (squares of light versus indentations on the digit of the hand), comparable distance-dependent interactions could be demonstrated for visual and somatosensory cortex. This, again, suggests modality-independent modes of processing and representation of distance-dependencies (Dinse and Jancke, 2001a). Taken together, there is convincing experimental evidence that sensory cortices contain large pools of neurones that act in concert to represent aspects of the outside world. Depending on the methodology used, the outcome emphasizes either the aspect of “parametric maps” or the notion of “distributed representations”. In this scenario, parametric maps are often regarded as a mass activity-based feature detector, i.e. a rather robust representation of certain parameter regimes, invariant against further contextual influences. Accordingly, this view provides some problems concerning how plastic adaptive capacities can be implemented. The notion of “distributed representations” allows a higher degree of flexibility, at the cost of a less rigid representation of feature spaces. On the other hand, it has even been suggested that distributed representations might reflect a lack of orderly representational maps. Only further experiments and more refined techniques will solve these conceptual discrepancies in functional mapping. However, there are no clear modality specific differences in these general questions regarding principles of cortical organization.
3.3. Temporal Processing Many aspects of temporal processing across modalities can be compared rather directly, because the time-axis reflects an absolute measure, rather than a parametric distribution of activity which would allow differences between modalities in input energy. We will now consider response latencies, repetition rate coding, RF-dynamics (i.e. the spatio-temporal, or spectro-temporal organization of RFs), and oscillatory behavior.
3.3.1. Response latencies Cortical response latencies are basically determined pre-cortically, by the kinetics of the receptors, by subcortical processing and by properties of the propagating axons. There are
© 2002 Taylor & Francis
290
Hubert R. Dinse and Christoph E. Schreiner
considerable differences in the temporal properties of the different receptors, and in the number of subcortical stages, which in turn results in significant timing differences across modalities. Axon properties such as myelination and size are comparable across modalities (Brown, 1987; Hunt and McIntyre, 1960). For the visual system, fast and slow conducting fibres constitute two segregated information channels that may play differing roles in form and motion processing (Ungerleider and Mishkin, 1982; Dreher et al., 1976; Bullier and Henry, 1980; Petersen et al., 1988). By contrast, segregated pathways in the somatosensory system that also may contribute to form and motion processing, that is, neurones with slowly and rapidly adapting responses, are not distinguished by different conduction times (Tremblay et al., 1993). Comparing cortical response latencies across modalities reveals significant differences, even given the uncertainty of comparing and scaling the stimuli (see above discussion of Point spread function). In any case, auditory and somatosensory latencies have the shortest latencies (ranging 10 to 20 msec), and visual latencies are the longest (40 to 60 ms). The somatosensory system has an unusual property: cortical latencies vary with skin position (in contrast to the ears and eyes, which have a fixed distance with respect to the cortex). Response latencies to hindleg stimulation can be twofold longer than latencies following vibrissa or foreleg stimulation. Thus, latencies in S1 reflect the distance between skin site and cortex. Other cortical factors such as synaptic integration and threshold behaviour may contribute to response latencies. Such factors probably do not contribute significantly to timing differences of peripheral and subcortical origin. The significant differences across modalities in the time at which sensory information arrives in the cortex provide a clear modality-specificity. However, it also poses a problem when information from different modalities has to be combined to yield an integrative, behaviourally-meaningful output. This raises the question of how information from different modalities is combined. In one possible scenario, the slowest modality could set the pace (for an account on insula contributions to auditory-visual interactions via short-latency connections with the tectal system see Bushara et al., 2001). Compensatory effects on sensory processing in blind subjects provide some insights. Late components of auditory event-related potentials (ERPs) have shorter latencies in blind than in sighted humans (Naveen et al., 1998; Niemeyer and Starlinger, 1981; Röder et al., 1996). In auditory discrimination tasks ERPs show an enhanced amplitude recovery in blind subjects as compared to normal sighted subjects, indicating an improved ability to process fast sequences (Röder et al., 1999). Studies on the speed of language processing revealed a similar superiority for blind people, as compared to normal subjects (Röder et al., 2000). On the other hand, the texture segmentation and visual search capacities in deaf subjects did not exceed that of age-and gender-matched hearing subjects. Rather, deaf school children showed deficits in visual processing, which were partially compensated in adult deaf subjects (Rettenbach et al., 1999). These findings from blind and deaf subjects indicate that sensory deprivation has no general effect on processing times, resulting either in acceleration or deceleration. The data are compatible with the hypothesis that the multi-sensory processing speed in normal subjects arises from integrative processes, which are dominated by the slowest information stream. Once the modality with the longest latencies is removed, sensory processing is accelerated. 3.3.2. Repetition rate coding Temporal integration, as expressed in repetition rate coding or sequence representation, is the capacity of cortical cells to respond to consecutive stimuli. In the periphery and the
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
291
thalamus many neurones in each modality can follow high repetition rates, of more than 100 events/s. In contrast, cortical cells usually have a low-pass characteristic which limits the following of more than 10 to 20 events/s, in all modalities (Movshon et al., 1978; Creutzfeldt et al., 1980; Foster et al., 1985; Simons, 1985; Philipps et al., 1989; Nelson, 1991,a,b,c; Gardner et al., 1992; Reid et al., 1992; Merzenich et al., 1993; Brosch and Schreiner, 2000; Krukowski, 2000). It has been proposed that specific properties of the NMDA-receptor channel kinetics mediate this reduction in stimulus-following capacity (Thomson and West, 1993; Crair and Malenka, 1995; Denham, 2000; Krukowski, 2000). In addition, GABAergic mechanisms might contribute to this property (Dykes et al., 1984) as well as fatigue and depletion of synaptic transmission (Chance et al., 1998, Galarreta and Hestrin, 1998; Wang and Kaczmarek, 1998; Buonomano, 1999, 2000). Presumably, these all act in concert to constrain the representation of fast event sequences in the cortex. Further work shows that active behaviour such as exploration can modulate the temporal processing capacities, compared to passive stimulation (Fanselow and Nicolelis, 1999; Moore et al., 1999), and that plastic reorganization alters and modifies temporal processing (Kilgrad and Merzenich, 1998; Buonomano, 1999; Dinse and Merzenich, 2002). Thus, in the cortex there is a significant reduction in repetition frequency response compared to subcortical processing. Such uniformity is another argument for homogeneity of cortical sensory processing across modalities. 3.3.3. RF-dynamics It has been known for decades that RFs have complex spatio-temporal behaviour (in the visual cortex) or spectro-temporal behaviour (in the auditory cortex) involving the time domain. This is seen, when the complete temporal profile of neurone responses is assessed (de Boer and Kuyper, 1968; Podvigin et al., 1974; van Gisbergen et al., 1975; Aertsen and Johannesma, 1981; Eggermont et al., 1981; Krause and Eckhorn, 1983; Jones and Palmer, 1987; Shevelev, 1987; Best et al., 1989; Dinse et al., 1990; for an update of more recent results see: Eckhorn et al., 1993; DeAngelis et al., 1993; Dinse, 1994; Wörgötter and Eysel, 2000; Dinse and Jancke, 2001b; Dinse, 2001). The substantial changes in RFs over time indicate a significant interaction of space and time—or of spectrum and time—as well as temporal dependence of tuning characteristics (Dinse et al., 1990, 1991; Dinse and Schreiner, 1996; Ringach et al., 1997; Ghazanfar and Nicolelis, 2001). Dynamic RFs are a common denominator in all sensory modalities. Their basic dynamic properties are influenced by aspects of latencies that reflect subcortical processes and by aspects of response duration. Response duration is governed by feedforward, feedback, and lateral interactions (von Seelen et al., 1986; Krone et al., 1986; Dinse, 1994; Dinse and Schreiner, 1996; Omurtag et al., 2000). These interactions constitute an essential and unifying feature of cortical functions. Another interpretation of RF dynamics attempts to link cortical processing dynamics and dynamics of the environment (Dinse et al., 1993; Dinse, 1994; Wiemer et al., 2000). Sensory signals typically have complex time-variant properties, which are manifested on a variety of time scales, that may be reflected in the dynamics of RFs. In the auditory system, one can differentiate timing based on the period of syllable sequences, in the range of hundreds of ms, from the duration of consonants and formant transitions, in the range of only 10 to 20 ms, from onset information, whose precision is in the millisecond range (for single events). In the visual system, the timing schedule provided by eye movements also imposes ecologically relevant dynamics. For the somatosensory system the movement of
© 2002 Taylor & Francis
292
Hubert R. Dinse and Christoph E. Schreiner
objects across the receptor surface reflects largely the velocity of limb movements relative to the objects (in the range of hundreds of milliseconds) and, by vibrational correlates, also reflects the texture properties (in the range of tens of milliseconds). RF dynamics may represent specific adaptations for processing inherently time-variant signals specific for each modality. A common feature of cortical signal processing would undergo some specific adaptations to match the requirements of the signal space. 3.3.4. Oscillations Perhaps the most widely recognized but least understood electrophysiological activity of the cerebral cortex is its characteristic electrical oscillations, which comprise a broad spectrum of periodic events from high-frequency oscillations (30–90 Hz, the so-called gamma range) to frequencies as low as seconds or minutes (besides ultradian and circadian rhythms). Stimulus-evoked oscillatory responses in the 10 Hz range have been studied for several decades at cortical and subcortical levels (Bishop and O’Leary, 1936; Chang, 1950; Andersen and Andersson, 1968). However, at present the analysis of these low frequency oscillations is purely phenomenological, and it is uncertain whether the different low-frequency patterns seen in different brain regions have common origins and functions, despite their temporal similarities (Dinse et al., 1990; Kopecz et al., 1993; Ahmed, 2000; Cotillon et al., 2000; Miller and Schreiner, 2000). Accordingly, their functional role remains obscure. Low-frequency oscillatory events are phase-locked to sensory stimulation, which explains their appearance as distinct oscillatory peaks in post-stimulus-time histograms (PSTHs). They cover frequencies between 5 to 20 Hz, encompassing several EEG bands. A detailed analysis of area- and modality-specific properties of low frequency oscillatory patterns recorded in 4 visual cortical areas, as well as in AI and SI (Dinse et al., 1997) found low-frequency oscillations in all areas. These differed in probability of occurrence, and each area had a characteristic frequency range of its own. Thus, in VI, frequencies ranged from 8 to 22 Hz, in AI from 6 to 10 Hz, and in SI from 10 to 20 Hz. Accordingly, the phenomenon of low-frequency oscillations is very general and found in all areas. There is a striking uniformity of the overall pattern, but there are substantial differences in the sequence and timing of the individual oscillatory peaks, differences that reflect an area-specific signature of the oscillations. Interestingly, human auditory and visual EPs also differ in their amplitude-frequency characteristics: Frequency maxima for visual stimuli were significantly higher than for auditory stimuli (Schurmann and Basar, 1999). Recent inactivation experiments in AI revealed that both the auditory thalamus, and the auditory sector of the thalamic reticular nucleus, but not the auditory cortex, have a role in the genesis of a specific type of low frequency stimulus-evoked oscillations (Cotillon and Edeline, 2000). The tendency to oscillate can be modified through external stimuli with appropriately chosen spatio-temporal properties (Miller and Schreiner, 2000). These findings suggest a common drive or origin of this oscillatory behaviour, that is modified by localized parameters. High-frequency gamma oscillations were described first for the olfactory bulb by Freeman and co-workers (Freeman, 1968; Freeman and Skarda, 1985; Eeckman and Freeman, 1990). They have since been found in visual (Eckhorn et al., 1988; Gray et al., 1989), auditory (Franowicz and Barth, 1995; MacDonald and Barth, 1995; Sukov and Barth, 1998) and somatosensory (MacDonald and Barth, 1995; Jones and Barth, 1997; Hashimoto, 2000) cortex, and they have gained much attention due to their possible involvement in
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
293
“feature binding” and higher cognitive processes (Eckhorn, 1994; Singer, 1998; Sauve, 1999). In contrast to low-frequency oscillations, high-frequency oscillations are not stimuluslocked, so they are only detectable in single trial analyses, or by computation of the power spectra of spike trains, and not in PSTHs that are based on a temporal averaging. Highfrequency oscillations cover a wide frequency range, from the higher beta range (~20 Hz) to >100 Hz. There is debate about the stimulus-specificity of these oscillations and uncertainty about the features and states that drives them optimally. While moving bars of light or moving gratings drive high-frequency oscillations strongly, flash stimuli are unable to induce oscillations, although they have the same perceptual relevance (Tovee and Rolls, 1992). Several studies have emphasized the importance of high-frequency oscillations in tasks relevant to behaviour, and there is ample evidence for a task-specific emergence of such oscillations (Hamada et al., 1999). Comparing oscillations across several neocortical areas shows that the spatial patterns formed by synchronous activity change when the contingency of reinforcement is reversed, vary with respect to stimuli, and had a dependence on context and learning, as seen in the olfactory bulb and prepyriform cortex (Barrie et al., 1996). In monkeys performing a motion-discrimination task, significant temporal correlations exist between simultaneously recorded pairs of neurones, in areas MT and MST and other extrastriate cortical areas. Interestingly, temporal decorrelation of MT and MST neurones could be used to detect the stimulus, but synchronization did not convey specific information about its direction of motion and was, thus, unlikely to contribute to behavioural performance (de Oliveira et al., 1997). While the functional role of this type of oscillation remains unresolved (Eeckman and Freeman, 1990; Ghose and Freeman, 1992; Kirschfeld, 1992; Tovee and Rolls, 1992; Kirschfeld et al., 1996, Ghose and Freeman, 1997; Singer, 1998; Engel et al., 1999), it is clear that even single cells display complex patterns of oscillatory behaviour across the whole frequency spectrum (Nunez et al., 1992; von Krosigk et al., 1993; Gray and McCormick, 1996; Jones et al., 2000). Accordingly, the origin of high-frequency oscillations is usually considered as a combination of cellular and network properties. For the present purposes, there appear to be no modality-specific effects, since high frequency oscillations are found in all areas discussed. The variability from cell to cell, or across behavioural states certainly exceeds any modality-specific aspect. However, comparative studies across modalities might reveal more specific insights into the functional role and relevance of cortical oscillatory patterns. 3.4. Spontaneous Activity The most conspicuous neural activity in sensory cortices occurs in the absence of sensory stimulation. The interpretation of this “spontaneous” activity, as either random background noise, or as functionally-relevant signal, has remained controversial. The high level of spontaneous discharge emanating from the retina, the cochleae, and several mechanoreceptors may reflect true noise, since it is believed to result from the stochastic nature of transmitter release from the receptors (Koerber et al., 1966; Rodieck, 1967). On the other hand, it has been argued that “ongoing activity” in higher brain centres might contain codes for global states and conditions, that reflect meaningful aspects of brain function, as yet unknown to the experimenter, and usually uncontrolled (Perkel and Bullock, 1968; but see: Miller and Schreiner, 2000). Spontaneous activity has also been shown to play an
© 2002 Taylor & Francis
294
Hubert R. Dinse and Christoph E. Schreiner
important role in creating and maintaining connections in the developing nervous system (Shatz and Stryker, 1988; Penn et al., 1998). Straightforward ways of analyzing spontaneous activity are to utilize spike count and frequency, while more sophisticated tools include analysis of interval distribution. In the case of a completely random series of action potentials, the interval distribution follows a Poisson-process. As neurones are characterized by refractory periods, such processes are more adequately described as renewal processes (Cox and Miller, 1965; Moore et al., 1966; Perkel et al., 1967; Wilbur and Rinzel, 1983). Even after decades of extensive study there is still controversy about the nature of the underlying interval distribution, as well as the nature of variability and stationarity (Softky and Koch, 1993; Shadlen and Newsome, 1998; Nawrot et al., 2000). However, perhaps more importantly, spontaneous activity in central nervous stations can show pronounced deviations from the predictions of a Poisson-process, as reflected in spontaneous oscillations. A general scheme holds that the spontaneous activity is lowest at cortical levels, but increase successively when one descends a sensory pathway. According to Herz and coworkers (1964), retinal ganglion cells discharge at >30 spikes/s, geniculate (thalamic) neurones at about 15 spikes/s, and visual cortical cells at about 5 spikes/s. Similar conditions have been reported for auditory and somatosensory system (Kiang et al., 1965; Goldstein et al., 1968; Willis et al., 1975; Peschanski et al., 1980), although skin receptors can vary more widely in terms of spontaneous activity (Bergmans and Grillner, 1969; Dykes, 1975; Johnson and Hsiao, 1992). There is some agreement that the dynamic state of the brain is reflected in the level and character of spontaneous activity (Evarts et al., 1962; Noda and Adey, 1970; Burns and Webb, 1982; Miller and Schreiner, 2000; Steriade, 2000), which appears to hold across modalities. For example, increasing depth of anaesthesia leads to decrease of spontaneous firing rates from 2.5–11 Hz during light anaesthesia, to 0–2.5 Hz in deep anaesthesia (Armstrong-James and George, 1988). This implies that the level and character of ongoingactivity is subject to substantial modifications, making it difficult to assign a characteristic pattern of spontaneous activity to a given area. In fact, there are very few studies devoted to unravelling possible modality-specific aspects of spontaneous activity (cf. Eggermont, 1990). Swadlow (1990) published a series of papers on ongoing activity in awake rabbits, recorded in different cortical areas. He classified cortical recording sites according to layer and projection pattern. For the forelimb representation of somatosensory cortex he reported discharge frequency differing substantially between laminae, by a factor of 5, with highest rates found in layer V. Similar lamina-specific differences were found in visual cortex recordings in the same preparation (Swadlow, 1988). Interestingly, intra-areal comparison of the forelimb and vibrissae representations in the somatosensory cortex confirmed laminar differences, but these differences were higher—up to a factor of 15—in the vibrissae representation (Swadlow, 1989). According to these data, differences in spontaneous discharge rate appear comparable between areas for a given layer, but can be large across layers, or within a single sensory representation. Average spontaneous activity recorded in the motor cortex was about 11 Hz ranging from 0.4 to 26 Hz (Glass and Wollberg, 1973). These authors reported a slight correlation with cortical depth suggesting that this might be due to layer-dependent variation in excitability or richness of dendritic arborization. With regard to higher-level areas, prefrontal cortical areas are involved in the temporal organization of behaviour, and are discussed in relation to working memory (cf. for reviews Fuster, 2000; Goldman-Rakic, 1996). Spontaneous activity levels typically are in the range of about 5 Hz (Harden et al., 1998; Gulledge and Jaffe, 1998). One key aspect of
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
295
working memory functions consists of the temporary holding of goal-relevant information about sensory stimuli, even if that information is no longer present at the time of response generation. In this sense, prefrontal neurones have the property of “memory cells”, in being active during delay periods, suggested by changes in firing rates. Accordingly, the level of ongoing activity is strongly modulated, by a factor of 10, in a highly task-specific way. However, it should be noted that data about comparable firing rates do not imply that variability and interval distributions are comparable as well. Independent of the level of spontaneous activity, the instantaneous frequency undergoes dramatic variation on a very short time scale (Werner and Mountcastle, 1963; Tolhurst and Movshon, 1983; Holt et al., 1996; Gutkin and Ermentrout, 1998), which is also true for stimulus-driven activity (Tomko and Crapper, 1974; Schiller et al., 1976; Manley and Mueller-Preuss, 1978; Heggelund and Albus, 1978; Vogels et al., 1989). Recent development of real-time imaging made it possible to monitor large-scale spatio-temporal changes of cortical activity on a millimeter scale, and at a millisecond time resolution, by means of voltage-sensitive dyes (Lieke et al., 1989). Recording both ongoing and light-evoked spatio-temporal activity patterns in cat visual cortex revealed that the variability of evoked activity appeared deterministic, resulting from the dynamics of ongoing activity. It has been suggested that these dynamics might reflect the instantaneous state of cortical networks. In spite of the large variability, evoked responses in single trials could be predicted by linear summation of the deterministic response and the preceding ongoing activity (Arieli et al., 1996). Tsodyks et al. (1999) were able to demonstrate that the firing rate of a spontaneously active single neurone depends strongly on the instantaneous spatial pattern of ongoing population activity in a large cortical area. At the level of cortical maps, very similar spatial patterns of population activity were observed both when the neurone fired spontaneously, and when it was driven by its optimal stimulus. Accordingly, these studies provide a direct link between spontaneous activity and cortical representations arising from sensory stimulation. The processes underlying the transformation of random and uncorrelated peripheral activity to central spontaneous activity (that can show spatial and temporal correlations) remains to be resolved. In conclusion, spontaneous activity is a typical characteristic of primary cortical areas. It appears comparable in frequency, particularly when compared to sensory periphery or subcortical stages which all discharge at a much higher frequency. Spontaneous activity at the cortical level does reflect different neurological states and functional properties of neuronal assemblies. There is little evidence for modality-specific characteristics. It remains to be seen whether the striking correlation of the dynamics of spontaneous activity pattern with the organization of sensory processing exists in non-visual modalities as well.
4. CROSS-MODAL PLASTICITY We have noted some functional and potential conceptual similarities between primary sensory cortical areas. It should then be possible, in principle, to substitute one cortical area for another, if the machinery and the implemented processing algorithms in different sensory cortices are indeed the same (or at least very similar). Building on work by Frost and Metin (Frost and Métin, 1985; Métin and Frost, 1989), Sur and colleagues (Sur et al., 1990; Sharma et al., 2000; von Melchner et al., 2000) tested this hypothesis directly (see also the chapter by S. Pallas, this volume). In immature ferrets, they eliminated the
© 2002 Taylor & Francis
296
Hubert R. Dinse and Christoph E. Schreiner
auditory input to the auditory thalamus, and redirected retinal fibres to the auditory portion of the thalamus, which in turn projected to the auditory cortex. The auditory cortex, completely deprived of auditory input, but supplied with information originating in the retina, developed functional properties resembling VI receptive fields and topographies. Single cells were tuned for direction- and orientation-selectivity, and showed organized retinotopic and orientation maps (including “pinwheels”). Moreover, horizontal connections in the rewired and remodelled AI were more like those in normal VI than those in normal AI. The rewired animals responded to visual stimulation as if they experienced vision, and did not treat such stimuli as an auditory event (von Melchner et al., 2000). If the experiment involved auditory input to the deafferented VI, there remains the question of which auditory input is necessary to change VI into a replica of AI. It appears unlikely that input from the auditory nerve alone would suffice to generate all AI properties in VI, since major feature dimensions are not yet computed at this peripheral level. Rather, the output from the midbrain station, the inferior colliculus, would seem to be the more appropriate input to the visual thalamus needed to induce the formation of functionally correct AI properties in VI. It alone has an equivalent set of basic feature dimensions to generate a sufficiently complete and computationally accessible cortical representation of sound. This proposition awaits direct experimental investigation. The elegant experiments described above provide direct evidence that different cortical processing systems may be interchangeable, and are capable of implementing the processes and algorithms used in other modalities. Since developmental and reorganizational plasticity undoubtedly was involved in the process of “rewiring” the auditory cortex (e.g. Buonomano and Merzenich, 1998), the experiments do not prove that the same algorithms are implemented in AI and VI, but only that AI can implement the VI algorithms. Furthermore, the experiments show that sensory and perhaps perceptual qualities are modalityspecific, and not cortical field-specific. Such findings strongly support the hypothesis proposed at the outset of this chapter, that the different primary sensory cortical fields can be viewed as analogous structures anatomically, and perhaps more importantly, functionally.
5. GENERAL CONCLUSION AND PREDICTIONS We have reviewed evidence germane to the hypothesis that the different sensory modalities perform similar tasks, under similar statistical constraints imposed by sensory input, with the same structural elements, and, possibly, similar circuits: The common task is to transform receptor images to cortical representations of external objects preparatory to action. While the peripheral sensory systems have different structural adaptations at the receptor level, the neuronal implementations of the required algorithms at the early cortical level show much congruence and may be interchangeable between modalities. The main lines of argument in support of this notion were as follows: (a) Functional and structural heterogeneity in the subcortical systems: The role of the subcortical system may be understood as a transformation of receptor surface information into a multi-dimensional parameter space. This process adapts the different sensory streams to a thalamo-cortical stage that applies common schemes for information processing and modulation. The activity across a receptor surface codes information about local and global correlations in the scene, shown by coactivation
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
297
and sequential activation of the receptors. This permits higher-order stimulus correlations, including contextual events, to be integrated in this process. Multiple subcortical systems construct relevant, functionally-independent feature dimensions. Since the nature of the stimuli and the corresponding receptors differ, the nature of these subcortical processing stages also differs between the modalities. (b) Functional and structural homogeneity in the thalamo-cortical system: The role of the thalamus and primary sensory cortex is, according to this view, to establish a compatible representation of different feature dimensions, that allows extraction of object form, attribute, and location relative to the background, for an analysis of the environmental scene. Subsequent sensory cortical areas use these base features to analyse the global features of objects and to establish object representations that become less dependent on the perceptual frame-of-reference. Furthermore, higher processes establish the relationship of objects to the environment and the self, perform multi-sensory integration, assign and evaluate the behavioural significance according to context, and as a basis for action. (c) Computational homogeneity in the thalamo-cortical system: At the thalamo-cortical interface a similar set of operations is implemented that involves thalamo-cortical, cortico-cortical and cortico-fugal network interactions. The main mechanisms of these algorithms include the spatio-temporal arrangement of excitatory and inhibitory processes in columnar and horizontal networks, that operate on spatially- and temporallyrelated streams of input. While only few of the actual processing schemes have been dissected and modeled in detail, the processing that is accomplished at the thalamo-cortical level appears to be largely a local, differentiating analysis, as opposed to a global, integrative one. A case in point is the contrast-independent processing of orientation-selectivity of visual cells (for review see Ferster and Miller, 2000; Anderson et al., 2000). The algorithms used in this task are based on local correlations and anti-correlations of inputs, as expressed in local excitatory and inhibitory circuits. Such computations use temporal and spatial information arising from peripheral receptors. Correlations among the activity in the receptor space are exploited to establish computationally-useful and independent dimensions, that are representative of external objects and scenes, and not just the receptor-surface activity. These computed entities may serve several needs of central processing: (i) They precede a general representation of the external world; (ii) They are a basis for the determination of object form and position; (iii) They create reliable and stabilized feature properties, as reflected in the intensity and contrast invariance of orientation tuning in the visual cortex, or in independent tuning properties of some auditory neurones in the presence of background noise ; (iv) Such computed features allow subsequent multi-sensory integration, by transcending a pure receptor representation of the sensory events; (v) Finally, they allow the assignment of significance to particular environmental conditions, and ultimately, the emergence of different perceptual attributes and the initiation of behaviour. Certain of these tasks may take place beyond the primary sensory field. (d) Multi-sensory integration: The integration of information from several sensory modalities has many advantages for the individual, including increase of salience, removal of ambiguities, and unified object characterization and perception (e.g. Stein and Meredith, 1990). To accomplish such integration, the information from the different modalities has to be represented at an equivalent level of abstraction, and in
© 2002 Taylor & Francis
Hubert R. Dinse and Christoph E. Schreiner
298
compatible frames-of-reference. Many aspects of multi-sensory integration take place after the early cortical representation suggesting that major aspects of representational equivalence have been established at that level. For example, it has been demonstrated that auditory-visual stimulus-onset asynchrony activates a large-scale neural network of insular, posterior parietal, prefrontal, and cerebellar areas (Bushara et al., 2001) permitting auditory-visual interaction phenomena such as the ventriloquist, and the McGurk illusions. Subsequent processing can then employ similar mechanisms for similar tasks. Furthermore, it would be advantageous if attentional and emotional modulation of these representations takes place at equivalent stages of processing, to maintain compatibility between sensory systems. The common nature of the modulatory influence at the thalamus suggests that the early cortical processing operates at equivalent levels across the three modalities (cf. Sherman and Guillery, 2001). (e) Cross-modal equivalence: The induction of visual receptive field properties and map organization in AI after re-routing of sub-thalamic channels convincingly demonstrates that the normally-observed functional differences between early sensory cortical areas is not a consequence of immutable field-specificity but largely due to subthalamic preprocessing. The processing capacities in the primary cortical fields therefore have the same potential for expressing specific algorithms. However, they can function somewhat differently according to the actual input. From these general principles of cortical processing equivalencies, a number of specific predictions can be made. Among them are the following: • •
•
•
• •
Given the joint 2-dimensionality for vision and touch, functional maps should be present in SI that are comparable to the many aspects described for VI, including orientation sensitivity, and direction selectivity. The emergence of spatial frequency maps (relative to the receptor sheet) should also be a common factor. Spatial frequency in the visual system can be regarded as equivalent to the spacing of frequency bands in the auditory system. Given that this aspect is coded in functional maps in VI and AI, a corresponding spatial-frequency coding scheme should be present in SI. The visual cortex shows a relationship between spatial frequency maps and orientation maps. A similar relationship could be predicted for the somatosensory cortex. (One should keep in mind that map expression and map interrelations can be quite weak on a local and cellular scale.) Ocular dominance bands in the visual cortex have a specific relationship to orientation maps and, perhaps, to spatial frequency maps. Cortical domains with low spatial frequency tend to lie in the centre of the ocular dominance columns (Hübener et al., 1997). Accordingly, the position of binaural bands and spatial frequency/bandwidth distribution in auditory cortex may show a systematic relationship as well. The inhibitory push-pull mechanisms invoked for contrast-independent orientation selectivity (Troyer et al., 1998) may also be found in somatosensory orientation processing and, perhaps, in auditory processing of spectral-shape information. “Feature maps” are not invariant, resistant to variations of stimulus configuration, context or timing parameters. For example, direction selectivity of visual neurones is strongly dependent on the direction of a moving texture background (Orban et al., 1987; Dinse and Krüger, 1990). Also, in SI and AI, there is evidence that spatial/spectral
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
299
and probably also temporal topography can be modified substantially by behavioural discrimination training (e.g. Recanzone et al., 1992, 1993; Wang et al., 1995; Beitel et al., 1999). The cortical mechanisms that govern these processes should hold for all modalities. The common feature of parametric maps observed in early sensory areas might reflect a rather general principle of cortical organization and function, potentially holding also for all other cortical areas. The fundamental processing step consists of a local operation, modified (contextually) by long-range connections. As this type of processing is performed within a two-dimensional sheet, it warrants the combination of local operations based on excitatory and inhibitory interactions, with a continuous representation of parameters, maintaining local neighbourhood relationships. While local processing obeys per se rules of proximity, the two-dimensional sheet allows one to define proximities of various kinds. For sensory areas, the “representation” of the outside world implicates both two-dimensionality, and proximities within the various types of physical worlds. In this sense this scheme is highly intuitive for early cortical representation, where it is reasonably clear what is represented. However, this scheme has been shown to hold also for intermediate states: An example is the highly specialized and detailed maps as described by Suga and coworkers in echolocating bats (e.g. Suga, 1984). Because of the highly specialized ultrasound environment of bats it was possible to deduce and then to identify higher-order maps that contain ordered representations of echo frequency and echo delay. The main problem in higher cortical areas arises from the fact that the behaviourally or computationally relevant parameter space is unknown and, in principle, is difficult to deduce. The nature and properties of such parameter spaces need to be determined to understand cortical processing outside the primary sensory or motor domains. For example, it is likely that there are highly abstract parameter spaces representing a profile of a face in terms of its emotional expression. In any case, the basic principle is to compute and assemble behaviourally relevant aspects of proximity, or similarity and dissimilarity in the projected parameter space. In principle, “parametric mapping” can be regarded as equivalent to “distributed processing”. Differences that have been pointed out between these two concepts are mainly methodological, and arise largely from peculiarities of the reconstruction algorithms (e.g. the optical imaging-based feature maps are just due to an “iceberg effect”, by ignoring a large portion of neural activation). While “parametric-mapping” is more intuitive because it relates directly the sensory representations to known physical features, we believe that the concept of “distributed representation” encapsulates better the representational and computational principles expressed in the brain, and this concept holds equally in lower (or early) and higher cortical areas. The appearance of modality-specific differences and modality-independent properties of brain organization and function offers an opportunity to investigate systematically the underlying functional and structural constraints and consequences. Among the questions that can be addressed from a more general point of view are the following: What is the basis and consequence of modality-specificities? Why and how are certain general aspects of cortical processing modified in particular ways? What is the consequence of such modifications for the evolution of higher brain functions in humans? The emphasis of common structures, mechanisms, and operational goals in primary sensory cortex, in support of the notion of functional equivalency, does not imply a perfect
© 2002 Taylor & Francis
300
Hubert R. Dinse and Christoph E. Schreiner
correspondence across areas. An evolutionary advantage and consequence of the apparent homogeneity of cortical organization is the richness in potential network characteristics that can be realized. The substrate for cortical processing can apply in a flexible manner, during development and learning, to a wide range of environmental and behavioural conditions. However, the influence of these conditions on the different modalities may be quite similar. In any case, it remains to be seen whether the local circuit interactions and functions are identical in different modalities or merely constitute similar classes with different solutions in each modality. We are now ready to discover the local network properties and the algorithms which are implemented for a number of primary cortical processes in different modalities. A refinement of the modality-comparative approach to this difficult but exciting and fundamental problem of brain function could decipher this mystery.
ACKNOWLEDGEMENTS We thank Dr. Jeffery Winer for his insightful comments on a draft of this chapter. Supported by grants DC02260 and NS35438 (to C.E.S.) and DFG 334 / 10 and 15 (to H.D.).
REFERENCES Abeles, M. (1991) Corticonics. Neural circuits in the cerebral cortex. Cambridge, UK: Cambridge University Press. Aertsen, A. and Johannesma, P. (1981) The spectro-temporal receptive field. A functional characterization of auditory neurons. Biological Cybernetics, 42, 133–143. Ahmed, B. (2000) Low frequency damped oscillations of cat visual cortical neurones. NeuroReport, 11, 1243–1247. Albus, K. (1975) A quantitative study of the projection area of the central and the paracentral visual field in area 17 of the cat. I. The precision of the topography. Experimental Brain Research, 24, 159–179. Allman, J., Miezin, F. and McGuiness, E.L. (1985) Stimulus specific responses from beyond the classical receptive field. Annual Review of Neuroscience, 8, 407–430. Andersen, P. and Andersson, S.A. (1968) Physiological basis of the alpha rhythm. Appleton-Century-Crofts, New York. Anderson, J.C., Binzegger, T., Kahana, O., Martin, K.A. and Segev, I. (1999) Dendritic asymmetry cannot account for directional responses of neurons in visual cortex. Nature, Neuroscience, 2, 820–824. Anderson, J.S., Lampl, I., Gillespie, D.C. and Ferster, D. (2000) The contribution of noise to contrast invariance of orientation tuning in cat visual cortex. Science, Washington, 290, 1968–1972. Arieli, A., Sterkin, A., Grinvald, A. and Aertsen, A. (1996) Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses. Science, Washington, 273, 1868–1871. Armstrong-James, M. and George, M.J. (1988) Influence of anesthesia on spontaneous activity and receptive field size of single units in rat Sm1 neocortex. Experimental Neurology, 99, 369–387. Atick, J.J. (1992) Could information theory provide an ecological theory of sensory processing? Network Computation in Neural Systems, 3, 213–251. Attias, H. and Schreiner, C.E. (1998) Coding of naturalistic stimuli by auditory midbrain neurons. In: M.I. Jordan et al. (eds), Advances in Neural Information Processing Systems, Vol. 10. Cambridge, MA: MIT Press, pp. 103–109. Bakin, J.S., Kwon, M.C., Masino, S.A., Weinberger, N.M. and Frostig, R.D. (1996) Suprathreshold auditory cortex activation visualized by intrinsic signal optical imaging. Cerebral Cortex, 6, 120–130. Barrie, J.M., Freeman,W.J. and Lenhart, M.D. (1996) Spatiotemporal analysis of prepyriform, visual, auditory and somesthetic surface EEGs in trained rabbits. Journal of Neurophysiology, 76, 520–539. Beitel, R.E., Schreiner, C.E. and Merzenich, M.M. (1999) Spectral receptive field plasticity in auditory cortex of owl monkeys trained with sinusoidally amplitude modulated (SAM) tones. Association for Research in Otolaryngology, Abstracts (U.S.A.), Midwinter meeting, 21, 201. Bergmans, J. and Grillner, S. (1969) Reciprocal control of spontaneous activity and reflex effects in static and dynamic flexor gamma-motoneurones revealed by an injection of DOPA. Acta Physiologica Scandinavica, 77, 106–124.
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
301
Best, J., Mallot, H.A., Krüger, K. and Dinse, H.R. (1989) Dynamics of visual information processing in cortical systems. In: L. Personnaz and G. Dreyfus (eds), Neural Networks. From Models to Application. IDSET, Paris, pp. 107–116. Bialek, W., Rieke, F., Van Steveninck, R.R. and Warland, D. (1991) Reading a neural code. Science, Washington, 252, 1854–1857. Bishop, G.H. and O’Leary, J. (1936) Components of the electric responses to the optic cortex of the rabbit. American Journal of Physiology, 117, 292–308. Blasdel, G.G. and Salama, G. (1986) Voltage-sensitive dyes reveal a modular organization in monkey striate cortex. Nature, London, 321, 579–585. Bolz, J. and Gilbert, C.D. (1989) The role of horizontal connections in generating long receptive fields in the cat visual cortex. European Journal of Neuroscience, 1, 263–268. Bonhoeffer, T. and Grinvald, A. (1991) Iso-orientation domains in cat visual cortex are arranged in pinwheellike patterns. Nature, London, 353, 429–431. Bregman, A.S. (1990) Auditory Scene Analysis: The Perceptual Organization of Sound. Cambridge, Mass.: Bradford Books, MIT Press. Brosch, M. and Schreiner, C.E. (2000) Sequence sensitivity of neurons in cat primary auditory cortex. Cerebral Cortex, 10, 1155–1167. Brown, M.C. and Ledwith, J.V. (1990) Projection of thin (type–II) and thick (type–I) auditory nerve fibers onto the cochlear nucleus of the mouse. Hearing Research, 49, 105–118. Brown, M.C. (1987) Morphology of labeled afferent fibers in the guinea pig cochlea. Journal of Comparative Neurology, 260, 591–604. Bullier, J. and Henry, G.H. (1980) Ordinal position and afferent input of neurons in monkey striate cortex. Journal of Comparative Neurology, 193, 913–935. Buonomano, D.V. and Merzenich, M.M. (1998) Cortical plasticity: From Synapses to Maps. Annual Review of Neuroscience, 21, 149–186. Buonomano, D.V. (1999) Distinct functional types of associative long-term potentiation in neocortical and hippocampal pyramidal neurons. Journal of Neuroscience, 19, 6748–6754. Buonomano, D.V. (2000) Decoding temporal information: A model based on short-term synaptic plasticity. Journal of Neuroscience, 20, 1129–1141. Burkitt, G.R., Lee, J. and Ts’o, D.Y. (1998) Functional organization of disparity in visual area V2 of the macaque monkey. Society for Neuroscience Abstracts, 24, 1978. Burns, B.D. and Webb, A.C. (1982) Mechanisms governing the relation between activity in the cerebral cortex and level of arousal. Proceedings of the Royal Society, London, series B, 214, 325–334. Burr, D. (1980) Motion smear. Nature, London, 284, 164–165. Bushara, K.O., Grafman, J. and Hallett, M. (2001) Neural correlates of auditory-visual stimulus onset asynchrony detection. Journal of Neuroscience, 21, 300–304. Capuano, U. and McIlwain, J.T. (1981) Reciprocity of receptive field images and point images in the superior colliculus of the cat. Journal of Comparative Neurology, 196, 13–23. Casagrande, V.A. and Norton, T.T. (1991) The lateral geniculate nucleus: A review of its physiology and function. In: A.G. Leventhal, (ed.), The Neural Basis of Visual Function, Volume 4, of Vision and Visual Disfunction series (ed. J.R. Cronley-Dillon), London: MacMillan Press, pp. 41–84. Chance, F.S., Nelson, S.B. and Abbott, L.F. (1998) Synaptic depression and the temporal response characteristics of V1 cells. Journal of Neuroscience, 18, 4785–4799. Chang, H.T. (1950) The repetetive discharges of cortico-thalamic reverberating circuit. Journal of Neurophysiology, 13, 235–257. Chen-Bee, C.H. and Frostig, R.D. (1996) Variability and interhemispheric asymmetry of single-whisker functional representations in rat barrel cortex. Journal of Neurophysiology, 76, 884–894. Clark, S.A., Allard, T.T., Jenkins, W.M. and Merzenich, M.M. (1988) Receptive fields in the body-surface map in adult cortex defined by temporally correlated input. Nature, London, 332, 444–445. Cotillon, N. and Edeline, J.M. (2000) Tone-evoked oscillations in the rat auditory cortex result from interactions between the thalamus and reticular nucleus. European Journal of Neuroscience, 12, 3637–3650. Cotillon, N., Nafati, M. and Edeline, J.M. (2000) Characteristics of reliable tone-evoked oscillations in the rat thalamo-cortical auditory system. Hearing Research, 142, 113–130. Cox, D.R. and Miller, H.D. (1965) The Theory of Stochastic Processes. New York: John Wiley. Crair, M.C. and Malenka, R.C. (1995) A critical period for long-term potentiation at thalamocortical synapses. Nature, London, 375, 325–328. Creutzfeldt, O., Hellweg, F.C. and Schreiner, C. (1980) Thalamocortical transformation of responses to complex auditory stimuli. Experimental Brain Research, 39, 87–104. Das, A. and Gilbert, C.D. (1995) Long-range horizontal connections and their role in cortical reorganization revealed by optical recording of cat primary visual cortex. Nature, London, 375, 780–784. Das, A. and Gilbert, C.D. (1997) Distortions of visuotopic map match orientation singularities in primary visual cortex. Nature, London, 387, 594–598.
© 2002 Taylor & Francis
302
Hubert R. Dinse and Christoph E. Schreiner
de Boer, F. and Kuyper, P. (1968) Triggered correlation. I.E.E.E., 15, 169–179. de Oliveira, S.C., Thiele, A. and Hoffmann, K.P. (1997) Synchronization of neuronal activity during stimulus expectation in a direction discrimination task. Journal of Neuroscience, 17, 9248–9260. DeAngelis, G.C., Ohzawa, I. and Freeman, R.D. (1993) Spatiotemporal organization of simple-cell receptive fields in the cat’s striate cortex. I. General characteristics and postnatal development. Journal of Neurophysiology, 69, 1091–1117. deCharms, C. and Zador, A. (2000) Neural representations and the cortical code. Annual Review of Neuroscience, 23, 613–847. Denham, S.L. (2000) Cortical Synaptic Depression and Auditory Perception. In: S. Greenberg and M. Slaney (eds), Computational Models of Auditory Function, NATO ASI Series. Amsterdam: IOS Press. DiCarlo, J.J., Johnson, K.O. and Hsiao, S.S. (1998) Structure of receptive fields in area 3b of primary somatosensory cortex in the alert monkey. Journal of Neuroscience, 18, 2626–2645. Dinse, H.R. (1986) Foreground-background-interaction—Stimulus dependent properties of the cat’s area 17, 18 and 19 neurons outside the classical receptive field. Perception, 15, A6. Dinse, H.R. and Krüger, K. (1990) Contribution of area 19 to the foreground-background-interaction of the cat. An analysis based on single cell recordings and behavioural experiments. Experimental Brain Research, 82, 107–122. Dinse, H.R., Krüger, K. and Best, J. (1990) A temporal structure of cortical information processing. Concepts in Neuroscience, 1, 199–238. Dinse, H.R., Krüger, K., Mallot, H.P. and Best, J. (1991) Temporal structure of cortical information processing: cortical architecture, oscillations, and non-separability of spatio-temporal receptive field organization. In: J. Krüger (ed.), Neuronal Cooperativity. Berlin, Heidelberg: Springer Verlag, pp. 67–104. Dinse, H.R., Spengler, F., Godde, B. and Hartfiel, B. (1993) Dynamic aspects of cortical functions: Processing and plasticity in different sensory modalities. In: A. Aertsen (ed.), Brain Theory: Spatio-temporal aspects of brain functions. Amsterdam: Elsevier, pp. 209–230. Dinse, H.R. (1994) A time-based approach towards cortical functions: Neural mechanisms underlying dynamic aspects of information processing before and after postontogenetic plastic processes. Physica D, 75, 129–150. Dinse, H.R. and Schreiner, C.E. (1996) Dynamic frequency tuning of cat auditory cortical neurons: specific adaptations to the processing of complex sounds. In: W. Ainsworth and S. Greenberg (eds), Proceedings of the workshop on: The auditory basis of speech perception. (European Speech Communication Association ETRW Series). Keele, U.K., Keele University, pp. 45–48. Dinse, H.R., Jancke, D., Akhavan, A.C., Kalt, T. and Schöner, G. (1996) Dynamics of population representations of visual and somatosensory cortex based on spatio-temporal stimulation. In: S.I. Amari, L. Xu, L.W. Chan, I. King and K.S. Leung (eds), Progress in Neural Information Processing. International Conference on Neural Information Processing (ICONIP’96). Singapore: Springer, pp. 1285–1290. Dinse, H.R., Krüger, K., Akhavan, A.C., Spengler, F., Schöner, G. and Schreiner, C.E. (1997) Low-frequency oscillations of visual, auditory and somatosensory cortical neurons evoked by sensory stimulation. International Journal of Psychophysiology, 26, 205–227. Dinse, H.R., Godde, B., Hilger, T., Reuter, G., Cords, S.M., Lenarz, T. and von Seelen, W. (1997) Optical imaging of cat auditory cortex cochleotopic selectivity evoked by acute electrical stimulation of a multichannel cochlear implant. European Journal of Neuroscience, 9, 113–119. Dinse, H.R., Godde, B., Hilger, T. and Schreiner, C.E. (2000) Optical imaging of relationships between functional topographies in cat auditory cortex. Institut für Neuroinformatik, Ruhr-Univ. Bochum, Internal Report IRINI 2000–01, pp. 1–11. Dinse, H.R. and Jancke, D. (2001a) Comparative population analysis of cortical representations in parametric spaces of visual field and skin: a unifying role for nonlinear interactions as a basis for active information processing across modalities. In: M.A.L. Nicolelis (ed.), Advances in Population Coding, (Progress in Brain Research, Vol. 130). Elsevier, pp. 155–173. Dinse, H.R. and Jancke, D. (2001b) Time-variant processing in V1: Indication for qualitative transformational steps from microscopic (single cell) to mesoscopic (population) levels. Trends in Neuroscience, 24, 203–205. Dinse, H.R. (2002) Multiple and variant timescales in dynamic information processing. Behavioural and Brain Sciences, in press Dinse, H.R. and Merzenich, M.M. (2002) Adaptation of Inputs in the Somatosensory System. In: M. Fahle and T. Poggio (eds), Perceptual Learning, MIT Press, Boston, MA, pp. 19–42. Doetsch, G.S. (2000) Patterns in the brain. Neuronal population coding in the somatosensory system. Physiology and Behavior, 69, 187–201. Douglas, R.J., Martin, K.A. and Whitteridge, D. (1988) Selective responses of visual cortical cells do not depend on shunting inhibition. Nature, London, 332, 642–644. Douglas, R.J., Martin, K.A. and Whitteridge, D. (1991) An intracellular analysis of the visual responses of neurones in cat visual cortex. Journal of Physiology (London), 440, 659–696.
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
303
Dreher, B., Fukada, Y. and Rodieck, R.W. (1976) Identification, classification and anatomical segregation of cells with X-like and Y-like properties in the lateral geniculate nucleus of old-world primates. Journal of Physiology, (London), 258, 433–452. Dykes, R.W. (1975) Afferent fibers from mystacial vibrissae of cats and seals. Journal of Neurophysiology, 38, 650–662. Dykes, R.W., Landry, P., Metherate, R. and Hicks, T.P. (1984) Functional role of GABA in cat primary somatosensory cortex: shaping receptive fields of cortical neurons. Journal of Neurophysiology, 52, 1066–1093. Eckhorn, R., Bauer, R., Jordan, W., Brosch, M., Kruse, W., Munk, M. and Reitboeck, H.J. (1988) Coherent oscillations: a mechanism of feature linking in the visual cortex? Multiple electrode and correlation analyses in the cat. Biological Cybernetics, 60, 121–130. Eckhorn, R., Krause, F. and Nelson, J.I. (1993) The RF cinematogram. A cross-correlation technique. Biological Cybernetic, 69, 37–55. Eckhorn, R. (1994) Oscillatory and non-oscillatory synchronizations in the visual cortex and their possible roles in associations of visual features. In: J. van Pelt, M.A. Corner, H.B.M. Uylings and F.H. Lopes da Silva (eds), The self-organizing brain: from growth cones to functional networks (Progress in Brain Research, Vol. 102). Amsterdam: Elsevier, pp. 405–426. Eeckman, F.H. and Freeman, W.J. (1990) Correlations between unit firing and EEG in the rat olfactory system. Brain Research, 528, 238–244. Eggermont, J.J., Aertsen, A., Hermes, D.J. and Johannesma, P.I.M. (1981) Spectro-temporal characterization of auditory neurons: redundant or necessary? Hearing Research, 5, 109–121. Eggermont, J.J. (1990) The Correlative Brain: Theory and Experiment in Neural Interaction. Berlin, Heidelberg: Springer Verlag. Ehret, G. and Merzenich, M.M. (1988) Complex sound analysis (Frequency resolution, filtering and spectral integration) by single units of the inferior colliculus. Brain Research Reviews, 13, 139–163. Ehret, G. and Schreiner, C.E. (1997) Frequency resolution and spectral integation (critical band analysis) in single units of the cat primary auditory cortex. Journal of Comparative Physiology, A 181, 635–650. Engel, A.K., Fries, P., Konig, P., Brecht, M. and Singer, W (1999) Temporal binding, binocular rivalry, and consciousness. Consciousness and Cognition, 8, 128–151. Erlhagen, W., Bastian, A., Jancke, D., Riehle, A. and Schöner, G. (1999) The distribution of neuronal population activation (DPA) as a tool to study interaction and integration in cortical representations. Journal of Neuroscience Methods, 94, 53–66. Evans, P.M. and Craig, J.C. (1991) Tactile attention and the perception of moving tactile stimuli. Perception and Psychophysics, 49, 355–364. Evarts, E.V., Bental, E., Bihari, B. and Huttenlocher, P.R. (1962) Spontaneous discharge of single neurons during sleep and waking. Science, Washington, 135, 727–728. Fanselow, E.E. and Nicolelis, M.A. (1999) Behavioral modulation of tactile responses in the rat somatosensory system. Journal of Neuroscience, 19, 7603–7616. Ferster, D. and Miller, K.D. (2000) Neural mechanisms of orientation selectivity in the visual cortex. Annual Reviews of Neuroscience, 23, 441–471. Fischer, B. (1972) Optical and neuron basis of visual image transfer: a uniform mathematical treatment of the retinal image and the excitability of the retinal ganglion cells with the aid of the linear systems theory. Vision Research, 12, 1125–1144. Foster, K.H., Gaska, J.P., Nagler, M. and Pollen, D.A. (1985) Spatial and temporal frequency selectivity of neurones in visual cortical areas V1 and V2 of the macaque monkey. Journal of Physiology (London), 365, 331–363. Franowicz, M.N. and Barth, D.S. (1995) Comparison of evoked potentials and high-frequency (gamma-band) oscillating potentials in rat auditory cortex. Journal of Neurophysiology, 74, 96–112. Freeman, W.J. (1968) Patterns of variation in waveform of averaged evoked potentials from prepyriform cortex of cats. Journal of Neurophysiology, 31, 1–13. Freeman, W.J. and Skarda, C.A. (1985) Spatial EEG patterns, non-linear dynamics and perception: the NeoSherringtonian view. Brain Research Reviews, 10, 147–175. Frost, D.O. and Metin, C. (1985) Induction of functional retinal projections to the somatosensory system. Nature, London, 317, 162–164. Furukawa, S., Xu, L. and Middlebrooks, J.C. (2000) Coding of sound-source location by ensembles of cortical neurons. Journal of Neuroscience, 20, 1216–1228. Fuster, J.M. (2000) Executive frontal functions. Experimental Brain Research, 133, 66–70. Galarreta, M. and Hestrin, S. (1998) Frequency-dependent synaptic depression and the balance of excitation and inhibition in the neocortex. Nature, Neuroscience, 1, 587–594. Gardner, E.P., Palmer, C.I., Hamalainen, H.A. and Warren, S. (1992) Simulation of motion on the skin. V. Effect of stimulus temporal frequency on the representation of moving bar patterns in primary somatosensory cortex of monkeys. Journal of Neurophysiology, 67, 37–63.
© 2002 Taylor & Francis
304
Hubert R. Dinse and Christoph E. Schreiner
Geldard, F.A. and Sherrick, C.E. (1972) The cutaneous “rabbit”: a perceptual illusion. Science, Washington, 178, 178–179. Ghazanfar, A.A. and Nicolelis, M.A. (2001) The structure and function of dynamic cortical and thalamic receptive fields. Cerebral Cortex, 11, 183–193. Ghose, G.M. and Freeman, R.D. (1992) Oscillatory discharge in the visual system: does it have a functional role? Journal of Neurophysiology, 68, 1558–1574. Ghose, G.M. and Freeman, R.D. (1997) Intracortical connections are not required for oscillatory activity in the visual cortex. Visual Neuroscience, 14, 963R–979R. Gibson, J.J. (1966) The Senses Considered as Perceptual Systems. London: George Allen and Unwin. Gilbert, C., Ito, M., Kapadia, M. and Westheimer, G. (2000) Interactions between attention, context and learning in primary visual cortex. Vision Research, 40, 1217–1226. Gilbert, C.D. and Wiesel, T.N. (1990) The influence of contextual stimuli on the orientation selectivity of cells in primary visual cortex of the cat. Visual Research, 30, 1689–1701. Glass, I. and Wollberg, Z. (1973) Spontaneous activity of single neurons in the precruciate area of the cat’s sensory motor cortex. Brain Research, 64, 391–396. Godde, B., Hilger, T., von Seelen, W., Berkefeld, T. and Dinse, H.R. (1995) Optical imaging of rat somatosensory cortex reveals representational overlap as topographic principle. NeuroReport, 7, 24–28. Goldman-Rakic, P.S. (1996) Regional and cellular fractionation of working memory. Proceedings of the National Academy of Sciences, U.S.A., 93, 13473–13480. Goldstein, M.H., Hall, J.L.2d and Butterfield, B.O. (1968) Single-unit activity in the primary auditory cortex of unanesthetized cats. Journal of the Acoustic Society of America, 43, 444–455. Gray, C.M., König, P., Engel, A.K. and Singer, W. (1989) Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature, London, 338, 334–337. Gray, C.M. and McCormick, D.A. (1996) Chattering cells: superficial pyramidal neurons contributing to the generation of synchronous oscillations in the visual cortex. Science, Washington, 274, 109–113. Grinvald, A., Lieke, E.E., Frostig, R.D. and Hildesheim, R. (1994) Cortical point-spread function and long-range lateral interactions revealed by real-time optical imaging of macaque monkey primary visual cortex. Journal of Neuroscience, 14, 2545–2568. Gulledge, A.T. and Jaffe, D.B. (1998) Dopamine decreases the excitability of layer V pyramidal cells in the rat prefrontal cortex. Journal of Neuroscience, 18, 9139–9151. Gutkin, B.S. and Ermentrout, G.B. (1998) Dynamics of membrane excitability determine interspike interval variability: a link between spike generation mechanisms and cortical spike train statistics. Neural Computation, 10, 1047–1065. Hamada, Y., Miyashita, E. and Tanaka, H. (1999) Gamma-band oscillations in the “barrel cortex” precede rat’s exploratory whisking. Neuroscience, 88, 667–671. Harden, D.G., King, D., Finlay, J.M. and Grace, A.A. (1998) Depletion of dopamine in the prefrontal cortex decreases the basal electrophysiological activity of mesolimbic dopamine neurons. Brain Research, 794, 96–102. Hartline, H.K. (1938) The response of single optic nerve fibres of the vertebrate eye to illumination of the retina. American Journal of Physiology, 121, 400–415. Hashimoto, I. (2000) High-frequency oscillations of somatosensory evoked potentials and fields. Clinical Neurophysiology, 17, 309–320. Heggelund, P. and Albus, K. (1978) Response variability and orientation discrimination of single cells in striate cortex of cat. Experimental Brain Research, 32, 197–211. Hendry, S.H.C. and Reid, R.C. (2000) The Koniocellular Pathway in Primate Vision. Annual Review of Neuroscience, 23, 127–153. Herz, A., Creutzfeldt, O. and Fuster, J. (1964) Statistical properties of neuron activity in the ascending visual system [in German]. Kybernetik, 2, 61–71. Holt, G.R., Softky, W.R., Koch, C. and Douglas, R.J. (1996) Comparison of discharge variability in vitro and in vivo in cat visual cortex neurons. Journal of Neurophysiology, 75, 1806–1814. Hubel, D.H. and Wiesel, T.N. (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Physiology (London), 160, 106–154. Hübener, M., Shoham, D., Grinvald, A. and Bonhoeffer, T. (1997) Spatial relationships among three columnar systems in cat area 17. Journal of Neuroscience, 17, 9270–9284. Hunt, C. and McIntyre, A.K. (1960) An analysis of fiber diameter and receptor characteristics of myelinated cutaneous afferent fibers in cat. Journal of Physiology (London), 153, 99–112. Imig, T.J. and Adrian, H.O. (1977) Binaural columns in the primary field (AI) of cat auditory cortex. Brain Research, 138, 241–257. Issa, N.P., Trepel, C. and Stryker, M.P. (2000) Spatial frequency maps in cat visual cortex. Journal of Neuroscience, 20, 8504–8514. Jancke, D., Akhavan, A.C., Erlhagen, W., Giese, M., Steinhage, A., Schöner, G. and Dinse, H.R. (1996) Population coding in cat visual cortex reveals nonlinear interactions as predicted by a neural field model.
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
305
In: Ch. von der Malsburg, W. von Seelen, J.C. Vorbrüggen and B. Sendhoff (eds), Artificial Neural Networks—ICANN 96. Berlin, Heidelberg: Springer Verlag, pp. 641–648. Jancke, J., Erlhagen, W., Dinse, H.R., Akhavan, A.C., Giese, M., Steinhage, A. and Schöner, G. (1999) Parametric population representation of retinal location: Neuronal interaction dynamics in cat primary visual cortex. Journal of Neuroscience, 19, 9016–9028. Jancke, D. (2000) Orientation formed by a Spot’s Trajectory: a two-dimensional population approach in primary visual cortex. Journal of Neuroscience, 20, RC86. Johnson, K.O. and Hsiao, S.S. (1992) Neural mechanisms of tactual form and texture perception. Annual Review of Neuroscience, 15, 227–250. Jones, J.P. and Palmer, L.A. (1987) The two-dimensional spatial structure of simple receptive fields in cat striate cortex. Journal of Neurophysiology, 58, 1187–1211. Jones, M.S. and Barth, D.S. (1997) Sensory-evoked high-frequency (gamma-band) oscillating potentials in somatosensory cortex of the unanesthetized rat. Brain Research, 768, 167–176. Jones, M.S., MacDonald, K.D., Choi, B., Dudek, F.E. and Barth, D.S. (2000) Intracellular correlates of fast (>200 Hz) electrical oscillations in rat somatosensory cortex. Journal of Neurophysiology, 84, 1505–1518. Kalt, T., Akhavan, A.C., Jancke, D. and Dinse, H.R. (1996) Dynamic population coding in rat somatosensory cortex. Society for Neuroscience Abstracts, 22, 105. Kanizsa, G. (1976) Subjective contours. Scientific American, 234, 48–52. Kapadia, M.K., Westheimer, G. and Gilbert, C.D. (2000) Spatial distribution of contextual interactions in primary visual cortex and in visual perception. Journal of Neurophysiology, 84, 2048–2062. Kiang, N.Y., Watanabe, T., Thomas, E.C. and Clark, L.F. (1965) Discharge patterns of single fibers in the cat auditory nerve. MIT, Cambridge. Kilgard, M.P. and Merzenich, M.M. (1998) Plasticity of temporal information processing in the primary auditory cortex. Nature, Neuroscience, 1, 727–731. Kim, D.S., Matsuda, Y., Ohki, K., Ajima, A. and Tanaka, S. (1999) Geometrical and topological relationships between multiple functional maps in cat primary visual cortex. NeuroReport, 10, 2515–2522. Kirman, J.H. (1974) Tactile apparent movement: the effects of number of stimulators. Journal of Experimental Psychology, 103, 1175–1180. Kirschfeld, K. (1992) Oscillations in the insect brain: do they correspond to the cortical gamma-waves of vertebrates? Proceedings National Academy of Science, U.S.A., 89, 4764–4768. Kirschfeld, K., Feiler, R. and Wolf-Oberhollenzer, F. (1996) Cortical oscillations and the origin of express saccades. Proceedings of the Royal Society of London, series B, 263, 459–468. Koerber, K.C., Pfeiffer, R.R., Warr, W.B. and Kiang, N.Y. (1966) Spontaneous spike discharges from single units in the cochlear nucleus after destruction of the cochlea. Experimental Neurology, 16, 119–130. Koffka, K. (1935) Principles of Gestalt Psychology. London: Routledge and Kegan Paul. Kohonen, T. and Hari, R. (1999) Where the abstract feature maps of the brain might come from. Trends in Neuroscience, 22, 135–139. Kopecz, K., Schöner, G., Spengler, F. and Dinse, H.R. (1993) Dynamic properties of cortical, evoked (10 Hz) oscillations. Theory and experiment. Biological Cybernetics, 69, 463–473. Krause, F. and Eckhorn, R. (1983) Receptive fields for motion determined for different types of cat visual neurons. Neuroscience Letters, S14, 209. Krone, G., Mallot, H.A., Palm, G. and Schüz, A. (1986) Spatio-temporal receptive fields: a dynamical model derived from cortical architectonics. Proceedings of the Royal Society of London, series B, 226, 421–444. Krukowski, A.E. (2000) A model of cat primary visual cortex and its thalamic input. Doctoral Dissertation, University of California, San Francisco. Langner, G. (1997) Temporal processing of pitch in the auditory system. Journal of New Music Research, 26, 116–132. Lennie, P. (1998) Single units and visual cortical organization. Perception, 27, 889–935. LeVay, S., Stryker, M.P. and Shatz, C.J. (1978) Ocular dominance columns and their development in layer IV of the cat’s visual cortex: a quantitative study. Journal of Comparative Neurology, 179, 223–244. Lieke, E.E., Frostig, R.D., Arieli, A., Ts’o, D.Y., Hildesheim, R. and Grinvald, A. (1989) Optical imaging of cortical activity: real-time imaging using extrinsic dye-signals and high resolution imaging based on slow intrinsic-signals. Annual Review of Physiology, 51, 543–559. Lin, C.S., Friedlander, M.J. and Sherman, S.M. (1979) Morphology of physiologically identified neurons in the visual cortex of the cat. Brain Research, 172, 344–348. Livingstone, M. and Hubel, D. (1988) Segregation of form, color, movement, and depth: anatomy, physiology, and perception. Science, Washington, 240, 740–749. Livingstone, M.S. (1998) Mechanisms of direction selectivity in macaque V1. Neuron, 20, 509–526. Lund, J.S., Yoshioka, T. and Levitt, J.B. (1993) Comparison of intrinsic connectivity in different areas of macaque monkey cerebral cortex. Cerebral Cortex, 3, 148–162.
© 2002 Taylor & Francis
306
Hubert R. Dinse and Christoph E. Schreiner
Luria, A.R. (1973) The working brain. London: Penguin. MacDonald, K.D. and Barth, D.S. (1995) High frequency (gamma-band) oscillating potentials in rat somatosensory and auditory cortex. Brain Research, 694, 1–12. MacKay, D.M. (1958) Perceptual stability of a stroboscopically lit visual field containing self-luminous objects. Nature, London, 181, 507–508. Malach, R. (1994) Cortical columns as devices for maximizing neuronal diversity. Trends in Neuroscience, 17, 101–104. Manley, J.A. and Mueller-Preuss, P. (1978) Response variability in the mammalian auditory cortex: an objection to feature detection? Federation Proceedings, 37, 2355–2359. Martin, K.A. and Whitteridge, D. (1984) The relationship of receptive field properties to the dendritic shape of neurones in the cat striate cortex. Journal of Physiology (London), 356, 291–302. Martin, K.A. (1988) The Wellcome Prize lecture. From single cells to simple circuits in the cerebral cortex. Quarterly Journal of Experimental Physiology, 73, 637–702. Maunsell, J.H. (1992) Functional visual streams. Current Opinion in Neurobiology, 2, 506–510. McIlwain, J.T. (1986) Point images in the visual system: new interest in an old idea. Trends in Neuroscience, 9, 354–358. Mendola, J.D., Dale, A.M., Fischl, B., Liu, A.K. and Tootell, B.H. (1999) The representation of illusory and real contours in human cortical visual areas revealed by functional magnetic resonance imaging. Journal of Neuroscience, 19, 8560 –8572. Merigan, W.H. and Maunsell, J.H. (1993) How parallel are the primate visual pathways? Annual Review of Neuroscience, 16, 369–402. Merzenich, M.M., Schreiner, C., Jenkins, W. and Wang, X. (1993) Neural mechanisms underlying temporal integration, segmentation, and input sequence representation: some implications for the origin of learning disabilities. Annals of the New York Academy of Science, 682, 1–22. Métin, C. and Frost, D.O. (1989) Visual responses of neurons in somatosensory cortex of hamsters with experimentally induced retinal projections to somatosensory thalamus. Proceedings of the National Academy of Science, U.S.A., 86, 357–361. Metzger, W. (1932) Versuch einer gemeinsamen Theorie der Phänomene Fröhlichs und Hazelhoffs und Kritik ihrer Verfahren zur Messung der Empfindungszeit. Psychologische Forschung, 16, 176–200. Middlebrooks, J.C., Dykes, R.W. and Merzenich, M.M. (1980) Binaural response-specific bands in primary auditory cortex (AI) of the cat: Topographical organization orthogonal to isofrequency contours. Brain Research, 181, 31–48. Middlebrooks, J.C. and Knudsen, E.I. (1984) A neural code for auditory space in the cat’s superior colliculus. Journal of Neuroscience, 4, 2621–2634. Miller, L.M. and Schreiner, C.E. (2000) Stimulus-based state control in the thalamocortical system. Journal of Neuroscience, 20, 7011–7016. Moore, C,I., Nelson, S.B. and Sur, M. (1999) Dynamics of neuronal processing in rat somatosensory cortex. Trends in Neuroscience, 22, 513–520. Moore, G.P., Perkel, D.H. and Segundo, J.P. (1966) Statistical analysis and functional interpretation of neuronal spike data. Annual Reviews of Physiology, 28, 493–522. Movshon, J.A., Thompson, I.D. and Tolhurst, D.J. (1978) Spatial and temporal contrast sensitivity of neurones in areas 17 and 18 of the cat’s visual cortex. Journal of Physiology (London), 283, 101–120. Naveen, K.V., Srinivas, R. and Nirmala, K.S. (1998) Differences between congenitally blind and normally sighted subjects in the P1 component of middle latency auditory evoked potentials. Perceptual and Motor Skills, 86, 1192–1194. Nawrot, M.P., Riehle, A., Aertsen, A. and Rotter, S. (2000) Spike Count Variability in Motor Cortical Neurons. European Journal of Neuroscience, 12 (suppl.11), abstr. 225.27. Nelson, S.B. (1991a) Temporal interactions in the cat visual system. I. Orientation-selective suppression in the visual cortex. Journal of Neuroscience, 11, 344–356. Nelson, S.B. (1991b) Temporal interactions in the cat visual system. II. Suppressive and facilitatory effects in the lateral geniculate nucleus. Journal of Neuroscience, 11, 357–368. Nelson, S.B. (1991c) Temporal interactions in the cat visual system. III. Pharmacological studies of cortical suppression suggest a presynaptic mechanism. Journal of Neuroscience, 11, 369–380. Nicolelis, M.A., Ghazanfar, A.A., Faggin, B.M., Votaw, S. and Oliveira, L.M. (1997) Reconstructing the engram: simultaneous, multisite, many single neuron recordings. Neuron, 184, 529–537. Nicolelis, M.A., Ghazanfar, A.A., Stambaugh, C.R., Oliveira, L.M., Laubach, M., Chapin, J.K., Nelson, R.J. and Kaas, J.H. (1998) Simultaneous encoding of tactile information by three primate cortical areas. Nature, Neuroscience, 1, 621–630. Nicolelis, M.A.L. (1996) Beyond maps: a dynamic view of the somatosensory system. Brazilian Journal of Medical and Biological Research, 29, 401–412. Nicolelis, M.A.L. (1999) Methods in Neural Ensemble Recordings (M.A.L. Nicolelis, ed.). Baton Rouge: CRC Press.
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
307
Niemeyer, W. and Starlinger, I. (1981) Do blind hear better? Investigations on auditory processing in congenital early acquired blindness. II. Central functions. Audiology, 20, 510–515. Noda, H. and Adey, W.R. (1970) Firing of neuron pairs in cat association cortex during sleep and wakefulness. Journal of Neurophysiology, 33, 672–684. Nunez, A., Amzica, F. and Steriade, M. (1992) Voltage-dependent fast (20–40 Hz) oscillations in long-axoned neocortical neurons. Neuroscience, 51, 7–10. Obermayer, K., Ritter, H. and Schulten, K. (1990) A principle for the formation of the spatial structure of cortical feature maps. Proceedings of the National Academy of Science, U.S.A., 87, 8345–8349. Omurtag, A., Kaplan, E., Knight, B. and Sirovich, L. (2000) A population approach to cortical dynamics with an application to orientation tuning. Network, 11, 247–260. Orban, G.A., Gulyas, B. and Vogels, R. (1987) Influence of a moving textured background on direction selectivity of cat striate neurons. Journal of Neurophysiology, 57, 1792–1812. Parraga, C.A., Troscianko, T. and Tolhurst, D.J. (2000) The human visual system is optimised for processing the spatial information in natural visual images. Current Biology, 10, 35–38. Penn, A.A., Riquelme, P.A., Feller, M.B. and Shatz, C.J. (1998) Competition in retinogeniculate patterning driven by spontaneous activity. Science, Washington, 279, 2108–2112. Perkel, D.H. and Bullock, T.H. (1968) Neural Coding. Neuroscience Research Program Bulletin, 6, 221–348. Perkel, D.H., Gerstein, G.L. and Moore, G.P. (1967) Neuronal spike trains and stochastic point processes. I. The single spike train. Biophysical Journal, 7, 391–418. Peschanski, M., Guilbaud, G. and Gautron, M. (1980) Neuronal responses to cutaneous electrical and noxious mechanical stimuli in the nucleus reticularis thalami of the rat. Neuroscience Letters, 20, 165–170. Petersen, S.E., Miezin, F.M. and Allman, J.M. (1988) Transient and sustained responses in four extrastriate visual areas of the owl monkey. Experimental Brain Research, 70, 55–60. Phillips, D.P., Hall, S.E. and Hollett, J.L. (1989) Repetition rate and signal level effects on neuronal responses to brief tone pulses in cat auditory cortex. Journal of the Acoustic Society of America, 85, 2537–2549. Podvigin, N.F., Kuperman, A.M. and Chuyeva, I.V. (1974) Space-time properties of excitation and inhibition and wave processes in the receptive fields of the external geniculate body of the cat. Biofizika, 19, 341–346. Ramachandran, V.S. and Anstis, S.M. (1983) Perceptual organization in moving patterns. Nature, London, 304, 529–531. Ramachandran, V.S. and Anstis, S.M. (1986) The perception of apparent motion. Scientific American, 254, 102–109. Ramachandran, V.S., Ruskin, D., Cobb, S. and Rogers-Ramachandran, D. (1994) On the perception of illusory contours. Vision Research, 34, 3145–3152. Recanzone, G.H., Merzenich, M.M., Jenkins, W.M., Grajski, K. and Dinse, H.R. (1992) Topographic reorganization of the hand representation in cortical area 3b of owl monkeys trained in a frequency discrimination task. Journal of Neurophysiology, 67, 1031–1056. Recanzone, G.H., Schreiner, C.E. and Merzenich, M.M. (1993) Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys. Journal of Neuroscience, 13, 87–103. Recanzone, G.L., Schreiner, C.E., Sutter, M.L., Beitel, R. and Merzenich, M.M. (1999) Functional organization of spectral receptive fields in the primary auditory cortex of the owl monkey. Journal of Comparative Neurology, 415, 460–481. Reichardt, W. (1961) Autocorrelation, a principle for the evaluation of sensory information by the central nervous system. In: W.A. Rosenblith (ed.), Sensory Communication. New York: MIT Press, pp. 303–317. Reid, R.C., Victor, J.D. and Shapley, R.M. (1992) Broadband temporal stimuli decrease the integration time of neurons in cat striate cortex. Visual Neuroscience, 9, 39–45. Rettenbach, R., Diller, G. and Sireteanu, R. (1999) Do deaf people see better? Texture segmentation and visual search compensate in adult but not in juvenile subjects. Journal of Cognitive Neuroscience, 11, 560–583. Rieke, F., Bodnar, D.A. and Bialek, W. (1995) Naturalistic stimuli increase the rate and efficiency of information-transmission by primary auditory afferents. Proceedings of the Royal Society, London, series B, 262, 259–265. Ringach, D.L., Hawken, M.J. and Shapley, R. (1997) Dynamics of orientation tuning in macaque primary visual cortex. Nature, London, 387, 281–284. Rockel, A.J., Hiorns, R.W. and Powell, T.P.S. (1980) The basic uniformity in structure of the neocortex. Brain, 103, 221–244. Röder, B., Rosler, F., Henninghausen, E. and Nacker, F. (1996) Event-related potentials during auditory and somatosensory discrimination in sighted and blind human subjects. Cognitive Brain Research, 4, 77–93. Röder, B., Rosler, F. and Neville, H.J. (1999) Effects of interstimulus interval on auditory event-related potentials in congenitally blind and normally sighted humans. Neuroscience Letters, 264, 53–56. Röder, B., Rosler, F. and Neville, H.J. (2000) Event-related potentials during auditory language processing in congenitally blind and sighted people. Neuropsychologia, 38, 1482–1502. Rodieck, R.W. (1967) Maintained activity of cat retinal ganglion cells. Journal of Neurophysiology, 30, 1043–1071.
© 2002 Taylor & Francis
308
Hubert R. Dinse and Christoph E. Schreiner
Romanski, L.M., Tian, B., Fritz, J., Mishkin, M., Goldman-Rakic, P.S. and Rauschecker, J.P. (1999). Dual streams of auditory afferents target multiple domains in the primate prefrontal cortex. Nature, Neuroscience, 2, 1131–1136. Sauve, K. (1999) Gamma-band synchronous oscillations: recent evidence regarding their functional significance. Consciousness and Cognition, 8, 213–224. Schiller, P.H., Finlay, B.L. and Volman, S.F. (1976) Short-term response variability of monkey striate neurons. Brain Research, 105, 347–349. Schreiner, C.E. (1998) Spatial distribution of responses to simple and complex sounds in primary auditory cortex. Audiology and Neurotology, 3, 104–122. Schreiner, C.E., Read, H.L. and Sutter, M.L. (2000) Modular organization of frequency integration in primary auditory cortex. Annual Review of Neuroscience, 23, 501–529. Schurmann, M. and Basar, E. (1999) Alpha oscillations shed new light on relation between EEG and single neurons. Neuroscience Research, 33, 79–80. Shadlen, M.N. and Newsome, W.T. (1998) The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. Journal of Neuroscience, 18, 3870–3896. Shamma, S.A. and Versnel, H. (1995) Ripple analysis in ferret primary auditory cortex. II. Prediction of unit responses to arbitrary spectral profiles. Auditory Neuroscience, 1, 207–320. Sharma, J., Angelucci, A. and Sur, M. (2000) Induction of visual orientation modules in auditory cortex. Nature, London, 404, 841–847. Shatz, C.J. and Stryker, M.P. (1988) Prenatal tetrodotoxin infusion blocks segregation of retinogeniculate afferents. Science, Washington, 242, 87–89. Sherman, S.M. and Guillery, R.W. (2001) Exploring the thalamus. Academic Press: San Diego. Sheth, B.R., Sharma, J., Rao, S.C. and Sur, M. (1996) Orientation maps of subjective contours in visual cortex. Science, Washington, 274, 2110 –2115. Shevelev, I.A. (1987) Temporal processing of orientation signals in visual cortex. Sensornje Sistemje, 1, 73–82. Shoham, D., Hübener, M., Schulze, S., Grinvald, A. and Bonhoeffer, T. (1997) Spatio-temporal frequency domains and their relation to cytochrome oxidase staining in cat visual cortex. Nature, London, 385, 529–533. Sillito, A.M., Grieve, K.L., Jones, H.E., Cudeiro, J. and Davis, J. (1995) Visual cortical mechanisms detecting focal orientation discontinuities. Nature, London, 378, 492–496. Simons, D.J. (1985) Temporal and spatial integration in the rat S1 vibrissa cortex. Journal of Neurophysiology, 54, 615–635. Singer, W. (1998) Consciousness and the structure of neuronal representations. Philosophical Transactions of the Royal Society of London, series B, 353, 1829–1840. Softky, W.R. and Koch, C. (1993) The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. Journal of Neuroscience, 13, 334–350. Stein, B.E. and Meredith, M.A. (1990) Multisensory integration. Neural and behavioral solutions for dealing with stimuli from different sensory modalities. Annals of the New York Academy of Sciences, 608, 51–65. Steriade, M. (2000) Corticothalamic resonance, states of vigilance and mentation. Neuroscience, 101, 243–276. Steriade, M. and Llinas, R.R. (1988). The functional states of the thalamus and the associated neuronal interplay. Physiology Reviews, 68, 649–742. Suga, N. (1984) The extent to which biosonar information is represented in the bat auditory cortex. In: G.M. Edelman, W.E. Gall and W.M. Cowan (eds), Dynamic Aspects of Neocortical Function. New York: Wiley and Sons, pp. 315–373. Sukov, W. and Barth, D.S. (1998) Three-dimensional analysis of spontaneous and thalamically evoked gamma oscillations in auditory cortex. Journal of Neurophysiology, 79, 2875–2884. Sur, M., Pallas, S.L. and Roe, A.W. (1990) Cross-modal plasticity in cortical development: Differentiation and specification of sensory neocortex. Trends in Neuroscience, 13, 227–233. Sutter, M.L. and Schreiner, C.E. (1991) Physiology and topography of neurons with multipeaked tuning curves in cat primary auditory cortex. Journal of Neurophysiology, 65, 1207–1226. Swadlow, H.A. (1988) Efferent neurons and suspected interneurons in binocular visual cortex of the awake rabbit: receptive fields and binocular properties. Journal of Neurophysiology, 59, 1162–1187. Swadlow, H.A. (1989) Efferent neurons and suspected interneurons in S-1 vibrissa cortex of the awake rabbit: receptive fields and axonal properties. Journal of Neurophysiology, 62, 288–308. Swadlow, H.A. (1990) Efferent neurons and suspected interneurons in S-1 forelimb representation of the awake rabbit: receptive fields and axonal properties. Journal of Neurophysiology, 63, 1477–1498. Swindale, N.V., Matsubara, J.A. and Cynader, M.S. (1987) Surface organization of orientation and direction selectivity in cat area 18. Journal of Neuroscience, 7, 1414–1427. Swindale, N.V. (1991) Coverage and the design of striate cortex. Biological Cybernetics, 65, 415–424. Swindale, N.V. (2000) How many maps are there in visual cortex? Cerebral Cortex, 10, 633–643. Swindale, N.V., Shoham, D., Grinvald, A., Bonhoeffer, T. and Hübener, M (2000) Visual cortex maps are optimized for uniform coverage. Nature, Neuroscience, 3, 822–826.
© 2002 Taylor & Francis
Functional Equivalence of Sensory Areas
309
Theunissen, F.E. and Doupe, A.J. (1998) Temporal and spectral sensitivity of complex auditory neurons in nucleus HVc of male zebra finches. Journal of Neuroscience, 18, 3786–3802. Thomson, A.M. and West, D.C. (1993) Fluctuations in pyramid-pyramid excitatory postsynaptic potentials modified by presynaptic firing pattern and postsynaptic membrane potential using paired intracellular recordings in rat neocortex. Neuroscience, 54, 329–346. Tolhurst, D.J., Movshon, J.A. and Dean, A.F. (1983) The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vision Research, 23, 775–785. Tomko, G.J. and Crapper, D.R. (1974) Neuronal variability: Non-stationary responses to identical visual stimuli. Brain Research, 79, 405–418. Tovee, M.J. and Rolls, E.T. (1992) Oscillatory activity is not evident in the primate temporal visual cortex with static stimuli. NeuroReport, 3, 369–372. Traub, R.D., Jefferys, J.G.R. and Whittington, M.A. (1999) Fast Oscillations in Cortical Circuits. Cambridge, Mass: MIT Press. Tremblay, N., Bushnell, M.C. and Duncan, G.H. (1993) Thalamic VPM nucleus in the behaving monkey. II. Response to air-puff stimulation during discrimination and attention tasks. Journal of Neurophysiology, 69, 753–763. Troyer, T.W., Krukowski, A.E., Priebe, N.J. and Miller, K.D. (1998) Contrast-invariant orientation tuning in cat visual cortex: thalamocortical input tuning and correlation-based intracortical connectivity. Journal of Neuroscience, 18, 5908–5927. Tsodyks, M., Kenet, T., Grinvald, A. and Arieli, A. (1999) Linking spontaneous activity of single cortical neurons and the underlying functional architecture. Science, Washington, 286, 1943–1946. Ungerleider, L.G. and Haxby, J.V. (1994) ‘What’ and ‘where’ in the human brain. Current Opinion in Neurobiology, 4, 157–165. Ungerleider, L.G. and Mishkin, M. (1982) Two cortical visual systems. In: D.J. Ingle, M.A. Goodale, R.J.W. Mansfield (eds), Analysis of Visual Behavior. MIT Press, Cambridge, pp. 549–586. Van Essen, D. and Kelly, J. (1973) Correlation of cell shape and function in the visual cortex of the cat. Nature, London, 241, 403–405. van Gisbergen, J.A., Grashuis, J.L., Johannesma, P. and Vendrik, A.J. (1975) Spectral and temporal characteristics of activation and suppression of units in the cochlear nuclei of the anesthetized cat. Experimental Brain Research, 23, 367–386. Vogels, R., Spileers, W. and Orban, G.A. (1989) The response variability of striate cortical neurons in the behaving monkey. Experimental Brain Research, 77, 432–436. von der Heydt, R., Peterhans, E. and Baumgartner, G. (1984) Illusory contours and cortical neuron responses. Science, Washington, 224, 1260–1262. von Krosigk, M., Bal, T. and McCormick, D.A. (1993) Cellular mechanisms of a synchronized oscillation in the thalamus. Science, Washington, 261, 361–364. von Melchner, L., Pallas, S.L. and Sur, M. (2000) Visual behaviour mediated by retinal projections directed to the auditory pathway. Nature, London, 404, 871–876. von Seelen, W., Mallot, H.P., Krone, G. and Dinse, H.R. (1986) On information processing in the cat’s visual cortex. In: G. Palm and A. Aertsen (eds), Brain Theory. Berlin, Heidelberg: Springer, pp. 49–79. Wang, L.Y. and Kaczmarek, L.K. (1998) High-frequency firing helps replenish the readily releasable pool of synaptic vesicles. Nature, London, 394, 384–388. Wang, X., Merzenich, M.M., Sameshima, K. and Jenkins, W.M. (1995) Remodeling of hand representation in adult cortex determined by timing of tactile stimulation. Nature, London, 378, 71–75. Warren, R.M. (1970). Restoration of missing speech sounds. Science, Washington, 167, 392–393. Weliky, M., Bosking, W.H. and Fitzpatrick, D. (1996) A systematic map of direction preference in primary visual cortex. Nature, London, 379, 725–728. Werner, G. and Mountcastle, V.B. (1963) The variability of central neural activity in a sensory system, and its implications for the central reflection of sensory events. Journal of Neurophysiology, 26, 958–977. Wiemer, J., Spengler, F., Joublin, F., Stagge, P. and Wacquant, S. (2000) Learning cortical topography from spatiotemporal stimuli. Biological Cybernetics, 82, 173–187. Wiesel, T.N., Hubel, D.H. and Lam, D.M. (1974) Autoradiographic demonstration of ocular-dominance columns in the monkey striate cortex by means of transneuronal transport. Brain Research, 79, 273–279. Wilbur, W.J. and Rinzel, J. (1983) A theoretical basis for large coefficient of variation and bimodality in neuronal interspike interval distributions. Journal of Theoretical Biology, 105, 345–368. Willis, W.D., Maunz, R.A., Foreman, R.D. and Coulter, J.D. (1975) Static and dynamic responses of spinothalamic tract neurons to mechanical stimuli. Journal of Neurophysiology, 38, 587–600. Winer, J. (1992) The functional architecture of the medial genicuate body and the primary auditory cortex. In: D.B. Webster, A.N. Popper and R.R. Fay (eds), The Mammalian Auditory Pathway: Neuroanatomy. New York: Springer, pp. 222–409. Winer, J.A., Larue, D.T. and Huang, C.L. (1999) Two systems of giant axon terminals in the cat medial geniculate body: Convergence of cortical and GABAergic inputs. Journal of Comparative Neurology, 413, 181–197.
© 2002 Taylor & Francis
310
Hubert R. Dinse and Christoph E. Schreiner
Wörgötter, F. and Eysel, U. (2000) Context, state and the receptive fields of striate cortex cells. Trends in Neuroscience, 23, 497–503. Yan, J. and Suga, N. (1999) Corticofugal amplification of facilitative auditory responses of subcortical combinationsensitive neurons in the mustached bat. Journal of Neurophysiology, 81, 817–824. Zhang, K. and Sejnowski, T.J. (1999) A theory of geometric constraints on neural activity for natural threedimensional movement. Journal of Neuroscience, 19, 3122–3145. Zhang, Y., Suga, N. and Yan, J. (1997) Corticofugal modulation of frequency processing in bat auditory system. Nature, London, 387, 900–903.
© 2002 Taylor & Francis
14 Plastic-Adaptive Properties of Cortical Areas Hubert R. Dinse1 and Gerd Boehmer2 1
Institute for Neuroinformatics, Dept. of Theoretical Biology, Group Experimental Neurobiology, Ruhr-University Bochum, Germany 2 Institute of Physiology and Pathophysiology, Gutenberg-University, Mainz, Germany Correspondence to: Hubert R. Dinse, Institute for Neuroinformatics, Dept. of Theoretical Biology, Group Experimental Neurobiology, Ruhr-University Bochum, ND 04 D-44780 Bochum, Germany Tel: +49-234-32-25565; FAX: +49-234-32-14209 e-mail:
[email protected]
This chapter summarizes recent findings about plastic changes in adult early sensory and motor cortices. We discuss mechanisms leading to enduring changes of synaptic efficacy and of neural response behaviour in terms of receptive fields and cortical representational maps, with special emphasis on behavioural and perceptual consequences of cortical reorganizations, after peripheral lesion or injury, differential use and training. Given the assumption that the presence of plasticadaptive abilities are a prerequisite for coping successfully with an ever-changing environment, we focus on comparative aspects, evaluating apparent similarities and dissimilarities emerging across different modalities. Most of the material reviewed is from animal studies that allow the study of adaptations and underlying mechanisms induced under a large variety of natural and laboratory conditions, at all levels from channels and synapses, to groups of neurones and cortical maps. Owing to the recent development of non-invasive imaging technologies, it has become possible to explore the significance of cortical plasticity for humans, occurring in “every-daylife”. Massive and enduring reorganizations are present for all areas and modalities discussed, corroborating the view that cortical maps and response properties are in a permanent state of use-dependent fluctuation. We discuss various mechanisms controlling synaptic plasticity, the role of input statistics and attention, the top-down modulation of plastic changes, the “negative”, (maladaptive) consequences of cortical reorganization, and the coding and decoding of adaptational processes. Despite the convincing evidence for profound reorganizational changes in all areas, specifically for injury-related plasticity, there exist also clear modality-specific differences, an observation holding at both the cellular and the systemic level. Differences include magnitude of changes, readiness of induceability and specificity of neural parameters that are affected. While reorganization of somatosensory and auditory cortex appears to follow comparable rules and constraints, adult visual cortex plasticity shows a number of particularities, indicating that visual cortical maps might be more difficult to change. We discuss a number of possible explanations based on different levels of abstraction. Among these are differences in control mechanisms of synaptic plasticity, the limiting character of complex topological maps, and the possible limitations of the metaphor of “use”, as a driving force of adult plasticity. KEYWORDS: Bienenstock-Cooper model, cortical map, Hebbian rules, input probability, lesion, LTP, LTD, maladaptivity, neural coding, perceptual learning, receptive field, sensory areas, simultaneity, synaptic plasticity, synaptic control mechanism, training, top-down modulation
311 © 2002 Taylor & Francis
312
Hubert R. Dinse and Gerd Boehmer
1. GENERAL ASPECTS OF PLASTIC ADAPTIVE CAPACITIES Heritable features evolving during evolutionary time spans are of ultimate advantage for survival and are, without exception, structurally fixed. To cope successfully with the ongoing changes of environmental conditions occurring during the lifespan of individuals, additional mechanisms are required that allow rapid and effective adaptations that are not specified by genetic constraints. In spite of the substantial amount of adaptational capacities, systems must possess sufficient generic stability to allow secure processing. Conceivably, there is a trade-off between modifiability and stability. In contrast to developmental plasticity, adaptations of adult brains do not, in the first place, rely on maturational and growth processes. Specifically, for learning-induced alterations there is a consensus that there is a crucial role for so-called functional plasticity, based on rapid and reversible modifications of synaptic efficacy. However, large-scale amputations have been shown to involve sprouting and outgrowth of afferent connections into neighbouring regions at cortical and subcortical levels (Florence et al., 1998; Jain et al., 2000). Technically, the term “adaptation” is used in a rather neutral sense, i.e. there are no implicit assumptions whether “adaptational changes” might yield a positive or negative outcome. Given this general overview, it appears conceivable that plastic-adaptive capacities of various forms represent a general and ubiquitous cortical feature present in all sensory modalities as well as in higher cortical areas. Before summarizing details and possible deviations from that scheme, some basic properties of cortical plasticity, observable in early sensory areas are briefly discussed. 1.1. Driving Forces that Lead to Adaptational Changes What are crucial factors that are potentially effective in inducing changes of neural representations? We assume a dynamically-maintained steady state of representations that emerged during development and adulthood from maturational and learning processes, reflecting the history of adaptation to a “mean environment”. Mean environment is defined as the accumulated and idiosyncratic experience of an individual. Adaptational processes are assumed to operate on these representations, and long-lasting changes are likely to occur when sensory input patterns are altered such that they deviate from the mean environment. (a) A simple way to alter the average steady state is by changing the input statistics. Features that are specifically effective in driving adaptational changes are simultaneity, repetition, or more generally, spatio-temporal proximity. These changes occur without involving attention or other cognitive processes. Accordingly, a class of non-cognitive adaptations is largely based on bottom-up processing. (b) Alternatively or additionally, attention can be drawn to a stimulus, thereby selecting it in comparison to others. Furthermore, the relevance of a stimulus can change dependent on context, history and behavioural task, thereby modifying the processing of the physically-defined attributes. There is general agreement that modification of early sensory processing by attention and stimulus relevance reflects top-down influences arising from cognitive processes. (c) Reinforcement of learning processes by reward or punishment usually accelerates adaptational processes. Such influences are assumed to be mediated by specific brain regions modifying early sensory processing.
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
313
1.2. The Hebbian Metaphor A central paradigm in the description and analysis of cortical plasticity is built around the Hebbian concept (1949) stating that episodes of high temporal correlation between pre- and postsynaptic activity are prerequisite for inducing changes of synaptic efficacy. Historically, the idea that cooperative processes are crucially involved in generating long-lasting changes of excitability can be traced back to the 19th century (James, 1890). In fact, since the time of Hebb, the aspect of simultaneity has become a metaphor in neural plasticity, although the exact role of Hebbian mechanisms in use-dependent plasticity remains controversial (Carew et al., 1984; Fox and Daw, 1993; Granger et al., 1994; Montague and Sejnowski, 1994; Joublin et al., 1996; Buonomano and Merzenich, 1996; Edeline, 1996; Cruikshank and Weinberger, 1996a,b; Ahissar et al., 1998). It has been suggested that the definition of Hebbian mechanisms must be extended beyond “simultaneity”, in the sense of strict coincidence, to cover all facets arising from learning processes. Such a definition must include a large number of pre- and post synaptic patterns, as well as a broad timewindow for what neural systems regard as “simultaneous”.
1.3. Use-dependent Plasticity as a Basis for Perceptual and Motor Skills One of the striking features of use-dependent plasticity is the correlation of cortical changes with performance. The acquisition of skills has often been used as an index for the build-up of implicit memories. Implicit memories are acquired automatically and without consciousness. Many repetitions, over a long time and without higher-level cognitive processes, are sufficient to improve perceptual and motor skills. This non-cognitive feature in combination with many repetitions characterizes an important aspect of use-dependent neural plasticity. It has been speculated that use-dependent plasticity might be strongly related to, if not a substrate for, implicit memory function.
1.4. Perceptual Learning Perceptual learning is the ability to improve perceptual performance by training and practice (cf. Gibson, 1953). In this sense, perceptual learning occurs largely independent of conscious experience (cf. Fahle and Poggio, 2001). Perceptual learning is usually characterized by a high specificity to stimulus parameters such as location or orientation of a stimulus, with little generalization of what is learned to other locations or to other stimulus configurations. Selectivity and locality of this type implies that the underlying neural changes most probably occur within early cortical representations that contain well-ordered topographic maps to allow for this selectivity, but where generalization with respect to spatial location and orientation has not yet occurred. Transfer of newly acquired abilities is considered an important marker of that processing level at which changes are most likely to occur: limited generalization indicates high locality of effects in early representations. In contrast, transfer of learned abilities implies the involvement of higher processing levels, as is often observed in task and strategy learning. There is increasing evidence that changes in early cortical areas might be more directly linked to perceptual learning than previously thought (Karni and Sagi, 1991; Recanzone et al., 1992a; Schoups et al., 1995; Crist et al., 1997; Fahle, 1997; Fahle and Poggio, 2001). While there is a large literature on
© 2002 Taylor & Francis
Hubert R. Dinse and Gerd Boehmer
314
perceptual learning in the visual system, much less data are available for comparable experiments in the auditory and somatosensory system.
2. POST-ONTOGENETIC PLASTICITY OF CORTICAL MAPS AND RECEPTIVE FIELDS It is useful to distinguish between two different forms of adult plasticity: • •
Lesion-induced plasticity subsumes the reorganization after injury and lesion, induced either centrally or at the periphery. This type of plasticity refers to aspects of compensation and repair of functions that have been acquired prior to injury or lesion. Training- and learning-induced reorganization is often denoted as “use-dependent plasticity” and describes plastic changes parallel to the behavioural improvement of performance, i.e. the acquisition of perceptual and motor skills.
In view of the fact that amputation changes the pattern of use entirely, a more accurate distinction would be between “lesion-induced” vs “non-lesion-induced” plasticity. Just how far the two forms are different, or possibly based on similar mechanisms, is a matter of ongoing debate. There are a number of detailed reviews providing an excellent overview covering all facets of cortical plasticity in early sensory and motor areas (Merzenich et al., 1988; Kaas, 1991; Scheich et al., 1991; Eysel, 1992; Garraghty and Kaas, 1992; Sameshima and Merzenich, 1993; Donoghue, 1995; Weinberger, 1995; Cruishank and Weinberger, 1996a; Edeline, 1996; Dinse et al., 1997a; Kaas and Florence, 1997; Sanes and Donoghue, 1997; Buonomano and Merzenich, 1998; Gilbert, 1998; Nicolelis et al., 1998a; Rauschecker, 1999; Recanzone, 2000; Dinse and Merzenich, 2002). In the following, the main focus is on comparative aspects. It should be noted that this comparative approach is hindered by the fact that there are, with rare exceptions, few studies explicitly exploring possible area- and modality-specific properties of cortical plasticity. 2.1. Lesion-Induced Plasticity Large-scale reorganizations were first described following digit amputation or deafferentation in the primary somatosensory cortex of cats, monkeys and racoons (Kalaska and Pomeranz, 1979; Kelahan et al., 1981; Rasmusson, 1982; Merzenich et al., 1983, 1984; Wall and Cusick, 1984; Calford and Tweedale, 1988; Florence and Kaas, 1995; Kaas et al., 1999). The main result was that the cortical territory representing the skin surface removed by amputation or deafferentation did not remain silent, but was activated by stimulation of bordering skin sites. Major topographic changes for cutaneous afferent representations were limited to a cortical zone extending about 1 mm on either side of the initial boundaries of the amputated digits. These early data indicated that the sensory cortical representations in adults were not hard-wired, but retain a self-organizing capacity operational throughout life (Merzenich et al., 1984). Much more dramatic cortical reorganizations were reported after mapping the cortex of monkeys that had undergone deafferentation of the dorsal roots (C2-T4) several years before, thereby depriving a cortical area of over 1 cm2 of its normal input from the arm and
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
315
hand (Pons et al., 1991). These authors found that all of the deprived area had developed novel responses to neighbouring skin areas, including the face and chin. It is now well established that comparable large-scale remodeling occurs in human somatosensory and motor cortical areas, weeks or months following limb amputation (Fuhr et al., 1992; Cohen et al., 1993; Kew et al., 1994; Yang et al., 1994; Flor et al., 1995, 1998; Knecht et al., 1998) implying that similar (if not identical) rules govern lesion-induced plastic reorganizations in humans (see also section 2.5. “Therapeutic consequences of cortical plasticity”). A series of lesion experiments performed a couple of years later in auditory and visual system confirmed the tremendous capacities of the cortex for reorganization described for SI. Several months after a restricted unilateral lesion of the cochlear of guinea-pigs, the area of contralateral auditory cortex representing the lesioned frequency range was partly occupied by an expanded representation of sound frequencies adjacent to the lesioned frequency range. Thresholds at their new characteristic frequencies (CFs) were close to normal (Robertson and Irvine, 1989). In extending these experiments, it was found that unilateral restricted cochlear lesions in adult cats altered the topographic representations of the lesioned cochleas along the tonotopic axis of primary auditory cortex, extending up to 3 mm rostal to the area of normal representation, with no apparent topographic order within this enlarged representation (Rajan et al., 1993). Interestingly, no comparable signs of plastic changes of the frequency map were found in the dorsal cochlear nucleus of adult cats following unilateral partial cochlear lesions (Rajan and Irvine, 1998). A striking overrepresentation of the frequency corresponding to the border area of the cochlear lesion has been observed after amikacin-induced cochlear lesions in primary auditory cortex of the adult chinchilla. The amount of reorganization was similar in extent to that previously seen during development (Kakigi et al., 2000). Fairly large lesions (5 by 10 degrees of visual angle) of the retina markedly altered the systematic representations of the contralateral eye in primary and secondary visual cortex, when matched inputs from the ipsilateral eye were also removed. Cortical neurones that normally have receptive fields in the lesioned region of the retina acquired new receptive fields in portions of the retina surrounding the lesions (Kaas et al., 1990). In another study, removal of visual inputs by focal binocular retinal lesions resulted in an immediate increase in receptive field size for cortical cells with receptive fields near the edge of the retinal scotoma. After a few months even the cortical areas that were initially silenced by the lesion recovered visual activity, representing retinotopic loci surrounding the lesion (Gilbert and Wiesel, 1992). Anatomical studies showed that the spread of geniculocortical afferents is insufficient to account for the cortical recovery (Darian-Smith and Gilbert, 1995), indicating that the topographic reorganization within the cortex was largely due to synaptic changes intrinsic to the cortex, most probably through the system of long-range horizontal connections. In a series of studies, the reorganizational properties of adult visual cortex following various forms of retinal injuries and lesions has been well established (Schmid et al., 1996; Calford et al., 1999, 2000). Quantitative studies of the response characteristics of visual neurones after retinal lesions indicated that these neurones develop fairly normal processing features. After three months of recovery, newly activated units exhibited strikingly normal orientation tuning, direction selectivity, and spatial frequency tuning, when high-contrast stimuli were used. However, contrast thresholds of most neurones were abnormally elevated, and the maximum response amplitude under optimal stimulus conditions was significantly reduced. The results suggest that the striate cortical neurones reactivated during topographic reorganization are
© 2002 Taylor & Francis
316
Hubert R. Dinse and Gerd Boehmer
capable of sending functionally meaningful signals to more central structures, provided that the visual scene contains relatively high contrast images (Chino et al., 1995). In animals that were allowed to recover from a complete monocular deactivation for up to several months, there was also rearrangement of the retinotopic maps. However, in this case, most neurones in the deprived peripheral representation remained unresponsive to visual stimuli even more than one year after treatment (Rosa et al., 1995). This is in marked contrast with the extensive reorganization that is observed in the central representation of V1 after restricted retinal lesions. The low potential for reorganization of the monocular sector of V1 demonstrates that the capacity for plasticity of mature sensory representations varies with location in cortex: even small pieces of cortex, such as the monocular crescent representations, may not reorganize completely if certain conditions are not met. These results suggest the existence of natural boundaries that may limit the process of reorganization of sensory representations. Taken together, all sensory areas display a well-documented capacity for profound reorganizations following peripheral lesions. However, there might exist differences in the magnitude of changes. Whether this reflects some modality-specific limitation of the visual cortex to reorganize after large retinal lesions requires further investigations. (For a discussion of perceptual consequences of lesion-induced reorganizations, and general aspects of maladaptive consequences see also section 2.5. below “Therapeutic consequences of cortical plasticity”). 2.2. Training- and Learning-Induced Use-Dependent Reorganization Perceptual skills improve with training (cf. Gibson, 1953). Accordingly, one of the key questions in cortical plasticity is how cortical changes are linked to parallel changes of perceptual and/or motor performance. This question requires the simultaneous assessment of both neurophysiological and behavioural changes. For example, Recanzone and coworkers showed that tactile frequency discrimination training in the adult owl monkeys over several months led to a significant reduction of frequency discrimination threshold (Recanzone et al., 1992a). When the cortical areas representing the skin area of the trained fingers were mapped, large-scale cortical reorganization became apparent, which included changes of receptive fields and of topography of cortical representational maps. Most notable, there was a significant correlation between the enlargement of cortical territory representing the skin surface stimulated during training and the improvement in performance, indicating a close relationship between cortical and perceptual changes (Recanzone et al., 1992b). In addition, sinusoidal stimulation of the trained skin elicited larger-amplitude responses, peak responses earlier in the stimulus cycle, and temporally sharper responses, than did stimulation applied to control skin sites. Analysis of cycle histograms for neuronal responses in area 3b revealed that the decreased variance of each stimulus cycle could account for behaviourally-measured frequency discrimination improvements (Recanzone et al., 1992c). With the somatosensory system as an example, these data demonstrated for the first time a direct relation between cortical plasticity and improvement of performance. A largely identical approach was taken for an analysis of training-induced changes in AI. Monkeys, trained for several weeks to discriminate small differences in the frequency of sequentially presented tonal stimuli, revealed a progressive improvement in performance with training. At the end of the training period, the tonotopic organization of Al was defined
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
317
electrophysiologically. The cortical representation, the sharpness of tuning, and the latency of the response were greater for the behaviourally-relevant frequencies of trained monkeys when compared to the same frequencies of control monkeys. Notably, the cortical area of representation was the only studied parameter that was correlated with behavioural performance. These results demonstrate that attended natural stimulation during a long-term training protocol can modify the tonotopic organization of Al in the adult primate, and that this alteration is correlated with changes in perceptual acuity (Recanzone et al., 1993). While there is a large body of information about perceptual learning in the visual domain in human subjects (see also section 2.7), little is known about parallel changes in visual cortex. In a study on perceptual learning in humans (discrimination of orientations), subjects showed a marked improvement over days, which was highly specific for position and orientation (Vogels and Orban, 1985; Schoups et al., 1995). However, the precise nature of the accompanying changes still remains unclear. As in the studies performed in SI and AI, one could expect that there would be a recruitment of cells toward the trained orientation. However, in contrast to the previous SI/AI studies, no comparable expansion in representation was found. Instead, the proportion of cells recorded in primary visual cortex (in monkeys trained to discriminate orientations) that preferred the orientation to which they had been trained, was not larger than the proportion of cells preferring any other orientation (Schoups, 2001). Parallel experiments using autoradiographic labelling of deoxyglucose as an indirect marker of neural activity (based on the close relation between oxygen consumption and firing activity) confirmed the electrophysiological data. No broadening of the orientation columns was observed as a consequence of the perceptual learning, and thus no recruitment occurred of cells responding to the trained orientation (Schoups, 2001). Moreover, outside the primary visual cortex no major and systematic changes have been found. Recordings in the inferotemporal cortex from rhesus monkeys trained to judge whether or not two successively presented gratings differed in orientation revealed no consistent effects either on the responsiveness or on the orientation tuning (Vogels and Orban, 1994). More recent findings on the firing behaviour of neurones in striate cortex, recorded from monkeys trained in an orientation discrimination task, provided evidence for rather complex changes. In the population of trained neurones, those that preferred the trained orientation exhibited a lower firing rate than the neurones preferring other orientations (Schoups et al., 2000). At first sight this result seems counterintuitive. However, models of perceptual learning involving orientation discrimination (Qian and Matthews, 1999) predicted that lower firing rates by the neurones that prefer the trained orientation could lead to selective changes in the tuning patterns of neurones that prefer the orientations bordering the one trained, which then would lead to a better performance in the discrimination task. These results raise some interesting possibilities related to the coding of plastic changes (cf. also Dinse and Merzenich, 2002; Schoups, 2001), which will be discussed later (see also section 2.6. “Coding of plastic changes”). The paradigm of “modified use” as a determinant of cortical organization has been applied in a large number of investigations, mostly performed in somatosensory cortex (cf. Dinse and Merzenich, 2002), with few studies in other modalities. In this approach, plastic changes are analyzed in a rather natural context, where the link between behaviour and cortical reorganization is often less quantifiable, but still intuitively obvious. For example, the implications of episodes of differential use, following nursing behaviour, occurring during the normal life-span of an animal, have been shown in a study of lactating rats. The SI representation of the ventral trunk skin was significantly larger than in matched postpartum
© 2002 Taylor & Francis
318
Hubert R. Dinse and Gerd Boehmer
non-lactating or virgin controls (Xerri et al., 1994). After training squirrel monkeys on a task involving retrieval of small objects, which required skilled use of the digits, their motor digit representations expanded, whereas their evoked-movement wrist/forearm representational zones contracted. In a second task, a monkey was trained in a key-turning task. In this case, the representation of the forearm expanded, whereas the digit representational zones contracted. Movement combinations that were used more frequently after training were selectively magnified (Nudo et al., 1996). Interestingly, repetitive motor activity alone appeared not to produce functional reorganization of cortical maps indicating that skill acquisition or motor learning is a prerequisite factor for induction (Plautz et al., 2000; but see section 2.3.2. “Coactivation” on this issue). In the auditory system, abnormal cochleotopic organization in the auditory cortex of cats reared in a frequency-augmented environment has been observed (Stanton and Harrison, 1996). For the visual system, Sugita (1996) reported that V1 neurones in monkeys can develop novel receptive fields to the ipsilateral hemifield after monkeys have worn reversing spectacles for several months. These studies suggest that visual cortical neurones can in fact acquire novel inputs, not only from neighbouring retinal areas, but also from distant nonadjacent areas. This report contradicts earlier findings, according to which the visual field representation in the striate cortex is rigidly prewired with reference to the anatomical fovea (cf. Pöppel et al., 1987). It is well established that perceived orientation can be influenced by previous adaptation to a tilted stimulus (tilt aftereffect), an illusion that decays rapidly over time. Following short-term adaptation to one stimulus orientation, systematic “rebound” shifts in orientation preference were observed, that included changes in orientation tuning away from the adapting stimulus indicating the involvement of widespread network interactions that mediate these effects (Dragoi et al., 2000). The recent development of non-invasive imaging techniques has made it possible to study the impact of modified use and practice in humans. Imaging studies performed over the last few years provided overwhelming evidence that extensive use and practice result in substantial changes of associated cortical representations. For example, in the somatosensory cortex of blind Braille readers (Pascual-Leone and Torres, 1993; Sterr et al., 1998a,b) and of string players (Elbert et al., 1995), selective enlargement was found for those cortical territories representing the digits engaged in more extensive use, as exemplified by the reading fingers (Braille readers) or the fingering digits (string player). In adults who were studied before and after surgical separation of webbed fingers, a cortical reorganization of the finger representation over several millimeters was observed (Mogilner et al., 1993), a finding reminiscent to what had been reported some years ago for artificial induction of syndactyly in monkeys (Clark et al., 1988). Subjects engaged in long-term perceptual training in tactile discrimination revealed changes in responsivensss of the somatosensory cortex (Spengler et al., 1997). When subjects received passive tactile stimulation of thumb and little finger over a period of 4 weeks, the representations of the fingers in primary somatosensory cortex were closer together after training. However, when subjects had to discriminate stimuli, MEG imaging revealed that the digital representations were further apart than before. Thus, the same prolonged repetitive stimulation produced two opposite effects, suggesting that activation in the same region of cortex is specific to different tasks (Braun et al., 2000). In order to demonstrate the perceptual relevance of the neural changes induced by a tactile coactivation protocol (see also section “2.3.2. Coactivation”), spatial discrimination performance was investigated in human subjects who underwent a similar passive coactivation,
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
319
as described in the animal study by Godde et al. (1996). A small skin area on the index finger was coactivated. Discrimination thresholds were used as a marker of reorganizational effects on human perception. It was found that two hours of coactivation were sufficient to drive a significant improvement of the spatial discrimination performance (Godde et al., 2000), demonstrating the potential role of pure input statistics for the induction of cortical plasticity without involving cognitive factors such as attention or reinforcement. A combined assessment of discrimination thresholds and recording of somatosensory evoked potentials in human subjects revealed that the individual gain of discrimination performance was correlated with the amount of cortical reorganization, as inferred from the shifts of the location of the “N20” dipole (Pleger et al., 2001). For the human motor system, similar fast adaptational regulations have been reported: using mapping of responses to transcranial magnetic stimulation (TMS), in human subjects who had unilateral immobilization of the ankle joint (i.e. they had to wear a cast for a couple of weeks), the area of motor cortex representing the tibialis anterior muscle were significantly reduced compared to the representation of the unaffected leg. The amount of areal reduction was correlated with the duration of immobilization, an effect rapidly reversed by voluntary muscle contractions (Liepert et al., 1995). An hour of synchronous movements of the thumb and foot resulted in a reduction of the distance of the centre of gravity of their respective output maps in the primary motor cortex, whereas asynchronous movements evoked no significant changes, indicating that similar principles of coactivation hold for both the sensory and motor system (Liepert et al., 1999). In highly skilled musicians, functional magnetic source imaging revealed an enlargement of dipole moments for piano tones, but not for pure tones of similar fundamental frequency, which was correlated with the age at which musicians began to practice (Pantev et al., 1998). In addition, musicians with absolute pitch were characterized by distinct neural activities in the auditory cortex (Hirata et al., 1999). Similarly, auditory cortical representations for tones of different timbre (violin and trumpet) were enhanced compared to sine tones in violinists and trumpeters, preferentially for timbres of the instrument of training (Pantev et al., 2001). Reminiscent of the reorganizations after frequency-discrimination training in monkeys (Recanzone et al., 1993), human subjects have been reported to show plastic reorganization in the auditory cortex induced by frequency-discrimination training over several weeks. Changes consisted of an increase of amplitude of the slow auditory evoked (wave “N1m”) and mismatch field (Menning et al., 2000). The human visual system is able to determine very precisely the relative positions of objects in space. Using an artificial scotoma, by occluding part of the visual field, while a pattern was shown over a surrounding region, resulted in severe mislocalization. This was due to a strong bias toward the interior of the scotoma, indicating a significant short-term cortical plasticity in adult human vision (Kapadia et al., 1994). In a psychophysical and functional imaging study of adaptation to inverting spectacles, subjects showed rapid adaptation of visuomotor functions within several days, but did not report return of upright vision. This was corroborated by the functional magnetic resonance images (fMRI) that failed to show alteration of the retinotopy of early visual cortical areas (Linden et al., 1999), a finding in contrast to recent animal data demonstrating indeed a functional reversal in area 17 after months of training (Sugita, 1996). Transcranial magnetic stimulation (TMS) of the occipital cortex evokes the perception of phosphenes. In human subjects, a reduced phosphene threshold was detected 45 min after a short period of light deprivation, and this persisted for the whole deprivation period of 3 h. Similarly, fMRI showed increased visual
© 2002 Taylor & Francis
320
Hubert R. Dinse and Gerd Boehmer
cortex activation after 60 min of light deprivation that persisted following 30 min of re-exposure to light, demonstrating a substantial increase in visual cortex excitability (Boroojerdi et al., 2000). It was speculated that such changes may underlie behavioural gains such as a lowered visual recognition thresholds reported in humans associated with light deprivation (Suedfeld, 1975). The tilt aftereffect leads to wide-spread changes of visual cortex orientation maps (Dragoi et al., 2000). When alphanumeric characters were presented to human subjects with a clockwise tilt, they were perceived as less tilted than the same stimulus horizontally inverted. In contrast, subjective perception of tilt magnitude for horizontally inverted nonalphanumeric stimuli was similar to that for non-inverted stimuli reflecting a persistent sensory recalibration of orientation perception as a result of previous long-term visual experience (Whitaker and McGraw, 2000). Taken together, these studies suggest that even small alterations in behaviour due to special demands imposed in everyday life alter early cortical representations rapidly and reversibly. The human studies summarized confirm the close relation between intensified or altered use (on the one hand) and enlargement of associated cortical representational maps (on the other hand), supporting the relevance of the concept of cortical plasticity for everyday life. From a comparative point of view, it appears fair to state that while there is a large body of information about use- and experience-related plastic changes in somatosensory and auditory cortex, comparatively little is known about visual cortex. 2.3. The Role of Input Statistics Human studies of the type summarized are very helpful in revealing signatures of cortical plasticity under everyday life conditions. However, these studies are not designed to control precisely for input pattern. Accordingly, it remains unclear what are the “driving factors” leading to reorganization. In the case of the blind Braille readers, potential candidates are: the frequency of finger usage, the spatial pattern of the Braille signs, the spatio-temporal pattern arising when the finger is moved across the Braille signs, the level of attention, and the duration of practice. In addition, many lines of evidence have shown that cortical systems adapt to input patterns characterized by different probabilities, implying that variations of input statistics alone are sufficient to induce reorganization of cortical maps, i.e. without involving cognitive processes such as those present in training protocols. Therefore, animal studies are required that complement and extend human studies by a systematic variation of input pattern. 2.3.1. Intracortical microstimulation Intracortical microstimulation (ICMS) is used to evoke selective motor responses by applying current through microelectrodes inserted into defined regions of motor representations. More recently, this technique has been utilized to study short-term and reversible plastic changes in various cortical regions, including motor (Nudo et al., 1990; Gu and Fortier, 1996; Kimura et al., 1996), somatosensory (Dinse et al., 1990; Recanzone et al., 1992d; Dinse et al., 1993; Spengler and Dinse, 1994; Joublin et al., 1996; Xing and Gerstein, 1996; Dinse et al., 1997a; Heusler et al., 2000), auditory (Sil’kis and Rapoport, 1995; Maldonado and Gerstein, 1996a,b; Maldonado et al., 1998; Sakai and Suga, 2001) and visual (Leonhardt et al., 1997; Godde et al., 2002) cortices, as well as thalamic relay nuclei of the somatosensory system (Dinse et al., 1997a).
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
321
A specific advantage of ICMS is that it allows one to investigate locally the properties of functional plasticity, independent of the peripheral and subcortical pathways and independent of the constraints provided by particularities of a sensory pathway and its preprocessing. In a typical ICMS experiment, repetitive electrical pulse trains of very low currents (usually less than 10 µA) are delivered via a microelectrode. Based on theoretical calculations, ICMS of that intensity activates a cortical volume of only 50 microns in diameter (Stoney et al., 1968). The resulting synchronized discharges are assumed to be crucial for mediating plastic changes. The short time scale and reversibility of ICMS-induced effects support the hypothesis that modulations of synaptic efficiency in neuronal networks can occur very rapidly without necessarily involving anatomical changes. Consequently, ICMS is an ideal method for studying possible modality- and area-specific constraints of cortical plasticity. In the rat motor cortex, significant changes in representations of movement were observed after a few hours of ICMS, these being fully reversible. Changes were characterized by border shifts up to more than 500 microns (Nudo et al., 1990). Application of ICMS in the hindpaw representation of the adult rat somatosensory cortex caused an overall but selective expansion of receptive field size up to 1 mm around the ICMS site (Recanzone et al., 1992d; Dinse et al., 1993; Spengler and Dinse, 1994). Receptive fields close to the stimulation site were enlarged, and comprised large skin territories, always including the receptive field at the ICMS-site, revealing a distance-dependent, directed enlargement towards the ICMS-receptive field. Early ICMS-related reorganization could already be detected after 15 min of ICMS, and much greater effects emerged after 2 to 3 hours, which were reversible within 6 to 8 hours after termination of ICMS (Dinse et al., 1993; Spengler and Dinse, 1994). In the auditory cortex, ICMS induced fast changes in the tonotopic map, and in the receptive field properties of cells at the electrically stimulated and adjacent electrodes. There was an enlargement of the cortical domain tuned to the acoustic frequency that had been represented at the stimulating electrode (Maldonado and Gerstein, 1996a,b; Maldonado et al., 1998). Comparison of reorganization evoked by focal electric stimulation (ICMS) in AI of an ecologically highly specialized animal (the mustached bat—Pteronotus parnellii) and a non-specialized one (the Mongolian gerbil—Meriones unguiculatus) revealed differences in the ICMS-induced shifts of best frequency, implying differences between specialized and nonspecialized (ordinary) areas of the auditory cortex (Sakai and Suga, 2001). ICMS-induced reorganization in somatosensory and visual cortex of pigmented rats was compared in individual animals (Leonhardt et al., 1997, 1998). In visual cortex, ICMS led to small (~20%), but significant expansions of receptive fields for a subpopulation of neurones with small receptive fields pre-ICMS. Neurones characterized by initially large RFs did not change. In contrast, RFs recorded in SI in the same animal, exhibited the well-known several-fold enlargement. In addition, in visual cortex, the time-structure of the neuronal responses was systematically altered, by suppressing late response components, leading to profound changes in the temporal structure of the receptive field dynamics. Comparable changes have not been observed in SI. As a further difference, in visual cortex, the observed changes were not reversible within the observation period of 3–6 h after ICMS. To further investigate the plasticity of functional maps in the visual cortex, orientationpreference maps were recorded by means of optical imaging. A few hours of ICMS induced major changes of orientation-preference maps in adult cats (Godde et al., 1999, 2002). These results showed that orientation-preference maps undergo substantial expansions
© 2002 Taylor & Francis
322
Hubert R. Dinse and Gerd Boehmer
reminiscent of non-visual cortical maps. However, changes were much more wide-spread and enduring, indicating that the large-scale changes of the functional architecture resulted from a restructuring of the entire underlying cortical network. Parallel electrophysiological single cell recordings revealed distinct shifts of the individual orientation tuning towards the preferred orientation present at the ICMS site. Again, no changes of receptive field sizes were found. Taken together, these results from ICMS experiments imply that sensory cortex, including visual cortex, is modifiable in adults, both in terms of functional maps and in terms of single cell properties. However, there appear to exist a number of differences, best documented for somatosensory and visual cortex, concerning reversibility, spatial range of changes, and neural response parameters most susceptible to modifications. 2.3.2. Coactivation A number of protocols have been introduced in which neural activity, necessary to drive plastic changes, was generated by an associative pairing protocol. In the pioneering studies by Fregnac and coworkers (Fregnac et al., 1988, 1992), persistent functional changes in response properties of single neurones of cat visual cortex were induced by a differential pairing procedure, during which iontophoresis was used to increase artificially the visual response for a given stimulus, and to decrease the response for a second stimulus. Neuronal selectivity was nearly always displaced towards the stimulus paired with the reinforced visual response, thereby leading to long-term modifications of orientation selectivity in about one third of the neurones tested. The largest changes were obtained at the peak of the critical period in normally reared and visually deprived kittens, but changes were also observed in adults. From a conceptual point of view, these findings supported the role of temporal correlation between pre- and postsynaptic activity in the induction of long-lasting modifications of synaptic transmission in associative learning during development and in adults. A similar modifiability of response properties of visual cortex neurones in adult cats has been observed after a conditioning protocol, where the presentation of particular visual stimuli was repeatedly paired with the iontophoretic application of either GABA or glutamate to control postsynaptic firing rates (McLean and Palmer, 1998). The modification in orientation tuning was not accompanied by a shift in preferred orientation, but rather, responsiveness to stimuli at or near the positively-reinforced orientation was increased relative to controls, and responsiveness to stimuli at or near the negatively-reinforced orientation was decreased. These studies are in contrast to a previous study, where lasting (maximal 1 h) modifications of the receptive fields of neurones in the visual cortex were observed by pairing visual stimuli with iontophoretic application of the neuromodulators acetylcholine and noradrenaline or the excitatory amino acids N-methyl-D-aspartate (NMDA) and L-glutamate in kitten, but not in adult animals (Greuel et al., 1988). While these experiments emphasize the potential capacities of adult visual cortex for change of its response properties, it remains an interesting question, why comparable experiments (drug-pairing) have been performed in the other modalities very rarely (Maalouf et al., 1998; Shulz et al., 2000). Possibly, induction of plastic changes in adult visual cortex might be more “difficult” to drive than in other areas using more natural types of stimulation, without direct drug application (see also below). However, independent of this speculation, the present findings from drug-pairing experiments point to a rather common form of modifiability across cortical areas.
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
323
In contrast to the cellular pairing protocols, a number of studies utilized a pairing of adequate (i.e. sensory) stimuli, for the somatosensory cortex (Diamond et al., 1993; Wang et al., 1995; Godde et al., 1996, 2000) and visual cortex (Eysel et al., 1998). In the study by Diamond et al. (1993) sensory experience was altered by a few days of “whisker pairing”: whiskers D2 and either D1 or D3 were left intact, while all other whiskers were trimmed. During whisker pairing, the receptive fields of cells in barrel D2 changed in distinct ways: the response to the centre receptive field increased, the response to the paired surround receptive field nearly doubled, and the response to all clipped, unpaired surround receptive fields decreased. These findings indicate that a brief change in the pattern of sensory activity induced by pairing of tactile stimuli can alter the configuration of cortical receptive fields of adult animals. To test the hypothesis that consistently non-coincident inputs may be actively segregated from one another in their distributed cortical representations, monkeys were trained to respond to specific stimulus sequence events (Wang et al., 1995). Animals received temporally-coincident inputs across fingertips and fingerbases, but distal vs proximal digit segments were non-coincidentally stimulated. Electrophysiological recordings in somatosensory cortex (area 3b) showed that synchronously applied stimuli resulted in integration of inputs in the cortical maps, whereas stimuli applied asynchronously were segregated by two band-like zones, in which all neurones had multiple digit receptive fields representing the stimulated skin surfaces. Interestingly, maps derived in the ventroposterior portion of the thalamus were not reorganized in an equivalent way, suggesting that this particular type of representational plasticity appears to be cortical in origin. In the study by Godde et al. (1996), receptive fields on the hindpaw of adult rats were used for coactivation. These authors reported reversible reorganization consisting of a selective enlargement of the cortical territory, and of the receptive fields representing the co-stimulated skin fields. In addition, a large representation emerged that included a joint representation of both skin sites. A control protocol applied to only a single skin site evoked no changes indicating that coactivation was essential for induction (see also “2.2. Training and learning induced reorganization”). It has been stressed that passive stimulation, or repetitive motor activity alone appeared not to produce comparable functional reorganization of cortical maps (Recanzone et al., 1992b; Plautz et al., 2000). On the other hand, the coactivation studies reported here showed a clear effect on cortical as well as on perceptual levels, in spite of the fact that attention was not involved. One explanation is that during the coactivation protocol, which was on average applied at a rate of 1 Hz for several hours, selected skin regions were stimulated 10 000 times or more. This is a much stronger stimulation in terms of stimulus number per time than the monkeys received during the passive discrimination training. Conceivably, the intensity of the stimulation/movement protocol might be the crucial factor responsible for its effectiveness. For the visual system, early studies claimed that retinal stimulation alone does not induce plastic changes (Buisseret et al., 1978). However, a recent coactivation study, performed in mature visual cortex, revealed the capacity for significant changes of receptive field organization: single cortical cells expanded their receptive fields, within minutes, into previously-unresponsive regions, and changed their functional receptive field structure for hours after associative co-stimulation of active and primarily-unresponsive regions (Eysel et al., 1998). While this study corroborates the sensitivity of visual cortex to a coactivation protocol consisting of natural sensory stimuli, the magnitude of changes are clearly below
© 2002 Taylor & Francis
324
Hubert R. Dinse and Gerd Boehmer
those reported for SI. However, given the many differences in methodological details, more experiments are required to provide a final answer about possible underlying modalityspecific differences. 2.3.3. Classical conditioning Another type of associative learning, as exemplified by classical conditioning, has been studied for decades, in several variations, in the auditory cortex (for review see Weinberger et al., 1990; Weinberger, 1995). Using a classical conditioning protocol, a tone of a given frequency (as the CS+) was paired with an aversive electrical shock. Tuning curves recorded in the auditory cortex before and after conditioning revealed a shift in the best frequencies in the direction of the frequency of the CS+; these shifts lasted up to a few weeks and could be reversed by extinction training (Diamond and Weinberger, 1986: Bakin and Weinberger, 1990; Edeline and Weinberger, 1993; Ohl and Scheich, 1996). Most conspicuously, the approach of classical conditioning to studying aspects of cortical plasticity appears to be entirely restricted to studies of the auditory system, ruling out any comparative analysis, although the restriction to the auditory system might, in itself, hint at some particular modality-specific constraints that might be worth exploring in more detail. Anecdotally, classical conditioning was discovered by Durup and Fessard in the visual cortex of humans many decades ago (cited by Weinberger et al., 1990). However, this was conditioning of the alpha rhythm, and may have involved conditioning of subcortical control mechanisms, rather than at the cortical level. Such conditioning, is likely to be important, but is not the focus of the present chapter. 2.4. Pharmacological Modulation of Adult Plasticity There are many sources modulating cortical responsiveness and plasticity. The major source of cholinergic inputs that have long been implicated in learning and memory comes from several groups of neurones within the basal forebrain, which receives inputs from limbic and paralimbic structures. These inputs have been assumed to represent one example of a top-down system providing modulatory information of higher-order—presumably cognitive—processes. For example, in animal experiments, pairing of sensory stimulation with electrical stimulation of the nucleus basalis has been shown to result in rapid and selective reorganization (in the somatosensory cortex by Rasmusson and Dykes [1988], in the auditory cortex by Edeline et al. [1994], Bakin and Weinberger [1996], Bjordahl et al. [1998] and by Kilgard and Merzenich [1998a]). On the other hand, lesions of the cholinergic system have been shown to prevent plastic reorganization in the somatosensory cortex (Baskerville et al., 1997; Sachdev et al., 1998). However, using a whisker pairing protocol, in which all but a few whiskers were trimmed, the animal’s active use of its remaining intact whiskers can restore some aspects of plasticity in an acetylcholine-depleted cortex (Sachdev et al., 2000). Direct administration of acetylcholine (ACh) to cortical neurones facilitates or suppresses responses to sensory stimuli, and these effects can endure well beyond the period of ACh application. In primary auditory cortex, analysis of single neurone frequency receptive fields, before and after such pairing of acoustic stimulation with ACh application revealed that in half of the cases, the receptive field alterations were highly specific to the frequency of the tone previously paired with ACh (Metherate and Weinberger, 1990). The involvement of neuromodulatory effects in cortical Hebbian-like
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
325
plasticity of acetylcholine (ACh) and noradrenaline (NE) was related to the timing of drug applications, relative to the conditioning time, their local concentrations and/or the site of application with respect to the relevant synapses (Ahissar et al., 1996). Ocular dominance plasticity is strongly expressed in early postnatal life and is usually assumed to be absent in the mature visual cortex. Local perfusion of kitten visual cortex with 6-hydroxydopamine (6-OHDA) prevented the effects of monocular deprivation in kittens, while locally perfused norepinephrine restored visual cortical ocular dominance plasticity (Kasamatsu et al., 1979). The effect of norepinephrine perfusion was seen both in kittens and, though to a lesser degree, in older animals which had outgrown the susceptible period. More recently, it was demonstrated that activation of cAMP-dependent protein kinase A could restore ocular dominance plasticity in visual cortex of adult cats (Imamura et al., 1999). These findings indicate that various forms of visual cortex organization can be affected, and persistently modified in adults. More generally, the former data are in line with the well-documented catecholaminergic and cholinergic modulation of post-ontogenetic cortical plasticity well established for auditory and somatosensory cortex. 2.5. Therapeutic Consequences of Cortical Plasticity The final outcome of reorganizational processes must not necessarily be beneficial. There is increasing evidence that abnormal perceptual experiences, such as the phantom limb sensation, arise from reorganizational changes induced by the amputation of the limb (Flor et al., 1995, 1998). In amputees, a number of perceptual correlates of cortical reorganizations have been described, such as a precise topographic mapping of the phantom onto the face area, these being explained on the basis of the topography of the border of the face-hand maps (Ramachandran et al., 1992; Halligan et al., 1993; Aglioti et al., 1997). In patients with chronic pain, the power of the early evoked magnetic field, elicited by painful stimulation, was elevated relative to that elicited by the same stimulation in healthy controls. Furthermore, this enlargement was a function of the chronicity of pain (Flor et al., 1997). Repetitive strain injuries, such as focal dystonia, have a high prevalence in workers who perform heavy schedules of rapid alternating movements, or repetitive, sustained, coordinated movements. It has been hypothesized that use-dependent plastic changes, as reviewed in this chapter, may cause these injuries (Byl et al., 1996, 1997). Monkeys trained in repetitive hand closing and opening developed typical signs of movement control disorders. Electrophysiological recordings within the primary somatosensory cortex revealed a de-differentiation of cortical representations of the skin of the trained hand, manifested by receptive fields that were 10 to 20 times larger than normal (Byl et al., 1996). A recent study using MEG in musicians suffering from focal hand dystonia revealed a smaller distance between the representations of the affected digits in somatosensory cortex, compared to the same digits in non-musician controls (Elbert et al., 1998) indicating similar neural changes in humans as a consequence of repetitive strain injuries. The negative outcome of neuroplasticity may also play a major role in some forms of age-related changes. It has been suggested that reorganizational processes lead to maladaptive changes, as a result of walking impairments, developed in rats of high age as a secondary response to muscle atrophy and other factors promoting limited agility (Spengler et al., 1995; Jürgens and Dinse, 1997a; Dinse and Merzenich, 2002; Dinse, 2001). In the auditory domain, some forms of dysfunctions in normal phonological processing, which are critical to the development of oral and written language, have been speculated
© 2002 Taylor & Francis
326
Hubert R. Dinse and Gerd Boehmer
to derive from initial difficulties in perceiving and producing basic sensory-motor information in rapid succession, emphasizing the crucial role of temporal parameters. In fact, when children with a particular type of language-based learning deficit were engaged in adaptive training of their temporal processing skills, they showed a marked improvement in their abilities to recognize brief and fast sequences of non-speech and speech stimuli. This suggests that the reorganizational changes are specifically sensitive to temporal parameters of the input (Tallal et al., 1993, 1996; Merzenich et al., 1996). People with amputations often have the feeling that the amputated limb is still present (phantom limb sensation). Subjective tinnitus, the hearing of a disturbing tone or noise in the absence of a real sound source, shares many similarities with the sensation of phantom limb, experienced by many amputees. Therefore, tinnitus has been thought of as an auditory phantom phenomenon (Jastreboff, 1990; Lockwood et al., 1998; Rauschecker, 1999). A marked shift of the cortical representation of the tinnitus frequency into an area adjacent to the expected tonotopic location was observed in subjects suffering from tinnitus. Importantly, a strong positive correlation was found between the subjective strength of the tinnitus and the amount of cortical reorganization (Mühlnickel et al., 1998), indicating that tinnitus is related to plastic alterations in auditory cortex. Studies using 2-deoxyglucose autoradiography in gerbils treated with salicylate (known to generate tinnitus) demonstrated increased activation in areas of the auditory cortex (Wallhäusser-Franke et al., 1996). Cochlear implants (CI) are a frequent measure to provide sound perception in patients with sensorineural hearing loss. Utilizing the critical period for speech acquisition, clinical data suggest that children implanted before 2 years of age have an excellent chance of acquiring speech understanding. For implanted children, maturational delays for corticallyevoked potentials, that approximated the period of auditory deprivation prior to implantation, were reported (Ponton et al., 1996). In cats implanted with multichannel intracochlear electrodes, long-term electrical CI stimulation was found to induce substantial reorganization of cortical auditory maps, consisting of a selective enlargement of that territory representing the frequency representations stimulated during chronic CI (Dinse et al., 1997b). It was suggested that the outcome of these reorganizations was due to Hebbian mechanisms, utilizing the simultaneity induced by the CI stimulation strategy (continuous interleaved sampler) in which at high stimulation rates all frequency channels are stimulated virtually simultaneously (viz within a single millisecond). Notably, the CIS strategy has proven highly effective in human patients in providing a high level of open speech understanding (Wilson et al., 1991). Similar reorganizational changes were observed in cats deafened and chronically CI-stimulated as adults (Dinse et al., 1997b, 1998; Godde et al., 1998). Major differences in cortical response distributions on the ectosylvian gyrus of adult cats due to deafening were also observed in long-term deafened animals. The authors speculated that these changes may reflect electrode-specific effects or reorganizational changes, as a consequence of the altered inputs (Raggio and Schreiner, 1999). Recent imaging data obtained using positron emission tomography (PET), in prelingually deaf patients before and after cochlear implantation support the relevance of these animal findings for human patients (Lee et al., 2000). After cochlear implantation, these authors found a positive correlation between the size of the hypometabolic area and a hearing-capability score. Accordingly, several lines of evidence indicate that the underlying plastic adaptational properties of cortical auditory neurones might provide the substrate involved in mediating the highly variable improvement of open speech understanding with practice, often observed in patients with hearing aids.
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
327
That adult visual cortex is indeed also capable of long-term changes, induced by simple training procedures, comes from recent studies in which partially blind subjects obtained some restitution of their visual field (Kasten et al., 1998). In visual restitution training, visual stimuli were presented on a computer screen in such a manner that the majority of stimuli appeared in the transition zone, which is usually located in the border region between the intact and damaged visual-field, as well as near the border of the transition zone and the defective field. When post-chiasma patients were trained for 6 months, (1 h per day), subjects showed a 30% improvement in the ability to detect visual stimuli. In optic nerve patients, the effects were even more pronounced. While in the past, partial blindness after brain injury has been considered non-treatable, these data are in line with a profound capacity of the visual cortex to reorganize, even in adults. Of particular interest are findings on cross-modal plasticity in blind subjects, this contributing to sensory compensation when vision is lost early in life (Cohen et al., 1997; Weeks et al., 2000, for a general account of cross-modal plasticity; see also Pallas, this volume). To identify differences in cross-modal reorganization, depending on the time of onset of blindness, and thereby distinguishing effects due to ontogenetic or post-ontogenetic plastic processes, blind subjects were studied by means of positron emission tomography, to identify cerebral regions activated in association with Braille reading. In the congenitally blind and early-onset blind groups, the occipital cortex was strongly activated, but this did not occur in the late-onset blind group. These results indicate that the susceptible period for this form of functionally relevant cross-modal plasticity does not extend beyond 14 years (Cohen et al., 1999). To determine whether the visual cortex receives input from the somatosensory system during a Braille reading task, positron emission tomography (PET) was used to measure activation in Braille readers blinded in early life. Blind subjects showed activation of primary and secondary visual cortical areas during tactile tasks, whereas normal controls showed deactivation. Importantly, a simple tactile stimulus that did not require discrimination produced no activation of visual areas (Sadato et al., 1996). Comparing behavioural and electrophysiological markers of spatial tuning within central and peripheral auditory space in congenitally blind and normally sighted but blindfolded adults, the hypothesis was tested that the effects of visual deprivation might be more pronounced for processing peripheral sounds. In fact, blind participants displayed localization abilities that were superior to those of sighted controls, but only when attending to sounds in peripheral auditory space. Electrophysiological recordings obtained at the same time revealed sharper tuning of early spatial attention mechanisms in the blind subjects. Differences in the scalp distribution of brain electrical activity between the two groups suggest compensatory reorganization in the blind, which may contribute to the improved spatial resolution for peripheral sound sources (Röder et al., 1999). Taken together, the maladaptive consequences of cortical plasticity are more and more acknowledged as a major factor in various forms of dysfunctions, an assumption apparently valid across modalities. 2.6. Coding of Plastic Changes How are plastic changes coded? What neural response parameters are affected by the various forms of manipulations leading to reorganizations, and the parallel changes of perception and behavior? The studies discussed so far have in common that they almost exclusively describe reorganizational changes in terms of receptive field size and in size of cortical
© 2002 Taylor & Francis
328
Hubert R. Dinse and Gerd Boehmer
representational territory. Particularly, the new imaging techniques such as fMRI allow one to study adaptational changes in humans, describing neural representations in terms of activation size of cortical maps. At least one simple rule-of-thumb appears to hold: extensive use leads to enlarged cortical territories, while limited use or no-use results in a reduction of cortical representational size, indicating a form of proportionality between representational area and use. Representational size correlates with the number of neurones activated by a given task or stimulation. This view implies that enhanced performance is at least partially achieved by recruitment of processing resources. However, a recent animal study suggested that exceptions might exist (Polley et al., 1999b): allowing an animal to use its deprived receptor organ in active exploration appeared to determine the direction of plastic changes in the adult cortex. Further studies are needed to explore whether a similar potential for a use-dependent direction of reorganizational changes holds true in normal, nondeprived animals. From a more general point of view, this study suggests that the outcome of plastic processes might depend on far more subtle constraints imposed by the individual task than previously thought. In fact, as discussed for the visual cortex, a simple recruitment after perceptual learning of orientation discrimination could not be demonstrated (see also section 2.2. “Training and learning induced reorganization”). More recently, temporal aspects of processing, i.e. aspects of coding in the time domain, have been recognized as an additional and highly significant candidate code. As a consequence, aspects of synchronicity and correlated activity have been intensively studied, revealing that cooperativity among many neurones is indeed subject to profound modification during plastic reorganization. (This has been shown in the somatosensory cortex by Dinse et al. [1990, 1993]; Faggin et al. [1997] and by Ghazanfar et al. [2000], in the auditory cortex by Ahissar et al. [1992, 1998] and Maldonado et al. [1996a,b] and in the motor cortex by Laubach et al. [2000]). These findings imply that changes in temporal coding are crucial for our understanding of use-dependent plasticity. Accordingly, a critical step for the investigation of how distributed cell assemblies process behaviourally-relevant information is therefore the introduction of methods for data analysis that can identify functional neuronal interactions within highdimensional data sets (cf. Nicolelis, 1999). Laubach et al. (2000) applied such methods by chronically recording from neuronal ensembles located in the rat motor cortex. Based on such an elaborate approach they could demonstrate that motor learning was correlated with an increase in the experimenter’s ability to predict a correct or incorrect single trial, based on measures of neuronal ensemble activity such as firing rate, temporal patterns of firing, and correlated firing. On the other hand, temporal processing, i.e. the computation of sequential events, which is particularly important in the auditory system, is still poorly understood. Under natural conditions, stimuli never appear in isolation. Therefore, timing and sequencing impose severe temporal constraints that modulate neurone responses (Zucker, 1989; Chance et al., 1998). There is, in fact, clear experimental evidence that repetitively applied stimuli alter the cortical response behaviour, as compared to a single stimulus (Gardner and Costanzo 1980; Lee and Whitsel, 1992; Dinse, 1994; Merzenich et al., 1993; Tommerdahl et al., 1998; Polley et al., 1999a). So far, only a few studies have explored how far temporal processing is affected and altered by plastic reorganizations. For example, as described above, Recanzone et al. (1992c) demonstrated that behavioural training of a frequencydiscrimination task affected entrainment of repetitive stimuli in the somatosensory cortex. To test whether experience can modify the maximum rate of following in adult rats, trains
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
329
of brief tones with random carrier frequency, but fixed repetition rate, were paired with electrical stimulation of the nucleus basalis. This was continued 300–400 times per day for 20–25 days. Pairing nucleus basalis stimulation with 5-p.p.s. stimuli markedly decreased the cortical response to rapidly presented stimuli, whereas pairing with 15-p.p.s. stimuli significantly increased the maximum cortical following rate, indicating an extensive cortical remodeling of temporal response properties (Kilgard and Merzenich, 1998b). In the studies on age-related changes of the hindpaw representation in old rats, the neural response behaviour following repetitive stimulation was studied with trains of tactile stimuli of variable interstimulus intervals (ISIs). Dramatic impairment of repetition coding and input sequence representations were observed in old rats as compared to young controls (Jürgens and Dinse, 1995), and comparable changes of the neural input sequence representation were found in rats with artificially induced walking alterations (Jürgens and Dinse, 1997b). As discussed in the next main section of this chapter, there is an extensive literature about changes of temporal aspects in in-vitro studies. Taken together, neural changes as a consequence of adaptational mechanisms include a large number of both spatial and temporal parameters of sensory processing. However, even under normal conditions, i.e. without involving adaptive processes, we have a poor understanding of both sensory processing and how performance is coded. That is why it is not clear what is meant by receptive field size. Is it “good” when a tuning curve gets sharper? “Good” for what? As exemplified by the study of Schoups et al. (2000), perceptual learning can be accompanied by rather unexpected changes: those neurones that preferred the trained orientation exhibited a lower firing rate than the neurones preferring other orientations. Conceivably, an apparent lack of plastic capacities might simply reflect hidden changes in parameter regimens presently not recognized or understood. 2.7. Is There an Area-specificity of Particular Cortical Visual Cortex Plasticity? Though not representative, inspection of the quantity of publications as provided by Medline offers some insight into areas regarded important and accessible for scientific explorations. Comparing papers published in the field of somatosensory and visual plasticity revealed a number of interesting differences. When normalized to the absolute number of published papers in both fields, the same percentage (about 5%) were devoted to the exploration of “plasticity”. Searching among these papers for “developmental plasticity” revealed 18% for visual, but only 5%” for somatosensory cortex, a discrepancy already noted by Weinberger (1995). “LTP” plus “NMDA” were found in 17% of visual cortex papers, but only in 6% dealing with somatosensory cortex. On the other hand, “adult reorganization” showed up in 4% of visual, but in 24% of somatosensory cortex studies. Similarly, “adult plasticity of cortical maps and receptive fields” was found in 8% of visual, but in 24% of somatosensory cortex papers. Finally, “perceptual learning” revealed 0.5% in the visual, but only 0.1% in the tactile modality. Combined, even if treated with ample caution, these data imply the existence of some particular differences between these areas concerning plastic changes. These particularities are reflected in the discrepancy that, as shown above, training and learning induce powerful cortical reorganizations, but most of what we know about cortical plasticity in adults comes from experiments in somatosensory or auditory cortex. However, as summarized in the next section, in-vitro studies using slice preparations demonstrate very convincingly the existence of the whole spectrum of cellular mechanisms
© 2002 Taylor & Francis
330
Hubert R. Dinse and Gerd Boehmer
in adult visual cortex crucial for mediating synaptic plasticity. Moreover, recent findings on perceptual learning in the visual and other domains imply the modifiability of adult visual cortex. Although cortical maps are widely believed to emerge in the developing brain by activitydependent mechanisms, the apparent stability of the basic layout of orientation preference maps of visual cortex has raised the suspicion that orientation-preference maps may be governed not only by activity-dependent processes, but may even be pre-specified intrinsically (Blakemore, 1977; Sengpiel et al., 1998; Miller et al., 1999). In the visual cortex, the precise match of orientation is a prerequisite for stereoscopic vision. Whether visual experience is responsible for the match was tested in a reverse-suturing experiment, in which kittens were raised so that both eyes were never able to see at the same time. A comparison of the layout of the two maps formed under these conditions showed them to be virtually identical. Considering that the two eyes never had common visual experience, this indicates that correlated visual input is not required for the alignment of orientation preference maps (Gödecke and Bonhoeffer, 1996). It was therefore suggested that the geometry of functional maps in the visual cortex might be intrinsically determined, while the relative strength of representation of different response properties can be modified through visual experience (Sengpiel et al., 1998). On the other hand, kittens reared in a striped environment responded to all orientations, but devoted up to twice as much cortical area to the experienced orientation as to the orthogonal one. This effect has been attributed to the instructive role of visual experience whereby some neurones shift their orientation preferences toward the experienced orientation. Thus, although cortical orientation maps are remarkably rigid, in the sense that orientations that have never been seen by the animal are still represented and occupy a large portion of the cortical territory, visual experience can nevertheless alter neuronal responses to oriented contours (Sengpiel et al., 1999). While these latter data refer to the critical sensitive period, there have been a number of studies many years ago that reported a substantial capacity of the adult visual cortex to reorganize. Creutzfeldt and coworkers reported that adult cats exposed to a visual environment consisting only of vertical stripes showed clear signs of plastic changes. The number of neurones sensitive to the vertical orientations relative to those sensitive to horizontal was markedly decreased (Creutzfeldt and Heggelund, 1975). In a previous study, Spinelli and coworkers analyzed response properties of visual cortical neurones in kittens which had viewed (from birth) horizontal lines with one eye and vertical lines with the other eye. Neurones with horizontal preferred orientations could be activated only by the eye exposed to horizontal lines, the analogous result was found for the vertical line. There was a consistent lack of binocularity (Hirsch and Spinelli, 1970). In a continuing study, these animals were re-exposed to a normal environment after about 2 months, for up to 19 months. After that period, the animals regained a high, but variable amount of binocularity (Spinelli et al., 1972) indicating that plastic capacities of visual cortical units extend well into adulthood (see their paper for an extensive discussion). Taking the above results together, there appears at present to be a lack of striking readiness for adult visual cortex to reorganize as reported for the other modalities. Just how far these differences reflect a genuine area-specificity, or the outcome of the experimenters’ belief that the adult visual system displays a higher degree of rigidity, remains a matter of debate. At any rate, evidence is accumulating that the adult visual cortex can be modified as well, although the parameters affected, and the modes of induction of plastic changes may be different.
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
331
3. CELLULAR MECHANISMS INVOLVED IN CORTICAL PLASTICITY As far as neuronal mechanisms possibly involved in cortical plasticity are concerned, Hebbian synapses are thought to play an important role not only in cortical development in young animals, but also in cortical reorganization in adult animals (Buonomano and Merzenich, 1998). In the Hebbian rule (Hebb, 1949) a synaptic input to a neurone is strengthened when it repeatedly or persistently causes the postsynaptic neurone to discharge. Thus, the examination of cellular mechanisms involved in neuronal plasticity is mainly focused on the alteration of synaptic efficacy by adequate stimulation of axonal inputs to small populations of neurones, or to single pyramidal cells. 3.1. Synaptic Plasticity: The Hippocampus Several forms of activity-dependent alteration of synaptic efficacy have been established since the first demonstration of long-lasting potentiation (LTP) of synaptic transmission from axons of the perforant path to neurones in the dentate gyrus of the hippocampus (Bliss and Lømo, 1973). In this study LTP was induced by tetanic stimulation in vivo. Subsequently, most studies on mechanisms involved in the induction of hippocampal synaptic plasticity were performed on brain slices. High-frequency stimulation (HFS; e.g. 1 sec at 100 Hz) of the Schaffer collaterals of CA3 pyramidal cells results in LTP of EPSPs recorded from pyramidal cells of the CA1 subfield, lasting at least 30 min and in most cases up to several hours. This form of synaptic plasticity of the hippocampus has been intensively studied (for reviews see Bliss and Collingridge, 1993; Malenka and Nicoll, 1993). Short-lasting forms of enhancement of synaptic strength (for review see Zucker, 1989) include short-term potentiation (STP) lasting up to 30 min, post-tetanic potentiation (PTP) lasting 30–40 s (Malenka and Nicoll, 1993) and paired-pulse facilitation lasting less than a second (PPF; Debanne et al., 1996). In contrast to HFS inducing LTP, low-frequency stimulation (LFS; e.g. 900 pulses at 1 Hz) of Schaffer-collaterals results in long-term depression (LTD) of EPSPs in CA1 pyramidal cells, i.e. in a sustained reduction of the synaptic efficacy (for review see Bear and Abraham, 1996). In some experimental protocols, synaptic depression of less than 30 min, or even less than a second, was observed. In analogy with STP and PPF, these forms of transient depression were termed STD (Artola and Singer, 1993) and PPD (Debanne et al., 1996), respectively. However, the term STD covers a variety of physiological processes including postsynaptic mechanisms, e.g. desensitization of neurotransmitter receptors, and presynaptic reduction of transmitter release from readily releasable stores (for review see Zucker, 1989). The latter is also true for PPD. With some remarkable exceptions (for review see Johnston et al., 1992), LTP as well as LTD depend on activation of the NMDA type of glutamate receptor as indicated by the blocking action of NMDA receptor antagonists applied during conditioning stimulation (Collingridge et al., 1983; Mulkey and Malenka, 1992). In addition, the induction of either LTP or LTD was demonstrated to depend on the intracellular concentration of calcium (Mulkey and Malenka, 1992). According to the results of this study, LTD is induced by a moderate increase of the intracellular calcium concentration, while a strong increase of the intracellular calcium concentration results in the induction of LTP. Furthermore, intracellular studies revealed that the induction of either LTP or LTD strongly depends on the time-relation between presynaptic and postsynaptic activities (for review see Bliss and Collingridge, 1993, and the following section).
© 2002 Taylor & Francis
332
Hubert R. Dinse and Gerd Boehmer
3.2. Synaptic Plasticity: The Adult Neocortex There is some evidence that the first and primary site of synaptic modification involved in the reorganization of cortical maps is in the cortex (for review Buonomano and Merzenich, 1998). Thus, the focus of the present short review is on synaptic plasticity induced within the cortex. In a study examining the susceptibility of the neocortex to the induction of synaptic plasticity, compared to that in CA1 of the hippocampus, it was demonstrated that—depending on the stimulation frequency—conditioning stimulation in cortical layer IV induced either LTP (with HFS) or LTD (with LFS) in layer III of the adult rat visual cortex (Kirkwood et al., 1993). Thus, common forms of synaptic plasticity can be induced in the hippocampus and the neocortex. However, the amount of enhancement in neocortical LTP is smaller and develops more slowly than in hippocampal LTP (Malenka, 1995). The features of synaptic plasticity of neocortical neurones have been studied most frequently in the primary visual cortex (Artola and Singer, 1987; Aroniadou and Teyler, 1991; Kirkwood and Bear, 1994a,b; Bear, 1996), but also in the primary and secondary somatosensory cortices (Castro-Alamancos et al., 1995; Kitagawa et al., 1997; Feldman, 2000; Heusler et al., 2000; Kawakami et al., 2001), the primary auditory cortex (Kudoh and Shibuki, 1994, 1997) and the motor cortex (Baranyi and Szente, 1987; Castro-Alamancos et al., 1995; Hess and Donoghue, 1994, 1996) as well as in the prefrontal cortex (Hirsch and Crepel, 1990). As with synaptic plasticity in the hippocampus, activation of NMDA glutamate receptors is essential for the induction of most forms of neocortical LTP and LTD (e.g. Kirkwood et al., 1993; Castro-Alamancos et al., 1995). Furthermore, by analogy with hippocampal plasticity, the induction of either LTP or LTD in layer II/III of the neocortex depends on the level of postsynaptic depolarization (Artola et al., 1990). Either LTP or LTD could be induced by the same stimulation pattern, depending on the level of depolarization during conditioning. Moreover, the same holds for the dependence of synaptic plasticity on the concentration of intracellular free calcium (Tsumoto and Yasuda, 1996): in neurones of the visual cortex, a stimulation pattern suitable to induce LTP was demonstrated to induce LTD when the effective free calcium was reduced by calcium chelators, i.e. substances binding free calcium (Hansel et al., 1997). In these neurones, the intracellular calcium concentration was higher and decayed more slowly with stimulation protocols inducing LTP than with stimulation protocols inducing LTD (Hansel et al., 1997). Quantitative results concerning the calcium concentration for the induction of either LTD or LTP in the neocortex are not available. However, in the hippocampus, fura-2-based quantification of calcium-dependence of the induction of synaptic plasticity revealed a calcium threshold of about 180 nM Ca2+ for the induction of LTD and of about 540 nM for the transition from LTD to LTP (Cormier et al., 2001). These results on the voltage- and calciumdependence of LTD and LTP are congruent with the “BCM” theory (Bienenstock, Cooper and Munro, 1982). This model of synaptic plasticity proposes that active synapses are potentiated when the total postsynaptic response exceeds a critical value, the modification threshold θm, and that active synapses are depressed when the activity is less than θm (for review see Bear, 1996). Moreover, during conditioning the timing of evoked EPSPs and the postsynaptic action potential is essential for the induction of either LTP or LTD (Feldman, 2000). LTP is induced when both events coincide or when the EPSP leads the postsynaptic action potential, whereas LTD is induced when the postsynaptic action potential leads the evoked EPSP. The NMDA receptor seems to be ideally suited as a molecular coincidence-detector, since
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
333
it is activated by the presynaptic release of glutamate, only if the postsynaptic membrane is sufficiently depolarized by other mechanisms. This associative signal may be provided by strong synaptic activation or alternatively by postsynaptic action potentials backpropagating along the apical dendrite (Paulsen and Sejnowski, 1999). Since the NMDA receptor is part of a non-selective cation channel with an important calcium conductance (for review see Collingridge and Lester, 1989; Kaczmarek et al., 1997), activation of the NMDA receptor results in the activity-related increase of the intracellular calcium concentration necessary for the induction of synaptic plasticity. Recent studies indicate that the induction of synaptic plasticity is associated with modulation of the number of functional synapses. During the induction of LTP in the visual cortex, previously “silent” synapses can be activated, whereas during the induction of LTD previously functional synapses can be inactivated (Voronin et al., 1996). In hippocampal neurones, “silent synapses” have NMDA receptors but lack AMPA receptors, which can be acquired rapidly after induction of LTP (Isaac et al., 1995). Conversely, induction of LTD results in an increase of the number of AMPA receptors but in an unaltered number of NMDA receptors (Carroll et al., 1999; for review see Scannevin and Huganir, 2000). Recent studies revealed not only that long-term synaptic plasticity, i.e. LTP and LTD could be induced in neocortical areas, but that also short-term alterations of synaptic strength may occur. PPF of EPSPs as observed in the visual cortex (Volgushev et al., 1997) as well as PPD of EPSPs as observed in the motor cortex (Thomson et al., 1993) are associated with alterations of the release probability of neurotransmitter. However, conditions known to increase the release of neurotransmitter were less effective in the neocortex compared with the hippocampus, while conditions known to decrease the release probability were similarly effective in the neocortex and hippocampus (Castro-Alamancos and Connors, 1997). These results were interpreted in terms of a relatively high probability of release in the neocortex. Furthermore, STP and STD were also reported to occur in the neocortex (Castro-Alamancos et al., 1995). Thus, it is evident that the neocortex is susceptible to the induction of transient and persistent modification of synaptic strength and that neocortical synaptic plasticity, in spite of some important differences, shares many features with hippocampal synaptic plasticity. However, there is accumulating evidence that—depending on the neocortical area—axonal connections do not respond homogeneously to conditioning stimulation under at least comparable conditions. Comparison of synaptic plasticity between functionally and cytoarchitectonically different neocortical areas has revealed different susceptibility to the induction of synaptic plasticity. Both, the granular somatosensory cortex and the agranular motor cortex were equally capable of generating LTD as well as STD (CastroAlamancos et al., 1995). In contrast, the capability of the two areas to generate LTP was unequal: in the somatosensory cortex, “theta burst” stimulation reliably induced LTP, whereas it induced STP in the motor cortex. Induction of LTP in the motor cortex required a reduction of the GABAA receptor-mediated intracortical inhibition, but the resulting LTP still differed from that in the somatosensory cortex, e.g. by its slow onset. Similarly, unequal capabilities to generate LTP were reported for the auditory and visual cortices (Kudoh and Shibuki, 1997). In this study, whole cell recordings revealed that the postsynaptic depolarization elicited by theta burst stimulation was significantly larger in the auditory cortex than that in the visual cortex. These differences were diminished when horizontal connections in supragranular layers were cut.
© 2002 Taylor & Francis
334
Hubert R. Dinse and Gerd Boehmer
3.3. Reorganization of Cortical Maps: Associative Synaptic Plasticity and Stabilization of Cortical Neuronal Networks LTP and LTD have been implicated as the cellular mechanisms involved in the experiencedependent reorganization of neocortical representational maps (e.g. Garraghty and Muja, 1996; Glazewski et al., 1996; Kirkwood et al., 1996). In this context, two basic properties of LTP and LTD are of importance: (i) input-specificity, i.e. only synapses are modified that were activated during stimulation of a given input (“homosynaptic plasticity”), and (ii) associativity, i.e. a “weak” input can be modified if it is active at the same time as a separate but convergent input is activated by tetanic stimulation (Bliss and Collingridge, 1993). The latter feature may be of particular importance for the interaction of different inputs, e.g. horizontal and vertical intracortical connections. Synaptic plasticity of horizontal connections within cortical layer II/III is discussed as a mechanism possibly involved in reorganization of cortical maps in the motor cortex (Hess and Donoghue, 1994, 1996), the visual cortex (Hirsch and Gilbert, 1993), and the somatosensory cortex (Lee et al., 1991). It was demonstrated in a behavioural study that motor skill learning is at least partly due to LTP-like mechanisms in the motor cortex (Rioult-Pedotti et al., 1998). HFS applied to intrinsic horizontal connections in layer II/III of different areas of the visual cortex, i.e. the primary visual cortex and the inferotemporal cortex, resulted in the induction of LTP in the inferotemporal cortex, whereas LTD was induced in the primary visual cortex (Murayama et al., 1997). This difference in the susceptibility to the induction of LTP is proposed to be due to differences in the distribution of neurochemicals, e.g. protein kinase-C, implicated with the expression of LTP (for review see Elgersma and Silva, 1999). Hebbian plasticity is indeed a powerful mechanism for the modification of synaptic strength of vertical and horizontal cortical connections. However, Hebbian plasticity tends to destabilize neuronal activity: strong inputs are further strengthened whereas weak inputs are further weakened, resulting in either excessive discharge or inactivity of neurones. Therefore, supplementary mechanisms are necessary to stabilize neuronal responses within neuronal networks modified during sensory experience. The BCM proposal (see above) of an activity-dependent shift of the LTD-LTP transition threshold θm, depending on the level of the postsynaptic discharge rate provides such a stabilizing mechanism: during high postsynaptic activity the threshold for LTP is high, making depression easier and further potentiation more difficult; but after a period of low neuronal activity the threshold for LTP should be low, resulting in a higher susceptibility to the induction of LTP. Indeed, it was demonstrated in the visual cortex that LTP induced in light-deprived animals was stronger than in control animals, whereas the magnitude of LFS-induced LTD in light-deprived animals was significantly less compared to control animals (Kirkwood et al., 1996). Weakening of LTD was reversible: the magnitude of LTD returned nearly to control levels when light-deprived animals were exposed to light for two days. These results were interpreted in terms of a light deprivation-dependent promotion of LTP over LTD by a shift of the LTD-LTP transition threshold θm. Additional candidate mechanisms important for stabilization of Hebbian plasticity have been proposed: synaptic scaling, spike-timing dependent plasticity (STDP) and synaptic redistribution (for review see Abbott and Nelson, 2000). Synaptic scaling, a mechanism globally modifying synaptic strength, was demonstrated to occur in cultured neocortical networks (Turrigiano et al., 1998). In these experiments, blocking spontaneous discharge activity caused a multiplicative increase of synaptic strength in all afferents, whereas
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
335
enhancing spontaneous activity caused a multiplicative decrease of synaptic strength. An important component of synaptic scaling is the postsynaptic modification of available functional glutamate receptors. Activity manipulation scales both AMPA and NMDA receptor-mediated transmission (Watt et al., 2000). Scaling of the NMDA receptor may influence the influx of calcium and consequently the LTP-LTD induction threshold in a manner comparable to that proposed by the BCM model. The importance of STDP as a stabilizing mechanism is suggested by the observation that the induction of either LTP or LTD depends on the temporal order of pre- and postsynaptic activity (see above; Feldman, 2000). This mechanism contributes to the stabilization of neuronal discharge (for review see Abbott and Nelson, 2000), since only inputs that discharge in a narrow time window before the postsynaptic discharge are potentiated whereas inputs that fire in a wider time window after the postsynaptic discharge are depressed. When presynaptic action potentials arrive randomly in time with respect to the postsynaptic discharge, LTD dominates over LTP. This was demonstrated in layer II/III pyramidal neurones of the somatosensory (barrel) cortex (Feldman, 2000). In this study, random pairing of pre- and postsynaptic activity resulted in an overall reduction of synaptic strength. STDP leads to a non-uniform distribution of synaptic strengths and to irregular postsynaptic firing on a reasonable average rate. Thus, STDP stabilizes Hebbian plasticity and leads to a noisy but temporally-sensitive state (Abbott and Nelson, 2000). The occurrence of synaptic redistribution as a stabilizing mechanism is suggested by the observation that postsynaptic LTP acts presynaptically to modify the probability of transmitter release (for review see Abbott and Nelson, 2000). In neocortical pyramidal neurones, an increase of synaptic response was observed only when a synaptic input occurred at low frequency, an effect that was interpreted in terms of a redistribution of the available synaptic efficacy (Markram and Tsodyks, 1996). In the visual cortex, postsynaptic intracellular tetanization resulted in LTP of inputs with strong PPF (low release probability) but in LTD of inputs with small PPF (high release probability) (Volgushev et al., 1997). This mechanism, probably involving a retrograde messenger, allows Hebbian modification to act on transient activation without increasing the steadystate response or the steady state excitability of postsynaptic neurones, since it increases the probability of transmission early in a sequence of activity, but decreases the availability of releasable transmitter late in a sequence. Thus, this mechanism additionally influences the short-term dynamics of synaptic transmission. Short-term synaptic dynamics of vertical and horizontal intracortical connection as observed in the barrel cortex may be involved in cortical reorganization by sensory experience (Finnerty, Roberts and Connors, 1999). Taken together, a variety of mechanisms may contribute to experience-dependent modification of cortical representational maps. Associative long-term synaptic modifications, i.e. LTP and LTD of vertical and horizontal connections are suited to expand or shrink receptive fields of cortical neurones. In this context the activation of “silent synapses” has to be considered of high importance. Stabilization of the discharge level of cortical neurones that is modified by Hebbian synaptic plasticity may be brought about by synaptic scaling, spike timing-dependent synaptic plasticity and synaptic redistribution. The latter and other short-term mechanisms of synaptic plasticity may also be important for the selective sensitivity of cortical neurones to dynamic changes of afferent activity. Furthermore, mechanisms involved in experience-dependent reorganization of cortical representational maps may include short-term and long-term modification of the synaptic
© 2002 Taylor & Francis
Hubert R. Dinse and Gerd Boehmer
336
efficacy of inhibitory input to cortical neurones, since it is generally accepted that inhibitory surrounds and subthreshold contributions determine the receptive field size, i.e. by a dynamically maintained balance between excitatory and inhibitory inputs. However, studies on this subject are scarce: in the visual cortex long-term modification of inhibitory synaptic transmission was demonstrated to occur in the developing visual cortex (Komatsu and Iwakiri, 1993). This form of LTP is not voltage-dependent, but depends on the activation of GABAB and monoamine receptors (Komatsu, 1996). Short-term alterations of the efficacy of inhibitory synapses have also been demonstrated to occur. PPF and PPD of inhibitory afferents were induced in the somatosensory cortex depending on the pulse interval (Fleidervish and Gutnick, 1995). PPF of IPSPs was induced when pulses were delivered at a brief interval, while PPD was induced when the interval was long. Synaptic depression induced by prolonged stimulation of afferents to cortical neurones was suggested to depend on the depletion of synaptic vesicles (Galarreta and Hestrin, 1998). In these experiments, sustained activation of neuronal afferents resulted in much weaker depression of synaptic currents at inhibitory synapses than at excitatory ones. The differential depression at excitatory and inhibitory synapses in the visual cortex indicates that the balance between excitation and inhibition can change dynamically as a function of activity (Varela et al., 1999). These alterations of the balance between excitation and inhibition may contribute importantly to the reorganization of cortical maps.
4. SUMMARY AND OUTLOOK We have reviewed recent findings about plastic changes in adult early sensory and motor cortices, that were induced by different approaches including peripheral lesions, differential use, and training. Generally, massive and enduring reorganizations have been described for all areas discussed, confirming the contemporary view according to which all cortical areas are modifiable, beyond the critical sensitive periods during development. The findings demonstrate impressively that the sensorimotor cortical representations in adults are not hard-wired, but retain a self-organizing capacity operational throughout life. On the other hand, for all forms of plasticity described, there also exist distinct modalityspecific differences. These differences include the magnitude of changes, the readiness of inducability, and the specificity of neural parameters that are affected. While plasticity in somatosensory and auditory cortex share many features, many lines of evidence suggest that visual cortex plasticity is characterized by a number of particularities. There exist also a number of area and modality-specific properties of cellular mechanisms mediating plasticity of synaptic transmission, indicating that dissimilarities observed at a systemic level are also present at the cellular level. Assuming that cortical plasticity in adults represents an ubiquitous feature required for survival of an individual, the emerging differences are difficult to understand. In the following, we offer a number of possible explanations touching on different levels of abstraction of possible underlying mechanisms and functional constraints. 1.
From a mechanistic point of view, different forms and magnitudes of plastic changes might be due to differences in cellular, pharmacological and histochemical properties, that reflect specific areal-specific constraints of the molecular equipment present in an area (Huntley et al., 1994; Elgersma and Silva, 1999). While this can explain
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
2.
3.
4.
5.
6.
7.
337
existing differences in the outcome of plastic changes, the question remains, what are the reasons for the emergence of such differences in cellular properties? Besides cellular and pharmacological aspects, anatomical particularities can have a decisive impact on the outcome of plastic changes. The network of horizontal longrange connections is a particularly interesting candidate for mediating specific forms of alterations of synaptic efficacy. Differences in the overall pattern of horizontal connections in different areas would explain differences in reorganizations observed both cellularly and at a systemic level. From in-vitro experiments evidence has been accumulated that there exist a number of different mechanisms controlling and stabilizing the outcome of plastic processes. Among these, spike-timing effectively controls synaptic potentiation (Abbott and Nelson, 2000). Given the profound area- and modality-specific differences in the timing of the afferent inflow of information (see also Dinse and Schreiner, this volume), it appears conceivable that such mechanisms are highly suited to govern the effectiveness of input-dependent plastic reorganizations. Similarly, the Bienenstock-Cooper-Munro model – BCM (Bienenstock, Cooper and Munro, 1982) provides a mechanism that controls the transition from synaptic depression to synaptic potentiation, by means of the level of the postsynaptic discharge rate. Active synapses are potentiated when the total postsynaptic response exceeds a critical value, the modification threshold θm, and synapses are depressed when the activity is less than θm. As the level of postsynaptic activity can be regulated in a complex way by selecting and integrating inputs from many different sources, the BCM model can potentially regulate the threshold for inducing diverse forms of plasticity, including the failure to induce any changes. Reorganization in early sensory areas is modified by so-called “top-down” routes conveying information about cognitive and attentional aspects processed in high-level areas, as exemplified by the modulatory action exerted by the cholinergic system. It is possible that differences in the reorganizational outcome are due to a differential sensitivity to this top-down modulation, thereby establishing a differential effectiveness of input- vs. attentional-driven plasticity. In somatosensory plasticity, the aspect of “use” and “no-use” provide the key features that allows an easy and intuitive description and classification of plastic changes. Given the obvious lack of typical “use-dependent” plasticity in the visual domain, it is quite possible that the scheme of differential use is an inappropriate concept that does not fit to the specific constraints of the visual system. By the same token, searching for adequate driving forces that are particularly effective in the visual system might allow one to reveal a specific form of visual cortex reorganization. The visual cortex is characterized by a number of so-called functional maps, that are overlaid across the retinotopic gradient, thereby generating a highly complicated form of topological structure (Malach, 1994; Swindale et al., 2000). Up to now comparable topological features have not been described for the somatosensory and auditory cortex (see also Dinse and Schreiner, this volume). It is suggested that these global topological constraints impose forces that stabilize the underlying cortical networks (Wolf and Geisel, 1998), thereby limiting and restricting plastic changes, particularly those of receptive fields. Consequently, only under severe circumstances such as lesions or massive changes of input statistics, do distinct changes of receptive fields or other parameters of neural response properties develop.
© 2002 Taylor & Francis
Hubert R. Dinse and Gerd Boehmer
338
8.
9.
Neural changes as a consequence of adaptational mechanisms include a large variety of both spatial and temporal parameters of neural response characteristics. However, even under normal conditions, i.e. without involving adaptive processes, sensory processing and how performance is coded is only poorly understood. As a consequence, perceptual learning or training can be accompanied by rather unexpected changes. Conceivably, an apparent lack of plastic capacities might simply reflect hidden changes in parameter regimens presently not recognized or understood. In other words, there can be significant changes, but we do not see them. Finally, it is possible that part of the observed dissimilarities reflect genuine modalityspecific differences, building on important constraints associated to the processing of sensory-specific information or constraints emerging from anatomical and morphological requirements, that in turn evolved in response to the processing requirements of a sensory area. Consequently, comparative studies focusing on modality-specific features of cortical plasticity will reveal insights into principles governing neocortical organization.
REFERENCES Abbott, L.F. and Nelson, S.B. (2000) Synaptic plasticity: taming the beast. Nature, Neuroscience, 3, 1178–1183. Aglioti, S., Smania, N., Atzei, A. and Berlucchi, G. (1997) Spatio-temporal properties of the pattern of evoked phantom sensations in a left index amputee patient. Behavioral Neuroscience, 111, 867–872. Ahissar, E., Vaadia, E., Ahissar, M., Bergman, H., Arieli, A. and Abeles, M. (1992) Dependence of cortical plasticity on correlated activity of single neurons and on behavioral context. Science (Washington), 257, 1412–1415. Ahissar, E., Haidarliu, S. and Shulz, D.E. (1996) Possible involvement of neuromodulatory systems in cortical Hebbian-like plasticity. Journal of Physiology (Paris), 90, 353–360. Ahissar, E., Abeles, M., Ahissar, M., Haidarliu, S. and Vaadia, E. (1998) Hebbian-like functional plasticity in the auditory cortex of the behaving monkey. Neuropharmacology, 37, 633–655. Aroniadou, V.A. and Teyler, T.J. (1991) The role of NMDA receptors in long-term potentiation (LTP) and depression (LTD) in rat visual cortex. Brain Research, 562, 136–143. Artola, A., Bröcher, S. and Singer, W. (1990) Different voltage-dependent thresholds for the induction of long-term depression and long-term potentiation in slices of the rat visual cortex. Nature, London, 347, 69–72. Artola, A. and Singer, W. (1987) Long-term potentiation and NMDA receptors in rat visual cortex. Nature (London), 330, 649–652. Artola, A. and Singer, W. (1993) Long-term depression of excitatory synaptic transmission and its relationship to long-term potentiation. Trends in Neuroscience, 16, 480–487. Bakin, J.S. and Weinberger, N.M. (1996) Induction of a physiological memory in the cerebral cortex by stimulation of the nucleus basalis. Proceedings of the National Academy of Science, U.S.A., 93, 11219–11224. Baranyi, A. and Szente, M.B. (1987) Long-lasting potentiation of synaptic transmission requires postsynaptic modifications in the neocortex. Brain Research, 423, 378–384. Baskerville, K.A., Schweitzer, J.B. and Herron, P. (1997) Effects of cholinergic depletion on experiencedependent plasticity in the cortex of the rat. Neuroscience, 80, 1159–1169. Bear, M.F. (1996) NMDA-receptor-dependent synaptic plasticity in the visual cortex. In: R.R. Mize and R.S. Erzurumlu (eds), Neural development and plasticity (Progress in Brain Research, Vol. 108). Amsterdam: Elsevier Science, pp. 205–218. Bear, M.F. and Abraham, W.C. (1996) Long-term depression in hippocampus. Annual Reviews of Neuroscience, 19, 437–462. Bienenstock, E.L., Cooper, L.N. and Munro, P.W. (1982) Theory for the development of neuron selectivity, orientation specifity and binocular interaction in visual cortex. Journal of Neuroscience, 2, 32–48. Bjordahl, T.S., Dimyan, M.A. and Weinberger, N.M. (1998) Induction of long-term receptive field plasticity in the auditory cortex of the waking guinea pig by stimulation of the nucleus basalis. Behavioral Neuroscience, 112, 467–479. Blakemore, C. (1977) Genetic instructions and developmental plasticity in the kitten’s visual cortex. Philosophical Transactions of the Royal Society, London, B Biological Sciences, 278, 425–434.
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
339
Bliss, T.V.P. and Lømo, T. (1973) Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. Journal of Physiology (London), 232, 331–356. Bliss, T.V.P. and Collingridge, G.L. (1993) A synaptic model of memory: long-term potentiation in the hippocampus. Nature (London), 361, 31–39. Boroojerdi, B., Bushara, K.O., Corwell, B., Immisch, I., Battaglia, F., Muellbacher, W. and Cohen, L.G. (2000) Enhanced excitability of the human visual cortex induced by short-term light deprivation. Cerebral Cortex, 10, 529–534. Braun, C., Schweizer, R., Elbert, T., Birbaumer, N. and Taub, E. (2000) Differential activation in somatosensory cortex for different discrimination tasks. Journal of Neuroscience, 20, 446–450. Buisseret, P., Gary-Bobo, E. and Imbert, M. (1978) Ocular motility and recovery of orientational properties of visual cortical neurones in dark-reared kittens. Nature (London), 272, 816–817. Buonomano, D.V. and Merzenich, M.M. (1998) Cortical plasticity: from synapses to maps. Annual Review of Neuroscience, 21, 149–186. Byl, N.N., Merzenich, M.M. and Jenkins, W.M. (1996) A primate genesis model of focal dystonia and repetitive strain injury: I. Learning-induced dedifferentiation of the representation of the hand in the primary somatosensory cortex in adult monkeys. Neurology, 47, 508–520. Byl, N.N., Merzenich, M.M., Cheung, S., Bedenbaugh, P., Nagarajan, S.S. and Jenkins, W.M. (1997) A primate model for studying focal dystonia and repetitive strain injury: effects on the primary somatosensory cortex. Physiological Therapy, 77, 269–284. Calford, M.B. and Tweedale, R. (1988) Immediate changes in responses of somatosensory cortex in adult flyingfox after digit amputation. Nature (London), 332, 446–447. Calford, M.B., Schmid, L.M. and Rosa, M.G. (1999) Monocular focal retinal lesions induce short-term topographic plasticity in adult cat visual cortex. Proceedings of the Royal Society, London, B, Biological Sciences, 266, 499–507. Calford, M.B., Wang, C., Taglianetti, V., Waleszczyk, W.J., Burke, W. and Dreher, B. (2000) Plasticity in adult cat visual cortex (area 17) following circumscribed monocular lesions of all retinal layers. Journal of Physiology (London), 524, 587–602. Carew, T.J., Hawkins, R.D., Abrams, T.W. and Kandel, E.R. (1984) A test of Hebb’s postulate at identified synapses which mediate classical conditioning in Aplysia. Journal of Neuroscience, 4, 1217–1224. Carroll, R.C., Lissin, D.V., von Zastrow, M., Nicoll, R.A. and Malenka, R.C. (1999) Rapid redistribution of glutamate receptors contributes to long-term depression in hippocampal cultures. Nature, Neuroscience, 2, 454–460. Castro-Alamancos, M.A., Donoghue, J.P. and Connors, B.W. (1995) Different forms of synaptic plasticity in somatosensory and motor areas of the neocortex. Journal of Neuroscience, 15, 5324–5333. Castro-Alamancos, M.A. and Connors, B.W. (1997) Distinct forms of short-term plasticity at excitatory synapses of hippocampus and neocortex. Proceedings of the National Academy of Science, U.S.A., 94, 4161–4166. Chance, F.S., Nelson, S.B. and Abbott, L.F. (1998) Synaptic depression and the temporal response characteristics of V1 cells. Journal of Neuroscience, 18, 4785–4799. Chino, Y.M., Smith III, E.L., Kaas, J.H., Sasaki, Y. and Cheng, H. (1995) Receptive-field properties of deafferentated visual cortical neurons after topographic map reorganization in adult cats. Journal of Neuroscience, 15, 2417–2433. Clark, S.A., Allard, T.T., Jenkins, W.M. and Merzenich, M.M. (1988) Receptive fields in the body-surface map in adult cortex defined by temporally correlated input. Nature (London), 332, 444–445. Cohen, L.G., Brasil, N., Pascual-Leone, A. and Hallett, M. (1993) Plasticity of cortical motor output organization following deafferentation, cerebral lesions, and skill acquisition. Advances in Neurology, 63, 187–200. Cohen, L.G., Celnik, P., Pascual-Leone, A., Corwell, B., Falz, L., Dambrosia, J., Honda, M., Sadato, N., Gerloff, C., Catala, M.D. and Hallett, M. (1997) Functional relevance of cross-modal plasticity in blind humans. Nature, London, 389, 180–183. Cohen, L.G., Weeks, R.A., Sadato, N., Celnik, P., Ishii, K. and Hallett, M. (1999) Period of susceptibility for cross-modal plasticity in the blind. Annals of Neurology, 45, 451–460. Collingridge, G.L., Kehl, S.J. and McLennan, H.J. (1983) Excitatory amino acids in synaptic transmission in the Schaffer collateral-commisural pathway of the rat hippocampus. Journal of Physiology (London), 334, 33–46. Collingridge, G.L. and Lester, R.A.J. (1989) Excitatory amino acid receptors in the vertebrate central nervous system. Pharmacological Reviews, 40, 143–210. Cormier, R.J., Greenwood, A.C. and Connor, J.A. (2001) Bidirectional synaptic plasticity correlated with the magnitude of dendritic calcium transients above a threshold. Journal of Neurophysiology, 85, 399–406. Creutzfeldt, O.D. and Heggelund, P. (1975) Neural plasticity in visual cortex of adult cats after exposure to visual patterns, Nature (London), 188, 1025–1027. Crist, R.E., Kapadia, M.K., Westheimer, G. and Gilbert, C.D. (1997) Perceptual learning of spatial localization: specificity for orientation, position, and context. Journal of Neurophysiology, 78, 2889–2894.
© 2002 Taylor & Francis
340
Hubert R. Dinse and Gerd Boehmer
Cruikshank, S.J. and Weinberger, N.M. (1996a) Evidence for the Hebbian hypothesis in experience-dependent physiological plasticity of neocortex: A critical review. Brain Research Reviews, 22, 191–228. Cruikshank, S.J. and Weinberger, N.M. (1996b) Receptive-field plasticity in the adult auditory cortex induced by Hebbian covariance. Journal of Neuroscience, 16, 861–875. Darian-Smith, C. and Gilbert, C.D. (1995) Topographic reorganization in the striate cortex of the adult cat and monkey is cortically mediated. Journal of Neuroscience, 15, 1631–1647. Debanne, D., Guérineau, N.C., Gähwiler, B.H. and Thompson, S.M. (1996) Paired-pulse facilitation and depression at unitary synapses in rat hippocampus: quantal fluctuation affects subsequent release. Journal of Physiology (London), 491, 163–176. Diamond, D.M. and Weinberger, N.M. (1986) Classical conditioning rapidly induces specific changes in frequency receptive fields of single neurons in secondary and ventral ectosylvian auditory cortical fields. Brain Research, 372, 357–360. Diamond, M.E., Armstrong-James, M. and Ebner, F.F. (1993) Experience-dependent plasticity in adult rat barrel cortex. Proceedings of the National Academy of Science, U.S.A., 90, 2082–2086. Dinse, H.R. (1994) A time-based approach towards cortical functions: neural mechanisms underlying dynamic aspects of information processing before and after postontogenetic plastic processes. Physica D, 75, 129–150. Dinse, H.R., Recanzone, G. and Merzenich, M.M. (1990) Direct observation of neural assemblies during neocortical representational reorganization. In: R. Eckmiller, G. Hartmann and G. Hauske (eds), Parallel Processing in Neural Systems and Computers. Amsterdam: Elsevier, pp. 65–70. Dinse, H.R., Recanzone, G.H. and Merzenich, M.M. (1993) Alterations in correlated activity parallel ICMSinduced representational plasticity. NeuroReport, 5, 173–176. Dinse, H.R., Godde, B., Hilger, T., Haupt, S.S., Spengler, F. and Zepka, R. (1997a) Short-term functional plasticity of cortical and thalamic sensory representations and its implication for information processing. Advances in Neurology, 73, 159–178. Dinse, H.R., Reuter, G., Cords, S.M., Godde, B., Hilger, T. and Lenarz, T. (1997b) Optical imaging of cat auditory cortical organization after electrical stimulation of a multichannel cochlear implant: differential effects of acute and chronic stimulation. American Journal of Otology, 18, 6S, 17–18. Dinse, H.R., Reuter, G., Godde, B., Cords, S.M., Hilger, T., Fischer, M. and Lenarz, T. (1998) Effects of chronic electrical stimulation of the cochlea on the cochleotopic organization of cat primary auditory cortex. Society of Neuroscience Abstracts, 24, 905. Dinse, H.R. and Merzenich, M.M. (2002) Adaptation of Inputs in the Somatosensory System. In: M. Fahle and T. Poggio (eds), Perceptual Learning. Cambridge: MIT Press, in press. Dinse, H.R. (2001) The Aging Brain. Introductory remarks. In: G.W. Kreutzberg and N. Elsner (eds), Proceedings of the 4th Meeting of the German Neuroscience Society. The 28th Göttingen Neurobiology Conference 2001. Stuttgart, New York: Thieme, pp. 356–363. Donoghue, J.P. (1995) Plasticity of adult sensorimotor representations. Current Opinion in Neurobiology, 5, 749–754. Dragoi, V., Sharma, J. and Sur, M. (2000) Adaptation-induced plasticity of orientation tuning in adult visual cortex. Neuron, 28, 287–298. Edeline, J.M. and Weinberger, N.M. (1993) Receptive field plasticity in the auditory cortex during frequency discrimination training: selective retuning independent of task difficulty. Behavioral Neuroscience, 107, 82–103. Edeline, J.M., Hars, B., Maho, C. and Hennevin, E. (1994) Transient and prolonged facilitation of tone-evoked responses induced by basal forebrain stimulations in the rat auditory cortex. Experimental Brain Research, 97, 373–386. Edeline, J.M. (1996) Does Hebbian synaptic plasticity explain learning-induced sensory plasticity in adult mammals? Journal of Physiology (Paris), 90, 271–276. Elbert, T., Pantev, C., Wienbruch, C., Rockstroh, B. and Taub, E. (1995) Increased cortical representation of the fingers of the left hand in string players. Science (Washington), 270, 305–307. Elbert, T., Candia, V., Altenmuller, E., Rau, H., Sterr, A., Rockstroh, B., Pantev, C. and Taub, E. (1998) Alteration of digital representations in somatosensory cortex in focal hand dystonia. NeuroReport, 9, 3571–3575. Elgersma, Y. and Silva, A.J. (1999) Molecular mechanisms of synaptic plasticity and memory. Current Opinion in Neurobiology, 9, 209–213. Eysel, U.T. (1992) Remodelling receptive fields in sensory cortices. Current Opinion in Neurobiology, 2, 389–391. Eysel, U.T., Eyding, D. and Schweigart, G. (1998) Repetitive optical stimulation elicits fast receptive field changes in mature visual cortex. NeuroReport, 9, 949–954. Faggin, B.M., Nguyen, K.T. and Nicolelis, M.A. (1997) Immediate and simultaneous sensory reorganization at cortical and subcortical levels of the somatosensory system. Proceedings of the National Academy of Science, U.S.A., 94, 9428–9433. Fahle, M. (1997) Specificity of learning curvature, orientation, and vernier discriminations. Vision Research, 37, 1885–1895. Fahle, M. and Poggio, T. (2002) Perceptual Learning. Cambridge: MIT Press.
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
341
Feldman, D.E. (2000) Timing-based LTP and LTD in vertical inputs to layer II/III pyramidal cells in rat barrel cortex. Neuron, 27, 45–56. Feldman, D.E., Nicoll, R.A. and Malenka, R.C. (1999) Synaptic plasticity at thalamocortical synapses in developing rat somatosensory cortex: LTP, LTD, and silent synapses. Journal of Neurobiology, 41, 92–101. Finnerty, G.T., Roberts, L.S.E. and Connors, B.W. (1999) Sensory experience modifies the short-term dynamics of neocortical synapses. Nature (London), 400, 367–371. Fleidervish, I.A. and Gutnick, M.J. (1995) Paired-pulse facilitation of IPSCs in slices of immature and mature mouse somatosensory neocortex. Journal of Neurophysiology, 73, 2591–2595. Flor, H., Braun, C., Elbert, T. and Birbaumer, N. (1997) Extensive reorganization of primary somatosensory cortex in chronic back pain patients. Neuroscience Letters, 224, 5–8. Flor, H., Elbert, T., Knecht, S., Wienbruch, C., Pantev, C., Birbaumer, N., Larbig, W. and Taub, E. (1995) Phantom-limb pain as a perceptual correlate of cortical reorganization following arm amputation. Nature (London), 375, 482–484. Flor, H., Elbert, T., Mühlnickel, W., Pantev, C., Wienbruch, C. and Taub, E. (1998) Cortical reorganization and phantom phenomena in congenital and traumatic upper-extremity amputees. Experimental Brain Research, 119, 205–212. Florence, S.L. and Kaas, J.H. (1995) Large-scale reorganization at multiple levels of the somatosensory pathway follows therapeutic amputation of the hand in monkeys. Journal of Neuroscience, 15, 8083–8095. Florence, S.L., Taub, H.B. and Kaas, J.H. (1998) Large-scale sprouting of cortical connections after peripheral injury in adult macaque monkeys. Science (Washington), 282, 1117–1121. Fox, K. and Daw, N.W. (1993) Do NMDA receptors have a critical function in visual cortical plasticity? Trends in Neuroscience, 16, 116–122. Fregnac, Y., Shulz, D., Thorpe, S. and Bienenstock, E. (1988) A cellular analogue of visual cortical plasticity. Nature (London), 333, 367–370. Fregnac, Y., Shulz, D., Thorpe, S. and Bienenstock, E. (1992) Cellular analogs of visual cortical epigenesis. I. Plasticity of orientation selectivity. Journal of Neuroscience, 12, 1280–1300. Fuhr, P., Cohen, L.G., Dang, N., Findley, T.W., Haghighi, S., Oro, J. and Hallett, M. (1992) Physiological analysis of motor reorganization following lower limb amputation. Electroencephalography and Clinical Neurophysiology, 85, 53–60. Galarreta, M. and Hestrin, S. (1998) Frequency-dependent synaptic depression and the balance of excitation and inhibition in the neocortex. Nature, Neuroscience, 1, 587–594. Gardner, E.P. and Costanzo, R.M. (1980) Temporal integration of multiple-point stimuli in primary somatosensory cortical receptive fields of alert monkeys. Journal of Neurophysiology, 43, 444–468. Garraghty, P.E. and Kaas, J.H. (1992) Dynamic features of sensory and motor maps. Current Opinions in Neurobiology, 2, 522–527. Garraghty, P.E. and Muja, N. (1996) NMDA receptors and plasticity in adult primate somatosensory cortex. Journal of Comparative Neurology, 367, 319–326. Ghazanfar, A.A., Stambaugh, C.R. and Nicolelis, M.A. (2000) Encoding of tactile stimulus location by somato sensory thalamocortical ensembles. Journal of Neuroscience, 20, 3761–3775. Gibson, E.J. (1953) Improvement in perceptual judgements as a function of controlled practice or training. Psychology B, 50, 401–431. Gilbert, C.D. (1998) Adult cortical dynamics. Physiological Reviews, 78, 467–485. Gilbert, C.D. and Wiesel, T.N. (1992) Receptive field dynamics in adult primary visual cortex. Nature (London), 356, 150–152. Glazewski, S., Chen, C.-M., Silva, A. and Fox, K. (1996) Requirement for a-CaMKII in experience-dependent plasticity of the barrel cortex. Science (Washington), 272, 421–423. Godde, B., Spengler, F. and Dinse, H.R. (1996) Associative pairing of tactile stimulation induces somatosensory cortical reorganization in rats and humans. NeuroReport, 8, 281–285. Godde, B., Reuter, G. and Dinse, H.R. (1998) Cochlear Implant Stimulation—From Single Cells to Behavior. Introductory remarks. In: R. Wehner and N. Elsner (eds), New Neuroethology on the move: The 26-th Göttingen Neurobiology Conference 1998, Vol. I. Stuttgart, New York: Thieme, pp. 458–464. Godde, B. and Dinse, H.R. (1999) Intracortical microstimulation is efficient to induce systematic plastic reorganization of orientation maps in area 18 of adult cats. Neuroimage, 9, S286. Godde, B., Stauffenberg, B., Spengler, F. and Dinse, H.R. (2000) Tactile coactivation induced changes in spatial discrimination performance. Journal of Neuroscience, 20, 1597–1604. Godde, B., Leonhardt, R., Cords, S. and Dinse, H.R. (2002) Plasticity of orientation preference maps in the visual cortex of adult cats. Proceedings of the National Academy of Science, U.S.A. in press. Gödecke, I. and Bonhoeffer, T. (1996) Development of identical orientation maps for two eyes without common visual experience. Nature (London), 379, 251–254. Granger, R., Whitson, J., Larson, J. and Lynch, G. (1994) Non-Hebbian properties of long-term potentiation enable high-capacity encoding of temporal sequences. Proceedings of the National Academy of Science, U.S.A., 91, 10104–10108.
© 2002 Taylor & Francis
342
Hubert R. Dinse and Gerd Boehmer
Greuel, J.M., Luhmann, H.J. and Singer, W. (1988) Pharmacological induction of use-dependent receptive field modifications in the visual cortex. Science (Washington), 242, 74–77. Gu, X. and Fortier, P.A. (1996) Early enhancement but no late changes of motor responses induced by intracortical microstimulation in the ketamine-anesthetized rat. Experimental Brain Research, 108, 119–128. Halligan, P.W., Marshall, J.C., Wade, D.T., Davey, J. and Morrison, D. (1993) Thumb in cheek? Sensory reorganization and perceptual plasticity after limb amputation. NeuroReport, 4, 233–236. Hansel, C., Artola, A. and Singer, W. (1997) Relation between dendritic Ca2+ levels and the polarity of synaptic long-term modifications in rat visual cortex neurons. European Journal of Neuroscience, 9, 2309–2322. Hebb, D.O. (1949) The Organization of Behavior. New York: J Wiley and Sons. Hess, G. and Donoghue, J.P. (1994) Long-term potentiation of horizontal connections provides a mechanism to recognize cortical maps. Journal of Neurophysiology, 71, 2543–2547. Hess, G. and Donoghue, J.P. (1996) Long-term depression of horizontal connections in rat motor cortex. European Journal of Neuroscience, 8, 658–665. Heusler, P., Cebulla, B., Boehmer, G. and Dinse, H.R. (2000) A repetitive intracortical microstimulation pattern induces long-lasting synaptic depression in brain slices of the rat primary somatosensory cortex. Experimental Brain Research, 135, 300–310. Hirata, Y., Kuriki, S. and Pantev, C. (1999) Musicians with absolute pitch show distinct neural activities in the auditory cortex. NeuroReport, 10, 999–1002. Hirsch, H.V.B. and Spinelli, D.N. (1970) Visual experience modifies distribution of horizontally and vertically oriented receptive fields in cats. Science (Washington), 168, 869–871. Hirsch, J.C. and Crepel, F. (1990) Use-dependent changes in synaptic efficacy in rat prefrontal neurons in vitro. Journal of Physiology (London), 427, 31–49. Hirsch, J.C. and Gilbert, C.D. (1993) Long-term changes in synaptic strength along specific intrinsic pathways in the cat visual cortex. Journal of Physiology (London), 461, 247–262. Huntley, G.W., Vickers, J.C. and Morrison, J.H. (1994) Cellular and synaptic localization of NMDA and nonNMDA receptor subunits in neocortex: organizational features related to cortical circuitry, function and disease. Trends in Neuroscience, 17, 536–543. Imamura, K., Kasamatsu, T., Shirokawa, T. and Ohashi, T. (1999) Restoration of ocular dominance plasticity mediated by adenosine 3′,5′-monophosphate in adult visual cortex. Proceedings of the Royal Society, London, B Biological Sciences, 266, 1507–1516. Isaac, J.T., Nicoll, R.A. and Malenka, R.C. (1995) Evidence for silent synapses: implications for the expression of LTP. Neuron, 15, 427–434. Jain, N., Florence, S.L., Qi, H.X. and Kaas, J.H. (2000) Growth of new brainstem connections in adult monkeys with massive sensory loss. Proceedings of the National Academy of Science, U.S.A., 97, 5546–5550. James, W. (1890) Psychology: Brief Course. Cambridge: Harvard University Press. Jastreboff, P.J. (1990) Phantom auditory perception (tinnitus): mechanisms of generation and perception. Neuroscience Research, 8, 221–254. Johnston, D., Williams, S., Jaffe, D. and Gray, R. (1992) NMDA-receptor-independent long-term potentiation. Annual Reviews of Physiology, 54, 489–505. Joublin, F., Spengler, F., Wacquant, S. and Dinse, H.R. (1996) A columnar model of somatosensory reorganizational plasticity based on Hebbian and non-Hebbian learning rules. Biological Cybernetics, 74, 275–286. Jürgens, M. and Dinse, H.R. (1995) Spatial and temporal integration properties of cortical somatosensory neurons in aged rats—Lack of age-related cortical changes in behaviorally unimpaired individuals of high age. Society of Neuroscience Abstracts, 21, 197. Jürgens, M. and Dinse, H.R. (1997a) Differential effects of the Ca2+-influx blocker nimodipine on receptive field properties and response latencies of somatosensory cortical neurons in aged rats. Institut für Neuroinformatik, Ruhr-University Bochum, Internal Report, 97–10, 1–23. Jürgens, M. and Dinse, H.R. (1997b) Use-dependent plasticity of SI cortical hindpaw neurons induced by modification of walking in adult rats: a model for age related alterations. Society of Neuroscience Abstracts, 23, 1800. Kaas, J.H. (1991) Plasticity of sensory and motor maps in adult mammals. Annual Review of Neuroscience, 14, 137–167. Kaas, J.H., Krubitzer, L.A., Chino, Y.M., Langston, A.L., Polley, E.H. and Blair, N. (1990) Reorganization of retinotopic cortical maps in adult mammals after lesions of the retina. Science (Washington), 248, 229–231. Kaas, J.H. and Florence, S.L. (1997) Mechanisms of reorganization in sensory systems of primates after peripheral nerve injury. Advances in Neurology, 73, 147–158. Kaas, J.H., Florence, S.L. and Jain, N. (1999) Subcortical contributions to massive cortical reorganizations. Neuron, 22, 657–660. Kaczmarek, L., Kossut, M. and Skangiel-Kramska, J. (1997) Glutamate receptors in cortical plasticity: molecular and cellular biology. Physiological Reviews, 77, 217–255. Kakigi, A., Hirakawa, H., Harel, N., Mount, R.J. and Harrison, R.V. (2000) Tonotopic mapping in auditory cortex of the adult chinchilla with amikacin-induced cochlear lesions. Audiology, 39, 153–160.
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
343
Kalaska, J. and Pomeranz, B. (1979) Chronic paw denervation causes an age-dependent appearance of novel responses from forearm in “paw cortex” of kittens and adult cats. Journal of Neurophysiology, 42, 618–633. Kapadia, M.K., Gilbert, C.D. and Westheimer, G. (1994) A quantitative measure for short-term cortical plasticity in human vision. Journal of Neuroscience, 14, 451–457. Karni, A. and Sagi, D. (1991) Where practice makes perfect in texture discrimination: evidence for primary visual cortex plasticity. Proceedings of the National Academy of Science, U.S.A., 88, 4966–4970. Kasamatsu, T., Pettigrew, J.D. and Ary, M. (1979) Restoration of visual cortical plasticity by local microperfusion of norepinephrine. Journal of Comparative Neurology, 185, 163–181. Kasten, E., Wust, S., Behrens-Baumann, W. and Sabel, B.A. (1998) Computer-based training for the treatment of partial blindness. Nature (Medicine), 4, 1083–1087. Kawakami, Y., Miyata, M. and Oshima, T. (2001) Mechanical vibratory stimulation of feline forepaw skin induces long-lasting potentiation in the secondary somatosensory cortex. European Journal of Neuroscience, 13, 71–178. Kelahan, A.M., Ray, R.H., Carson, L.V., Massey, C.E. and Doetsch, G.S. (1981) Functional reorganization of adult raccoon somatosensory cerebral cortex following neonatal digit amputation. Brain Research, 223, 152–159. Kew, J.J., Ridding, M.C., Rothwell, J.C., Passingham, R.E., Leigh P.N., Sooriakumaran, S., Frackowiak, R.S. and Brooks, D.J. (1994) Reorganization of cortical blood flow and transcranial magnetic stimulation maps in human subjects after upper limb amputation. Journal of Neurophysiology, 72, 2517–2524. Kilgard, M.P. and Merzenich, M.M. (1998a) Cortical map reorganization enabled by nucleus basalis activity. Science (Washington), 279, 1714–1718. Kilgard, M.P. and Merzenich, M.M. (1998b) Plasticity of temporal information processing in the primary auditory cortex. Nature, Neuroscience, 1, 727–727. Kimura, A., Melis, F. and Asanuma, H. (1996) Long-lasting changes of neuronal activity in the motor cortex of cats. NeuroReport, 22, 869–872. Kirkwood, A., Dudek, S.D., Gold, J.T., Aizenman, C.D. and Bear, M.F. (1993) Common forms of synaptic plasticity in hippocampus and neocortex in vitro. Science (Washington), 260, 1518–1521. Kirkwood, A. and Bear, M.F. (1994a) Hebbian synapses in visual cortex. Journal of Neuroscience, 14, 3404–3412. Kirkwood, A. and Bear, M.F. (1994b) Homosynaptic long-term depression in the visual cortex. Journal of Neuroscience, 14, 3404–3412. Kirkwood, A., Rioult, M.G. and Bear, M.F. (1996) Experience-dependent modification of synaptic plasticity in visual cortex. Nature (London), 381, 526–528. Kitagawa, H., Nishimura, Y., Yoshioka, K., Lin, M. and Yamamoto, T. (1997) Long-term potentiation and depression in layer III and V pyramidal neurons in the cat sensorimotor cortex in vitro. Brain Research, 751, 339–343. Knecht, S., Henningsen, H., Hohling, C., Elbert, T., Flor, H., Pantev, C. and Taub, E. (1998) Plasticity of plasticity? Changes in the pattern of perceptual correlates of reorganization after amputation. Brain, 121, 717–724. Komatsu, Y. (1996) GABAB receptors, monoamine receptors, and postsynaptic inositol triphosphate-induced Ca2+ release are involved in the induction of long-term potentiation at visual cortical inhibitory synapses. Journal of Neuroscience, 16, 6342–6352. Komatsu, Y. and Iwakiri, M. (1993) Long-term modification of inhibitory synaptic transmission in developing visual cortex. NeuroReport, 4, 907–910. Kudoh, M. and Shibuki, K. (1994) Long-term potentiation in the auditory cortex of adult rats. Neuroscience Letters, 171, 21–23. Kudoh, M. and Shibuki, K. (1997) Importance of polysynaptic inputs and horizontal connectivity in the generation of tetanus-induced long-term potentiation in the rat auditory cortex. Journal of Neuroscience, 17, 9458–9465. Laubach, M., Wessberg, J. and Nicolelis, M.A. (2000) Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task. Nature (London), 405, 567–571. Lee, C.J. and Whitsel, B.L. (1992) Mechanisms underlying somatosensory cortical dynamics: I. In vivo studies. Cerebral Cortex, 2, 81–106. Lee, D.S., Lee, J.S., Oh, S.H., Kim, S.K., Kim, J.W., Chung, J.K., Lee, M.C. and Kim, C.S. (2000) Cross-modal plasticity and cochlear implants. Nature (London), 409, 149–150. Lee, S.M., Weisskopf, M.G. and Ebner, F.F. (1991) Horizontal long-term potentiation of responses in rat somatosensory cortex. Brain Research, 544, 303–310. Leonhardt, R., Spengler, F. and Dinse, H.R. (1997) Low reorganizational efficiency of intracortical microstimulation (ICMS) in primary visual cortex of pigmented rats. Society of Neuroscience Abstracts, 23, 2363. Leonhard, R., Churs, L., Spengler, F. and Dinse, H.R. (1998) Intracortical microstimulation induces plastic changes in the time-structure of neuronal responses in adult rat area 17. Forum of European Neuroscience, European Journal of Neuroscience, 10, S10, pp. 227. Liepert, J., Tegenthoff, M. and Malin, J.P. (1995) Changes of cortical motor area size during immobilization. Electroencephalography and Clinical Neurophysiology, 97, 382–386. Liepert, J., Terborg, C. and Weiller, C. (1999) Motor plasticity induced by synchronized thumb and foot movements. Experimental Brain Research, 125, 435–439.
© 2002 Taylor & Francis
344
Hubert R. Dinse and Gerd Boehmer
Linden, D.E., Kallenbach, U., Heinecke, A., Singer, W. and Goebel, R. (1999) The myth of upright vision. A psychophysical and functional imaging study of adaptation to inverting spectacles. Perception, 28, 469–481. Lockwood, A.H., Salvi, R.J., Coad, M.L., Towsley, M.L., Wack, D.S. and Murphy, B.W. (1998) The functional neuroanatomy of tinnitus: evidence for limbic system links and neural plasticity. Neurology, 50, 114–120. Maalouf, M., Dykes, R.W. and Miasnikov, A.A. (1998) Effects of D-AP5 and NMDA microiontophoresis on associative learning in the barrel cortex of awake rats. Brain Research, 793, 149–168. Malach, R. (1994) Cortical columns as devices for maximizing neuronal diversity. Trends in Neuroscience, 17, 101–104. Maldonado, P.E. and Gerstein, G.L. (1996a) Reorganization in the auditory cortex of the rat induced by intracortical microstimulation: a multiple single-unit study. Experimental Brain Research, 112, 420–430. Maldonado, P.E. and Gerstein, G.L. (1996b) Neuronal assembly dynamics in the rat auditory cortex during reorganization induced by intracortical microstimulation. Experimental Brain Research, 112, 431–441. Maldonado, P.E., Altman, J.A. and Gerstein, G.L. (1998) Neuron discharges in the rat auditory cortex during electrical intracortical stimulation. Neuroscience and Behavioral Physiology, 28, 48–59. Malenka, R.C. and Nicoll, R.A. (1993) NMDA-receptor-dependent synaptic plasticity: multiple forms and mechanisms. Trends in Neurosciences, 16, 521–527. Malenka, R.C. (1995) Synaptic plasticity in hippocampus and neocortex: a comparison. In: M.J. Gutnick and I. Mody (eds), The Cortical Neuron. New York: Oxford University Press, pp. 98–110. Markram, H. and Tsodyks, M. (1996) Redistribution of synaptic efficacy between neocortical pyramidal neurons. Nature (London), 382, 807–810. McLean, J. and Palmer, L.A. (1998) Plasticity of neuronal response properties in adult cat striate cortex. Visual Neuroscience, 15, 177–196. Menning, H., Roberts, L.E. and Pantev, C. (2000) Plastic changes in the auditory cortex induced by intensive frequency discrimination training. NeuroReport, 11, 817–822. Merzenich, M.M., Kaas, J.H., Wall, J.T., Sur, M., Nelson, R.J. and Felleman, D.J. (1983) Topographic reorganization of somatosensory coertical areas 3b and 1 in adult monkeys following restricted deafferentation. Neuroscience, 8, 3–55. Merzenich, M.M., Nelson, R.J., Stryker, M.P., Cynader, M.S., Schoppmann, A. and Zook, J.M. (1984) Somatosensory cortical map changes following digit amputation in adult monkeys. Journal of Comparative Neurology, 224, 591–605. Merzenich, M.M., Recanzone, G., Jenkins, W.M., Allard, T.T. and Nudo, R.J. (1988) Cortical representational plasticity. In: P. Rakic and W. Singer (eds), Neurobiology of Neocortex. New York: Wiley, pp. 41–67. Merzenich, M.M., Schreiner, C., Jenkins, W. and Wang, X. (1993) Neural mechanisms underlying temporal integration, segmentation, and input sequence representation: some implications for the origin of learning disabilities. Annals of the NewYork Academy of Science, 682, 1–22. Merzenich, M.M., Jenkins, W.M., Johnston, P., Schreiner, C., Miller, S.L. and Tallal, P. (1996) Temporal processing deficits of language-learning impaired children ameliorated by training. Science (Washington), 271, 77–81. Metherate, R. and Weinberger, N.M. (1990) Cholinergic modulation of responses to single tones produces tonespecific receptive field alterations in cat auditory cortex. Synapse, 6, 133–145. Miller, K.D., Erwin, E. and Kayser, A. (1999) Is the development of orientation selectivity instructed by activity? Journal of Neurobiology, 41, 44–57. Mogilner, A., Grossman, J.A., Ribary, U., Joliot, M., Volkmann, J., Rapaport, D., Beasley, R.W. and Llinas, R.R. (1993) Somatosensory cortical plasticity in adult humans revealed by magnetoencephalography. Proceedings of the National Academy of Science, U.S.A., 90, 3593–3597. Montague, P.R. and Sejnowski, T.J. (1994) The predictive brain: Temporal coincidence and temporal order in synaptic learning mechanisms. Learning and Memory, 1, 1–33. Mühlnickel, W., Elbert, T., Taub, E. and Flor, H. (1998) Reorganization of auditory cortex in tinnitus. Proceedings of the National Academy of Science, U.S.A., 95, 10340–10343. Mulkey, R.M. and Malenka, J.C. (1992) Mechanisms underlying homosynaptic long-term depression in area CA1 of the hippocampus. Neuron, 9, 967–975. Murayama, Y., Fujita, I. and Kato, M. (1997) Contrasting forms of synaptic plasticity in monkey inferotemporal and primary visual cortices. NeuroReport, 8, 1503–1508. Nicolelis, M.A., Katz, D. and Krupa, D.J. (1998a) Potential circuit mechanisms underlying concurrent thalamic and cortical plasticity. Reviews in Neuroscience, 9, 213–224. Nicolelis, M.A.L. (1999) Methods in Neural Ensemble Recordings. New York: CRC Press. Nudo, R.J., Jenkins, W.M. and Merzenich, M.M. (1990) Repetitive microstimulation alters the cortical representation of movements in adult rats. Somatosensory and Motor Research, 7, 463–483. Nudo, R.J., Milliken, G.W., Jenkins, W.M. and Merzenich, M.M. (1996) Use-dependent alterations of movement representations in primary motor cortex of adult squirrel monkeys. Journal of Neuroscience, 16, 785–807.
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
345
Ohl, F.W. and Scheich, H. (1996) Differential frequency conditioning enhances spectral contrast sensitivity of units in auditory cortex (field Al) of the alert Mongolian gerbil. European Journal of Neuroscience, 8, 1001–1017. Pantev, C., Oostenveld, R., Engelien, A., Ross, B., Roberts, L.E. and Hoke, M. (1998) Increased auditory cortical representation in musicians. Nature (London), 392, 811–814. Pantev, C., Roberts, L.E., Schulz, M., Engelien, A. and Ross, B. (2001) Timbre-specific enhancement of auditory cortical representations in musicians. NeuroReport, 12, 169–174. Pascual-Leone, A. and Torres, F. (1993) Plasticity of the sensorimotor cortex representation of the reading finger in Braille readers. Brain, 116, 39–52. Paulsen, O. and Sejnowski, T.J. (2000) Natural patterns of activity and long-term synaptic plasticity. Current Opinion in Neurobiology, 10, 172–179. Plautz, E.J., Milliken, G.W. and Nudo, R.J. (2000) Effects of repetitive motor training on movement representations in adult squirrel monkeys: role of use versus learning. Neurobiology of Learning and Memory, 74, 27–55. Pleger, B., Dinse, H.R., Ragert, P., Schwenkreis, P., Malin, J.P. and Tegenthoff, M. (2001) Shifts in cortical representations predict human discrimation improvement. Proceedings of the National Academy of Science U.S.A., 98, 12255–12260. Polley, D.B., Chen-Bee, C.H. and Frostig, R.D. (1999a) Varying the degree of single-whisker stimulation differentially affects phases of intrinsic signals in rat barrel cortex. Journal of Neurophysiology, 81, 692–701. Polley, D.B., Chen-Bee, C.H. and Frostig, R.D. (1999b) Two directions of plasticity in the sensory-deprived adult cortex. Neuron, 24, 623–637. Pons, T.P., Garraghty, P.E., Ommaya, A.K., Kaas, J.H., Taub, E. and Mishkin, M. (1991) Massive cortical reorganization after sensory deafferenatation in adult macaques. Science (Washington), 252, 1857–1860. Ponton, C.W., Don, M., Eggermont, J.J., Waring, M.D., Kwong, B. and Masuda, A. (1996) Auditory system plasticity in children after long periods of complete deafness. NeuroReport, 8, 61–65. Pöppel, E., Stoerig, P., Logothetis, N., Fries, W., Boergen, K.P., Oertel, W. and Zihl, J. (1987) Plasticity and rigidity in the representation of the human visual field. Experimental Brain Research, 68, 445–448. Qian, N. and Matthews, N. (1999) A physiological theory for visual perceptual learning of orientation discrimination. Society of Neuroscience Abstracts, 25, 1316. Raggio, M.W. and Schreiner, C.E. (1999) Neuronal responses in cat primary auditory cortex to electrical cochlear stimulation. III. Activation patterns in short- and long-term deafness. Journal of Neurophysiology, 82, 3506–3526. Rajan, R. and Irvine, D.R. (1998) Absence of plasticity of the frequency map in dorsal cochlear nucleus of adult cats after unilateral partial cochlear lesions. Journal of Comparative Neurology, 399, 35–46. Rajan, R., Irvine, D.R., Wise, L.Z. and Heil, P. (1993) Effect of unilateral partial cochlear lesions in adult cats on the representation of lesioned and unlesioned cochleas in primary auditory cortex. Journal of Comparative Neurology, 338, 17–49. Ramachandran, V.S., Stewart, M. and Rogers-Ramachandran, D.C. (1992) Perceptual correlates of massive cortical reorganization. NeuroReport, 3, 583–586. Rasmusson, D.D. (1982) Reorganization of raccoon somatosensory cortex following removal of the fifth digit. Journal of Comparative Neurology, 205, 313–326. Rasmusson, D.D. and Dykes, R.W. (1988) Long-term enhancement of evoked potentials in cat somatosensory cortex produced by co-activation of the basal forebrain and cutaneous receptors. Experimental Brain Research, 70, 276–286. Rauschecker, J.P. (1999) Auditory cortical plasticity: a comparison with other sensory systems. Trends in Neuroscience, 22, 74–80. Recanzone, G. (2000) Cerebral cortex plasticity: perception and skill acquisition. In: M.S. Gazzaniga (ed.), The New Cognitive Neurosciences. Boston: MIT-Press, pp. 237–250. Recanzone, G.H., Jenkins, W.M., Hradek, G.T. and Merzenich, M.M. (1992a) Progressive improvement in discriminative abilities in adult owl monkeys performing a tactile frequency discrimination task. Journal of Neurophysiology, 67, 1015–1030. Recanzone, G.H., Merzenich, M.M., Jenkins, W.M., Grajski, K. and Dinse, H.R. (1992b) Topographic reorganization of the hand representation in cortical area 3b of owl monkeys trained in a frequency discrimination task. Journal of Neurophysiology, 67, 1031–1056. Recanzone, G.H., Merzenich, M.M. and Schreiner, C.E. (1992c) Changes in the distributed temporal response properties of SI cortical neurons reflect improvements in performance on a temporally-based tactile diskrimination task. Journal of Neurophysiology, 67, 1071–1091. Recanzone, G.H., Merzenich, M.M. and Dinse, H.R. (1992d) Expansion of the cortical representation of a specific skin field in primary somatosensory cortex by intracortical microstimulation. Cerebral Cortex, 2, 181–196. Recanzone, G.H., Schreiner, C.E. and Merzenich, M.M. (1993) Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys. Journal of Neuroscience, 13, 87–103. Rioult-Pedotti, M.-S., Friedman, D., Hess, G. and Donoghue, J.P. (1998) Strengthening of horizontal cortical connections following skill learning. Nature, Neuroscience, 1, 230–234.
© 2002 Taylor & Francis
346
Hubert R. Dinse and Gerd Boehmer
Robertson, D. and Irvine, D.R. (1989) Plasticity of frequency organization in auditory cortex of guinea pigs with partial unilateral deafness. Journal of Comparative Neurology, 282, 456–471. Röder, B., Teder-Sälejärvi, W., Sterr, A., Rösler, F., Hillyard, S.A. and Neville, H.J. (1999) Improved auditory spatial tuning in blind humans. Nature (London), 400, 162–166. Rosa, M.G., Schmid, L.M. and Calford, M.B. (1995) Responsiveness of cat area 17 after monocular inactivation: limitation of topographic plasticity in adult cortex. Journal of Physiology (London), 482, 589–608. Rumpel, S., Hatt, H. and Gottmann, K. (1998) Silent synapses in the developing rat visual cortex: evidence for postsynaptic expression of synaptic plasticity. Journal of Neuroscience, 18, 8863–8874. Sachdev, R.N., Lu, S.M., Wiley, R.G. and Ebner, F.F. (1998) Role of the basal forebrain cholinergic projection in somatosensory cortical plasticity. Journal of Neurophysiology, 79, 3216–3228. Sachdev, R.N., Egli, M., Stonecypher, M., Wiley, R.G. and Ebner, F.F. (2000) Enhancement of cortical plasticity by behavioral training in acetylcholine-depleted adult rats. Journal of Neurophysiology, 84, 1971–1981. Sadato, N., Pascual-Leone, A., Grafman, J., Ibanez, V., Deiber, M.P., Dold, G. and Hallett, M. (1996) Activation of the primary visual cortex by Braille reading in blind subjects. Nature (London), 380, 526–528. Sakai, M. and Suga, N. (2001) Plasticity of the cochleotopic (frequency) map in specialized and nonspecialized auditory cortices. Proceedings of the National Academy of Science, U.S.A., 98, 3507–3512. Sameshima, K. and Merzenich, M.M. (1993) Cortical plasticity and memory. Current Opinion Neurobiol., 3, 187–196. Sanes, J.N. and Donoghue, J.P. (1997) Static and dynamic organization of motor cortex. Advances in Neurology, 73, 277–296. Scannevin, R.H. and Huganir, R.L. (2000) Postsynaptic organization and regulation of excitatory synapses. Nature Reviews Neuroscience, 1, 133–141. Scheich, H. (1991) Auditory cortex: comparative aspects of maps and plasticity. Currrent Opinion in Neurobiology, 1, 236–247. Schmid, L.M., Rosa, M.G., Calford, M.B. and Ambler, J.S. (1996) Visuotopic reorganization in the primary visual cortex of adult cats following monocular and binocular retinal lesions. Cerebral Cortex, 6, 388–405. Schoups, A., Vogel, R., Qian, N. and Orban, G. (2001) Practising orientation identification improves orientation coding in VI neurons. Nature (London), 412, 549–553. Schoups, A.A., Vogels, R. and Orban, G.A. (1995) Human perceptual learning in identifying the oblique orientation: retinotopy, orientation specificity and monocularity. Journal of Physiology (London), 483, 797–810. Schoups, A.A., Vogels, R. and Orban, G.A. (2000) Orientation tuning changes after perceptual leaning in orientation identification. Society of Neuroscience Abstracts, 26, 1082. Sengpiel, F., Godecke, I., Stawinski, P., Hübener, M., Lowel, S. and Bonhoeffer, T. (1998) Intrinsic and environmental factors in the development of functional maps in cat visual cortex. Neuropharmacology, 37, 607–621. Sengpiel, F., Stawinski, P. and Bonhoeffer, T. (1999) Influence of experience on orientation maps in cat visual cortex. Nature, Neuroscience, 2, 727–732. Shulz, D.E., Sosnik, R., Ego, V., Haidarliu, S. and Ahissar, E. (2000) A neuronal analogue of state-dependent learning. Nature (London), 403, 549–553. Sil’kis, I.G. and Rapoport, S.S. (1995) Plastic reorganizations of the receptive fields of neurons of the auditory cortex and the medial geniculate body induced by microstimulation of the auditory cortex. Neuroscience and Behavioral Physiology, 25, 322–339. Spengler, F. and Dinse, H.R. (1994) Reversible relocation of representational boundaries of adult rats by intracortical microstimulation (ICMS). NeuroReport, 5, 949–953. Spengler, F., Godde, B. and Dinse, H.R. (1995) Effects of aging on topographic organization of somatosensory cortex. NeuroReport, 6, 469–473. Spengler, F., Roberts, T., Poeppel, D., Byl, N., Wang, X. and Merzenich, M.M. (1997) Cortical reorganization and transfer in humans trained in tactile discrimination. Neuroscience Letters, 232, 151–154. Spinelli, D.N., Hirsch, H.V., Phelps, R.W. and Metzler, J. (1972) Visual experience as a determinant of the response characteristics of cortical receptive fields in cats. Experimental Brain Research, 15, 289–304. Stanton, S.G and Harrison, R.V. (1996) Abnormal cochleotopic organization in the auditory cortex of cats reared in a frequency augmented environment. Auditory Neuroscience, 2, 97–108. Sterr, A., Muller, M.M., Elbert, T., Rockstroh, B., Pantev, C. and Taub, E. (1998a) Perceptual correlates of changes in cortical representation of fingers in blind multifinger Braille readers. Journal of Neuroscience, 18, 4417–4423. Sterr, A., Muller, M.M., Elbert, T., Rockstroh, B., Pantev, C. and Taub, E. (1998b) Changed perceptions in Braille readers. Nature (London), 391, 134–135. Stoney, Jr., S.D., Thompson, W.D. and Asanuma, H. (1968) Excitation of pyramidal tract cells by intracortical microstimulation: effective extent of stimulating current. Journal of Neurophysiology, 31, 659–669. Suedfeld, P. (1975) The benefits of boredom: sensory deprivation reconsidered. American Scientist, 63, 60–69. Sugita, Y. (1996) Global plasticity in adult visual cortex following reversal of visual input. Nature, Neuroscience, 380, 523–526.
© 2002 Taylor & Francis
Comparative Aspects of Cortical Plasticity
347
Swindale, N.V., Shoham, D., Grinvald, A., Bonhoeffer, T. and Hübener, M. (2000) Visual cortex maps are optimized for uniform coverage. Nature, Neuroscience, 3, 822–826. Tallal, P., Miller, S. and Fitch, R.H. (1993) Neurobiological basis of speech: a case for the preeminence of temporal processing. Annals of the New York Academy of Science, 682, 27–47. Tallal, P., Miller, S.L., Bedi, G., Byma, G., Wang, X., Nagarajan, S.S., Schreiner, C., Jenkins, W.M. and Merzenich, M.M. (1996) Language comprehension in language-learning impaired children improved with acoustically modified speech. Science (Washington), 271, 81–84. Thomson, A.M., Deuchars, J. and West, D.C. (1993) Large, deep layer pyramid-pyramid single axon EPSPs in slices of rat motor cortex display paired pulse and frequency-dependent depression, mediated presynaptically and self-facilitation, mediated postsynaptically. Journal of Neurophysiology, 70, 2354–2369. Tommerdahl, M., Delemos, K.A., Favorov, O.V., Metz, C.B., Vierck, Jr., C.J. and Whitsel, B.L. (1998) Response of anterior parietal cortex to different modes of same-site skin stimulation. Journal of Neurophysiology, 80, 3272–3283. Tsumoto, T. and Yasuda, H. (1996) A switching role of postsynaptic calcium in the induction of long-term potentiation or long-term depression in visual cortex. Seminars in the Neurosciences, 8, 311–319. Turrigiano, G.G., Leslie, K.R., Desai, N.S., Rutherford, L.C. and Nelson, S.B. (1998) Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature (London), 391, 892–896. Varela, J.A., Song, S., Turrigiano, G.G. and Nelson, S.B. (1999) Differential depression at excitatory and inhibitory synapses in visual cortex. Journal of Neuroscience, 19, 4293–4304. Vogels, R. and Orban, G.A. (1985) The effect of practice on the oblique effect in line orientation judgements. Vision Research, 25, 1679–1687. Vogels, B. and Orban, G.A. (1994) Does practice in orientation discrimination lead to changes in the response properties of macaque inferior temporal neurons? European Journal of Neuroscience, 6, 1680–1690. Volgushev, M., Voronin, L.L., Chistiakova, M. and Singer, W. (1997) Relations between long-term synaptic modifications and paired-pulse interactions in the rat neocortex. European Journal of Neuroscience, 9, 1656–1665. Voronin, L.L., Volgushev, M., Chistiakova, M., Kuhnt, U. and Singer, W. (1996) Involvement of silent synapses in the induction of long-term potentiation and long-term depression in neocortical and hippocampal neurons. Neuroscience, 74, 323–330. Wall, J.T. and Cusick, C.G. (1984) Cutaneous responsiveness in primary somatosensory (S-I) hindpaw cortex before and after partial hindpaw deafferentation in adult rats. Journal of Neuroscience, 4, 1499–1515. Wallhäusser-Franke, E., Braun, S. and Langner, G. (1996) Salicylate alters 2-DG uptake in the auditory system: a model for tinnitus? NeuroReport, 7, 1585–1588. Wang, X., Merzenich, M.M., Sameshima, K. and Jenkins, W.M. (1995) Remodelling of hand representation in adult cortex determined by timing of tactile stimulation. Nature (London), 378, 71–75. Watt, A.J., van Rossum, M.C.W., MacLeod, K.M., Nelson, S.B. and Turrigiano, G.G. (2000) Activity co-regulates quantal AMPA and NMDA currents at neocortical synapses. Neuron, 26, 659–670. Weeks, R., Horwitz, B., Aziz-Sultan, A., Tian, B., Wessinger, C.M., Cohen, L.G., Hallett, M. and Rauschecker, J.P. (2000) A positron emission tomographic study of auditory localization in the congenitally blind. Journal of Neuroscience, 20, 2664–2672. Weinberger, N.M. (1995) Dynamic regulation of receptive fields and maps in the adult sensory cortex. Annual Review of Neuroscience, 18, 129–158. Weinberger, N.M., Ashe, J.H., Metherate, R., McKenna, T.M., Diamond, D.M. and Bakin, J. (1990) Retuning auditory cortex by learning: a preliminary model of receptive field plasticity. Concepts in Neuroscience, 1, 91–132. Whitaker, D. and McGraw, P.V. (2000) Long-term visual experience recalibrates human orientation perception. Nature, Neuroscience, 3, 13. Wilson, B.S., Finley, C.C., Lawson, D.T., Wolford, R.D., Eddington, D.K. and Rabinowitz, W.M. (1991) Better speech recognition with cochlear implants. Nature (London), 352, 236–238. Wolf, F. and Geisel, T. (1998) Spontaneous pinwheel annihilation during visual development. Nature (London), 395, 73–78. Xerri, C., Stern, J.M. and Merzenich, M.M. (1994) Alterations of the cortical representation of the rat ventrum induced by nursing behavior. Journal of Neuroscience, 14, 1710–1721. Xing, J. and Gerstein, G.L. (1996) Networks with lateral connectivity. III. Plasticity and reorganization of somatosensory cortex. Journal of Neurophysiology, 75, 217–232. Yang, T.T., Gallen, C.C., Cobb, S., Schwartz, B.J. and Bloom, F.E. (1994) Noninvasive detection of cerebral plasticity in adult human somatosensory cortex. NeuroReport, 5, 701–704. Zucker, R.S. (1989) Short-term synaptic plasticity. Annual Reviews of Neuroscience, 12, 13–31.
© 2002 Taylor & Francis
Part V MORPHOLOGICAL SUBSTRATES OF SEGREGATION AND INTEGRATION
© 2002 Taylor & Francis
15 Connectional Organisation and Function in the Macaque Cerebral Cortex Malcolm P. Young Neural Systems Group, Department of Psychology, Claremont Place Newcastle upon Tyne, NE1 7RU, United Kingdom Tel: 0044-191-222-7525; FAX: 0044-191-222-5622; e-mail:
[email protected]
Experimental neuroanatomy has revealed a very numerous and complex set of connections between many discriminable brain structures. These data are uniquely important to defining the organisation of brain systems, and so represent the raw material for an unmistakable step toward understanding brain function. However, the interpretation of these data in terms of brain organisation has previously suffered from a widespread failure to appreciate two things. First, the data are sufficiently numerous and complex that principled analysis of the data is necessary, before reliable conclusions about organisation can be drawn from them, exactly as in all other areas of science where data are complex and numerous. Second, the organisational principles at the neural systems level do not emerge from informal inspection of the primary data, no matter how eminent the inspector. It is now almost universally acknowledged that neuroinformatics, the computer-based collation, management and analysis of these data, is a necessary step in drawing scientifically justified conclusions about the organisation of neural systems. This chapter reviews results from the “first generation” of neuroinformatics, and shows that fairly simple organisational principles and regularities underlie the complexity of the connections, and that that these principles together define the first reliable representations of the organisation of central brain systems. The “second generation” of neuroinformatics is presently more concerned with the interpretation of these organising principles and regularities in terms of their meaning for brain function. Some progress in this latter area is also reviewed in the context of the primate visual system, and arguments are presented that the functional architecture implied by the connection data is in an important sense the opposite of the feed-forward network widely supposed. The chapter ends by exploring whether a single theoretical net can be thrown over results from a wide range of neuroscience disciplines, if this net has as its central tenet that processing is principally inferential in nature. KEYWORDS: analysis of connectivity, Bayesian inference, connectivity, feedback, feedforward, neuroinformatics, receptive field
1. NEUROANATOMY AND NEUROINFORMATICS Almost all neuroscientists consider that understanding brain structure will aid understanding of brain function. This assumption is reproduced in studies at every level of the nervous system, and of every presumed functional subsystem. At the level of the whole cortex, for example, many researchers assume that information processing is closely determined by the inputs, the internal connectivity and computations, and the outputs of the network of areas and nuclei that make up the brain. Defining the connectivity of brain structures has therefore been a primary research focus for neuroanatomists over many decades. 351 © 2002 Taylor & Francis
352
Malcolm P. Young
Their enterprise has been enormously successful, perhaps among the most successful in all of biology. However, this success has brought with it a problem that is very similar to that faced by some other biological disciplines: the quantity and complexity of connection data, and their dispersion through an extensive and idiosyncratic literature make it very difficult indeed to derive reliable conclusions about the information they collectively bear about the organisation of the system. An example of the scale of this problem is provided by noting that more than 14,000 individual reports of connections between different gross structures of the rat brain have been made in the last 20 years (Burns and Young, 2000). Data so numerous and complex provide excellent opportunities for the derivation of false conclusions if examined only informally, simply through the ease by which inconvenient data can be overlooked. Similarly, for the macaque visual system, V1 is known to be connected to more than 50 other structures (e.g. Young et al., 1995). More than 300 ipsilateral cortico-cortical connections have been described between at least 30 differentiable visual processing regions (e.g. Felleman and Van Essen, 1991). These connections, together with the connections that visual areas make with other cortical regions, constitute a cortical network defined by almost a thousand gross connections (e.g. Young, 1993, 1995). Furthermore, a plethora of callosal and other commissural connections link the two hemispheres; and the cortical visual systems stand upon a thalamo-cortical network of almost equal complexity (e.g. Scannell et al., 1999). Quite surprisingly, given the complexities revealed by neuroanatomists’ experiments, it has only recently become widely acknowledged that statements about the organisation of
Figure 15.1. This diagram re-emphasizes the distinction between, on the one hand, primary information about those brain structures which are connected, and, on the other hand, principles of organisation of the neural systems which are defined by these connections. All connections collated from the neuroanatomical literature in Young (1993) are plotted, and so a very great deal of information about the connectivity between brain structures is represented. However, the positions of the points representing brain structures in the diagram have simply been placed at random, and so simulate complete innocence of the organisation of the systems defined by these connections. Organisational principles at the neural systems level do not emerge from informal inspection of primary data, no matter how eminent the inspector. Principled and detailed data analysis is necessary to untangle the connections, and so reveal the organisational principles of neural systems in the brain.
© 2002 Taylor & Francis
Connectivity and Function in the Cortex
353
neural systems need to be supported by analyses of these data. Most areas of science already employ methods of statistical data analysis to substantiate experimenters’ interpretations of their data. No study in neurophysiology, for instance, would be published without detailed analysis of the data, the results of which constrain rigorously the conclusions that can reliably be drawn from them. Areas that have not routinely employed data analysis methods have most often not done so because the experimental data have not appeared to be tractable for data analysis, or because the benefits of data analysis have not been particularly clear. Both these considerations seem to have previously applied to the application of methods of data analysis to data on connectional neuroanatomy. However, it is instructive to consider the kinds of issue that raw, un-analysed data can inform, and the kinds of issue that can only be informed by the results of analyses. Consider, for instance, that an experimental study revealed the carriage of retrograde label from V4 to MT 4. This datum would be sufficient (assuming no trans-neuronal labelling) to conclude that MT is connected to V4. A problem arises, however, when conclusions about the organisation of the system are made on the basis of individual data. For example, it has been argued that there cannot be two streams in visual cortex because V4 (the prototypical ventral-stream area) and MT (the prototypical dorsal-stream area) are reciprocally interconnected (see Young, 1995). In this case, a conclusion about the organisation of the visual system—that it is not organised into two streams—is based on readily replicable, uncontentious data. The problem arises because the organisation of the system is defined by many hundreds of connections (e.g. Felleman and Van Essen, 1991; Young, 1992), of which the connections mentioned are only two. It is clearly not possible to draw reliable conclusions about something defined by hundreds of data on the basis of only two data. In exactly the same way, a few spikes fired to null stimuli could not be used to argue that a neurone is not directionally tuned. Because large numbers of connections define neural systems, conclusions about their organisation unequivocally require the support of analyses (Young, 1995). For these reasons, both analysis of neuroanatomical data and neuroanatomical experiments are necessary before reliable conclusions about the organisation of neural systems can be drawn. Also, both experimental and data-analytic work are required to further refine knowledge of neural organisation. The complexity of connection data, and their often qualitative rather than quantitative nature, present a problem recognisable to any statistician: the problem of finding implicit structure or order in badly-behaved real-world data. A frequent analogy for this problem concerns the fable of the elephant in the dark room (e.g. McDonald, 1986). Many hands are required to feel around the mysterious creature in the dark. The coherence and agreement between these independent analyses then determines the degree to which belief should be invested in the results (Young et al., 1995). Accordingly, a rather wide variety of different data-analytic methods have now been applied to several different varieties of connection data. These have included the following methods: (i) modelling data on the quantitative distributions of connection strength by statistical geometry (Young et al., 1995); (ii) computational hierarchical analysis of laminar origin and termination data (e.g. Hilgetag et al., 1996, 2000b); (iii) seriation (Young et al., 1994, 1995); (iv) Optimal Set Analysis (Hilgetag et al., 2000a); (v) cluster analysis (Hilgetag et al., 2000a); and (v) non-metric multidimensional scaling analyses (NMDS: Young, 1992, 1993; Young et al., 1994,
4
For full list of abbreviations used in this chapter, see the Appendix (p. 371).
© 2002 Taylor & Francis
354
Malcolm P. Young
1995). Data for method (i) are unfortunately very rare. For the other methods data are very numerous. Happily, these many independent analyses of several different kinds of data produce a largely self-consistent picture of neural system organisation, of which, even more fortunately, rather few aggregate characteristics can be stated (Young et al., 1995; Hilgetag et al., 2000a). This chapter reviews some of these analyses of the connectional organisation of cortical systms in the macaque, with particular emphasis on the visual system, and attempts to place some interpretation on what the results might mean for how the brain mediates behaviour.
2. THE MACAQUE VISUAL SYSTEM The cortical visual system appears to occupy a little more than half the area of the macaque’s cerebral cortex (Felleman and Van Essen, 1991). It is composed of (the order of) a billion neurones, which reveal complex patterns of visual feature preferences to the microelectrode, and are distributed in more than thirty discriminable visual processing compartments or areas (e.g. Felleman and Van Essen, 1991; Zeki and Shipp, 1988), whose identities and borders continue to be debated. These visual cortical areas are interconnected by hundreds of ipsi- and contra-lateral cortico-cortical connections, as well as by a very rich subcortical network (Kaas and Huerta, 1988; Young, 1992, 1995; Young et al., 1995). Felleman and Van Essen (1991) collated neuroanatomical data on visual structures according to a detailed parcellation scheme, and we analysed their collation, with a number of small changes (see Young, 1992; Young et al., 1995). 2.1. Analysis of the Macaque Visual System Data by Non-Metric Multidimensional Scaling (NMDS) Consider that the function of any brain structure is constrained by its inputs and outputs. Its inputs determine the kinds of information it can process, and its outputs determine which other structures it can inform directly about its computations. The pattern of connections that any brain area makes and receives therefore will be a key determinant of its function. One can reason further that the more similar are the patterns of connections of any two brain areas, the more similar will be their functions. Hence, a very simple approach to the analysis of connectivity is to find some spatial configuration of points representing brain areas that optimally reflects the similarities and differences in these areas’ connection patterns. Brain areas with similar patterns of connection should be placed close together, while brain areas with very different patterns of connections should be placed far apart. In this way a system’s “functional architecture” should be simply read off the configuration using the developed capabilities of the human visual system. There are many methods available to perform such an analysis of connectivity data, but the readiest to hand, and in fact the first employed in this task, is non-metric multidimensional scaling (NMDS) (Young, 1992; Young et al., 1995). Figure 15.2 shows the results of such an analysis for the primate visual system. The points of the structure are concentrated into an annular region of the “space”, as is expected from considering the quantitative aspects of connection data (see Young et al., 1995). The dimensions of the solution correspond approximately to the anterior-posterior and dorsal-ventral distribution of the areas as they are placed within the brain. Parietal
© 2002 Taylor & Francis
Connectivity and Function in the Cortex
355
Figure 15.2. The structure derived from NMDS analysis of the macaque cortical visual system matrix. The features of this structure and the means by which it was derived are described in Young (1992), and in much more detail in Young et al. (1995). Briefly, the structure implies that the visual system is divided into two gross streams, both of which are hierarchically organized, and which reconverge in areas of the temporal and frontal lobes. The figure is reprinted with permission from Young et al. (1995).
areas, for example, are placed toward the top of the diagram, whereas infero-temporal areas are placed toward the bottom. If it is remembered that data on area-to-area connectivity were the only type of information that entered the analysis—no information regarding the spatial arrangement of the areas on the cortical sheet entered the analysis— this aspect of the configuration implies that the spatial location of an area predicts to a degree the areas to which that area is likely to be connected. A possible explanation is that nearby areas tend to exchange connections with one another (Young, 1992; Young and Scannell, 1996; see also Cowey, 1979; Mitchison, 1991). A different explanation, that brain areas have migrated to come into positions conducive to economical wiring volume, has been suggested (Cherniak, 1994), but we have argued that this alternative explanation is not biologically plausible (Young and Scannell, 1996). The primary visual cortex (V1) is located farthest left in Figure 15.2. A group of prestriate areas including V2, V3, VP, V4t, V3A, MT, and, surprisingly, as it is a posterior parietal area, PIP, are placed close to V1. MT and V3A are placed further from V1 than other members of this group. MT is further distinguishable from its topological neighbours by its weak projection to frontal cortex area 46 and its apparently stronger projection to the frontal eye fields (FEF) (see Felleman and Van Essen, 1991). Every area in the
© 2002 Taylor & Francis
356
Malcolm P. Young
prestriate group projects to a further set of areas, which consists of areas of the posterior parietal cortex and the caudal superior temporal sulcus, namely FST, MSTd, MSTl, VIP, PO, LIP and DP. These areas then project to FEF, area 7a, the posterior region of the superior temporal polysensory area (STPp), and to frontal area 46 and the anterior STP (STPa) (Young, 1992; Young et al., 1995). Returning to V1, but now concentrating on the lower part of the structure, V1 projects to V4, while V2 and VP project to VOT. Signals are relayed from V4 and VOT into the areas of the inferotemporal (IT) cortex. The IT areas appear to be rather serially organized, with more anterior areas generally being placed successively further toward the right of the diagram (except AITv), and so further away from the sensory periphery. The areas of IT at the greatest remove from the visual periphery, that is, the highest order areas according to this analysis, are associated with areas TF and TH of the parahippocampal cortex. The topologically higher-order IT areas project to and receive from STPa and area 46 (Young, 1992, 1995; Young et al., 1995). It is a feature of the structure that relatively few connections pass across the central region between the parietal and inferotemporal groupings of areas, by comparison to the number that pass around the rim. Hence, the analysis suggests that there are two relatively distinct sets of areas in the macaque cortical visual system, which are much more profusely interconnected within groupings than between them. These two sets of areas correspond in straightforward manner to the dorsal and ventral streams of visual processing, which were proposed most clearly by Ungerleider and Mishkin (1982) on the basis of the behavioural effects of lesions. The higher-order areas of both streams are interconnected. This feature of the structure implies that there are opportunities for the reconvergence of processed visual information using “feed-forward” pathways in the rostral parts of the temporal lobe and in the frontal lobe (Young, 1992; Young et al., 1995). These aspects of the structure that results from NMDS analysis of the macaque cortical visual system indicate that, at the gross level of connections between brain areas, four principles underlie its organization. (i) Neighbouring areas tend to exchange connections; (ii) the system is dichotomized into two streams; (iii) both streams are broadly hierarchical, and (iv) the streams may reconverge in temporal and frontal areas (Young, 1992; Young et al., 1995). How robust are these conclusions? The grossest possible perturbation of the matrix is to assume that all unreported connections exist (Young, 1992). We analysed a matrix derived from coding all unreported connections as existing. Connections explicitly reported as having been looked for and found absent remained “0”. This “control” analysis yields a configuration in many respects similar to Figure 15.2. In both structures, the parietal and infero-temporal structures are segregated, with V1 and prestriate areas between them at one side, and STP and area 46 between them at the other. Quantitatively, the relation between the two structures is characterised by 76% of the variance in the one structure being explained by the other (i.e. the two structures are 76% the same in quantitative terms: Young, 1992, 1995). The “control” structure implies the same gross organizing principles as the structure in Figure 15.2. Even when the grossest possible perturbation is applied to the dataset, therefore, the gross conclusions are not perturbed. The solutions are similar because a sufficiently large number of connections have been confirmed absent (see Felleman and Van Essen, 1991). The main difference between the structures lies in shifts of the positions of less well-studied areas, such as VOT and V4t. These areas have their positions shifted toward the centre of the structure by their acquiring a large number of
© 2002 Taylor & Francis
Connectivity and Function in the Cortex
357
hypothetical new connections, which correspond to possible connections that have not yet been explicitly ruled out. It would be very surprising if these structures were in reality to possess such rich connectivity. In any case, the similarity between these structures suggests that the organizational conclusions are workably robust against future changes to the possible connections concerning which neuroanatomists have not thus far reported. A further question about robustness concerns the question of whether the NMDS solution for the visual system data in Figure 15.2 systematically reflects the data structure of the matrix. This question arises because where the badness-of-fit of an NMDS solution is very high (or very low), an annular structure can emerge in the solution for entirely spurious reasons (see Young et al., 1995, for a detailed discussion). Randomly ordered data, for example, give rise to very high badness-of-fit, and to annular configurations, in which the ordering of the points within the annulus is random. However, some data structures are themselves annular, as for example in the celebrated colour circle (Shepard, 1962). The traditional means of deciding between cases of “systematic-annularity” and “spuriousannularity” is to compare the badness-of-fit of a solution with analyses of comparable random data (in this case, a matrix of connectivity re-connected at random), thereby determining whether the solution is drawn from the distribution in which very low fit could have produced annular artefact (Stenson and Knoll, 1969). The probability of the badnessof-fit of the solution in Figure 15.2 falling within the distribution of random data with very low fit was less than 10–30, a probability that corresponded to a very large z-score for the real solution’s badness-of-fit (Young et al., 1994; 1995). Hence, there is no ground for believing that the NMDS solutions for the visual system data should not be trusted to be a systematic reflection of the connection data’s structure (cf. Simmen et al., 1994).
2.2. Analysis of the Visual System Data using Data Conditioning Methods and NMDS We have previously shown (e.g. Young et al., 1995) that several data conditioning methods, when coupled with NMDS, are very successful in recovering known parameters from test data at the same level of measurement as anatomical connection data. Data conditioning methods “wdsm1” (weighted dissimilarity transform-one) and “pth1” (path-length transformone) were particularly successful (for mathematical definition of these transforms please see Young et al., 1995). Analyses of visual system data by these data transformations and NMDS are of interest as they are a means to ensure that as few aspects of data structure as possible are obscured by the connection data’s genuine sparsity, in the small number of dimensions required in an output configuration. Figure 15.3 shows the solutions derived by wdsm1-NMDS and pth1-NMDS for the visual system, with the solution at the top being that for wdsm1. Both solutions are similar, despite the very different algorithms by which the data were transformed in each case. In both configurations, all the “dorsal stream” areas are concentrated in the top portion of each diagram, while all the “ventral stream” areas are concentrated in the bottom part. The two streams originate in a number of occipital visual areas, including V1, which are placed at the left, and appear to reconverge via STP and area 46, which are placed at the right. The wdsm1 and pth1 structures both share 92% of their variability with the untransformed structure, in Figure 15.2. The wdsm1 and pth1 configurations share 93% of their variability with one-another. They are both therefore very similar to one another, and very
© 2002 Taylor & Francis
358
Malcolm P. Young
Figure 15.3. Configurations derived by submitting the macaque visual system matrix to the wdsm1 (top) and pth1 (bottom) data-conditioning routines, and then to analysis by untied NMDS (see Young et al., 1995, for full details). The same conclusions about the gross organization of the system would be drawn from these solutions as from the untransformed data analysis. The figure is reprinted with permission from Young et al. (1995).
similar to the untransformed structure. The probabilities that these correspondences could come about by chance, according to approximate randomization tests, are all less than 1 in a million (Young et al., 1995). Hence, the same conclusions would be drawn concerning the gross organization of the system from these solutions as from Figure 15.2. The wdsm1 and pth1 solutions suggest, however, that there may be a further division of labour within the two visual streams. This is particularly apparent for areas in the dorsal stream, for which there seems to be a further division that draws areas PO, MSTl and VIP away from their associates. Similarly, a possible distinction between V4 and TF and the other ventral stream areas is suggested.
© 2002 Taylor & Francis
Connectivity and Function in the Cortex
359
2.3. Simple Statistical Properties of the Connection Patterns A simple further means of testing whether these organizing principles hold is to employ a χ2 test to determine whether the observed connections are those expected under the various hypotheses. One important dispute is about whether the visual system is segregated into distinct processing streams. On the one hand, analysis of the behavioural effects of cortical lesions (e.g. Ungerleider and Mishkin, 1982) and all analyses of connectivity so far undertaken (Young, 1992; Young et al., 1995; Hilgetag et al., 2000a) and, to some extent, the physiological evidence (e.g. Merigan and Maunsell, 1993) suggest that the system is divided into two gross streams. On the other hand, it is maintained by some that the system is not internally segregated into parallel streams (e.g. Martin, 1992; Goodhill et al., 1994). These hypotheses about the organization of the macaque visual system are easy to test decisively. If the system is dichotomized into streams, then the elements of the dorsal stream should be significantly more connected with their dorsal associates than with the elements of the ventral stream, and vice versa. Similarly, there should be significantly more connections that have been confirmed absent between the dorsal and ventral areas than within each of these groupings. If these comparisons were to fail to reach significance, then the null hypothesis, that the areas are not segregated, could not be rejected. We divided all the macaque cortical visual areas into four sets, which corresponded to “early”, “late”, “dorsal” and “ventral” groupings (Young et al., 1995). The “early” set of areas contained V1, V2, V3, VP, V3A, PIP and V4t; the “late” set contained areas FEF, 46, STPa, STPp, TF and TH; the “dorsal” group contained MT, MSTd, MSTl, FST, PO, LIP, VIP, DP and 7a; the “ventral” group contained V4, VOT, PITd, PITv, CITd, CITv, AITd and AITv. The analysis of reported connections showed the dorsal and ventral areas are much more connected internally than the null hypothesis predicts (χ2 = 17.2, p < 0.00004; Young et al., 1995). The analysis of connections that have been demonstrated absent showed the dorsal and ventral areas exchange many fewer connections than the null hypothesis predicts (χ2 = 18.6, p < 0.00002; Young et al., 1995). The hypothesis that the visual areas are distributed without segregation into a dorsal and a ventral stream is hence rejected at a very high level of statistical significance (Young et al., 1995). Exactly comparable tests of the hypothesis that the visual areas are serially ordered have also been undertaken (Young et al., 1995). The analysis shows that early and late visual areas are significantly more connected internally within each group than would be expected from the Null hypothesis (χ2 = 9.8, p < 0.002; Young et al., 1995). Early and late areas are significantly less connected between each other than would be expected from the Null hypothesis (χ2 = 10.9, p < 0.001) (Young et al., 1995). The analyses of the “confirmed absent” entries in the connection matrix illustrate the fact that important information about the organization of the system is carried by “connections” that are not present, and therefore that it would be helpful if these absences were more often reported explicitly by experimental neuroanatomists.
2.4. Optimal Set Analysis of Visual System Data The χ2 tests described above show that the incidence of connections agrees with predictions, for example by the pre-classification of structures into two streams. However, the analysis does not explore the possibility that better classifications of the structures into other clusters could be found. Hence, χ2 analysis cannot itself rule out the possibility that
© 2002 Taylor & Francis
360
Malcolm P. Young
there are more than two streams within the system. We explored whether clusters of areas could be found by computation that fit optimally the explicit criteria, both in the macaque visual system and in cortical systems in general. The requirement to define (for the above χ2 analyses) the way in which real data might quantitatively support or refute hypotheses about neural organisation forced us to specify an explicit definition of a connectionally differentiated stream (or cluster or system) (Hilgetag et al., 2000a). According to our definition, a cluster is a set of structures that are more connected among themselves than they are with any other structures, and more disconnected from other structures than they are among themselves. This definition is quite generally applicable. We developed a computational method, Optimal Set Analysis (OSA), to find clusters of areas that optimally fit the explicit criteria, using an optimisation algorithm with an explicit cost function formalised from our definition of what a cluster is, as above (Hilgetag et al., 2000a). This process was implemented in a network processor (Hilgetag et al., 2000a). For the macaque visual system, the processor obtained optimal (lowest-cost) solutions, all of which showed a clear separation of the visual areas into two main clusters. The first group comprises areas MSTl, MT, LIP, V2, V3, VP, V3A, PIP, V4, V4t, FST, VIP, DP, PO, FEF, and MSTd, and the second STPp, PITd, TF, TH, AITd, PITv, CITd, 46, AITv, CITv, STPa, and 7a. Primary cortical area V1 was associated with the first group in one third of the solutions and formed a separate cluster in the others. The only area with an alternating preference for the two groups was area VOT, which appeared in the first cluster in one third of the solutions and in the second cluster in the remaining solutions. The keen eye will notice that the assignments of area 7a and V4 in these solutions are the reverse of those usually suspected to obtain. Area 7a would be expected to cluster with its dorsal stream associates and not with ventral stream stations, and V4 should cluster with its ventral stream associates and not dorsal stations. However, a specific aspect of data structure gives rise to the apparent misassignment. For the χ2 test (above) and in Young et al. (1995), the ventral stream was defined as V4, VOT, PITd, PITv, CITd, CITv, AITd, AITv and the dorsal as MT, MSTd, MSTl, FST, PO, LIP, VIP, DP, 7a. All other areas were classified as either “early” or “late”. We re-examined the connectivity between the groupings of Young et al. (1995) and found that they represented the optimal (and unique) arrangement for these areas according to the OSA cost functions. This arrangement had a “cost” of 30, while the same arrangement with V4 and 7a swapped carried a cost of 51. V4 and 7a swapped into the “incongruous” assignments derived by OSA only when the connections of all 32 areas were included. When all connections are included, the cost moves from 135 for our apparently paradoxical arrangement to 141, when V4 is placed in the ventral and 7a in the dorsal stream. Hence, adding the “early” and the “late” areas of Young et al. (1995) causes V4 and 7a to move to the anomalous groupings. The explanation is that the earlier visual areas tend to cluster with the dorsal stream stations. Many of these areas share many interconnections with V4, and so draw V4 toward the dorsal cluster when all areas are included. Correspondingly, all the “late” areas except FEF tend to cluster with the ventral stream stations, and share many connections with 7a, and hence draw this area toward the ventral grouping. Thus, the apparent misassignments of these areas reflect a bona-fide feature of data structure: the dorsal-ventral dichotomy is not orthogonal to the early-late organisation of areas. The dorsal stream tends to be “lower”, and the ventral stream “higher”. The positions of V4 and 7a in the latter respect affect their clustering assignments. V4 is lower than 7a, and so tends to cluster with its early associates, while the opposite is true for 7a.
© 2002 Taylor & Francis
Connectivity and Function in the Cortex
361
2.5. Seriation Analysis of the Macaque Cortical Visual System Data Seriation analysis has previously been used to examine the chronological order of archaeological grave site data (Wilkinson, 1971; Laporte and Taillefer, 1987), but has recently been suggested as a means of investigating serial ordering in connection data (Simmen et al., 1994). The method attempts to find the uni-dimensional ordering of the elements of a matrix, amongst the n! (factorial n) possible orderings, that minimizes a measure of distance between the elements. It does this by finding permutations of the rows that minimize the cumulative mismatch between all rows. The row order then indicates the serial ordering of the rows. In the case of connection data, this ordering then specifies the optimal serial ordering of the brain structures. We implemented a seriation algorithm using simulated annealing to find optimal orderings (Young et al., 1994, 1995). Several organizational features were common to optimal length orderings. Parietal and IT areas were always segregated as far apart as possible, being joined at the one side by V1 and the prestriate areas, and at the other side by area 46 and the parcellations of STP. Again, then, very similar organisational features emerged for the visual system from this analysis as for others. We used Procrustes rotation to provide a quantitative measure of the closeness of relation between the results of the seriation algorithm and that of NMDS. Coefficients were distributed about a mean of 0.9 (p < 0.000001) (Young et al., 1995).
2.6. Hierarchical Analysis of the Macaque Cortical Visual System Hierarchical analysis (Rockland and Pandya, 1979; Maunsell and Van Essen, 1983; Felleman and Van Essen, 1991) represents a widely acknowledged approach to finding organisational features in connection data. This approach examines the patterns of laminar origins and terminations of projections between cortical areas, using a framework of some simple rules. Connections from one area to another are classified as ascending if they originate from supragranular layers of the cortex, or bilaminarly from superficial and deeper layers, and terminate mainly in layer 4. Descending connections can arise in bilaminar fashion from upper and deeper layers, or in a unilaminar pattern from infragranular layers, but tend to terminate in laminae other than layer 4. Connections classified as lateral originate in superficial and infragranular layers, and terminate in a columnar pattern throughout the cortical mantle. This classification scheme fails to accommodate about 10% of the connections in the monkey visual system for which some laminar information is known. These rules thus determine whether projections should be classified as “ascending”, “descending” or “lateral”. Application of these rules to experimental data yields a set of constraints that defines pair-wise hierarchical relations between areas. It is then possible to arrange the cortical areas into largely consistent hierarchies, which involve few violations of the pair-wise hierarchical constraints. Hence, this analysis demonstrates that some sensory systems are hierarchically organised overall, and it gives insight into the ordering of structures in each hierarchy (e.g. Felleman and Van Essen, 1991). Re-analysing the issue of hierarchical classification of data from the monkey visual system, however, we calculated that the total number of possible hierarchies for these areas is greater than 1037 (Hilgetag et al., 1996, 2000b). Considering the huge numbers of possible hierarchies, the incompleteness and partial inconsistency of the experimental data, and the use of a discrete cost function (number of violated rule constraints), we greatly doubted that a uniquely optimal solution could be obtained by hand, despite such
© 2002 Taylor & Francis
362
Malcolm P. Young
orderings having been reported in the literature. Accordingly, we constructed a computer program that could perform hierarchical analysis automatically. To do this, we caused a simulated network’s structure itself, rather than just the activity of its elements (as would be the case in a conventional neural network), to reflect the relations between the cortical areas (Hilgetag et al., 2000b). An algorithm then manipulated the structure of this network by simulated annealing until its structure optimally reflected the input constraints. The processor found more than 150,000 different solutions, all of which had the same optimally-small number of rule violations (Hilgetag et al., 1996, 2000b). All the computed hierarchies possessed a cost of 6 violated rules. In all cases, this involved two symmetrical pairs of three anatomical connection constraints that could not be satisfied for any particular hierarchy. This is a smaller number of violations than for any previous manuallyobtained solution. The familiar Felleman and Van Essen scheme of the visual cortical hierarchy (Felleman and Van Essen, 1991), for instance, possesses 8 constraint violations, when violations are counted in the same way. This cost is impressive given the informal methods used, but it is unlikely that the hierarchy of Felleman and Van Essen (1991) is found in the top million hierarchies (Hilgetag et al., 2000b). The number of rule violations in our computed solutions was very small by comparison to optimal solutions derived from randomly shuffled tables, demonstrating that the regularities captured experimentally are surprisingly systematic. The number of levels in the optimal hierarchies ranged between 13 and 24 (Hilgetag et al., 2000b). Hence, optimal solutions possess markedly more hierarchical stages than
Figure 15.4. Frequency distribution of optimal hierarchies for visual areas (Hilgetag et al., 2000b). The boxes are shaded according to the relative occurrence of an area at a particular level in all 152 803 computed solutions. The main peaks of the area frequencies are denoted by frames in thicker lines, and the ordering of the peak solution also represents an optimal hierarchy. Violations of the hierarchical constraints are as follows: for the constraint FST £ MST, relative occurrence of violations, in all solutions circa: 17%; for FST < STPp: 14%; for LIP = PITv: 12%; for LIP £ MSTd: 2%; for FST ≥ TF: 2%; for MSTd < PITv: 2%; for FST ≥ PITd: <<1%; and for MSTd < PITd: <<1% (together with their corresponding symmetrical rules: e.g. MSTd ≥ FST). The first three violations, together with their counterparts, are the violations for the peak solution. Reprinted with permission from Hilgetag et al. (2000b).
© 2002 Taylor & Francis
Connectivity and Function in the Cortex
363
have been apparent hitherto. Because there are so many equally well-fitting optimal hierarchies, it is misleading to single out one particular hierarchy from the large solution set, and impractical to show all of the optimal hierarchies. A statistical summary of the many solutions may therefore be the most appropriate format in which to represent the hierarchical structure of the primate visual system revealed by current data and rules. Figure 15.4 reflects the frequency with which one area appears on a particular hierarchical level, taking into account all 152,803 different solutions. The hierarchical constraints were sufficient to fix only V1 and V2 uniquely. These areas were always found on levels 1 and 2, respectively. The placement of all other areas depended on the structure of a particular solution. The positions of some areas were strongly associated with those of others, since these areas had an identical frequency distribution. This indicates a fixed position relative to each other in all the solutions. These relationships concerned, aside from V1 and V2, areas V4t and MT (same level), MSTd and VIP (same level), and CITv, CITd, and STPp (same level). We compared these results from hierarchical analysis of laminar data with that from NMDS analysis of area-to-area data. In lieu of performing 150,000 Procrustes rotations of the new and optimal orderings against the NMDS solution in Figure 15.2, we compared the median hierarchical solution with the NMDS solution. The median hierarchical model, which possessed only one dimension, nonetheless accounted for 41% of the variability of the two-dimensional NMDS configuration. Hence, these completely independent methods, deployed on completely different types of data, show remarkable agreement about the ordering of cortical stations in the visual system. 2.7. Summary of Analyses of the Macaque Cortical Visual System All the various different analyses described in the foregoing sections agree, quantitatively, and significantly (in statistical terms). Recently, analysis of the propagation of activity through the visual system after experimental disinhibition has also concurred with the organisational principles derived above (Stephan et al., 2000). Hence, three propositions account for results from six independent methods of analysis of three different types of neurobiological data. These are: (i) that the macaque visual system is hierarchically organized; (ii) It is divided into gross streams; (iii) It provides opportunities for processed visual signals to reconverge. We suppose, therefore, that NMDS, χ2 analysis, seriation, Optimal Set Analysis, and computational hierarchical analysis have faithfully extracted underlying aspects of the structure from their particular types of data, and that these data themselves captured real aspects of a self-consistent neural system. Indeed, it becomes increasingly difficult to find explanations for the concordances between the different results that do not acknowledge that they have captured real aspects of the organisation of the visual system. Aspects of the functional interpretation of these purely structural results are examined in a later section.
3. MACAQUE CORTICAL SYSTEMS In the above account, analysis of connections between visual cortical areas proceeded by different methods and operated on several different kinds of connectivity data. Nonetheless, the results of all these different approaches concurred in their conclusions about the
© 2002 Taylor & Francis
364
Malcolm P. Young
organisation of the cortical visual system. This mutual corroboration prompts the hope that these approaches faithfully recover many aspects of the underlying structure of connectivity data, and that, consequently, they may be used to inform wider questions about the organisation of the whole macaque cerebral cortex. I turn now, therefore, to a brief review of the application of the same approach to the connections of the entire cerebral cortex, including the other major sensory systems in the macaque brain. Analysis of connectional data begins by identifying cortical regions of interest, and continues by collating information on the projections between these areas. This information can be represented in connection matrices for analysis by methods such as NMDS and seriation, and those aspects of it that bear on laminar patterns of connectivity may be represented in a table of constraints for analysis by hierarchical optimization. For the primate cerebral cortex, we based the division of the cortical sheet into areas principally on the parcellation developed by Felleman and Van Essen (1991), except for the areas of the superior temporal cortex for which we followed the parcellation by Pandya and Yeterian (1985). The neuroanatomical literature was examined for connections between the 72 areas of this parcellation, and reported connections were collated into a form tractable for data analysis (Young, 1993). We previously divided the connectivity matrix into subsets of areas that corresponded, according to conventional knowledge, to the cortical auditory and somatosensory-motor systems, and we also examined the whole matrix (Young, 1993; Young et al., 1994). These connection matrices were analysed using the full battery of methods from neuroinformatics, employed as above, for the visual structures. I turn first to results of these analyses for the central sensory systems, and then to those for the whole cortical system as collated. 3.1. Analysis of Macaque Auditory Cortex Analysis of data from the primate auditory cortex, showed a clustering of secondary areas, including paAc, reit, paAl, proA and Tpt, around the primary auditory area, the auditory koniocortical area (KA). Area paAr appeared to be less peripheral than its neighbours. Afferent signals would appear to be processed somewhat successively along the superior temporal gyrus (STG) from, for example, area Tpt, the caudalmost field of the STG, through TS3, TS2 to TS1, and thence through the dorsal temporal polar cortex, TGd. The auditory areas of the rostral STG interact with elements of the limbic system, namely the entorhinal cortex, perirhinal cortex (35) and, through these structures, the hippocampus, which are all positioned at the greatest connectional remove from the auditory periphery. The primate cortical auditory system hence appears to be hierarchically organized, with a primary area, secondary areas and auditory association areas progressively distant topologically from the auditory brainstem and periphery. Unlike the organization of the primate cortical visual system, however, the auditory system presents itself in the NMDS solution as a single hierarchy, since “horizontal” connections between areas at a similar remove from the primary area are as numerous as those between stations above and below each structure. 3.2. Analysis of the Macaque Somatosensory-Motor Cortex Analysis of data from the primate somatosensory-motor cortex shows the post-central primary somatosensory cortex in areas 3a, 3b, 1 and 2 to be grouped. These relatively
© 2002 Taylor & Francis
Connectivity and Function in the Cortex
365
peripheral areas are then associated with areas 5 and SII, and these areas with area 7b, retroinsular (Ri) and insular cortex (Ig and Id), which thus appear to comprise the higherorder somatosensory fields. These areas interact with limbic structures, such as the entorhinal cortex, perirhinal cortex (area 35), and these structures contact the hippocampus. These features suggest that the cortical somatosensory system is organized as a single hierarchy, somewhat as the cortical auditory system (Young, 1993): “horizontal” connections between areas placed at a similar remove from the primary area are as numerous as those between stations above and below each. One feature of this system, however, that is rather unlike either the visual or auditory systems, is that part of the system comprises the cortical motor system. This part includes the medial supplementary motor area (SMA), the premotor cortex (area 6), and the primary motor cortex (area 4). These motor areas are arranged in what appears to be a hierarchy, in which the primary motor area is associated with primary somatosensory areas, the premotor area with higher somatosensory areas, and SMA with still higher ones. Primary motor cortex (area 4) seems not to be connected at the cortical level to any area outside the somatosensory-motor system, which may imply that, as well as being a sensory hierarchy, this system plays an important role in the integration of cortical motor signals (Young et al., 1994). 3.3. Analysis of the Connectivity of the Whole Primate Cortex Figure15.5 represents 834 connections between 72 structures in the visual, auditory, somatosensory-motor, frontal and limbic cortex of the macaque monkey (see Young, 1993; Young et al., 1995). Despite the complexity of the solution structure, features of the gross organization of the systems are apparent in it, and in general every organizational feature apparent has been corroborated by subsequent analyses with other methods (e.g. Hilgetag et al., 2000a; Stephan et al., 2000; Young et al., 1995). Propitiously, areas within each of the visual, auditory and somatosensory-motor systems are clustered together. Each cluster of central sensory structures is topologically distant from the other clusters of sensory areas. Visual cortical stations are placed at the left, with earlier visual areas located toward the bottom, and successively higher visual areas located progressively toward the top left of the structure. The two streams of the visual system are clearly separated, with elements of the dorsal stream (e.g. FEF, LIP, 7a) positioned relatively further toward the centre of the diagram, where they are drawn by their greater connectivity outside the visual system, chiefly with the somatosensory-motor system. Auditory cortical areas are positioned at the top right of the diagram, with primary and secondary areas placed at the extreme right, and the areas of the higher auditory cortex located progressively further toward the top left. The somatosensory-motor cortex is located at the bottom right of the configuration, with the primary areas at the very bottom. The primary motor cortex (area 4) is placed in the centre of this group of areas and is positioned furthest from the centre of the structure. Area 7b, as well as being strongly connected to the other areas of the somatosensory-motor system, has rich interconnections with the other sensory systems and has been drawn nearer to the centre of the structure by these connections (Young, 1993). Structures in which the most elaborated sensory signals are likely to be processed are connected to a further cluster of areas positioned at the top left of the diagram. This cluster of areas is composed of the limbic system (the entorhinal, perirhinal and cingulate cortex and the hippocampus), in association with some of the areas of the frontal and prefrontal
© 2002 Taylor & Francis
366
Malcolm P. Young
Figure 15.5. The topological organization of the entire primate cortical processing system as represented in the collated connection data (Young, 1993; Young et al., 1994, 1995). Aspects of the organisation of these neural systems are described in the text. Reprinted with permission from Young (1993).
cortex (e.g. areas 13, 10 and 12). The amygdala is placed near the geometric centre of the configuration, mainly because of a very rich output connectivity: the basal nuclei of the amygdala project in varying degrees of density to all but 8 of the areas of the cortical parcellation. Its inputs, however, arise selectively in the higher order sensory processing areas. The “fronto-limbic complex”, at the top left of the diagram, is the cluster of areas furthest from the sensory-motor periphery of the cortical processing system, which is represented at the bottom and right-hand edge of the structure. This result confirms the idea that the limbic system is topologically central, but also shows the topological association between limbic structures and elements of frontal cortex. Frontal cortex (in contrast to occipital cortex) however, may not be a connectionally homogeneous set of areas, and therefore not functionally homogeneous either, since some frontal cortical areas are associated with the limbic system and the fronto-limbic complex, while others are more associated with one of the sensory modalities (e.g. areas 8 [FEF] and 46 with vision, and area 45 with the somatosensory-motor system). Areas 14, 32 and 25 appear in some analyses to form a somewhat distinct grouping, which is increasingly difficult to impute solely to the low number of connections reported for area 14 (cf. Young et al., 1995). The anterior superior temporal polysensory area (STPa) appears to be a central area in the topology of the cortical processing system, and the relative positioning of this area in
© 2002 Taylor & Francis
Connectivity and Function in the Cortex
367
the system of connections may be related to the elaborate stimulus preferences of cells in that area (e.g. Perrett et al., 1990; Young and Yamane, 1992). The somatosensory-motor system is located between the clusters of areas associated with processing the other two sensory modalities: it appears connectionally closer to the visual and auditory systems than vision and audition are to each other. Cortical communication between the auditory and visual systems, except within the fronto-limbic complex, seems to be limited to interactions mediated through the frontal eye fields (FEF). Interactions between the somatosensory-motor system and audition are more abundant, and particularly involve the connections of areas 7b and Tpt. Interactions of the visual and somatosensory-motor systems are also rich, especially those mediated by areas 7b, FEF, 46 and LIP. 3.4. Summary of Analyses of the Macaque Cortical Systems At the aggregate level, the analyses of connectivity reviewed above suggest a division of the cortical areas into four major topological clusters of areas. These clusters correspond to the visual, auditory, somatosensory-motor systems, and the fronto-limbic complex.
Somato-motor system
Vi su
al
ry ito ud em A syst
sy
ste
m
Fronto-limbic complex
Visual input
Somatosensory input
Auditory input
Motor output
Figure 15.6. Summary diagram showing gross features of the organization of the macaque cerebral cortex. The sensory-motor periphery is represented as being at the bottom of the diagram. Three hierarchical sensory systems, the visual, auditory and somatosensory-motor systems, connect the periphery with a fourth system, the fronto-limbic complex, which is at the greatest connectional remove from the periphery. There is restricted cross-talk between the sensory hierarchies outside the fronto-limbic complex. Amended from Young (1993).
© 2002 Taylor & Francis
368
Malcolm P. Young
The analyses suggest that all the major cortical sensory systems are organized in a hierarchical manner, and that the fronto-limbic complex is situated at the furthest remove from the sensory-motor periphery, due to its being connected mainly with the higher-order areas of each sensory system. The primate visual system is unlike the other cortical sensory systems in that it is larger and more complex, and in that it, alone of any central cortical sensory system, possesses a clear division into two internal streams of processing. These features of the organization of the primate cortical systems are summarized in Figure 15.6.
4. WHAT DOES NEUROANATOMICAL ORGANISATION MEAN? Thus far, I have reviewed a variety of analyses of purely structural data. It has been a hope of long standing that better knowledge of brain organisation would aid improved understanding of brain function, and so I now turn to a number of functional interpretations of these structural results. This entails trying to forge explicit links between structure and function. It is already apparent that structure and function are sufficiently closely linked at the systems level to allow successful predictions from aspects of organisation to aspects of function. For example, the mystery of where cells with particular complex motion preferences might be found in the cat brain was resolved by scrutinizing structural diagrams of exactly the sort described above (Scannell et al., 1996). The strategy was to employ the patchy information available from neurophysiology on the properties of some stations, together with the structural diagrams of that system, to predict the most likely location for cells with the sought-after property. Such locations were then studied neurophysiologically. This strategy has succeeded twice, in the two studies in which it has been explored (Scannell et al., 1996, 1997; cf. Merabet et al., 1998). Doubtless, some other search strategy might have succeeded, but in fact none did. Similarly, this strategy might not have succeeded, but in fact it did. Another example of an explicit structure-function relationship is that the patterns of activation over the cortex following experimental disinhibition could be predicted using connectivity matrices (Young and Scannell, 2000; Stephan et al., 2000; Kötter and Sommer, 2000). In this case, it is particularly interesting that exclusion of the many indirect pathways by which activity might propagate from one station to another diminishes the goodness-of-fit between structure and observed activity propagation (Kötter and Sommer, 2000). It is instructive also to compare those possible structure-function links that have led to successful predictions from those that have not. One informative example of a “failure” comes from the limbic system. Even though the prelimbic cortex of the rat (PL) receives a direct connection from CA1, electrophysiological experiments that attempted to find so-called “head-direction” or “place” cells in PL reported a null result (Poucet, 1997; Jung et al., 1998). Similarly, V4 and MT exchange quite robust projections, but each is the home of neurones with very different stimulus selectivities. An interpretation of these various successes and “failures” in exploring possible structurefunction relationships is that specific individual projections have a rather lower functional significance than many might have expected. What appears to matter most is the pattern of connections. This is reflected in the striking correspondence between the positions taken by particular cortical stations in the configurations from analysis of connectivity— which reflect the patterns of connections they make and receive—and the similarities or
© 2002 Taylor & Francis
Connectivity and Function in the Cortex
369
differences in physiological properties of neurones in these areas (Young, 1992). Areas containing cells with similar response properties tend to have similar patterns of connections, and to be placed close together in the configurations. This correspondence implies that local connectivity, and perhaps also the biophysics of cortical cells, may not vary very greatly across the cortex (Mountcastle, 1982), so that the “location” of an area in the pattern of cortical macro-circuitry may determine in large part the area’s functional properties. The correspondence between patterns of connection and function has proved to be an aid to prediction, but the finding that patterns of connections are more important to function than individual projections cuts across widely held, though often implicit, views of cortical function. To illustrate this, and explain why there should be a correspondence of this kind and no other, I return to considering the cortical visual system. I believe there exists a very widely credited and largely unchallenged notion of how the visual system functions. This is the view that vision consists of hierarchical analysis or filtration of the retinal input generated by a scene or an image. This vision-as-analysis model supposes that most of the useful information about the visual world is present in the immediate input from the retina. Feed-forward connections then convey this information at high gain and high fidelity to (and through) a hierarchically organised visual cortex, information being successively extracted by increasingly sophisticated receptive fields, each effectively a filter, situated at each successive stage. The goal of this process is to provide representations of objects, surfaces and spatial relationships, each being extracted from the input information in the visual subsystem to which such computations are imputed (e.g. Lennie, 1998). In this framework, the content of the scene and its relationship to the observer’s knowledge of the world are of little importance, although feedback and lateral connections are thought to play some contextual role. In an extreme form, it has been suggested that the visual system is simply a stacked series of competitive learning networks, together forming a kind of “bagatelle board”, in which an input vector falls in at the eye, is fed forward through the system, and an output vector, possessing the virtues of invariance, emerges at the other end, presumably to inform frontal and limbic structures. The finding that patterns of connections are more important to function than individual projections is inimical to vision-as-analysis in the following way. If the visual system is designed to analyse retinal input, signals derived from this input should be relayed with high gain and fidelity throughout the system. Correspondingly, it is widely assumed that a large proportion of the synapses in V1 come from neurones in the LGN, the principal relay for signals from the eye to the cortex in primates. But this is not the case. Quantitative neuroanatomy shows that axons from the geniculate account for no more than 5% of the total excitatory synapses on the average pyramidal neurone in the layers in V1 receiving from the geniculate (e.g. Peters and Payne, 1993). Consequently, over 95% of the excitatory synapses, even in geniculo-recipient layers in primary visual cortex, are made by neurones that originate, not in the LGN, but from other parts of V1, other cortical areas, and other thalamic nuclei. Similarly, individual cortico-cortical projections take up only a low proportion of the synapses in their targets. The projection from V1 to MT provides fewer than 5% of the excitatory synapses in MT (Anderson et al., 1998), and the projection from V2 to V1 provides less than 6% of the excitatory synapses in V1 (Budd, 1998). However, while individual extrinsic thalamo-cortical or cortico-cortical projections only rarely contribute more than 5% of the synapses in a given area, each cortical area in this system typically receives a large number of inputs from other areas and thalamic nuclei (27 on average; Scannell and Young, 2000). When all the connections are considered (Scannell
© 2002 Taylor & Francis
370
Malcolm P. Young
and Young, 2000; Kennedy and Barone, 1999), perhaps 30% of the synapses within a given volume of visual cortex typically come from distant cortical areas or non-geniculate thalamic nuclei. The system, therefore, seems not to be nearly as concerned with transmission of retinal input with high gain and fidelity as one would expect, if analysis of the input were the principal computational goal. Retinal input appears not to be the primary or only information source, even at the very first cortical stage. Also, long-range connections reach into local circuitry and form a substantial part of it. Local computations will hence be markedly affected by information washing over them from a very wide variety of sources, almost all of them internal. In addition, because this cross-sharing of synapses occurs in every station, description of the dynamics of the system will involve interaction terms— which will be large—and so both local computation and global aspects of the activity in the whole visual system will reflect highly interactive dynamics. Hence, a highly dynamic “information soup”, in which retinal input plays only a part, seems a much more likely model than bottom-up sequential extraction of retinal information. This conclusion resonates with earlier ones from neuroanatomy, which I believe deserve greater attention than they have received (e.g. Braitenberg and Schüz, 1991; Miller, 1991). Furthermore, the inputs to an area are akin to a veritable “chorus”. They are a much more numerous influence than the strongest single input. Thus the overall pattern of connections will be an important determinant of the functional properties of cortical areas, explaining the above mentioned correspondence between the patterns of connections reflected in analyses of connectivity and aspects of function visible to neurophysiologists. Neuroanatomical, neurophysiological and psychophysical evidence now suggests that it may be better to think in terms of vision as inference rather than as analysis (see e.g. Knill and Richards, 1996). On this inferential model, the majority of useful information is present in the system and not in the immediate retinal input (Scannell and Young, 2000). The findings from neuroanatomy and neuroinformatics which I have reviewed reflect an architecture rather inconsistent with bottom-up analysis, and favourable for inference. The high degree of cross-sharing of synapses between stations, for example, suggests strongly interactive dynamics at the systems level, which might generate a dynamic and transitory global consensus in the system. Cross-sharing of synapses in many visual stations is precisely the architecture expected for a network implementing inference, because it provides for local computation of a wide range of information on which to base inferences. In the inferential framework, neurones signal the probability that some feature constellation is present at some external location, and do so on the basis of knowledge of the statistical structure of the visual world and of any other information available (e.g. memory for recent events). Hence, in this framework, a neurophysiologically-mapped field represents the neurone’s projection of an inferred probability onto the world. It does not represent the result of simple analysis of the local features within the classical RF. However, electrophysiological effects made apparent with improbable stimuli do not dissociate inference from analysis, or projective from receptive fields. If the prior probabilities of stimuli (“priors” hereafter) are flat, the function of afferent likelihood maps directly onto the posterior probability, so that inference reduces to analysis. Improbable stimuli hence may themselves suggest, potentially misleadingly, that vision is bottom-up analysis. When priors are not flat, as for example during the processing of normal visual scenes, neurones should behave in predictably different ways, and the two models will dissociate experimentally. Indeed, neurophysiology already suggests that neurones do behave in different
© 2002 Taylor & Francis
Connectivity and Function in the Cortex
371
ways when confronted with traditional experimental stimuli compared to normal vision (Baddeley et al., 1997; Gallant et al., 1998; Rieke et al., 1997), and that visual neurones generate tuned responses that can be only weakly dependent on analysis of the local image region corresponding to their classical RF (Grosof et al., 1993; Peterhans and von der Heydt, 1989; Treue and Maunsell, 1996). In general, priors should be made available to local computation via connections that are remodelled by activity-dependent processes, and so come to reflect properties of the visual diet and visual digestion. Extensively remodelled connections include “feedack” projections and local connectivity (Barone et al., 1995; Kennedy et al., 1989). This may explain why the effects of feedback projections on their targets have been so elusive physiologically: traditional improbable stimuli would not engage these projections in the computational role they enjoy in normal vision. One of the ramifications of these suspicions about inferential processes in the visual cortex is that stimulus realism should matter. Natural, or at least, probable, stimuli may be required if normal vision is to be understood. The old certainties, among them that one can employ a bar or grating in a denuded visual scene and hope to understand normal vision, are beginning to give way. At present, there is insufficient information from well-designed experiments to conclude that visual computation is actually inferential, but the clearly different predictions that the traditional and inferential models make for neuronal processing when priors are peaked suggest that visual neurophysiologists may again be entering, as it is said in Chinese, interesting times. The fact that a single theoretical net can be cast over results from neuroanatomy, neuroinformatics, neurophysiology, and psychophysics suggests that brain organisation and function actually are now beginning to inform oneanother, even if the particular theory proves to be in error.
APPENDIX List of Abbreviations V1 V2 V3 VP V3A V4 VOT V4t MT MSTd MSTl FST PITd PITv CITd CITv AITd AITv
primary visual cortex, area 17 second visual area, part of area 18 third visual area ventral posterior visual area visual area 3A the fourth visual area visual occipito-temporal area transitional zone of V4 abutting MT middle temporal area dorsal middle superior temporal area lateral/ventral middle superior temporal area floor of superior temporal posterior inferotemporal, dorsal posterior inferotemporal, ventral central inferotemporal, dorsal central inferotemporal, ventral anterior inferotemporal, dorsal anterior inferotemporal, ventral
© 2002 Taylor & Francis
Malcolm P. Young
372
STPp STPa TF TH PO PIP LIP VIP DP 7A FEF 46 TGv ER HIPP 3a 3b 1 2 5 Ri SII 7b Ig Id 35 4 6 SMA 30 23 24 9 32 25 14 10 15 12 11 13 G PaAr PaAl PaAc KA proA
superior temporal polysensory, posterior superior temporal polysensory, anterior area TF of the parahippocampal cortex area TH of the parahippocampal cortex parieto-occipital visual area posterior intraparietal lateral intraparietal ventral intraparietal dorsal prelunate area 7a area 8, frontal eye fields frontal area 46 ventral temporal polar cortex entorhinal cortex hippocampus primary somatosensory cortex area 3a primary somatosensory cortex area 3b primary somatosensory cortex area 1 primary somatosensory cortex area 2 area 5 of the somatosensory cortex retroinsular cortex second somatosensory area parietal area 7b insula granular insula dysgranular perirhinal cortex primary motor cortex premotor cortex supplementary motor cortex area 30 posterior cingulate anterior cingulate prefrontal area 9 area 32 area 25 prefrontal area 14 area 10 area 15 area 12 area 11 area 13 gustatory cortex auditory parakoniocortical, rostral auditory parakoniocortical, lateral auditory parakoniocortical, caudal auditory koniocortical, primary auditory prokoniocortex
© 2002 Taylor & Francis
Connectivity and Function in the Cortex
reit TGd TS1 TS2 TS3 Tpt
373
auditory retroinsular temporal cortex dorsal temporal polar cortex superior temporal auditory area 1 superior temporal auditory area 2 superior temporal auditory area 3 auditory area Tpt
REFERENCES Anderson, J.C., Binzegger, T., Martin, K.A.C. and Rockland, K.S. (1998) The connection from cortical area of V1 to V5: a light and electron microscopic study. Journal of Neuroscience, 18, 10525–10540. Baddeley, R., Abbot, L.F., Booth, M.C.A., Sengpiel, F., Freeman, T., Wakeman, E.A. and Rolls, E.T. (1997) Responses of neurons in primary and inferior temporal visual cortices to natural scenes. Proceedings of the Royal Society, series B, 264, 1775–1783. Barone, P., Dehay, C., Berland, M., Bullier, J. and Kennedy, H. (1995) Developmental remodelling of primate visual cortical pathways. Cerebral Cortex, 5, 22–38. Braitenberg, V. and Schüz, A. (1991) Anatomy of the Cortex: Statistics and Geometry of Neuronal Connectivity (Studies in Brain Functio series, Vol. 18). Berlin: Springer-Verlag. Budd, J.M.L. (1998) Extrastriate feedback to primary visual cortex in primates: a quantitative analysis of connectivity. Proceedings of the Royal Society, series B, 265, 1037–1044. Burns, G.A.P.C. and Young, M.P. (2000) Analysis of the connectional organisation of neural systems associated with the hippocampus in rats. Philosophical Transactions of the Royal Society: Biological Sciences, 355, 55–70. Cherniak, C. (1994) Component placement optimization in the brain. Journal of Neuroscience, 14, 2418–2427. Cowey, A. (1979) Cortical maps and visual perception. Quarterly Journal of Experimental Psychology, 31, 1–17. Felleman, D.J. and Van Essen, D.C. (1991) Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1–47. Gallant, J.L., Connor, C.E. and Van Essen, D.C. (1998) Neural activity in areas V1, V2, and V4 during free viewing of natural scenes compared to controlled viewing. Neuroreport, 9, 85–90. Goodhill, G.J., Simmen, M.W. and Willshaw D.J. (1994) An evaluation of the use of multidimensional scaling for understanding brain connectivity. Philosophical Transactions of the Royal Society of London—Series B: Biological Sciences, 348, 265–280. Grosof, D.H., Shapley, R.M. and Hawken, M.J. (1993) Macaque V1 neurons can signal illusory contours. Nature (London), 365, 550–552. Hilgetag, C.-C., O’Neill, M.A. and Young, M.P. (1996) Indeterminate organization of the visual hierarchy. Science (Washington), 271, 776–777. Hilgetag, C.-C., O’Neill, M.A. and Young, M.P. (2000a) Hierarchical organisation of macaque and cat cortical sensory systems explored with a novel network processor. Philosophical Transactions of the Royal Society: Biological Sciences, 355, 71–89. Hilgetag, C.-C., Stephan, K., Burns, G., O’Neill, M.A. and Young, M.P. (2000b) Clustered organisation of cortex and connectivity in macaque monkey and cat. Philosophical Transactions of the Royal Society: Biological Sciences, 355, 91–110. Jung, M., Qin, Y., McNaughton, B. and Barnes, C. (1998) Firing characteristics of deep layer neurons in prefrontal cortex in rats performing spatial working memory tasks. Cerebral Cortex, 8, 437–450. Kaas, J.H. and Huerta, M.F. (1988) The subcortical visual system of primates. In: H.D. Steklis and J. Erwin (eds), Comparative Primate Biology, Vol. 4: Neurosciences. New York: A.R. Liss, pp. 327–391. Kennedy, H. and Barone, P. (1999) Relative contribution of cortical projections to areas V1 and V4. Society for Neuroscience Abstracts, 25, 572.18. Kennedy, H., Bullier, J. and Dehay, C. (1989) Transient projections from the superior temporal sulcus to area 17 in the newborn macaque monkey. Proceedings of the National Academy of Science, U.S.A., 86, 8093–8097. Knill, D.C. and Richards, W. (1996) Perception as Bayesian inference. Cambridge: Cambridge University Press. Kötter, R. and Sommer, F.T. (2000) Global relationship between anatomical connectivity and activity propagation in the cerebral cortex. Philosophical Transactions of the Royal Society: Biological Sciences, 355, 127–134. Laporte, G. and Taillefer, S. (1987) An efficient interchange procedure for the Archaeological seriation problem. Journal of Archaeological Science, 14, 283–289. Lennie, P. (1998) Single units and visual cortical organization. Perception, 27, 889–935. Martin, K.A.C. (1992) Parallel pathways converge. Current Biology, 2, 555–557.
© 2002 Taylor & Francis
374
Malcolm P. Young
Maunsell, J.H. and Van Essen, D.C. (1993) The connections of the middle temporal visual area (MT) and their relationship to a cortical hierarchy in the macaque monkey. Journal of Neuroscience, 3, 2563–2586. McDonald, R.P. (1986) Describing the elephant: structure and function in multivariate data. Psychometrika, 51, 513–534. Merabet, L., Desautels, A., Minville, K. and Casanova, C. (1998) Motion integration in a thalamic visual nucleus. Nature (London), 396, 265–268. Merigan, W.H. and Maunsell, J.H.R. (1993) How parallel are the primate visual pathways? Annual Review of Neuroscience, 10, 363–401. Miller, R. (1991) Cortico-hippocampal interplay and the representation of contexts in the brain. (Studies of Brain Function series, Vol. 17), Berlin: Springer-Verlag. Mitchison, G. (1991) Neuronal branching patterns and the economy of cortical wiring”. Proceedings of the Royal Society, series B, 245, 151–158. Mountcastle, V.B. (1978) An organizing principle for cerebral function: the unit module and the distributed system In: G.M. Edelman and V.B. Mountcastle (eds), The Mindful Brain. Cambridge, Mass: MIT Press, pp. 7–50. Pandya, D.N. and Yeterian, E.H. (1985) Architecture and connections of cortical association areas. In: A. Peters and E.G. Jones (eds), Cerebral Cortex, Vol. 4. New York and London: Plenum Press, pp. 3–61. Perrett, D.I., Harries, M.H., Benson, P.J., Chitty, A.J. and Mistlin A.J. (1990) Neurones responsive to faces in the temporal cortex: studies of functional organization, sensitivity to identity and relation to perception. Human Neurobiology, 3, 197–208. Peterhans, E. and von der Heydt, R. (1989) Mechanisms of contour perception in monkey visual cortex: I. contours bridging gaps. Journal of Neuroscience, 9, 1749–1764. Peters, A. and Payne, B.R. (1993) Numerical relationships between geniculocortical afferents and pyramidal cell modules in cat primary visual cortex. Cerebral Cortex, 1, 69–78. Poucet, B. (1997) Searching for spatial unit firing in the prelimbic area of the rat medial prefrontal cortex. Behavioural Brain Research, 84, 151–159. Rieke, F., Warland, D., de Ruyter van Steveninck, R. and Bialek, W. (1997) Spikes. Exploring the neural code. Cambridge, Mass.: MIT Press. Rockland, K.S. and Pandya, D.N. (1979) Laminar origins and terminations of cortical connections of the occipital lobe in the rhesus monkey. Brain Research, 179, 3–20. Scannell, J.W. and Young, M.P. (2000) Primary visual cortex within the cortico-cortico-thalamic network. In: A. Peters, E.G. Jones and B.R. Payne (eds), Cerebral Cortex, Vol. 15. Cat Primary Visual Cortex. New York: Plenum, in Press. Scannell, J.W., Sengpiel, F., Benson, P.J., Tovee, M.J., Blakemore, C. and Young, M.P. (1996) Visual motion processing processing in the anterior ectosylvian sulcus of the cat. Journal of Neurophysiology, 76, 895–907. Scannell, J.W., Burns, G., O’Neill, M.A., Hilgetag, C.-C. and Young, M. (1997) The organization of the thalamo-cortical network of the cat. Society for Neuroscience Abstracts, 23, 514.12. Scannell, J.W., Burns, G., O’Neill, M.A. and Young, M.P. (1999) The connectional organisation of the thalamocortico-cortical system of the cat. Cerebral Cortex, 9, 277–299. Shepard, R.N. (1962) The analysis of proximities: multidimensional scaling with an unknown distance function. I. Psychometrika, 27, 219–246. Simmen, M.W., Goodhill, G.J. and Willshaw, D.J. (1994) Scaling and brain connectivity. Nature (London), 369, 448–50. Stenson, H.H. and Knoll, R.L. (1969) Goodness of fit for random rankings in Kruskal’s nonmetric scaling procedure. Psychological Bulletin, 71, 122–126. Stephan, K.E., Hilgetag, C.-C., Burns, G.A.P.C., O’Neill, M.A., Young, M.P. and Kötter, R. (2000) Computational analysis of global functional connectivity between areas of primate cerebral cortex. Philosophical Transactions of the Royal Society: Biological Sciences, 355, 111–126. Treue, S. and Maunsell, J.H.R. (1996) Attentional modulation of visual motion processing in cortical areas MT and MST. Nature (London), 382, 539–541 Ungerleider, L.G. and Mishkin, M. (1982) Two cortical visual systems. In: D.G. Ingle, M.A. Goodale and R.J.Q. Mansfield (eds), Analysis of Visual Behavior. Cambridge MA: MIT Press, pp. 549–586. Wilkinson, E.M. (1971) Archaeological seriation and the travelling salesman problem. In: F.R. Hodson, D.G. Kendall and P. Tautu (eds), Mathematics in the Archaeological and Historical Sciences. Edinburgh: Edinburgh University Press, pp. 276–283. Young M.P. (1992) Objective analysis of the topological organization of the primate cortical visual system. Nature (London), 358, 152–155. Young, M.P. (1993) The organization of neural systems in the primate cerebral cortex. Proceedings of the Royal Society, series B, 252, 13–18. Young, M.P. (1995) Open questions about the neural mechanisms of visual pattern recognition. In: M.S. Gazzaniga (ed.), The Cognitive Neurosciences. London: MIT Press, pp. 463–474.
© 2002 Taylor & Francis
Connectivity and Function in the Cortex
375
Young, M.P. and Scannell, J.W. (1996) Component placement optimization in the brain. Trends in Neurosciences, 19, 413–414. Young, M.P., Scannell, J.W. and Burns, G. (1994) The Analysis of Cortical Connectivity. New York: R.G. Landes; Heidelberg: Springer. Young, M.P., Scannell, J.W., O’Neill, M.A., Hilgetag, C.C., Burns, G. and Blakemore, C. (1995) Non-metric multidimensional scaling in the analysis of neuroanatomical connection data and the organization of the primate cortical visual system. Philosophical Transactions of the Royal Society, Biological Sciences, 348, 281–308. Young, M.P. and Yamane, S. (1992) Sparse population coding of faces in the inferotemporal cortex. Science (Washington), 256, 1327–1331. Young, M.P. and Scannel, J.W. (2000) Brain structure-function relationships: advances from neuroinformatics. Philosophical Transactions of the Royal Society, Biological Sciences, 355, 3–6. Zeki, S. and Shipp, S. (1988) The functional logic of cortical connections. Nature (London), 335, 311–317.
© 2002 Taylor & Francis
16 The Human Cortical White Matter: Quantitative Aspects of Cortico-Cortical Long-Range Connectivity Almut Schüz1,* and Valentino Braitenberg1,2,3 1
Max-Planck-Institut für biologische Kybernetik, Spemannstr. 38 72076 Tübingen, Germany Institut für medizinische Psychologie der Universität Tübingen Gartenstr. 29 72072 Tübingen, Germany 3 Laboratorio di Scienze Cognitive, Università di Trento, v.Tartarotti 7, Rovereto (Italy) *Correspondence: Max-Planck-Institut für biologische Kybernetik, Spemannstr. 38, 72076 Tübingen, Germany, Tel: 0049-7071-601 544; FAX: 0049-7071-601 577 e-mail:
[email protected];
[email protected] 2
We investigated the human cortical white matter in order to get insights into quantitative aspects of connectivity between cortical regions. We dissected the long-range bundles in the depth of the white matter which run over large distances and connect the cortical lobes to each other. Measuring the cross-sectional areas of these bundles and multiplying them by the assumed density of fibers we could estimate the number of fibres in these bundles. It turns out that the total number of fibers in the intrahemispheric long-range bundles is only about 2% of the total number of cortico-cortical fibres, and is of the same order as the number of fibers in the callosal system. Evidently, the vast majority of cortico-cortical fibers are of shorter range, connecting cortical areas within one lobe or neighbouring areas belonging to different lobes. As a rough rule, the number of fibres of a certain range of lengths is inversely proportional to their length. The results are discussed in relation to information processing within and between modalities. KEYWORDS: cell assembly, cortical areas, cortical hierarchy, cortical lobes, fascicle, fibre length
1. INTRODUCTION When we talk about the cerebral cortex, what we usually have in mind is the sheet of laminated grey matter surrounding the forebrain. This is where the phenomenon of areal diversification manifests itself most evidently. However, the white matter underneath it is an equally important constituent of the telencephalic cortex: hardly any other structural feature characterizes the cortex as much as the mass of fibres underlying it, mostly composed of axons of cortical neurones. The vast majority of these fibres connect the cortex to itself as is evident in the large amount of cortical white matter as compared to the thickness of the various subcortical fibre tracts (Braitenberg, 1974; Seitz, this volume). While the intrinsic connectivity within an area seems to be provided to a large extent by collaterals running within the grey matter (e.g. Yoshioka et al., 1992; Amir et al., 1993), the traffic between different areas is mainly the responsibility of the main axons, running through the white matter. This is certainly true for the majority of fibres in large brains, in spite of the fact that axon collaterals freely cross the borders between neighbouring cortical areas (e.g. DeFelipe et al., 1986). The spatial separation between the two systems 377 © 2002 Taylor & Francis
378
Almut Schüz and Valentino Braitenberg
is less distinct in small brains such as that of the mouse, in which areas measure no more than a few millimeters across.
2. WHITE MATTER AND BRAIN SIZE A large amount of white matter is by no means unique to the human brain, but common to large brains with a folded cortex. In contrast, in small, lissencephalic brains the white matter is confined to a thin sheet. The volume of white matter increases from about 6% in small insectivores to about 42% of the total neocortical volume (grey + white matter) in humans (Frahm et al., 1982). This is so for obvious combinatorial reasons, and in addition because with larger brains axons become longer on average (Braitenberg, 2001) and, to a certain degree, also thicker (Ringo, 1991; Jerison, 1992; Schüz and Preißl, 1996). The large amount of white matter in large brains does not necessarily mean that the cortex is more “self-reflexive” in larger brains, but may actually be a consequence of a common allometric principle (Braitenberg, 1978).
3. COMPOSITION OF THE HUMAN CORTICAL WHITE MATTER At first sight, the white matter has the appearance of a homogeneous mass. However, as has been known for a long time (Déjérine, 1895), fibres having the same origin and destination tend to stick together, forming sheets and fascicles, which can be recognized on myelin preparations and can be isolated by blunt dissection as practiced traditionally in laboratories of human anatomy. Cortico-cortical fibres which run over long distances within the same hemisphere are located in the depth of the white matter, while fibres of shorter range are located more superficially, the most superficial ones forming the so-called U-fibre system, and staying mostly within the compass of a single gyrus or sulcus. Also, the callosal and the afferent and efferent systems tend to run in sheets. The various systems are interdigitated in places where they cross each other, but in spite of this they can be visualized more or less separately at various locations, and their dissection can be facilitated by repeated freezing and unfreezing of the formalin-fixed tissue, as shown in the beautiful atlas by Gluhbegovic and Williams (1980). In the core of the white matter, five main fascicles can be discerned which connect the different cortical lobes with each other: the superior and inferior occipitofrontal fascicle, the superior and inferior longitudinal fascicle and the uncinate fascicle (Figure 16.1).
4. QUANTITATIVE ASPECTS We have made an estimate of the number of fibres in these fascicles, in order to see how large a part of the cortico-cortical long-range system they represent. We did this by first assessing the cross-sectional areas of the various fascicles. For this we used both, brain atlases (Gluhbegovic and Williams, 1980; Montemurro and Bruni, 1981) and our own dissection material. For the measurements, we used the central portion of the course of the fasicles where they can be assumed to contain a substantial proportion of the fibres which
© 2002 Taylor & Francis
The Human Cortical White Matter
379
Figure 16.1. Overview over the intrahemispheric long-range bundles in the depth of the white matter. 1. Superior occipitofrontal fascicle, 2. Location of the Corona radiata, 3–5. Superior longitudinal fascicle, 6. Outline of the insula, 7. Inferior occipitofrontal fascicle, 8. Inferior longitudinal fascicle, 9. Location of the anterior commissure, 10. Uncinate fascicle. From Nieuwenhuys et al. (1991). Copyright: Springer Verlag, with permission.
they collect, on a segment where they were most compact and not loosened by crossing fibres from other systems. The measured cross-sectional areas were then multiplied by the density of fibres, in order to assess the number of fibres in the fascicles. Since we could not find any data on the density of fibres in these fascicles, we used the density of fibers in the corpus callosum for which data are available (see collection of data in Blinkov and Glezer, 1968; Tomasch, 1954; Aboitiz et al., 1992). Since the corpus callosum is part of cortical white matter, and since it connects the cortex of both hemispheres over similar distances as the fascicles do between the lobes within one hemisphere, fibre thickness in these two systems may be assumed to be similar. The density of fibres in the human corpus callosum is about 380 000/mm2 (Aboitiz et al., 1992) including a few percent unmyelinated fibres. We also included in our measurements of the intrahemispheric long-range bundles the cingulum, which connects the hippocampal formation to the cingulate gyrus, as well as connecting portions of the cingulate gyrus to each other (Brodal, 1981). The cross-sectional areas of the cingulum and of the inferior longitudinal fascicle are of similar size, about 19.6 mm2 (slightly more than the diameter of the optic nerve). The uncinate fascicle and the inferior occipitofrontal fascicle form a compact bundle at their transition between temporal and frontal lobe where they have together a cross-sectional
© 2002 Taylor & Francis
Almut Schüz and Valentino Braitenberg
380 Table 16.1.
Density of axons in the corpus callosum (Aboitiz et al., 1992) Total number of axons in the corpus callosum (Blinkov and Glezer, 1968; Aboitiz et al., 1992) Number of axons in the Cingulum Superior longitudinal fascicle Uncinate fascicle + inferior occipitofrontal fascicle Inferior longitudinal fascicle Superior occipitofrontal Fascicle Total number of axons in the far-reaching fascicles in one hemisphere
380 000/mm2 2–3 × 108 7.4 × 106 4.3 × 107 3.1 × 107 7.4 × 106 between 6 × 106 and 1.2 × 107 about 108
area of about 81 mm2. The superior occipitofrontal fascicle is difficult to measure but has a cross-sectional area one- to two-times that of the optic nerve, i.e. 16 to 32 mm2. The largest bundle, the superior longitudinal fascicle, has a cross-sectional area of about 114 mm2. Table 16.1 shows the results of our estimates. The number of fibres ranges within the orders of magnitude of 106 and 107 in the various fascicles. The total number of fibres in all of them together in one hemisphere may therefore reach the order of 108. Interestingly, this is the same order as that of the number of fibres in the corpus callosum. We then wondered how large a part of the total cortico-cortical system of one hemisphere is contained in these long intrahemispheric fascicles. The calculation is shown in Table 16.2. For this we need the total number of neurones within the cortex of one hemisphere, which is, according to Haug (1986), 7.5 × 109. From this number, we have to subtract the following: (i) the non-pyramidal cells (which make only local connections), (ii) the callosal neurones projecting to the other hemisphere, and (iii) the efferent neurones projecting to other parts of the brain. The third of these is the number about which least is known. Our guess is of the order of 108 for one hemisphere. This is based on the assumption that the cortical neurones projecting to the thalamus are not much more than the thalamo-cortical neurones for which an upper limit is given by the number of thalamic neurones (at most 108 for one hemisphere, see data in Blinkov and Glezer, 1968), and on the assumption that cortico-striatal neurones are roughly of the same number, and neurones projecting to the brain stem are much fewer (of the order of 107 to the main output structure of the brain stem, the pontine nuclei [Brodal, 1981; Glickstein, 1987]).
Table 16.2. Neurones in 1 hemisphere = 7.5 × 109 (Haug, 1986) 1.5 × 1010: 2 minus 15% non-pyramidal cells (Braak and Braak, 1986) – 1.1 × 109 minus callosal neurones in 1 hemisphere (see Table 16.1) – 1 to 1.5 × 108 minus efferent neurones –2 × 108 = Cortico-cortical ipsilateral = 6 × 109 8 ⇒about 2% of those (10 ) are in the long fascicles
© 2002 Taylor & Francis
The Human Cortical White Matter
381
With the estimates given in Table 16.2, one ends up with a total number of about 6 × 103 neurons making cortico-cortical projections within the same hemisphere (80% of all the neurones in the cortex of one hemisphere). In spite of all the uncertainties contained in these estimates, it is evident that the 108 fibers in the deep fascicles constitute only about 2% of the cortico-cortical fibres in the white matter.
5. CONSIDERATIONS ON THE DISTRIBUTION OF FIBRES OF DIFFERENT LENGTH IN THE PYRAMIDAL-TO-PYRAMIDAL CELL SYSTEM What one would ultimately like to know in the context of a theory of the global operation of the cortex, is the distribution of fibre lengths in the system of fibres connecting the cortex to itself. Our estimate of a 2% share of each of the two long-range systems, the corpus callosum and the intrahemispheric bundles, is but a small contribution to this. Tentatively, we may go toward a complete picture in a rough way by comparing estimates of the numbers of (a) very short (intracortical) tangential connections, (b) longer fibers, linking the cortical grey matter as U-fibers and (c) long fibers in the deep bundles and the callosal system (Figure 16.2). The range of intracortical connections, provided by the horizontal collaterals of descending axons is known only for the cortex of smaller primates (a few millimeters in the macaque; e.g. Fisken et al., 1975; DeFelipe et al., 1986; Amir et al., 1993; Lund et al., 1993; Levitt et al., 1993; Levitt and Lund, this volume; Valverde et al., this volume) and
Figure 16.2. Number of fibers (in both hemispheres together) vs range (log–log plot) in the three compartments described in the text. Compartment A is assumed to contain intracortical horizontal fibres up to a length of 3 mm, compartment B corresponds to the fibres in the U-fibre system of the white matter, and compartment C to cortico-cortical fibres of longer range, which do not follow the pattern of cortical folding. The latter are assumed to include the deep fascicles, the more superficial bundles and the callosal system. The inset on the upper right shows a sketch of the 3 fibre systems quantified here.
© 2002 Taylor & Francis
382
Almut Schüz and Valentino Braitenberg
can be inferred only indirectly for humans. In Figure 16.3 we assume a maximum horizontal distance of 3 mm, spanned by the longest axon collaterals within the grey matter. The number of horizontal axon collaterals is a few times the number of pyramidal cells (column A in Figure 16.2). The next component for which an estimate is possible, are the fibres leaving the cortex for the white substance and re-entering it at a distance of one or a few centimeters. (We assumed up to 30 mm). They are largely contained in the fibre layer which underlies the cortex, following its convoluted shape. This layer, the so-called U-fibre system, has a thickness of about 1.5 mm, as measured beneath the sulci. The number of fibres can be estimated on the basis of the volume of this layer and the (estimated) thickness and length of the fibres. To estimate the volume, we take the surface of the cortex (about 1600 cm2 for both hemispheres together; see Blinkov and Glezer, 1968; Henery and Mayhew, 1989; Sisodiya et al., 1996), multiplied by the thickness of the U-fibre layer. We take 2 µm2 as the cross-section area of the fibres, slightly less than that of the fibres in the human corpus callosum, according to Blinkov and Chernyshev (see Table 16.1). The average length of the fibres is estimated as 15 mm. In order to assess the number of cortico-cortical fibres in the third compartment (C in Figure 16.2), we made an estimate of the volume occupied by these fibres in the white matter. The volume of the cortical white matter in both hemispheres together (including the corpus callosum, but excluding the internal capsule) is about 420 cm3 (Frahm et al., 1982). From this we subtract the U-fiber system as measured above (240 cm3), as well as an estimated volume for the efferent and afferent fibres. According to the assumptions made in the previous section, their number may reach 6 × 108 in both hemispheres together. Assuming an average length of 50 mm for these fibres, and a thickness of 2 µm2, we end up with a volume of 60 cm3. This leaves a volume of 120 cm3 for the longer cortico-cortical fibres in the intrahemispheric and commissural systems. These fibres may reach a length of 15 cm; for the average we took a length of 60 mm. With a fibre thickness of 2.6 µm2 corresponding to the fibre thickness in the corpus callosum, we end up with a number of about 8 × 108 in compartment C in Figure 16.2. The number of fibres in the compartments B and C in Figure 16.2 do not quite add up to the total number of pyramidal cells assumed in the previous section. This is not astonishing in view of the uncertainties inherent in these estimates: These include uncertainties in the total number of cortical neurones, in the percentage of pyramidal cells that send an axon to the white substance, in the range of volumes given for the white matter (Blinkov and Glezer, 1968) and the uncertain quantitative data on the efferent and afferent systems. Also, the fibre thickness in the various compartments of the white substance is known only in an approximate way. In spite of this, orders of magnitude are probably correct. Roughly, the relation between range and number of cortico-cortical fibres is such that for an increase of one order of magnitude in length, their number goes down by one order of magnitude.
6. OVERALL ORGANIZATION OF THE LONG-RANGE SYSTEM AND HEBB’S THEORY We end up with the following picture: (1) The two far-reaching fibre systems of the cortical white matter, the callosal system connecting both hemispheres and the ipsilateral
© 2002 Taylor & Francis
The Human Cortical White Matter
383
deep fascicles connecting the various lobes of one hemisphere with each other are of a similar magnitude. (2) Both of them together contain only a small percentage (no more than 4%) of the total number of cortico-cortical fibres in the white matter. The overwhelming majority of cortico-cortical fibres must be of shorter range, connecting areas within one lobe, or neighbouring areas belonging to different lobes. Most of these shorter fibres of the white substance are contained in the thin layer of so-called U-fibers lining the deep surface of the cortex. Even larger is the number of intracortical fibers which connect pyramidal cells of one neighbourhood without entering the white substance. The preponderance of fibres not exceeding a few centimeters in length agrees well with the results reported by the group of Young on the connectivity between cortical areas in cat and monkey, though based on a completely different approach (see Young, this volume). Bringing together all the connections which have been reported in the literature as obtained by tracer studies, it turned out that the connectional vicinity between cortical areas reflects—to an astonishing degree—their topographic vicinity in the cortex (Young et al., 1994, 1995). Almost half of the connections between areas are “nearest-neighbour-or-next-door-but-one” connections (47.8% in the cat; Scannell et al., 1995). This sheds an interesting light on the representation of concepts in the cortex. According to Hebb’s theory (1949), briefly sketched in the introductory chapter, a concept is represented in the cortex by a group of neurones binding together the various features pertaining to it. This leads us to ask at what level in the hierarchy of connections this occurs. Since direct connections between primary sensory areas of different modalities seem not to exist, it cannot be at the lowest level, at least as far as concepts which involve more than one modality are concerned. In principle, it is still possible that neurones in areas quite close to the primary sensory areas are connected into a multimodal cell assembly. As is evident from the scheme of connections between cortical areas in the monkey (Young, 1993), direct connections between areas of different modalities already exist two levels above the primary sensory areas of the visual, acoustic and somatosenory system. In addition, there are connections between the first level above the primary sensory area of one modality and the second level of the other modalities for all three sensory systems. However, the majority of connections seems not to be involved in this kind of lowlevel integration between different sensory systems which enter the cortex far from each other in the different cortical lobes. Obviously, the greater part of cortico-cortical fibres are involved in the processing of information between the areas of one neighbourhood, i.e. to a large extent within the same modality. This suggests that, normally, a great deal of preprocessing happens below the level at which cell assemblies representing a multimodal or abstract concept are assembled. This preprocessing could be both in the service of invariance of shape, size, etc. (Zeki, 1993), or of the establishment of repertoires of meaningful features (e.g. Tanaka et al., 1991; 1997; Oram and Perrett, 1994). To the neuroanatomist the surprizing result of our survey is probably that most of the cortico-cortical fibres in the white substance of the hemisphere are contained in the narrow layer just beneath the cortex. The brain theorist may find food for thought in the inverse relation of number and length of fibres, which may remind him of the well-known Zipf-law of linguistics.
© 2002 Taylor & Francis
384
Almut Schüz and Valentino Braitenberg
ACKNOWLEDGEMENT We are grateful to Prof. Wagner who gave us the possibility to use dissection material from the Anatomical Institute at the University of Tübingen.
REFERENCES Aboitiz, F., Scheibel, A.B., Fisher, R.S. and Zaidel, E. (1992) Fiber composition of the human Corpus callosum. Brain Research, 598, 143–153. Amir, Y., Harel, M. and Malach, R. (1993) Cortical hierarchy reflected in the organization of intrinsic connections in macaque monkey visual cortex. Journal of Comparative Neurology, 334, 19–46. Blinkov, S.M. and Glezer, I.I. (1968) The Central Nervous system in figures and tables, German edition of the Russian original (1964), Jena: VEB Fischer Verlag, pp. 140–148, p. 164. Blinkov, S.M. and Chernyshev, A.S. (1935) The variability of the human Corpus callosum (in Russian), Tr. In-ta mozga I, 175–237, Moscow, quoted in Blinkov, S.M. and Glezer, I.I. (1968). Braak, H. and Braak, E. (1986) Ratio of pyramidal cells versus non-pyramidal cells in the human frontal isocortex and changes in ratio with ageing and Alzheimer’s disease. In: D.F. Swaab, E. Fliers, M. Mirmiram, W.A. van Gool and F. van Haaren (eds), Aging of the brain and Alzheimer’s disease (Progress in Brain Research, Vol. 70). Amsterdam: Elsevier, pp. 185–212. Braitenberg, V. (1974) Thoughts on the cerebral cortex. Journal of Theoretical Biology, 46, 421–447. Braitenberg, V. (1978) Cortical architectonics: general and areal. In: M.A.B. Brazier and H. Petsche Architectonics of the cerebral cortex, New York: Raven Press, pp. 443–465. Braitenberg, V. (2001) Brain size and the number of neurons: an exercise in synthetic neuroanatomy. Journal of Computational Neurocience, 10, 71–77. Brodal, A. (1981) Neurological Anatomy in Relation to Clinical Medicine, 3rd edn. New York, Oxford: Oxford University Press, p. 669. DeFelipe, J. Conley, M. and Jones, E.G. (1986) Long-range focal collateralization of axons arising from corticocortical cells in the monkey sensory-motor cortex. Journal of Neuroscience, 6, 3749–3766. Déjérine, J. (1895) Anatomie des Centre Nerveux, Vol.1, edn. of 1980. Paris, New York: Masson. Fisken, R.A., Garey, L.J. and Powell, T.P.S. (1975) The intrinsic, association and commissural connections of area 17 of the visual cortex. Philosophical Transactions of the Royal Society of London, B, 272, 487–536. Frahm, H.D., Stephan, H. and Stephan, M. (1982) Comparison of brain structures in insectivores and primates. I. Neocortex. Journal für Hirnforschung, 23, 375–389. Glickstein, M., May III, J.G. and Mercier, B.E. (1985) Corticopontine projection in the Macaque: The distribution of labelled cortical cells after large injections of horseradish peroxidase in the pontine nuclei. Journal of Comparative Neurology, 235, 343–359. Gluhbegovic, N. and Williams, T.H. (1980) The Human Brain, a photographic guide. Hagerstown: Harper and Row, Publishers. Haug, H. (1986) History of neuromorphometry. Journal of Neuroscience Methods, 18, 1–17 (Special Issue: H.B.M. Uylings, R.W.H. Verwer and J. van Pelt [eds], Morphometry and Stereology in Neurosciences). Hebb, D.O. (1949) Organization of Behaviour. A Neuropsychological Theory, 2nd edn. 1961. New York: Wiley and Sons. Henery, C.C. and Mayhew, T.M. (1989) The cerebrum and cerebellum of the fixed human brain: efficient and unbiased estimates of volumes and cortical surface areas. Journal of Anatomy, 167, 167–180. Jerison, H. (1991) Brain size and the evolution of mind. 59th James Arthur Lecture. New York: American Museum of Natural History. Levitt, J.B., Lewis, D.A., Yoshioka, T. and Lund, J.S. (1993) Topography of pyramidal neuron intrinsic connections in macaque monkey prefrontal cortex (Areas 9 and 46). Journal of Comparative Neurology, 338, 360–376. Lund, J.S., Yoshioka, T. and Levitt, J.B. (1993) Comparison of intrinsic connectivity in different areas of macaque monkey cerebral cortex. Cerebral Cortex, 3, 148–162. Montemurro, D.G. and Bruni, J.E. (1981) The Human Brain in Dissection. Philadelphia, London: W.B. Saunders Company. Nieuwenhuys, R., Voogd, J. and van Huijzen, Chr. (1991) The Human Central Nervous System—A synopsis and Atlas, 2nd German edn. Berlin, Heidelberg: Springer-Verlag. Oram, M.W. and Perrett, D.I. (1994) Modeling visual recognition from neurobiological constraints. Neural Networks, 7, 945–972. Ringo, J.L. (1991) Neuronal interconnections as a function of brain size. Brain Behavior and Evolution, 38, 1–6. Scannell, M.P., Blakemore, C. and Young, M.P. (1995) Analysis of connectivity in the cat cerebral cortex. The Journal of Neuroscience, 15, 1463–1483.
© 2002 Taylor & Francis
The Human Cortical White Matter
385
Schüz, A. and Preißl, H. (1996) Basic connectivity of the cerebral cortex and some considerations on the Corpus callosum. Neuroscience and Biobehavioral Reviews, 20, 567–570. Sisodiya, S., Free, S., Fish, D. and Shorvon, S. (1996) MRI-based surface area estimates in the normal adult human brain: evidence for structural organisation. Journal of Anatomy, 188, 425–438. Tanaka, M. (1997) Mechanisms of visual object recognition: monkey and human studies. Current Opinion in Neurobiology, 7, 523–529. Tanaka, K., Fukada, S.H. and Moriya, M. (1991) Coding visual images of objects in the inferotemporal cortex of the macaque monkey. Journal of Neurophysiology, 66, 170–189. Tomasch, J. (1954) Size, distribution and number of fibers in the human Corpus callosum. Anatomical Record, 119, 119–135. Yoshioka, T., Levitt, J.B. and Lund, J. (1992) Intrinsic lattice connections of macaque monkey visual cortical area 4. Journal of Neuroscience, 12, 2785–2802. Young, M.P. (1993) The organization of neural systems in the primate cerebral cortex. Proceedings of the Royal Society, London, B, 252, 13–18. Young, M.P., Scannell, J.W., Burns, G.A.P.C. and Blakemore, C. (1994) Analysis of connectivity: neural systems in the cerebral cortex. Reviews in the Neursciences, 5, 227–249. Young, M.P., Scannell, J.W. and Burns, G. (1995) The Analysis of Cortical Connectivity. New York, Berlin: Springer. Zeki, S. (1993) A vision of the brain. Oxford: Blackwell Scientific Publications.
© 2002 Taylor & Francis
17 Fundamentals of Association Cortex Stewart Shipp Wellcome Department of Cognitive Neurology, University College, London Correspondence: S. Shipp, Wellcome Department of Cognitive Neurology, University College, Gower Street, London WC1E 6BT, U.K. Tel: (+44)207-679-3910; FAX: (+44)207-679-7316; e-mail:
[email protected]
There are many instances of parallelism in the neural architecture of the visual system. This review deals with three, expressed by various forms of anatomical segregation at progressively higher levels: (a) the three output channels of the retina (M, P and K); (b) the three ‘metabolic’ pathways at the level of V1 and V2 (blob-thin stripe, interblob-interstripe and layer 4B-thick stripe), and (c) the dual dorsal and ventral pathways, feeding the parietal and temporal lobes of the brain. Only if these three parallel systems dovetail with each other could the global architecture of the visual system be truly described as parallel, but this is not so: there is abundant cross-talk at the interface of (a) with (b), and (b) with (c)—and internally within each system. Cross-talk reflects integrative functions that tend to be neglected by parallel models. The ethos of the article is that cross-talk serves co-operative interactions that facilitate the operational goals of a particular pathway, rather than as acting to dilute its specific, specialised character. In that sense, the functional organisation of the visual system is more parallel than its underlying circuitry.
1. INTRODUCTION In the history of thinking about the brain, localisation of function is one of the oldest thoughts. As the resolution of anatomical studies has increased, it has stayed with us, to be applied to structures across the cortical sheet, ranging in scale from lobes and architectural fields, through areas, to compartments and columns. To give it another name, localisation can be thought of as segregation, the idea that any structurally (or neurochemically, or genetically) distinct module of tissue is likely to have a corresponding functional identity, a specialisation that distinguishes it from neighbouring structures. To anyone willing to accept the label “neuroanatomist”, this notion is probably axiomatic. Naturally enough, there are comparable levels of functional classification to accompany the varying scales of anatomical segregation. Within vision, we can identify the basic attributes of seeing colour, form (shape, depth and texture) and motion. At a higher level, we find the recruitment of these modalities to serve different types of action: grasping and manipulating an object, as opposed to verbal description of it (or perhaps just silent contemplation). At a subordinate level, it is common to identify, for example, neural selectivity for particular hues, orientations, or disparities. Clearly, any global theory of “how the brain works” has to match the functions to the anatomical structures. This is exactly what the most generally accepted theory of visual brain function, the dogma of two visual pathways, undertakes. It cites the mass of evidence for the divergence in function of the parietal and temporal 387 © 2002 Taylor & Francis
388
Stewart Shipp
lobes of the primate brain (allying human neuropsychology and functional imaging with monkey neurophysiology and behaviour) and links it to anatomical evidence that these two centres are fed by two separate pathways arising from the interconnections of prestriate areas and V1. Thus the dorsal (“WHERE”) pathway is held to terminate in the parietal lobe and to subserve location of objects in space, and the ventral (“WHAT”) pathway is thought to terminate in the temporal lobe, subserving object recognition (Mishkin et al., 1983; Haxby et al., 1991). This dogma has been extended to stress the role of the dorsal pathway in motor control, and of the ventral pathway in perceptual awareness (Goodale and Milner, 1992). For a passing moment, it had seemed possible that the cortical “two pathway” dogma could be extended still further downwards, to incorporate the fundamental distinction between the magno- (M) and parvocellular (P) retinogeniculate pathways, a kind of grand unification of two-pathway theories (Livingstone and Hubel, 1987b). The idea was that the dorsal pathway might be fed exclusively by the M system, and the ventral pathway by the P system, but this view began to unravel almost before it could be proposed: the P and M systems are not so completely segregated. In general, the problem is that the segregations existing at one stage do not necessarily exist in 1:1 relationship with those at the next. This feature (cross-talk, or cross-connectivity) is an omnipresent feature of cortical organisation, and it also applies to the post-striate levels of the two pathway dogma. More on that below. To complete the introduction, it remains to point out that cross-talk is a reflection of the other fundamental property of association cortex, integration. Like segregation, integration is a broad term, referring to anatomical relationships and functional processes over a range of different levels. The historical defining characteristic of association cortex is the association of sensations arising from different sensory modalities. The analogous property, within vision, is the binding of visual attributes recognised to belong to the same object, yet processed along separable pathways. Another example might be the synthesis of one attribute out of another, for instance form-from-motion. The element of integration is one that is neglected by parallel models of brain function. Segregation and integration are the twin fundamentals of cortical organisation. In an elementary sense, the latter cannot exist without the former. It is easy to cast them as opponent processes—but this, surely, is to miss the evident reality that they must cooperate to subserve the goals of cortical processing. The aim of what follows is, first, to ask precisely what is segregated in early visual analysis, and then to examine the greyer territory of where, how and why it is brought back together—if at all.
2. GENICULO-CORTICAL PATHWAYS There are three classes of relay neurone in the lateral geniculate nucleus (LGN): magnocellular (M) and parvocellular (P) cells, whose properties are well documented, and koniocellular (K) cells, which are not. That they are not is because K cells have tiny cell bodies and are scattered throughout the intercalated layers of the LGN, so that they are extremely difficult to study (Hendry and Yoshioka, 1994). All three classes seem to inherit their response properties from equivalent classes of retinal ganglion cells, avoiding cross-talk between channels. M and P cells have similar concentric receptive fields (ON-centre, OFF-surround or vice versa), although only P cells have cone opponent inputs (Wiesel and Hubel, 1966; Dreher et al., 1976; Schiller and Malpeli, 1978). The two channels are conceived as spatio-temporal filters with overlapping ranges of responsivity, although
© 2002 Taylor & Francis
Fundamentals of Association Cortex
389
these response ranges are notably dependent on the nature of stimulus contrast (i.e. luminance or chromatic). M cells have the higher absolute sensitivity to luminance contrast, and they respond to a higher range of temporal frequencies (Shapley et al., 1981; Hicks et al., 1983; Derrington and Lennie, 1984). With larger calibre axons, they also have faster signal conduction velocities. These properties give a higher resolution for timing of events, so that M cells are the natural substrate for coding dynamic changes in an image. P cells, with smaller receptive fields, can respond to a higher range of spatial frequencies (Derrington and Lennie, 1984); furthermore, P cells outnumber M cells by 10:1 in the LGN, and by a still greater ratio in the fovea (Connolly and Van Essen, 1984), so they are the prime source of fine-grain spatial representation. This picture changes considerably for chromatic contrast. The M system is “colour blind” in the sense that it provides no information about colour, and its spectral sensitivity is equivalent to that of the V(λ) human luminous efficiency curve; using the technique of heterochromatic flicker photometry, activity of the M system reaches a minimum at the point of minimal perceived flicker, which defines the psychophysical state of isoluminance between the tested pair of coloured lights (Lee et al., 1988). In P cells, chromatic contrast (tested with isoluminant stimuli) has the effect of shifting the response range to low-pass for spatial frequencies (DeValois et al., 1977; Derrington et al., 1984). This means that, with everyday visual images, P cells extract a chromatic signal from low spatial frequencies, and a multiplexed signal that is increasingly dominated by achromatic contrast, from higher spatial frequencies (Ingling and Martinez-Uriegas, 1983). Lesions of specific layers in the LGN support this picture (Schiller et al., 1990; Merigan et al., 1991a,b). Lesions in the M layers create deficits in seeing flicker and motion. Lesions in the P layers abolish colour vision, and severely impair detailed form vision and stereopsis. However the capacity to make certain coarse discriminations of form, brightness and size is unaffected by lesions in either layer (although abolished by wholesale LGN destruction) meaning that one channel can substitute for another in performing these simple tasks. The lesion studies are a more reliable method of distinguishing the roles of the M and P channels than purely psychophysical procedures using isoluminant coloured stimuli to “neutralise” the M system. Because, at isoluminance, the M system is not exactly silent, and because the P response is also of different character (Schiller and Colby, 1983; Lee et al., 1988; Hubel and Livingstone, 1990), it is unreliable to ascribe all residual perceptual capacities at isoluminance to the P system, and all deficits in performance to inactivity of the M system (Logothetis et al., 1990). These conclusions may be correct with regard to residual motion perception, which may seem slowed or jumpy, i.e. lacking smoothness (Livingstone and Hubel, 1987b; Gegenfurtner and Hawken, 1996). However, they are incorrect, for instance with regard to isoluminant deficits in stereovision (Livingstone and Hubel, 1987b): the lesion experiments suggest that stereopsis is primarily supported by the P system (Schiller et al., 1990). The demonstration that different percepts exploit different subcortical channels reveals nothing about how these channels are processed cortically. Thus, one must ask how far centrally the segregation between the M and P systems is maintained beyond their initial relay through layer 4C of V1. Layer 4B provides an immediate example of a layer with evident magnocellular character. It is driven by M input from 4Ca (Lund et al., 1994; Callaway, 1998), and has neurones with high contrast sensitivity, typical of the M system (Blasdel and Fitzpatrick, 1984; Hawken and Parker, 1984). Layer 4B has several other unique characteristics: it is the site of the dense lateral fibre plexus (the stria of Gennari, after which striate cortex is named); it is populated by large spiny stellate projection neurones
© 2002 Taylor & Francis
Stewart Shipp
390
(like pyramidal cells shorn of their apical dendrites), whose dendrites are thus confined to 4B, but whose axons leap several rungs of the cortical hierarchy to terminate in area V5 (Shipp and Zeki, 1989a); and it is the earliest site in the primate visual system where direction-selective cells are located (Dow, 1974; Orban et al., 1986). Hence it is but four synapses from the retina to area V5/MT, and this is generally recognised as a fast pathway devoted to motion analysis. The dominance of the M system in V5 can be demonstrated by selective inactivation of the LGN: blockade of the M layers essentially eliminates activity in V5, apart from a few sites where P-driven responses can be detected (Maunsell et al., 1990). The relationship of the M and P systems turns out to be asymmetrical, however. Progressive accounts differ in detail, but it seems clear that the domain of the P system (layers 4A, 3 and 2 of V1) is substantially invaded by M input. Selective blockade of the LGN, for instance, suggests relatively equal contributions from both systems to these layers, and convergence at the level of single neurones (Nealey and Maunsell, 1994). A detailed anatomical picture is provided by Figure 17.1. In brief, layer 4A is itself a direct target of P geniculocortical axons, which are reinforced by further relays from 4Cb, extending through 4A and lower layer 3. There is no detectable bias in these relays either toward or away from the characteristic cytochrome oxidase blobs. M input to interblobs derives from cells situated around the border zone of 4Ca and 4Cb, whose dendrites should be able to sample both M and P input. The weight of M input to layers 2/3 may be biased toward blobs, however, as cells in the upper part of 4Ca, and also in 4B, have axonal arbors that are focused within blobs. This concurs with evidence that contrast sensitivity is higher in blobs than interblobs (Tootell et al., 1988a; Edwards et al., 1995). The cytochrome oxidase blobs and interblobs of layer 2/3 can be pictured as de-multiplexing the P signal in different ways: blobs extract the low spatial frequency colour information, and interblobs extract the high frequency pattern information, using it to construct K system: input to V1 and superior colliculus M system: extrageniculate relays via pulvinar
M & P input and intrinsic connections of area V1
striate cortex (V1)
extrastriate cortex
1 2
1 2
1 2
3 4A 4B
3 4A 4B
3
4C a
4C a
4
4C b
4C b
V2
.
5 6
V5 V3 V2
.
5
5
6
6
LGN - K PARVO
. . . .. . . . . .. .
PULVINAR
PARVO MAGNO RELAYS RELAYS MAGNO
K
M K
LGN
SUPERIOR COLLICULUS
MIXED PARVO & MAGNO RELAYS
M&K RETINA
Figure 17.1. Left: magnocellular and parvocellular inputs to V1; intrinsic relays from layer 4; outputs to prestriate cortex. Right: koniocellular inputs to V1; extrageniculate relays via superior colliculus and pulvinar.
© 2002 Taylor & Francis
Fundamentals of Association Cortex
391
orientation selective cells (Livingstone and Hubel, 1984a; Zeki and Shipp, 1988). Given that orientation tuning is also found in 4B (and in 4Ca) M input can clearly support the latter process (Blasdel and Fitzpatrick, 1984). Similarly, the contrast sensitivity of the M system may be exploited by blobs in synthesising a 3D colour space (typically represented by orthogonal luminance (black/white), red/green, and blue/yellow axes. It is known that there is an preponderance of red/green units amongst P cells. Input from shortwave (blue) cones is rare in this system, but relatively more prominent amongst K cells (Martin et al., 1997). To complete the picture, the K system also provides a specific input to blobs (Casagrande, 1994); indeed, perhaps it is K input that constructs blobs, as all direct geniculate targets in V1 are zones of elevated metabolism.
3. THE NATURE OF SPECIALISATION The simple message of the above is that P, M and K input is mixed, as appropriate, into three distinct internal compartments in V1—blobs, interblobs and layer 4B—with specialisations related to colour, form and motion respectively. It is, however, potentially misleading to employ perceptual labels for such an early stage of analysis. At the level of the LGN, for instance, one could state that the M system is suitable for motion analysis, but not that it is specialised for motion. Although it has now developed direction and orientation tuning, the same could still be said of the M system at the level of layer 4B. It is only the component of motion orthogonal to the contour that is signalled, and there is no sensitivity to “pattern motion”, as found at higher levels (Movshon et al., 1985; Rodman and Albright, 1989). Furthermore, the activity of non-directional cells in 4B, signalling the orientation of moving contours, could be considered a specialisation for “dynamic form” (Zeki and Shipp, 1988). Finally, it is reported that layer 4B is rich in neurones that are tuned for stereo disparity (Hubel and Livingstone, 1990). This portfolio of properties in 4B is probably dictated by factors to do with economy of visual processing, and it is not satisfactorily encompassed by any simple perceptual category. With this thought in mind, it is also worth examining the specialisations of the other compartments more closely. The orientation selective cells of the interblobs are better suited for analysis of static patterns, at higher resolution, so this looks like the inception of texture and shape processing. Broadband spectral tuning is explicable by convergence of P relays of different wavelength sensitivity. This would also enable extraction of the maximum spatial information from P signals. Such convergence may not be universal, as some interblob cells display both orientation and colour tuning (Livingstone and Hubel, 1984a). Nevertheless, even when there is no overt sign of colour tuning, some orientation tuned cells do still respond to oriented contours defined by isoluminant colour borders, i.e. the response is not dependent on the sign of colour-contrast (Gouras and Krüger, 1979; Thorell et al., 1984; Hubel and Livingstone, 1990). Both the former “colour-signed” and latter “colour-unsigned” signals could thus serve “form-from-colour”, corresponding to the fact that recognisable forms still exist in uniformly isoluminant pictures. There is psychophysical and single-unit evidence for both signed and unsigned chromatic contributions to motion perception (Dobkins and Albright, 1993, 1994; Gegenfurtner and Hawken, 1996). Does this imply that motion is not served only by the M system? Residual responses of the M system to moving isoluminant stimuli could in theory account for the unsigned colour contribution, but there is actually direct evidence from the LGN lesion
© 2002 Taylor & Francis
392
Stewart Shipp
technique for a P contribution to motion: the same studies that reveal a motion deficit after M lesions also demonstrate a residual capacity to discriminate direction, if only at a substantially higher luminance contrast threshold (Merigan et al., 1991a). This, in turn could mean two things: either that the motion pathway does recruit some P input, or that what is labelled the motion pathway is not the exclusive domain of all motion-related processing. Such a question cannot be satisfactorily resolved without looking beyond V1. It is worth pointing out, however, that although signals from the P system are not directly relayed to layer 4B, they can be sampled by pyramidal output neurones in this layer whose apical dendrites ascend into layers 4A and 3 (Sawatari and Callaway, 1996). Is there less ambiguity concerning the role of blobs in colour vision? It is a common observation that blobs are present in the nocturnal owl monkey, an animal with a single type of cone and rudimentary colour vision (Jacobs et al., 1993). However, even if owl monkeys see only shades of grey, this lightness perception is akin to colour vision, and is a function likely to be served by blobs (Allman and Zucker, 1990). The chromatic analogue of relative lightness for shades of grey is colour constancy. Notably, the responses of V1 colour cells correlate with the intensity and spectral quality of their local stimulation, but not with the colour percepts of a human observer, which arise from the global pattern of stimulation (Zeki, 1983); thus the mechanisms achieving constancy are partly, at least, the properties of higher areas (e.g. V4). There is analogous evidence relating to the role of V1 in form vision; namely, the relative activity of V1 orientation cells does not correlate with the momentary percept of orientation, in an experimental set-up where orthogonal gratings, presented dichoptically, produce a state of retinal rivalry (Leopold and Logothetis, 1996). In general terms, the corpus of the “neural correlate of consciousness” is certainly larger than V1, and it may even exclude V1 (Crick and Koch, 1995). Thus, the three compartments at the level of V1 represent intermediate stages in the synthesis of percepts, a synthesis that depends on a degree of mixing of M, P and K inputs. The next question is whether the V1 output channels serve as a more stable basis for parallel (“two visual pathway”) models of the visual brain, or whether they, in turn, are desegregated and reconstituted/recombined.
4. METABOLIC PATHWAYS It is perhaps fairly well known—certainly well reviewed (DeYoe and Van Essen, 1988; Livingstone and Hubel, 1988; Zeki and Shipp, 1988)—that the three compartments of V1 lead on to functionally comparable metabolic subdivisions in area V2 (thick stripes, thin stripes and paler interstripes), and then to areas V4 and V5, the latter originally portrayed as the roots of the ventral and dorsal visual pathways (Mishkin et al., 1983). Two, three or whatever number, a parallel pathway model implies separable entities that are not strongly interlinked. The presence of cross-talk is always acknowledged, but how much cross-talk can there be without a network model becoming preferable to a parallel one? There is no certain answer to this question, despite the wealth of relevant anatomical and physiological data. The model of Figure 17.2 is cast in the traditional framework, with accentuated detail at the early levels of the pathway to complement this part of the article. The ventral pathway is the route from blobs and interblobs to thin stripes and interstripes, and then on to inferior temporal cortex, via area V4. The dorsal pathway is traditionally the route from
© 2002 Taylor & Francis
Fundamentals of Association Cortex
393
Figure 17.2. Origins and higher stages of the interblob-interstripe (Ib-Is), blob-thin stripe (B-N*) and layer 4B-thick stripe (4B-K*) pathways (small, medium and large arrows respectively at the output from V1, input to V2, and inputs to V3, V4 and V5). Input to V3 from thin stripes and interstripes is less certain than input from thick stripes (arrows in parentheses). V4 is shown with two types of internal module; input from interstripes to one of these is uncertain. The diagram is configured to suggest a relationship between the 4B-Ks and dorsal pathways, and between the B-Ns/Ib-Is and ventral pathways, but there is also substantial cross-talk: internally within V1 and V2 (pale arrows); in the distributed outputs of V3; and in cross-connectivity of the parietal and temporal lobes. * For these abbreviations, see note 6, p. 401.
© 2002 Taylor & Francis
394
Stewart Shipp
layer 4B to V5, via the thick stripes of V2 as well as directly, and from V5 on to areas of parietal cortex. However the status of V3, which feeds both these routes, is ambivalent, and there are other forms of cross-connection, at higher stages. To put it simply, the anatomical basis of a parallel, two pathway model is not its greatest strength. A parallel model is better served by the title “two visual streams”, if “stream” is understood to index both the properties of an area as well as its connectivity. The notion requires that the statistical weight of the internal links within a pathway exceeds the weight of the external links with the other pathway (Young, 1992). Each stream may therefore preserve a functional homogeneity despite receiving alien inputs. As we proceed, this idea will be examined in more detail, at successive levels in the visual hierarchy. 4.1. Diversity of Function Across the Metabolic Compartments 4.1.1. Segregated pathways? The projections from blobs to thin stripes, interblobs to interstripes and from layer 4B to thick stripes appear to be direct and non-divergent (e.g. no cross-connections from blobs to thick stripes—(Livingstone and Hubel, 1983, 1987a; Levitt et al., 1994b; Malach et al., 1994). Any cross-connectivity at this level must therefore be intrinsic to V1 and/or V2. Lateral connections in V1 are 80% specific, in that they link blob to blob, and interblob to interblob (Livingstone and Hubel, 1984b; Yoshioka et al., 1996). Also, blobs are not watertight compartments in that dendrites of some blob cells do extend into the interblob matrix, and vice versa (Hübener and Bolz, 1992). Finally there are inputs from layer 4B to the blobs. These form a vertical column of ascending input; the blobs do not provide reciprocal axonal output back to layer 4B (Callaway, 1998). Within V2, by contrast, reciprocal cross-talk appears to be the rule. Intrinsic connections between V2 stripes were initially described as being specific (i.e. thin stripe to thin stripe—(Livingstone and Hubel, 1984a) but subsequent studies have found that non-specific links are also common: each type of stripe communicates with each other type of stripe (Levitt et al., 1994a; Malach et al., 1994). What function do these cross-connections serve? The exact nature of the specialisation across stripes in V2 bears discussion in detail, as the drift of several recent reports has been to downgrade segregation, and to emphasise a similarity in functional characteristics across stripes (Levitt et al., 1994a,b; Gegenfurtner et al., 1996; Tamura et al., 1996; Kiper et al., 1997). One may thus ask whether (a) the stripes are less diverse than the compartments in V1, this trend toward homogeneity being brought about by intrinsic connectivity in V2? Or (b) the diversity of function within V1 has also been exaggerated? (Leventhal et al., 1995). Or, (c) the case against diversity and segregation has been overstated? 4.1.2. Segregated functions—form and colour? Overall, the body of single unit work on area V2 is ambivalent on the properties of each compartment (see Table 17.1); there is no shortage of potential explanations for these inconsistencies, although it is hard to know which applies where. Variations in stimuli, criteria for response selectivity, anaesthetic level, electrode characteristics/neural sampling, track reconstruction and criteria for stripe identification are all potential
© 2002 Taylor & Francis
Fundamentals of Association Cortex
395
Table 17.1.
% orientation selective
MEDIAN % direction selective
MEDIAN % colour selective
MEDIAN % disparity selective [2. = binocular interaction] MEDIAN % incidence end-stopping [1. includes length antagonism within RF] MEDIAN
Thick
Thin
Inter
report
n
1. 2. 3. 4. 5. 6. 7.
1. 2. 3. 4. 5. 6. 7.
1. 2. 3. 4. 5. 6. 7.
1. 2. 3. 4. 5. 6. 7.
667 86 390 83 426 100 55
1. 2. 3. 4. 6. 7.
667 62 190 83 100 55
1. 2. 4. 5. 6. 7.
660 81 111 426 72 55
2. 3. 5.
91 390 26
1. 3. 4. 5. 6.
601 390 111 426 76
1. 2. 3. 4. 6. 7. 1. 2. 4. 5. 6. 7. 2. 3. 5. 1. 3. 4. 5. 6.
72 51 87 92 86 85 21* 85% 16 19 30 50 28 18* 24% 24 16 10 16 39 28* 20% 68 38 77 68% 30 20 17 42 13 20%
1. 2. 3. 4. 6. 7. 1. 2. 4. 5. 6. 7. 2. 3. 5. 1. 3. 4. 5. 6.
21 20 64 48 38 73 21* 38% 1 7 21 4 9 18* 8% 55 86 27 75 65 28* 60% 33 21 10 21% 49 22 30 16 19 22%
1. 2. 3. 4. 6. 7. 1. 2. 4. 5. 6. 7. 2. 3. 5. 1. 3. 4. 5. 6.
67 17 82 78 84 96 38 78% 0 0 34 13 25 31 19% 24 64 27 12 33 63 30% 22 15 1 15% 22 30 18 37 41 30%
Notes: Summary of the incidence of visual selectivity across stripes, as reported in seven studies. 1. Shipp (2002) Visual Neuroscience (in press). 2. DeYoe and Van Essen (1985); 3. Peterhans and von der Heydt (1993); 4. Levitt et al. (1994); 5. Roe and Ts’o (1995); 6. Gegenfurtner et al. (1996); 7. Tamura et al. (1996). *—study 7 did not distinguish between thick and thin dark stripes.
factors—plus the fact that stripe borders cannot be localised to better than ±100 µm owing to the diffuse staining characteristics of cytochrome oxidase histology. Thus, with relatively small populations of neurones in some studies, plus the acknowledged clustering of similar neurones within some form of stripe sub-structure, there is ample scope for the observed variability in the reported incidence of response selectivities across stripes. In the original formulation, thin stripes were characterised by colour selectivity, interstripes by orientation selectivity accompanied by end-stopping, and thick stripes by orientation selectivity accompanied by disparity selectivity (Hubel and Livingstone, 1987). Groups of these cells were found in the appropriate sequence as the electrode traversed one stripe after
© 2002 Taylor & Francis
396
Stewart Shipp
another, but the authors (perhaps wisely) seem to have avoided defining stripe boundaries precisely, and refrained from tabulating the exact proportions of each cell class to be found in each stripe. Most other authors have done the decent thing, and provided exact percentage incidences; their work is summarised in Table 17.1. Going by the median % figures, colour and lack of orientation selectivity emerge from Table 17.1 as consensus characteristics for thin stripes, but medians are hardly a very satisfactory resolution of the discrepancies apparent across studies. Fortunately, there is much extra data to hand, in the form of functional imaging studies (2-deoxyglucose labelling and optical imaging). These techniques generate maps of functional architecture that may be compared to several cycles of the stripe pattern, and their conclusions have generally proven to be more consistent with each other, and concordant across V1 and V2, than the results of electrophysiological studies. For instance, imaging techniques show that the thick and thin stripes of V2 are responsive to a lower range of spatial frequencies than the interstripes, and the blobs and interblobs of V1 can be similarly distinguished (Tootell et al., 1988c; Tootell and Hamilton, 1989). Single unit findings agree that in V1, blobs respond to a lower spatial frequency than interblobs (Tootell and Born, 1991; Edwards et al., 1995) and, in V2, that low-pass units occur only within thin stripes (Levitt et al., 1994a)—yet, in this very study, the optimal spatial frequency across V2 compartments was assessed to be statistically indistinguishable (Levitt et al., 1994a). How about selectivity for colour? Using low frequency isoluminant chromatic stimuli, in place of luminance contrast, only V1 blobs and V2 thin stripes appear in the imaging data (Tootell et al., 1988b; Tootell and Hamilton, 1989). These low frequencies include diffuse fields of zero spatial structure, so it is fair to conclude that the colour specific response is related to hue discrimination, rather than any kind of chromatic form vision (call it “colour-from-colour”). This result holds up to frequencies of 2 cycles/deg in central visual field. The introduction of higher spatial frequencies into isoluminant stimuli activates all three stripes in V2, and both blobs and interblobs in V1 (the relative intensity across compartments depending on the interplay of the frequency content, and eccentricity of the stimulus). Thus, the whole of V2 can be responsive to isoluminant chromatic stimuli. However optical imaging reveals that the thin stripes are the only location where the magnitude of the isoluminant response can actually exceed that of a spatially equivalent luminance stimulus (Roe and Ts’o, 1997). The relative response of single neurones to luminance contrast, and to isoluminant chromatic contrast (call it “chrominance”) is well known to depend on spatial frequency. For red-green P units of the LGN, as mentioned above, chrominance alone is effective at zero spatial frequency, but the cell’s response is increasingly dominated by luminance at higher spatial frequencies (Derrington et al., 1984). Cortical units in V1 develop far tighter tuning for spatial frequency. Hence colour selective cells (i.e. those in which the chrominance responses are most dominant) are also tuned to the lowest ranges of spatial frequency (Lennie et al., 1990; Leventhal et al., 1995). Perversely, the latter two studies are just those which have questioned whether such colour selective cells are present in significant numbers within layers 2 and 3 of V1, or are restricted to blobs in their distribution. Both used sinsusoidal luminance and chrominance gratings as stimuli; the negative findings with regard to blobs conflict with the imaging work using similar stimuli, and with the previous single-unit work, using traditional stimulation by flashed coloured spots (Livingstone and Hubel, 1984a; Ts’o and Gilbert, 1988). The latter was based on substantially larger samples of cells, and demonstrates a high incidence of colour coding within V1 blobs using
© 2002 Taylor & Francis
Fundamentals of Association Cortex
397
tangential sections with good histological definition of the darker-staining cytochrome oxidase elements. A third variable associated with blobs and thin stripes is lack of orientation selectivity, yet this has been questioned, both in V2 (Levitt et al., 1994a; Gegenfurtner et al., 1996), and V1 (Leventhal et al., 1995): the latter study indicates that sinusoidal gratings are a very sensitive probe for resolving weak orientation preferences and, uniquely, reports orientation selectivity within layer 4Cb. Yet again, functional imaging data supports the conventional picture, since, in V1, blobs are known to be situated upon fractures in the orientation map of V1, i.e. regions across which there is both a rapid change in preferred orientation and the broadest tuning for orientation (Bartfeld and Grinvald, 1992; Blasdel, 1992). In V2, zones of minimal selectivity to orientation appear to coincide directly with thin stripes (Malach et al., 1994; Roe and Ts’o, 1997). As noted above, the minimal orientation tuning in blobs and thin stripes hints that the contribution of these structures to colour vision lies in seeing attributes such as hue, saturation and brightness. Outside blobs and thin stripes, chromatic selectivity is more likely to be accompanied by tuning for orientation and higher spatial frequencies, and hence to be subserving ‘form-from-colour’. In a similar vein, only simple oriented cells can provide a signed-colour signal (one that modulates in phase with the periodicity of a chromatic grating); complex cells produce an unsigned signal, for although they may be selective for modulation along a particular axis in colour space (e.g. red-green), the firing rate is independent of the phase of the grating (Thorell et al., 1984; Lennie et al., 1990). In V2 roughly 75% of cells may be complex (Levitt et al., 1994a); if responsive to chrominance gratings, they may show spectral tuning narrower than that of V1 cells and no bias toward thin stripes (Kiper et al., 1997)— yet an unsigned-colour signal cannot be subserving colour-from-colour. Thus, as a more general point, the discovery of certain selectivities across all compartments in V2 does not necessarily undermine the case for functional segregation, although it might compromise the simple labels commonly attached to the segregated functions. 4.1.3. Direction and disparity Direction and disparity selectivity are characteristic properties of the network with which thick stripes are associated, namely areas V5, V3 and layer 4B of V1, but the evidence for V2 (from Table 17.1) is equivocal, at least for direction; unfortunately, there is no available evidence from functional imaging to break this deadlock. It can be noted that responses contingent upon coherent motion of stimuli are most common in thick stripes, a specialisation related to form-from-motion (dynamic form) (Peterhans and von der Heydt, 1993). There is some tension between the view that the thick stripes are the most prominent compartment for disparity selectivity, and the conclusion from LGN lesion work that fine stereovision depends more on the P system than the M system (Schiller et al., 1990). One explanation is that P signals may reach thick stripes via dendrites of some layer 4B cells that stick into 4A and 3, although this may be more pertinent for the minority of colour sensitive cells in the thick stripes (median incidence = 16%), than for the more prevalent property of disparity selectivity (median incidence = 68%). Could there be hidden disparity tuning in interstripes? Note that the typical test for neural disparity tuning equates to psychophysical procedures for measuring absolute disparity sensitivity in human observers (i.e. judging the distance of a single isolated object); however it is a well documented result that sensitivity to relative disparity (the difference in distance of
© 2002 Taylor & Francis
398
Stewart Shipp
two objects) is far higher than the absolute disparity sensitivity (Westheimer, 1979). If thick stripes’ stereo properties do derive from the M system, it is a fair proposition that the P system may feed a stereo capacity on the part of interstripes that is much more proficient than so far demonstrated and which (unlike V1 [Cumming and Parker, 1999]) is sensitive to relative disparity. The distinct feature of interstripes was proposed to be end-stopping (Hubel and Livingstone, 1987), but this characteristic may be less singular than first suggested (Table 17.1). Otherwise, interstripes resemble thick stripes in orientation selectivity, and sensitivity to contrast. (Only the direction-selective cells of thick stripes show the high contrast sensitivity that is typical of the M system—Levitt et al., 1994a.) Nevertheless, despite the similar appearance of the orientation signals emanating from thick stripes and interstripes, some element of distinction is implied by the fact that they participate in different networks of higher areas. More on this later. 4.1.4. Single neurones with multiple selectivities Another observation meriting consideration is the combination of selectivities for different attributes in single neurones. If, for instance, colour and direction selectivity were found to be positively associated, each could not be characteristic of a separate compartment. Or, if colour and direction selectivity indices are independently assorted across neurones, as reported by Gegenfurtner et al. (1996) it is necessary to consider their stripe distribution more closely. A segregation model would require, minimally, that the majority of the neurones with the highest colour or direction indices would not be found in the “wrong” compartment. This was not examined, but it was commented that the highest indices of selectivity for colour or motion were mutually exclusive (Gegenfurtner et al., 1996). Data from the author’s laboratory (Table 17.1 and Figure 17.3) support the idea that the neurones most selective for colour or motion are tightly restricted to thin stripes and thick stripes respectively5. Another consideration is that a joint colour- and direction-tuned cell, if found in a thick stripe, could be serving motion-from-colour (the converse notion, for the same cell in a thin stripe to be serving “colour-from-motion”, is clearly nonsensical). Another study finds that neurones with joint colour and direction selectivities are substantially more frequent in V2 than V1 (Tamura et al., 1996). The underlying idea here is that these cells may be created within V2, by intrinsic connections. This is an important notion, and it is instructive to pursue its ramifications. Suppose that the association neurone receives convergent colour and direction selective signals, and consider the outcome if the integration were to follow either logical AND or logical OR rules. In the former case the spatial properties of both inputs must be concordant (e.g. both are driven by spots) and a successful stimulus must combine both the preferred colour and the preferred direction of motion, or else the association neurone will not be activated. In this respect it would resemble a direction selective unit that had been directly constructed from signed-colour inputs. By contrast, OR integration would produce a very different “animal”. A successful stimulus need possess just one of the requisite features, and the form selectivities need not be concordant. This may seem a bizarre conception, but it may be closer to the experimental 5
The overall proportion of units classified as direction-selective or biased is far smaller than in other studies, implying a stricter criterion; hence these must be the units with the most extreme selectivities, and they were virtually all within thick stripes.
© 2002 Taylor & Francis
Fundamentals of Association Cortex
© 2002 Taylor & Francis
399
Figure 17.3. Distribution of orientation and wavelength selectivity across layers and stripes of area V2. Top left: orientation across stripes (K: ThicK stripes; N: ThiN stripes; I: Interstripes; K:I and N:I represent border zones at thick/inter and thin/inter junctions respectively). Top right: wavelength selectivity across stripes. Bottom left: wavelength selectivity across layers of thick stripes (“3.0” indicates mid layer 3; “3.5” indicates layer 3/4 border zone, etc). Bottom right: wavelength selectivity across layers of thin stripes. Wavelength-related responses classified as selective or biased for spectral stimuli, “dark” indicates preference for dark stimulus on light background.
400
Stewart Shipp
picture: the paper illustrates a superficial V2 neurone driven by either (a) rightward motion of an achromatic slit, or (b) flashed presentation of a relatively large, blue spot; the effect of amalgamating these features in a single stimulus is not mentioned (Tamura et al., 1996). It is perplexing to think how the ambiguity inherent in the signal from this cell might be interpreted by higher visual centres. Carrying the interpretation further is bedevilled by a lack of evidence, but it is worth sketching an outline hypothesis. Suppose that the OR association neurone does not have an output to other cortical areas, and that its connections are all local intrinsic ones. Note that output cells, projecting from the thick stripes to higher areas such as V5, reside mainly in layer 3B (Shipp and Zeki, 1989b). Recordings from patches of such cells (efferent to V5) show that they are heavily direction selective, i.e. they conform to the thick stripes’ particular character (DeYoe and Van Essen, 1985). By contrast, the author’s data (Figure 17.3) shows that the majority of colour cells in thick stripes are found more superficially, in layers 2 or 3A. So perhaps it is cells in these layers, i.e. non-output cells, that show the less characteristic (multiple) response selectivities. What could their function be? It must be some form of multimodal integration, or binding (encoding the fact that attributes processed along separate channels belong to the same object). Evidently, building neurones with multiple selectivities is one way of doing this—a simple tactic, but one with a recognised fundamental flaw, often dignified as the “combinatorial explosion”: there are more potential combinations of features than neurones available to represent them. A more recent idea is that binding is achieved by synchronisation of the neurones activated by a given object (Engel et al., 1997). The hypothetical synchronisation of colour and direction cells has yet to be examined experimentally. Nonetheless, the author’s speculation is that superficial, multimodal association neurones act as part of the local machinery in V2 that induces sets of functionally dissimilar (unimodal) projection neurones to adopt correlated rates of firing: putatively, the projection neurones in a stripe embody its functional characteristics, and the role of local neurones is to represent not features themselves, but their contingencies. 4.1.5. Interim summary Of the three choices listed above (4.1.1.), regarding the status of functional segregation in V2, the viewpoint on offer here is option (c): that functional architecture of V2 is most effectively summarised as a set of three segregated pathways linking V1 to distant prestriate areas. Indications to the contrary, such as evidence for the “wrong” kind of selectivity in each stripe, could be counted (1) as quirks of histological analysis (or any other factor responsible for the diversity apparent in Table 17.1); or (2) an alternative means of representing the variable of interest in a particular stripe; or even (3) as part of mechanism for achieving binding-by-synchrony. The possibility of (1–3) means that discordant physiological evidence cannot yet be held to overturn a segregationist model of V2 that is strongly supported by anatomical and functional imaging data. A potential development of the model is the idea that segregation is more robust for output cells, and less prominent for local neurones. This builds on the earlier notion that, although V2 is a vehicle for the continuation of three compartments established by V1, it does also play an important role in facilitating cross-talk between these channels (Shipp and Zeki, 1989b; Roe and Ts’o, 1995). Such cross-talk is not pictured as weakening the identity of the three segregated pathways, but as co-ordinating their mutual activity at a stage just before they diverge into the wildnerness of prestriate cortex.
© 2002 Taylor & Francis
Fundamentals of Association Cortex
401
5. PARALLEL PATHWAYS FROM V2 If the metabolic compartments maintain their identity in the passage through V2, what happens next? Further diversification and segregation? Reconvergence and desegregation? Or, somewhere between these two, maintenance of a three-fold parallel process? A generalised answer to this question is complicated by two facts: that V2 projects to at least ten additional cortical areas, nominally up to four (or more) rungs above V2 in the cortical hierarchy (Felleman and Van Essen, 1991; Gattass et al., 1997); and that the source of most of these outputs has yet to be specified at the level of stripes. So, whatever the rule, it is virtually guaranteed that future exceptions will be found (and the connections of V3 seem likely to afford an exception on the basis of presently available evidence). However, the best documented pathways are those leading to areas V4 and V5, and their best description does appear to be “parallel”: thick stripes lead to V5 (MT), thin stripes and interstripes lead to V4 (DeYoe and Van Essen, 1985; Shipp and Zeki, 1985). Cross-talk, e.g. output from thick stripes to V4, is not unknown, but it is not prevalent (Zeki and Shipp, 1989; Nakamura et al., 1993). Furthermore, the ascending outputs from V4 and V5 do not themselves show a great deal of convergence, even though some of them reach into similar zones of cortex. On this basis it is possible to carve out groups of higher areas served by thick stripes, or by thin stripes/interstripes (see Figure 17.4), that partially equate to the dorsal and ventral streams. The aim of this part is to describe the outer limits of parallelism in the visual cortex; ultimately there is reconvergence, and this forms the subject of the final part. 5.1. The Output of Thin Stripes and Interstripes to V4, and Beyond Having created the separate blob-thin stripe (B-Ns)6 and interblob-interstripe (Ib-Is) pathways, it is something of a mystery why the visual system requires them to stay in close proximity as they relay through V4, to inferotemporal cortex. How high up this chain do they retain their separate identities? It is difficult to resolve this question because metabolic compartments are not visible in V4, and no other marker has yet been found to directly visualise the modular substructure of V4. Yet V4-modules are believed to exist, because localised deposits of retrograde tracer, made blindly into V4, reveal two distinct patterns of output from V2 (Shipp and Zeki, 1985; Zeki and Shipp, 1989; Munk et al., 1995). The source cells may be found only within interstripes; or, they are within thin stripes bracketed by interstripes. The former suggests a type of module in V4 that is the exclusive target of interstripes. The latter suggests one of two things: (a) a second type of module that receives input from both thin stripes and interstripes; or (b) a module that receives input only from thin stripes, but has smaller dimensions than the Ib-Is recipient module, such that tracers injected into it tend to overflow into the adjoining Ib-Is recipient territory. The second option, (b)—implying continued segregation in V4 of the B-Ns and Ib-Is pathways— is the one that has been favoured by subsequent work (Felleman et al., 1997), and especially by the most recent study, directly visualising separate axonal fields in V4 arising from a pair of adjacent inter- and thin stripes (Xiao et al., 1999).
6
The abbreviation ‘Ns’ for thiN stripe uses its last letter, to avoid ambiguity with thicK stripes. The latter are abbreviated ‘Ks’, to avoid confusion with ‘K’ for koniocellular.
© 2002 Taylor & Francis
402
Stewart Shipp
Figure 17.4. Ascending pathways from V2 divided into two domains: left (dark ground) pathways derived from thick stripes; right (light ground) from interstripes/thin stripes. Dashed paths represent cross-talk between domains. Not all known pathways, or areas are illustrated. Pairs or triplets of areas grouped in boxes are mutually connected (arrows not shown). Arrow-heads terminating (or arrow-stems commencing) on outline of box represent a connection to all the boxed areas; arrow-heads (or -stems) penetrating box are specific to the indicated area: this system is used, for instance, to indicate specific input to V3 from thick stripes, but unspecific/undetermined input to V3/V3A/PIP from V2 generally. Indicated at right are ranks in the cortical hierarchy—NB that, at level 6+, relative elevation within the diagram ceases to be an indication of relative rank.
The same anatomical experiments that reveal the sources, within V2, of ascending inputs to V4 also show the sources of descending inputs to V4 (and, assuming the connections to be reciprocal, the likely targets of ascending projections from V4) (DeYoe et al., 1994; Shipp and Zeki, 1995; Felleman et al., 1997). These are multiple sites in IT cortex (e.g. areas TEO and TE) and also in STS (e.g. V4A, PITd and PITv). This means that a V4 module can be identified by its input from V2, and further characterised by the distribution of its output. So far, the putative modules in V4 do not differ appreciably in the general distribution of their outputs, as they all seem to connect with virtually the same set of areas, in IT cortex and elsewhere. There is, however, some distinctiveness at a local level, such that ascending connections of different modules tend to be segregated from each other (note that, experimentally, this requires the use of dual tracers at nearby sites in V4) (DeYoe et al., 1994; Felleman et al., 1997). In other words, it is as if the Ib-Is and B-Ns pathways retain some elements of a separate identity not only through V4 but in higher
© 2002 Taylor & Francis
Fundamentals of Association Cortex
403
areas also. Yet the potential for mixing is by no means eliminated. As noted above, it will be necessary to delineate the suspected modules by some means other than their connectivity in order to trace the evolution of the Ib-Is and B-Ns pathways with greater certainty. 5.2. Evidence from Human Neuropathology There are two contrasting clinical conditions—arising from occipital lobe lesions in the region of the fusiform gyrus, or from carbon monoxide (CO) poisoning—whose associated syndromes involve colour vision: There is either selective loss (achromatopsia) in the first case, or selective preservation (chromatopsia) in the second (Zeki, 1993). If the B-Ns pathway is the principal agency for extracting attributes of hue, brightness and saturation from P signals (dubbed “colour from colour”), then the clinical observations would seem to speak to the issue of the separability of the B-Ns pathway. Do these two syndromes provide evidence that the B-Ns pathway does achieve a fuller form of isolation, at least in human visual cortex? Let us first consider the more general problem of assimilating primate data across species. Monkey and human psychophysical data are highly similar, and yet the human brain is not simply a scaled up version of a monkey brain with a 1:1 homology between all identifiable areas and pathways. Somewhere in the system, there may be psychophysical correlates of structural dissimilarities. With regard to the pathways under consideration, human V1 and V2 are known to possess blobs and some form of stripes, respectively (Wong-Riley et al., 1993), but even at this early stage there is at least one difference in the metabolic signature of these areas, as human V1 lacks layer 4A (Horton and Hedley-White, 1984). It is awkward to go further, for the details of human connectivity are unknown. So, the tactic in monkey-human comparisons must be to assess the potential insights that might be gained on the assumption of structural identity across species brains, with a common pool of functional data. Let us suppose, therefore, that monkeys can talk and describe human-like perceptual consequences of focal, and diffuse, brain damage. Take the latter first. CO pathophysiology is not totally understood, but, if residual colour vision is present in several cases (Milner et al., 1991; Sparr et al., 1991), this seems likely to hinge on the coincidence of the B-Ns pathway with metabolically active regions. In V1, layer 4Cb and the blobs of the upper layers are known to be more vascular than the surrounding tissue (Zheng et al., 1991). In normal conditions this sustains an elevated rate of firing and, in the presence of CO, provides a slightly greater cushion against the effects of hypoxia. Since higher areas do not differ substantially in their overall metabolism, the selective preservation of colour might result from differential survival of the B-Ns pathway at an early level (i.e. V1 and perhaps V2); there need not be a strong implication for selective sparing of a higher area that is the preserve of the B-Ns pathway and devoted to “colour-from-colour”. However, the support for this notion from the evidence of cerebral achromatopsia is stronger, especially from those cases where relatively small lesions give rise to a relatively pure colour deficit (Damasio et al., 1980; Kolmel, 1988). These patients see only black and white (and shades of grey), but otherwise their vision is described as close to normal. The inference from these cases of pure achromatopsia for a “colour-from-colour” area that is selectively damaged, is hard to resist—especially when specific tests reveal preserved motion- and form-from-colour capacities (Barbur et al., 1994; Cavanagh et al., 1998; Heywood et al., 1998).
© 2002 Taylor & Francis
404
Stewart Shipp
But could such an area still receive signals relating to contours (i.e. surface boundaries) from colour-signed (or even colour-unsigned) components of the Ib-Is pathway? Perceptually, coloured surfaces are filled-in between visible borders (Krauskopf, 1963). It is as if the colour contrast signal at the border is more important than the colour content sampled from points in the surface’s interior. The Cornsweet illusion (in achromatic and chromatic versions), is another demonstration of this principle (Ware and Cowan, 1983; Purves et al., 1999). It is not only surface colour (including brightness) but also texture that can be filled in (Ramachandran and Gregory, 1991). A powerful theoretical formulation describes neural circuitry in terms of a “boundary contour system”, and a “feature contour system”, one determining the boundaries of segmented objects, the other filling in their surface properties (Grossberg, 1994). It is natural to equate these with the Ib-Is and B-Ns pathways. The theoretical account posits repeated feedback interactions between the two systems at successive stages in order to account for diverse perceptual phenomena. This is a fairly accurate analogue for the anatomical status of the Ib-Is and B-Ns pathways. In short, there is no firm evidence for large scale divergence of the Ib-Is and B-Ns pathways. The two may remain tightly integrated over a succession of relays, whilst retaining some form of separate identity. If so, perhaps they truly deserve the tag “parallel pathways”: never far apart, they merge only at infinity! 5.3. Pathways from the Thick Stripes The early stages of the motion pathway constitute a fully interconnected network of four stations: layer 4B of V1, the thick stripes of V2, and total areas V3 and V5 (the “4B-Ks” pathway). All four are characterised by directional selectivity, but V5, three rungs above V1 in hierarchical status, has the highest incidence, verging on 100% (Zeki, 1974). There is a wealth of additional evidence linking primate area V5 to motion processing and perception, and satellite areas such as MST to optic flow (Orban, 1997). Area V5 is held to be a pivotal area in the dorsal pathway—so that initially, the higher reaches of the pathway were virtually defined to be those sites receiving ascending inputs, directly or indirectly, from V5 (Mishkin et al., 1983; Van Essen and Maunsell, 1983; Ungerleider and Desimone, 1986). These sites include areas VIP, LIP and 7a of the inferior parietal lobule, and V6/PO and V6A in the superior lobule. The functions of these areas are related to visual control of reaching, in near or far personal space (VIP and V6A—Duhamel et al., 1998; Shipp et al., 1998) or saccadic eye movements (7a/LIP). More recently, a branch of V5 output leading to mid-temporal areas has also been recognised (Boussaoud et al., 1990). Should it be considered a ventral limb of the dorsal pathway, or as part of the ventral consortium, or (as the authors choose) a third pathway? Whatever the verdict, this degree of distribution is ultimately the undoing of a totally parallel model of visual connectivity. The parallelism of the two visual pathways model originates with separate outputs from V2 stripes to V4 and V5, and relies on the ascending outputs of V4 (also widely distributed) avoiding those from V5. But, crucially, outputs from V4 and V5 very nearly come together in the middle temporal and inferior parietal lobes. Their fields of connections are adjacent, with restricted instances of direct overlap— a pattern that has been termed “juxtaconvergence” (Shipp and Zeki, 1995)—see Figure 17.5. The ultimate convergence of the two pathways is but one (or at most two) stages away—see below. In this sense the overall set of connections is not parallel, because the highways passing via V4 and V5 do eventually amalgamate. Furthermore, the element of
© 2002 Taylor & Francis
Fundamentals of Association Cortex
405
parallelism is unduly magnified by restricting attention to V4 and V5: other, earlier instances of convergence can be seen in examining the connections of V3. V3 certainly receives input from the thick stripes. Contributions from the thin stripes and interstripes are also likely, but appear with greater variability in the author’s own material. Like V5, V3 has many direction-selective cells, at 40% not as many as V5, but also some with the advanced property of pattern motion (Felleman and Van Essen, 1987; Gegenfurtner et al., 1997). The functional distinction between V3 and V5 is addressed by the notion that V3 serves dynamic form, that is, the analysis of the orientation of moving contours, perhaps deriving contours from motion contrast (“form-from-motion”). This rationalises its output to V4 (as well as V5), the dynamic form information presumably complementing static form information in the relays to IT cortex subserving object recognition. V3 also projects to V3A, and the caudal intraparietal sulcus where neurones are sensitive to the 3D orientation of objects, very likely as part of a pathway for controlling the hand in grasping movements (Sakata et al., 1998). Another of V3’s specialities could be the analysis of slowly moving coloured patterns. There is psychophysical evidence that these are detected by the colour opponent (P) system and that V5, although activated by isoluminant, colour-signed signals, is insufficiently sensitive to be the neural basis (Gegenfurtner and Hawken, 1996). V3, by contrast, has more abundant neurones with colour tuning and lower optimal speeds (Gegenfurtner et al., 1997). Furthermore, if V3 is the site for such processing, it does not appear to be handed on to V5; so there is almost evidence for an alternative motion pathway arising in V3, although whether it proceeds via V3A, or V4 (or both) is totally unknown. These latter examples are potential instances of convergence between the outputs from V2 compartments, but the circuitry is insufficiently determined to be sure. For instance, there is a hypothetical possibility that V3 (like V4) might possess hidden modules, and that its output to V4 emerges from a module not receiving from the thick stripes. Details of the output from V2 stripes to other prestriate areas (e.g. V3A/PIP, V6/PO) are equally scarce. There is some likelihood that they will be dominated by the M system/thick stripes, like other routes to parietal cortex, but perhaps not to the same degree as the V5 motion pathway. This, as stated at the beginning, is the cleanest known example of a segregated, specialised pathway, synthesising luminant motion signals from basic M inputs. It was also one of the earliest to be traced—perhaps it has been over-influential. And finally, even the motion pathway must find its end within association cortex.
6. CONVERGENCE AND INTEGRATION Cross-connections between pathways exist at all levels, but following our earlier analysis (Zeki and Shipp, 1988) can be divided into three categories: ascending, lateral and descending. To these can be added a fourth, namely re-entrant subcortical loops. These linkages serve a variety of functions of which few, if any are properly understood. Yet all could be considered, in one way or another, as forms of integration. 6.1. Integration through Cortico-Thalamo-Cortical Circuitry? This topic is considered in detail elsewhere in this volume, so the treatment here is brief. A general principle that has emerged from anatomical studies is that if two cortical areas
© 2002 Taylor & Francis
406
Stewart Shipp
are linked directly (i.e. by cortico-cortical connections), then they are also likely to be linked via the thalamus (i.e. via the pulvinar nucleus for interactions between visual association areas). Connections between the cortex and thalamus are reciprocal, and retain elements of both retinal and cortical topography. However the topography is far from precise—and, evidently, there must be some topographic jitter to permit non-adjacent cortical areas to inter-communicate via thalamic circuitry. A single site in the pulvinar thus tends to connect with a rather broad expanse of cortex, spanning several separate areas (Sherman and Guillery, 1996). It is something of a surprise, therefore, to find that the pulvinar projections of V5 and V4 are basically separate from each other (Shipp and Zeki, 1995); it is experimentally difficult to exclude all possibility of overlap, but certainly the pulvinar circuitry does not look as if it is engineered to promote intercommunication of these areas via the thalamus. Even so, if the projection fields of V5 and V4 do not overlap in the physical sense, they are both in register with the retinotopic map of the pulvinar (or to be specific, the ventral pulvinar, containing what has traditionally been termed the inferior nucleus [Bender, 1981]). To understand how this can be, imagine that the pulvinar map is expressed in a particular anatomical plane, and replicated many times along the axis orthogonal to that plane (e.g. like cortical maps extending through the layers of the cortex). The retinotopic order seen in the V5 and V4 projection fields is simply expressed within separate replicas of the pulvinar map. This organisation also gives rise to a simple functional hypothesis. Some inputs to the ventral pulvinar, for instance from V1 or from the superior colliculus, traverse many or all of these replica maps. Thus focal activity arising in V1 or the colliculus would be transmitted, via the pulvinar maps, to retinotopically equivalent sites in the maps of areas V5 and V4 (and several other prestriate areas with similarly organised connections). The resultant activity in this set of prestriate areas would be spatially co-ordinated, pointing to a role in exogenously driven spatial attention. In summary, the thalamic circuitry of V4 and V5 may not act as a form of communication between the two areas, but it may be instrumental in co-ordinating activity within their respective spatial maps, perhaps facilitating integration within other circuits.
6.2. Integration by Ascending Connections 6.2.1. Temporal cortex Inferotemporal (IT) cortex is the traditional mainstay of the ventral pathway, and receives widespread input from V4, but the status of mid-temporal visual cortex (i.e. all the temporal lobe cortex buried within the superior temporal sulcus [STS]), is less clear-cut. It could be portrayed as a second limb of the dorsal pathway. This is because V5 has been found to project, directly and indirectly, to a series of sites along the STS. The direct projections are to a pair of areas MST and FST (Boussaoud et al., 1990), and these in turn project to an ill-defined conglomerate area, STP (superior temporal polysensory) with components known as areas TPO, PGa and IPa, respectively occupying the anterior bank, fundus and lower posterior bank of the STS (Seltzer and Pandya, 1978). V4 projections target a succession of IT areas that dip into the lower STS bank—PITd, CITd and AITd (Van Essen et al., 1990)—often subsumed within areas TEO and TE. If projections of both V4 and V5 are examined concurrently, their respective fields within the STS are
© 2002 Taylor & Francis
Fundamentals of Association Cortex
407
seen, as expected, to be largely non-overlapping (Shipp and Zeki, 1995). However these STS and IT areas are massively interconnected, as demonstrated by tracers deposited in TEO or TE (Morel and Bullier, 1990; Baizer et al., 1991; Distler et al., 1993), or within the STS (Seltzer and Pandya, 1978, 1994; Boussaoud et al., 1990). Why should this be so? IT cortex is responsible for object recognition in general, and face recognition in particular. Face sensitive cells are found in clusters throughout IT cortex, on the IT gyral surface and within the STS (e.g. Tanaka et al., 1991; Perrett et al., 1992). Neighbouring clusters of cells in the STS have very different properties, such as selectivity for static views of the head (Perrett et al., 1991), or shape-indeterminate motion selectivity (Oram et al., 1993). Nonetheless, as these authors point out, the tuning in either category is clustered around the same body-centred axes. Intriguingly, there are also cells responsive to dynamic views of particular bodily movements (e.g. front or rear view of a standing, sitting, or walking action—Perrett et al., 1990). These types of cells are thought to subserve percepts of biological motion, the characteristic gait and limb movements produced by primate locomotion, a function that can also be identified at comparable locations of human cortex (Bonda et al., 1996). In other words, what is being identified is not an object so much as a particular pattern of motion—but this is still a form of recognition. The juxtaposition of biological motion and face sensitivity makes sense in terms of a general role for this part of the brain in determining the intentions and behaviour of other individuals in a social setting (Brothers and Ring, 1993). There are clearly conspecific gestures that require both facial identity, expression and body movement to be faithfully interpreted—requiring the ultimate convergence of static form and motion processing. 6.2.2. Inferior parietal cortex Inferior parietal cortex is part of the dorsal pathway, yet it does receive a strong input from V4, as well as V5. On closer examination, the two projections are found to be largely separate. V5 projects to the lower, ventral part of the lateral bank of the intraparietal sulcus, a region of relatively heavy myelination that probably includes areas LIP and VIP (Lateral and Ventral Intra-Parietal areas) (Maunsell and Van Essen, 1983; Ungerleider and Desimone, 1986). V4 projects mainly to the lightly myelinated dorsal part, LIPd (Blatt et al., 1990; Morel and Bullier, 1990). When both projections are examined concurrently, the V4 and V5 fields are seen to be contiguous, with small zones of overlap (Shipp and Zeki, 1995); an additional complication is that both of these fields are discontinuous, being segmented into parallel bands that run orthogonal to the axis of the sulcus (see Figure 17.5). Analogous perhaps to the situation in temporal cortex, convergence between the 4B-Ks and B-Ns/Ib-Is pathways is then achieved by the dense interconnectedness of LIPv and LIPd7 (Andersen et al., 1990; Blatt et al., 1990; Morel and Bullier, 1990; Baizer et al., 1991). What does LIP do? The area was first identified as a parietal region heavily connected with the frontal eye field (Andersen et al., 1985), and is best known for its presaccadic
7
A technical limitation is noteworthy: a large proportion of experiments with tracers placed in LIP and demonstrating intrinsic connections, simultaneously reveal inputs to LIP from both V5 and V4—suggesting that the tracer placements are neither confined to LIPv nor LIPd, but involve both. It is therefore the minority of experiments that fail to label one of V5 or V4 that provide stronger evidence of interconnections between LIPd and LIPv.
© 2002 Taylor & Francis
408
Stewart Shipp
Figure 17.5. Distribution of connections with areas V4 (red) and V5 (green) within parietal and temporal lobes. Paired injections of separate tracers were made into retinotopically corresponding sites in areas V4 and V5 within a single hemisphere. Fields of connectivity are shown on a flattened cortical surface, reconstructed from serial horizontal sections at 300 µm intervals. Injection sites appear as solid and dashed lines (core and halo respectively). Top: lateral bank of IPS; the limits of the sulcus are shown by + symbols; also shown is the distribution of interhemispheric connections (“callosal degeneration”). Bottom: superior temporal sulcus, with sections aligned at the point of maximal convexity in the fundus of the sulcus; red (V4) labelled regions outside the left hand margin of the sulcus lie on the surfaces of the prelunate and inferior temporal gyri. (see Color Plate 6)
© 2002 Taylor & Francis
Fundamentals of Association Cortex
409
activity (Barash et al., 1991); subsequent studies have not unearthed any regional variation that might correspond to the anatomical subdivisions LIPd and LIPv. Visual responses of LIP neurones are activated only by salient stimuli (Gottlieb et al., 1998)— likely targets for a saccadic eye movement (or any other behavioural response)—and evidently stimuli can be salient with respect to either of the sensory domains associated with V4 or V5. However, the particular quality responsible for salience is not itself explicitly represented in LIP. LIP seems to act as a working memory spatial map (for locations of salient objects); the map is updated after each eye movement, and even starts to update itself before the movement is initiated (Colby et al., 1995). However, since activity of LIP neurones is not irrevocably followed by an eye movement, there are good arguments for attributing LIP with a more general function, namely that it is an important centre for control of the locus of spatial attention (Colby and Goldberg, 1999). This concurs with the “premotor theory” of attention, which holds that the cortical system for directing attention within space evolved from the mechanisms controlling eye movements, and may share much of the same circuitry (Rizzolatti et al., 1987; Sheliga et al., 1995); and there is persuasive evidence from human imaging work, showing that fronto-parietal activations induced by either ocular or attentional shifts are massively overlapping (Corbetta, 1998; Corbetta et al., 1998). From this viewpoint, LIP would be the source (or at least one important source) of an attentional “spotlight”, whose effects are felt elsewhere in the visual system. There are well documented neural effects related to spatial attention (both facilitatory and suppressive) in areas such as V4 and IT cortex (Moran and Desimone, 1985; Motter, 1993; Connor et al., 1997; Luck et al., 1997). These studies found only minor effects in V1, but there is now rapidly accumulating evidence from human functional imaging for significant effects of spatial attention in V1, as well as a broad spectrum of extrastriate areas (Brefczynski and DeYoe, 1999; Gandhi et al., 1999; Martinez et al., 1999; Somers et al., 1999). For present purposes, it is useful to enquire about the circuitry that could mediate these effects, especially vis-a-vis the likelihood of convergence, either across the tripartite channels stemming from V2, or across the dual dorsal and ventral processing streams. First of all, there are direct reciprocal connections from LIPd back to V4, and these obviously act to link up the dorsal and ventral streams, as do additional outputs from LIP to TEO and TE. Importantly, the latter derive from LIPv as well as LIPd (Blatt et al., 1990; Morel and Bullier, 1990; Baizer et al., 1991; Distler et al., 1993; Webster et al., 1994), so cross-talk from the 4B-Ks pathway that feeds LIPv is also substantial. Descending connections from LIP reach as far as V3 and V3A, but not to V2, nor V1. Thus, although a spatial attention “signal” might reach V1 and V2 indirectly, via a cortical relay, it is also interesting to consider subcortical pathways that could be more potent. Some blending of the B-Ns/Ib-Is and 4B-Ks pathways within subcortical output of LIP seems unavoidable. Outputs to the thalamus from LIPv and LIPd are convergent, and the thalamo-cortical loop returns to LIP as well as neighbouring areas (Asanuma et al., 1985; Schmahmann and Pandya, 1990; Hardy and Lynch, 1992). The circuitry does not, however, invade V4 or IT cortex to a great extent, as it has been demonstrated that LIP and IT cortex connect with largely distinct thalamic realms, centred in dorsal and ventral pulvinar respectively (Baleydier and Morel, 1992; Baizer et al., 1993). A potentially far more pervasive re-entrant loop operates via the superior colliculus: LIP has a very dense output to the deep layers of the colliculus (Lynch et al., 1985)—this is the circuitry that mediates LIP’s control of eye movements. Moreover, as well as the descending output to brainstem
© 2002 Taylor & Francis
410
Stewart Shipp
oculomotor nuclei, the colliculus has ascending outputs to the thalamus, including substantial parts of the pulvinar (Harting et al., 1980). The loop is completed by pulvinocortical projections which, maximally, include the entire visual cortex (Sherman and Guillery, 1996). The re-entry into V4 and IT cortex establishes convergence between the parietal and temporal streams, and this is notably asymmetric, since there is no equivalent subcortical route from IT to LIP. Colliculo-pulvino-cortical circuitry was mentioned above in the context of exogenously driven (bottom-up) spatial attention, whereas spatial attention effects mediated via LIP are more likely to be endogenous (top-down). There may be some separation of the respective circuits, since retinal input to the colliculus targets its superficial layers, LIP input is directed to its intermediate/deep layers, and prestriate areas like V4 and V5 (and TEO) have convergent projections to the superficial/intermediate layers; furthermore, there is some evidence that different layers of the colliculus project to different parts of the pulvinar (Benevento and Standage, 1983). Unfortunately, the available anatomical data is insufficient to resolve the issue. It is nonetheless worth mentioning two experiments that imply participation of the colliculus in endogenous spatial attention: (a) small collicular lesions can interfere with a colour discrimination task, if the colour-target stimulus is distinguished from a distractor by a spatial cue, and the cue (and target) are placed into the lesioned field location (Desimone et al., 1990); (b) the saccadic vector induced by microstimulation of the colliculus is found to be deflected by a concurrent, covert shift of attention, even if the attended location is indicated by a symbolic cue and the trained behavioural response does not call for an eye movement (Kustov and Robinson, 1996). 6.2.3. Ventral-to-dorsal communication The general conclusion from the above is that a blend of 4B-Ks with B-Ns/Ib-Is outputs is broadcast from the dorsal stream to the ventral stream (perhaps principally via a subcortical route)—thus integrating all these modalities of the parallel pathways. There is good reason to believe that this dorsal-to-ventral communication, if it sustains focal spatial attention, must play a rather fundamental role in the normal operation of the ventral pathway. By contrast, the reverse interaction (ventral-to-dorsal) is rather less obtrusive. The supporting evidence is not so abundant, as much of it derives from careful neuropsychological assessment of a single patient. This famous patient is DF, who became agnosic following CO poisoning and ventraloccipital cortical damage. She has largely intact visuomotor ability, successfully reaching and grasping objects whose size and orientation she is unable to report verbally (Milner, 1997). This is held to represent independent (but unconscious) processing on the part of an intact dorsal pathway. Following this formula, any demonstrable visuomotor impairment in DF must reflect the absence of contributions from the ventral pathway to dorsal pathway functions, and this has now been demonstrated in several respects. For instance, DF is unusually reliant on stereoscopic cues: under monocular viewing her wrist orientation and grasp size are severely impaired compared to normals, who are presumed to make use of monocular pictorial cues (e.g. perspective) in order to achieve accuracy in grasping movements (Dijkerman et al., 1996; Marotta et al., 1997). For another example, note that DF is good at grasping tools, but does not always take hold of the correct bit (i.e. the handle) (Carey et al., 1996). The first example shows how ventral pathway contributions may, in certain circumstances, abet visuomotor control. The second, by contrast, is essential—there
© 2002 Taylor & Francis
Fundamentals of Association Cortex
411
Figure 17.6. Schema for dorsal pathway (oblongs), ventral pathway (ovals), and damaged cortex (diagonal shading) in patient DF. Partial overlap of the two pathways is depicted at all levels; in striate cortex this reflects greater allocation of peripheral field to the dorsal pathway, and of central visual field to the ventral pathway; overlap decreases as the pathways ascend through prestriate cortices to the parietal and occipito-temporal lobes. Areas of damage in occipito-temporal (and parts of ventral prestriate?) cortex are largely conjectural. Spared parts of the ventral pathway must mediate residual colour and pattern vision. The conscious realm is diminished in DF (she is agnostic for object identity, size and orientation); the “zone of lost consciousness” may exceed the region of physical damage due to loss of feedback from destroyed higher centres. Hence the “realm of the unconscious” is depicted to include some components of the ventral pathway.
has to be ventral-to-dorsal communication to select the correct target of a grasping action8. There being no analogous neuropsychological data for monkeys, it is somewhat groundless to specify pathways, except to underline the existence of reciprocal parieto-temporal connections, and to note the likelihood of participation by circuits involving frontal/premotor cortex in any kind of planned action, such as picking up a tool by its handle. The usual interpretation of case DF is that it not only demonstrates the operation of a separate dorsal pathway for visuomotor control, but also that the pathway incorporates a surprisingly autonomous level of form processing that is unavailable to the ventral pathway, and is not consciously perceived (Goodale and Milner, 1992; Milner, 1997). This may be true, but it is not unquestionably so. Figure 17.6 provides a (pitifully) crude schematic depiction. Firstly, it is unlikely that the diffuse damage caused by CO anoxia could restrict itself to the ventral pathway, when, as we have seen, the ventral pathway is so difficult to pin down anatomically. Perhaps some overlapping elements of the dorsal pathway are also damaged—at the very least, areas processing pictorial cues that seem capable of contributing to both perception and motor control, as noted above. Equally, 8
A degree of shape selectivity intrinsic to the dorsal pathway has been reported (Sereno and Maunsell, 1998)— but in the limit, this could not suffice in all circumstances, unless it were fully to duplicate the object identifying capacity of IT cortex.
© 2002 Taylor & Francis
412
Stewart Shipp
some areas of spared cortex could also possess dual functionality. These are shown within the “non-conscious” realm in Figure 17.6, in accordance with the fact that DF cannot report any form percepts, either verbally or, in analogue form, by gestures. However this does not mean that these areas are indubitably outside the NCC (neural correlate of consciousness); they may only have been rendered so by the effect of the lesion to higher areas, depriving them of feedback. There is a considerable body of thought implying the operation of feedback loops within the normal framework of perception (e.g. Pollen, 1999). None of this is to gainsay the fact that the processes subserving visuo-motor control and visual cognition may differ in many respects: e.g. egocentric versus allocentric spatial coding; residence in short term memory; relationship to a stored knowledge base, etc. Ultimately, the underlying circuitry must reflect this schism, and it is important properly to understand its evolution. Figure 17.6 could be viewed as a network, with progressively individuating modes that never achieve total separation. It would be ironic if the two pathway formulation, adhered to too tightly, were to become a roadblock to better understanding. 6.3. Integration through Non-specific Circuitry The circuitry that is the subject matter for this concluding section is ‘non-specific’ in two senses, either: (a) in the sense that it is anatomically diffuse, terminating freely across functionally specialised compartments and/or areas, and with a modulatory synaptic action; or (b) it underpins certain integrative functional phenomena that clearly demand some form of cross-modal interaction, and yet whose level and locale within the visual network cannot be specified with any degree of reliability. Included in (a) are all feedback connections, anatomically defined as axonal terminations focused upon layers 1 and 6; although excitatory, these probably avoid unstable positive feedback effects by acting through voltage dependent synapses that modulate the response to ascending (feed-forward) inputs (e.g. Crick and Koch, 1998). Additional circuitries in this category may be components of “intrinsic” and “lateral” connections (connections internal to an area, and between areas of equal hierarchical status) whose laminar patterns are a hybrid of the feedback and forward patterns (Felleman and Van Essen, 1991). Feedback connections are more diffusely organised than forward connections (Zeki and Shipp, 1988; Krubitzer and Kaas, 1989; Rockland et al., 1994). For instance, the feedback from areas V4 and V5 to V2 is partly reciprocal, in that it principally targets the appropriate source stripe (thin/inter or thick respectively), and partly diffuse, in that all the intervening territory in V2 also receives some degree of feedback, to layers 1 and 6 (Shipp and Zeki, 1989b; Zeki and Shipp, 1989). Under consideration in (b) are some phenomena related to forms of attention. Although spatial attention can be locked to an empty position in space (Posner, 1980), it is often synonymous with attention to an object present at a particular location: the perceptual resolution of this object is enhanced, whilst that of other nearby objects is attenuated (e.g. Lee et al., 1999). At the neural level, these features of attention are captured by models of competition (Desimone, 1998; Lee et al., 1999). Evidently there is a role here for local intrinsic connections, the competition being effected between neurones with overlapping receptive fields. There is a reduced response to non-attended items (Moran and Desimone, 1985): essentially, if there is an underlying plasticity in the selection of items to which
© 2002 Taylor & Francis
Fundamentals of Association Cortex
413
a neurone can respond, attention is a process of focusing local neural processing resources upon a single dominant stimulus (Luck et al., 1997; McAdams and Maunsell, 1999; Reynolds et al., 1999; Treue and Martinez Trujillo, 1999; Treue and Maunsell, 1999). The global integrative element in this dynamic allocation of processing resources becomes apparent when it is considered that attention tends to spread to all the attributes of the object at the target location: or that the (visual) “network tends to cascade into a state in which the same object is dominant throughout” (Duncan, 1998). This means not only that diverse object attributes, e.g. colour, form and motion, are better resolved but also that, in a crowded scene, the individual attributes of the attended object are tied together. This is known as binding (Treisman, 1996, 1998)9. For example, watching a game of snooker (or pool), the colour and velocity of only one attended ball is known; awareness of the remainder is for balls of diverse colour moving in diverse directions, but there is little knowledge of specific colour-motion pairings. Evidence for the role of attention in binding comes from psychological studies (Treisman, 1998), and the apparent dramatic rise in failures of binding (“illusory conjunctions”) suffered by a parietal lesion case demonstrating the classic features of Balint’s syndrome, including simultanagnosia (Friedman-Hill et al., 1995). It is thus obvious that binding (or concurrent, object-based, multimodal feature attention) is a prime example of large scale integrative action across the visual system. The neural basis of binding is far from understood, but there is hopeful progress in hypotheses relating to the role of neural synchronisation in feature integration (Engel et al., 1997). Synchronised activity can be demonstrated transcortically (e.g. between V1 and V2: Nowak et al., 1999; Roe and Ts’o, 1999). Importantly, some synchronised responses are contingent upon simple stimuli, e.g. oriented bars, moving coherently, as if they were part of the same object (Kreiter and Singer, 1996). As yet there is no comparable demonstration that synchronisation can support cross-modal feature binding, nor that synchronisation is contingent upon focal attention. Nonetheless, synchronisation has the promise of coding for the partnership of an object’s features processed along separate pathways. In other words, it has the capacity to code for transient associations between all possible feature combinations (brown and upward, red and leftward etc., in the snooker example). Hence the circuitry sustaining this capacity would in all likelihood be diffusely organised, including all those connections that are the usual suspects for mediating the construction of dual selectivity. Models of neural networks find that synchronisation is an emergent property of networks that are richly connected across levels, to model forward, backward and lateral connectivity (Tononi et al., 1992; Schillen and König, 1994; Lumer et al., 1997). The cross-connections between stripes within V2, and other examples of lateral connectivity, for instance V4/V5, or LIP/TEO, could all contribute to establishing transient states of synchrony between neurones responding to different features of the same object. In addition, there is the propensity of feedback connections to terminate diffusely, such that the feedback from V3, V4 and V5 spreads across all pathways at the level of V2. This contrasts to the situation in V1, where the feedback from areas V4 and V5 is more tightly reciprocal (Zeki and Shipp, 1988). This lends indirect support for the teleological view that cross-channel communication is a particular attribute of V2, its real raison d’être, as if
9
Readers of Duncan (1998) and Treisman (1998) will note that the former’s competition model and the latter’s feature integration theory are not quite so readily reconciled as this passage implies.
© 2002 Taylor & Francis
414
Stewart Shipp
the later divergence of the pathways within prestriate cortex is delayed in order to allow their more effective integration. Finally, it is worth noting that the relevant circuitry is unlikely to be confined to the visual cortex, as interactions with the frontal lobe are bound to be an integral component of all attentive phenomena.
7. CONCLUSION Although many different localities in the visual system display a parallel architecture, it is not clear that the global organisation of the system is best described as “parallel”. Take for instance the three distinct retinal outputs (M, P and K); these retain their identity through a couple of synapses, but are then variously remixed into three new cortical channels (B-Ns, Ib-Is and 4B-Ks). These cortical channels retain their separation to a degree (depending where you look) but again, they certainly do not function in splendid isolation. Cortical areas select from these channels the appropriate mix from which to construct their specialised representations; they may, perhaps, be even less discriminating in sampling each others’ output in order to co-ordinate their activities. Nevertheless, the functional specialisms of some areas are sufficiently distinct to identify separate dorsal and ventral “pathways”—each an assembly of particular areas and networks of connections. It is an interesting question whether this is a casual or a causal distinction. A casual distinction is one of convenience, a way of breaking down a complex system for ease of description. There may be an underlying continuum of function across the cortical mantle, but a partition enables certain generalisations to be made, just as a dividing line around the globe (the equator) enables North–South distinctions of macroeconomic activity. The simple tags for function (What vs Where) and location (parietal lobe vs temporal lobe) aid and abet this distinction. A causal distinction would reflect some deeper dualistic factor determining cerebral organisation—perhaps a duality between the goals of visual processing, a physical representation to guide immediate motor acts, and a categorical representation to permit cognitive manipulations. In the latter case there should be some observable discontinuity of the functional continuum across the parietal/temporal divide. This question is not resolved here. The burden of this article is that even if the dorsal/ ventral distinction is a fundamental one, it poorly encapsulates the architecture of the visual system, whose functional integrity depends on interactions between these two systems. Binding of stimulus attributes into coherent object properties is one notional function, that seems to make sense of cross-talk at all levels. This should be a reasonably symmetrical relationship: for instance, an object’s location and velocity can be important factors in knowing what it is, just as object identity is an important determinant in formulating how (and whether) to reach out and manipulate it. But there is also one very significant asymmetrical relationship, which concerns spatial attention (itself not unrelated to binding). It seems very likely that the control of spatial attention has evolved from the mechanisms that direct the eyes toward items of behavioural significance, a crucial dorsal pathway function. But the circuitry has diversified and, through cortical and subcortical loops, infiltrates the ventral pathway at all levels. It would seem that the dorsal system wields an intimate influence over its twin. The operational details of this neural cookery are obscure. Not surprisingly, it is easier to provide a list of ingredients than a recipe.
© 2002 Taylor & Francis
Fundamentals of Association Cortex
415
REFERENCES Allman, J. and Zucker, S. (1990) Cytochrome oxidase and functional coding in primate striate cortex: a hypothesis. Cold Spring Harbor Symposia on Quantitative Biology, 15, 979–982. Andersen, R.A., Asanuma, A. and Cowan, W.M. (1985) Callosal and prefrontal associational projecting cell populations in area 7A of the macaque monkey: a study using retrogradely transported fluorescent dyes. Journal of Comparative Neurology, 232, 443–455. Andersen, R.A., Asanuma, A., Essick, G. and Siegel, R.M. (1990) Corticocortical connections of anatomically and physiologically defined subdivisions within the inferior parietal lobule. Journal of Comparative Neurology, 296, 65–113. Asanuma, A., Andersen, A. and Cowan, W.M. (1985) The thalamic relations of the caudal inferior parietal lobule and the lateral prefrontal cortex in monkeys: divergent cortical projections from cell clusters in the medial pulvinar nucleus. Journal of Comparative Neurology, 241, 357–381. Baizer, J.S., Desimone, R. and Ungerleider, L.G. (1991) Organization of visual inputs to the inferior temporal and posterior parietal cortex in macaques. Journal of Neuroscience, 11, 168–190. Baizer, J.S., Desimone, R. and Ungerleider, L.G. (1993) Comparison of subcortical connections of inferior temporal and posterior parietal cortex in monkeys. Visual Neuroscience, 10, 59–72. Baleydier, C. and Morel, A. (1992) Segregated thalamocortical pathways to inferior parietal and inferotemporal cortex in macaque monkey. Visual Neuroscience, 8, 391–405. Barash, S., Bracewell, R.M., Fogassi, L., Gnadt, J.W. and Andersen, R.A. (1991) Saccade-related activity in the intraparietal area I. Temporal properties; comparison with area 7a. Journal of Neurophysiology, 66, 1095–1108. Barbur, J.L., Harlow, A.J. and Plant, G.T. (1994) Insights into the different exploits of color in the visual-cortex. Proceedings of the Royal Society (London) series B, 258, 327–334. Bartfeld, E. and Grinvald, A. (1992) Relationships between orientation-preference pinwheels, cytochrome oxidase blobs, and ocular-dominance columns in primate striate cortex. Proceedings of the National Academy of Sciences of the U.S.A., 89, 11905–11909. Bender, D.B. (1981) Retinotopic organization of macaque pulvinar. Journal of Neurophysiology, 46, 672–693. Benevento, L.A. and Standage, G.P. (1983) The organization of projections of the retinorecipient and nonretinorecipient nuclei of the pretectal complex and layers of the superior colliculus to the lateral pulvinar and medial pulvinar in the macaque monkey. Journal of Comparative Neurology, 217, 307–336. Blasdel, G.G. (1992) Orientation selectivity, preference, and continuity in monkey striate cortex. Journal of Neuroscience, 12, 3139–3161. Blasdel, G.G. and Fitzpatrick, D. (1984) Physiological organization of layer 4 in macaque striate cortex. Journal of Neuroscience, 4, 880–895. Blatt, G.J., Andersen, R.A. and Stoner, G.R. (1990) Visual receptive field organization and cortico-cortical connections of the lateral intraparietal area (area LIP) in the macaque. Journal of Comparative Neurology, 299, 421–445. Bonda, E., Petrides, M. and Evans, A. (1996) Specific involvement of human parietal systems and the amygdala in the perception of biological motion. Journal of Neuroscience, 16, 3737–3744. Boussaoud, D., Ungerleider, L.G. and Desimone, R. (1990) Pathways for motion analysis: cortical connections of the medial superior temporal and fundus of the superior temporal visual areas in the macaque. Journal of Comparative Neurology, 296, 462–495. Brefczynski, J.A. and DeYoe, E.A. (1999) A physiological correlate of the ‘spotlight’ of visual attention. Nature Neuroscience, 2, 370–374. Brothers, L. and Ring, B. (1993) Mesial temporal neurons in the macaque monkey with responses selective for aspects of social stimuli. Behavioural Brain Research, 57, 53–61. Callaway, E.M. (1998) Local circuits in primary visual cortex of the macaque monkey. Annual Review Neuroscience, 21, 47–74. Carey, D.P., Harvey, M. and Milner, A.D. (1996) Visuomotor sensitivity for shape and orientation in a patient with visual form agnosia. Neuropsychologia, 34, 329–337. Casagrande, V.A. (1994) A third parallel visual pathway to primate area V1. Trends in Neuroscience, 17, 305–310. Cavanagh, P., Henaff, M.A., Michel, F., Landis, T., Troscianko, T. and Intriligator, J. (1998) Complete sparing of high-contrast color input to motion perception in cortical color blindness. Nature Neuroscience, 1, 242–247. Colby, C.L., Duhamel, J.R. and Goldberg, M.E. (1995) Oculocentric spatial representation in parietal cortex. Cerebral Cortex, 5, 470–481. Colby, C.L. and Goldberg, M.E. (1999) Space and attention in parietal cortex. Annual Review of Neuroscience, 22, 319–349. Connolly, M. and Van Essen, D.C. (1984) The representation of the visual field in parvicellular and magnocellular layers of the lateral geniculate nucleus in the macaque monkey. Journal of Comparative Neurology, 226, 544–564.
© 2002 Taylor & Francis
416
Stewart Shipp
Connor, C.E., Preddie, D.C., Gallant, J.L. and Van Essen, D.C. (1997) Spatial attention effects in macaque area V4. Journal of Neuroscience, 17, 3201–3214. Corbetta, M. (1998) Frontoparietal networks for directing attention and the eye to visual locations: Identical, independent or overlapping neural systems. Proceedings of the National Academy of Sciences of the U.S.A., 95, 831–838. Corbetta, M., Akbudak, E., Conturo, T.E., Snyder, A.Z., Ollinger, J.M., Drury H.A. et al. (1998) A common network of functional areas for attention and eye movements. Neuron, 21, 761–773. Crick, F. and Koch, C. (1995) Are we aware of neural activity in primary visual cortex? Nature (London), 375, 121–123. Crick, F. and Koch, C. (1998) Constraints on cortical and thalamic projections: the no-strong-loops hypothesis. Nature (London), 391, 245–50. Cumming, B.G. and Parker, A.J. (1999) Binocular neurons in V1 of awake monkeys are selective for absolute, not relative, disparity. Journal of Neuroscience, 19, 5602–5618. Damasio, A., Yamada, T., Damasio, H., Corbett, J. and McKee, J. (1980) Central achromatopsia: behavioral, anatomic, and physiologic aspects. Neurology, 30, 1064–1071. Derrington, A.M., Krauskopf, J. and Lennie, P. (1984) Chromatic mechanisms in lateral geniculate nucleus of macaque. Journal of Physiology (London), 357, 241–265. Derrington, A.M. and Lennie, P. (1984) Spatial and temporal contrast sensitivities of neurones in lateral geniculate nucleus of macaque. Journal of Physiology (London), 357, 219–240. Desimone, R. (1998) Visual attention mediated by biased competition in extrastriate visual cortex. Philosophical Transactions of the Royal Society of London, series B, 353, 1245–1255. Desimone, R., Wessinger, M., Thomas, L. and Schneider, W. (1990) Attentional control of visual perception: cortical and subcortical mechanisms. Cold Spring Harbor Symposia on Quantitative Biology, 55, 963–971. DeValois, R.L., Snodderly, D.M., Yund, E.W. and Hepler, N.K. (1977) Responses of macaque lateral geniculate cells to luminance and color figures. Sensory Processes, 1, 244–259. DeYoe, E.A., Felleman, D.J., Van Essen, D.C. and McClendon, E. (1994) Multiple processing streams in occipitotemporal visual cortex. Nature (London), 371, 151–154. DeYoe, E.A. and Van Essen, D.C. (1985) Segregation of efferent connections and receptive field properties in visual area 2 of the macaque. Nature (London), 317, 58–61. DeYoe, E.A. and Van Essen, D.C. (1988) Concurrent processing streams in monkey visual cortex. Trends in Neuroscience, 11, 219–226. Dijkerman, H.C., Milner, A.D. and Carey, D.P. (1996) The perception and prehension of objects oriented in the depth plane. I. Effects of visual form agnosia. Experimental Brain Research, 112, 442–451. Distler, C., Boussaoud, D., Desimone, R. and Ungerleider, L.G. (1993) Cortical connections of inferior temporal area TEO in macaque monkeys. Journal of Comparative Neurology, 334, 125–150. Dobkins, K.R. and Albright, T.D. (1993) What happens if it changes color when it moves?: psychophysical experiments on the nature of chromatic input to motion detectors. Vision Research, 33, 1019–1036. Dobkins, K.R. and Albright, T.D. (1994) What happens if it changes color when it moves?: the nature of chromatic input to macaque visual area MT. Journal of Neuroscience, 14, 4854–4870. Dow, B.M. (1974) Functional classes of cells and their laminar distribution in monkey visual cortex. Journal of Neurophysiology, 37, 927–946. Dreher, B., Fukada, Y. and Rodieck, R.W. (1976) Identification, classification and anatomical segregation of cells with X-like and Y-like properties in the lateral geniculate nucleus of Old-World primates. Journal of Physiology (London), 258, 433–452. Duhamel, J.-R., Colby, C.L. and Goldberg, M.E. (1998) Ventral intraparietal area of the macaque: congruent visual and somatic response properties. Journal of Neurophysiology, 79, 126–136. Duncan, J. (1998) Converging levels of analysis in the cognitive neuroscience of visual attention. Philosophical Transactions of the Royal Society of London, series B, 353, 1307–1317. Edwards, D.P., Purpura, K.P. and Kaplan, E. (1995) Contrast sensitivity and spatial frequency response of primate cortical neurons in and around the cytochrome oxidase blobs. Vision Research, 35, 1501–1523. Engel, A.K., Roelfsema, P.R., Fries, P., Brecht, M. and Singer, W. (1997) Role of the temporal domain for response selection and perceptual binding. Cerebral Cortex, 7, 571–582. Felleman, D.J. and Van Essen, D.C. (1987) Receptive field properties of neurons in area V3 of macaque monkey extrastriate cortex. Journal of Neurophysiology, 57, 889–920. Felleman, D.J. and Van Essen, D.C. (1991) Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1–47. Felleman, D.J., Xiao, Y. and McClendon, E. (1997) Modular organization of occipito-temporal pathways: cortical connections between visual area 4 and visual area 2 and posterior inferotemporal ventral area in macaque monkeys. Journal of Neuroscience, 17, 3185–3200. Friedman-Hill, S.R., Robertson, L.C. and Treisman, A. (1995) Parietal contributions to visual feature binding: evidence from a patient with bilateral lesions. Science (Washington), 269, 853–855.
© 2002 Taylor & Francis
Fundamentals of Association Cortex
417
Gandhi, S.P., Heeger, D.J. and Boynton, G.M. (1999) Spatial attention affects brain activity in human primary visual cortex. Proceedings of the National Academy of Sciences of the USA, 96, 3314–3319. Gattass, R., Sousa, A.B.P., Mishkin, M. and Ungerleider, L.G. (1997) Cortical projections of area V2 in the macaque. Cerebral Cortex, 7, 110–129. Gegenfurtner, K.R. and Hawken, M.J. (1996) Interaction of motion and colour in the visual pathways. Trends in Neurosciences, 19, 394–401. Gegenfurtner, K.R., Kiper, D.C. and Fenstemaker, S.B. (1996) Processing of colour, form and motion in macaque area V2. Visual Neuroscience, 13, 161–172. Gegenfurtner, K.R., Kiper, D.C. and Levitt, J.B. (1997) Functional properties of neurons in macaque area V3. Journal of Neurophysiology, 77, 1906–1923. Goodale, M.A. and Milner, A.D. (1992) Separate visual pathways for perception and action. Trends in Neurosciences, 15, 20–25. Gottlieb, J.P., Kusunoki, M. and Goldberg, M.E. (1998) The representation of visual salience in monkey parietal cortex. Nature, London, 391, 481–484. Gouras, P. and Krüger, J. (1979) Responses of cells in foveal visual cortex of the monkey to pure color contrast. Journal of Neurophysiology, 42, 850–860. Grossberg, S. (1994) 3-D vision and figure ground separation by visual cortex. Perception and Psychophysics, 55, 48–120. Hardy, S.G.P. and Lynch, J.C. (1992) The spatial distribution of pulvinar neurons that project to two subregions of the inferior parietal lobule in the macaque. Cerebral Cortex, 2, 217–230. Harting, J.K., Huerta, M.F., Frankfurter, A.J., Strominger, N.L. and Royce, G.J. (1980) Ascending pathways from the monkey superior colliculus: an autoradiographic study. Journal of Comparative Neurology, 192, 853–882. Hawken, M.J. and Parker, A.J. (1984) Contrast sensitivity and orientation selectivity in lamina IV of the striate cortex of Old World primates. Experimental Brain Research, 54, 367–372. Haxby, J.V., Grady, C.L., Horwitz, B., Ungerleider, L.G., Mishkin, M., Carson, R.E., Herscovitch, P., Schapiro, M.P. and Rapoport, S.I. (1991) Dissociation of object and spatial processing pathways in human extrastriate cortex. Proceedings of the National Academy of Sciences of the USA, 88, 1621–1625. Hendry, S.H.C. and Yoshioka, T. (1994) A neurochemically distinct third channel in the macaque dorsal lateral geniculate nucleus. Science (Washington), 264, 575–577. Heywood, C.A., Kentridge, R.W. and Cowey, A. (1998) Form and motion from colour in cerebral achromatopsia. Experimental Brain Research, 123, 145–153. Hicks, T.P., Lee, B.B. and Vidyasagar, T.R. (1983) The responses of cells in macaque lateral geniculate nucleus to sinusoidal gratings. Journal of Physiology (London), 337, 183–200. Horton, J.C. and Hedley-White, E.T. (1984) Mapping of cytochrome oxidase patches and ocular dominance coulumns in human visual cortex. Philosophical Transactions of the Royal Society of London, series B, 304, 255–272. Hubel, D.H. and Livingstone, M.S. (1987) Segregation of form, color and stereopsis in primate area 18. Journal of Neuroscience, 7, 3378–3415. Hubel, D.H. and Livingstone, M.S. (1990) Color and contrast sensitivity in the lateral geniculate body and primary visual cortex of the macaque monkey. Journal of Neuroscience, 10, 2223–2237. Hübener, M. and Bolz, J. (1992) Relationships between dendritic morphology and cytochrome oxidase compartments in monkey striate cortex. Journal of Comparative Neurology, 324, 67–80. Ingling, Jr., C.R. and Martinez-Uriegas, E. (1983) The relationship between spectral sensitivity and spatial sensitivity for the primate r-g X-channel. Vision Research, 23, 1495–1500. Jacobs, G.H., Deegan, J.F., Deegan, D., Neitz, J., Crognale, M.A. and Neitz, M. (1993) Photopigments and color vision in the nocturnal monkey, Aotus. Vision Research, 33, 1773–1783. Kiper, D.C., Fenstemaker, S.B. and Gegenfurtner, K.R. (1997) Chromatic properties of neurons in macaque area V2. Visual Neuroscience, 14, 1061–1072. Kolmel, H.W. (1988) Pure homonymous hemiachromatopsia: findings with neuro-ophthalmolgic examination and imaging procedures. European Archives of Psychiatry and Neurological sciences, 237, 237–243. Krauskopf, J. (1963) Effect of retinal image stabilization on the appearance of heterochromatic targets. Journal of the Optical Society of America, 53, 741–744. Kreiter, A.K. and Singer, W. (1996) Stimulus-dependent synchronization of neuronal responses in the visual cortex of the awake macaque monkey. Journal of Neuroscience, 16, 2381–2396. Krubitzer, L.A. and Kaas, J.H. (1989) Cortical integration of parallel pathways in the visual system of primates. Brain Research, 478, 161–165. Kustov, A.A. and Robinson, D.L. (1996) Shared neural control of attentional shifts and eye movements. Nature (London), 384, 74–77. Lee, B.B., Martin, P.R. and Valberg, A. (1988) The physiological basis of heterochromatic flicker photometry demonstrated in the ganglion cells of the macaque retina. Journal of Physiology (London), 404, 323–347.
© 2002 Taylor & Francis
418
Stewart Shipp
Lee, D.K., Itti, L., Koch, C. and Braun, J. (1999) Attention activates winner-take-all competition among visual filters. Nature Neuroscience, 2, 375–381. Lennie, P., Krauskopf, J. and Sclar, G. (1990) Chromatic mechanisms in striate cortex of macaque. Journal of Neuroscience, 10, 649–669. Leopold, D.A. and Logothetis, N.K. (1996) Activity changes in early visual cortex reflect monkeys’ percepts during binocular rivalry. Nature (London), 379, 549–553. Leventhal, A.G., Thompson, K.G., Liu, D., Zhou, Y. and Ault, S.J. (1995) Concomitant sensitivity to orientation, direction, and color of cells in layers 2, 3 and 4 of monkey striate cortex. Journal of Neuroscience, 15, 1808–1818. Levitt, J.B., Kiper, D.C. and Movshon, J.A. (1994a) Receptive fields and functional architecture of macaque V2. Journal of Neurophysiology, 71, 2517–2542. Levitt, J.B., Yoshioka, T. and Lund, J.S. (1994b) Intrinsic cortical connections in macaque visual area V2: evidence for interaction between different functional streams. Journal of Comparative Neurology, 342, 551–570. Livingstone, M.S. and Hubel, D.H. (1983) Specificity of cortico-cortical connections in monkey visual system. Nature (London), 304, 531–534. Livingstone, M.S. and Hubel, D.H. (1984a) Anatomy and physiology of a color system in the primate visual cortex. Journal of Neuroscience, 4, 309–356. Livingstone, M.S. and Hubel, D.H. (1984b) Specificity of intrinsic connections in primate primary visual cortex. Journal of Neuroscience, 4, 2830–2835. Livingstone, M.S. and Hubel, D.H. (1987a) Connections between layer 4B of area 17 and the thick cytochrome oxidase stripes of area 18 in the squirrel monkey. Journal of Neuroscience, 7, 3371–3377. Livingstone, M.S. and Hubel, D.H. (1987b) Psychophysical evidence for separate channels for the perception of form, color, movement, and depth. Journal of Neuroscience, 7, 3416–3468. Livingstone, M.S. and Hubel, D.H. (1988) Segregation of form, color, movement, and depth: anatomy, physiology, and perception. Science (Washington), 240, 740–749. Logothetis, N.K., Schiller, P.H., Charles, E.R. and Hurlbert, A.C. (1990) Perceptual deficits and the activity of the color-opponent and broad-band pathways at isoluminance. Science (Washington), 247, 214–217. Luck, S.J., Chelazzi, L., Hillyard, S.A. and Desimone, R. (1997) Neural mechanisms of spatial selective attention in areas V1, V2 and V4 of macaque visual cortex. Journal of Neurophysiology, 77, 24–42. Lumer, E.D., Edelman, G.M. and Tononi, G. (1997) Neural dynamics in a model of the thalamocortical system. I. Layers, loops and the emergence of fast synchronous rhythms. Cerebral Cortex, 7, 207–227. Lund, S., Yoshioka, T. and Levitt, B. (1994) Substrates for interlaminar connections in area V1 of macaque monkey visual cortex. In: A. Peters and K.S. Rockland (eds), Primary Visual Cortex in Primates. New York: Plenum, pp. 37–60. Lynch, J.C., Graybiel, A.M. and Lobeck, L.J. (1985) The differential projection of two cytoarchitectonic subregions of the inferior parietal lobule of macaque upon the deep layers of the superior colliculus. Journal of Comparative Neurology, 235, 241–254. Malach, R., Tootell, R.B.H. and Malonek, D. (1994) Relationship between orientation domains, cytochrome oxidase stripes, and intrinsic horizontal connections in Squirrel monkey area V2. Cerebral Cortex, 4, 151–165. Marotta, J.J., Behrmann, M. and Goodale, M.A. (1997) The removal of binocular cues disrupts the calibration of grasping in patients with visual form agnosia. Experimental Brain Research, 116, 113–121. Martin, P.R., White, A.J., Goodchild, A.K., Wilder, H.D. and Sefton, A.E. (1997) Evidence that blue-on cells are part of the third geniculocortical pathway in primates. European Journal of Neuroscience, 9, 1536–1541. Martinez, A., Anllo-Vento, L., Sereno, M.I., Frank, L.R., Buxton, R.B., Dubowitz, D.J. et al. (1999) Involvement of striate and extrastriate visual cortical areas in spatial attention. Nature Neuroscience, 2, 364–369. Maunsell, J.H.R., Nealey, T.A. and DePriest, D.D. (1990) Magnocellular and parvocellular contributions to responses in the middle temporal visual area (MT) of the macaque monkey. Journal of Neuroscience, 10, 3323–3334. Maunsell, J.H.R. and Van Essen, D.C. (1983) The connections of the middle temporal area and their relationship to a cortical hierarchy in the macaque monkey. Journal of Neuroscience, 3, 2563–2586. McAdams, C.J. and Maunsell, J.H.R. (1999) Effects of attention on orientation-tuning functions of single neurons in macaque cortical area V4. Journal of Neuroscience, 19, 431–441. Merigan, W.H., Byrne, C.E. and Maunsell, J.H.R. (1991a) Does primate motion perception depend on the magnocellular pathway? Journal of Neuroscience, 11, 3422–3429. Merigan, W.H., Katz, L.M. and Maunsell, J.H. (1991b) The effects of parvocellular lateral geniculate lesions on the acuity and contrast sensitivity of macaque monkeys. Journal of Neuroscience, 11, 994–1001. Milner, A.D. (1997) Vision without knowledge. Philosophical Transactions of the Royal Society of London, series B, 352, 1249–1256. Milner, A.D., Perrett, D.I., Johnston, R.S., Benson, P.J., Jordan, T.R., Heeley, D.W., Beltucci, D., Mortara, F., Mutani, R., Terazzi, E. and Davidson, D.L.W. (1991) Perception and action in ‘visual form agnosia’. Brain, 114, 405–428.
© 2002 Taylor & Francis
Fundamentals of Association Cortex
419
Mishkin, M., Ungerleider, L.G. and Macko, K.A. (1983) Object vision and spatial vision: two cortical pathways. Trends in Neuroscience, 6, 414–417. Moran, J. and Desimone, R. (1985) Selective attention gates visual processing in the extrastriate cortex. Science (Washington), 229, 782–784. Morel, A. and Bullier, J. (1990) Anatomical segregation of two cortical visual pathways. Visual Neuroscience, 4, 555–578. Motter, B.C. (1993) Focal attention produces spatially selective processing in visual cortical areas V1, V2, and V4 in the presence of competing stimuli. Journal of Neurophysiology, 70, 909–919. Movshon, J.A., Adelson, E.H., Gizzi, M.S. and Newsome, W.T. (1985) The analysis of moving visual patterns. In: C. Chages, R. Gattass and C.G. Gross (eds), Pattern recognition mechanisms. Vatican: Pontifical Academy of Science, pp. 117–151. Munk, M.H., Nowak, L.G., Girard, P., Chounlamountri, N. and Bullier, J. (1995) Visual latencies in cytochrome oxidase bands of macaque area V2. Proceedings of the National Academy of Sciences of the USA, 92, 988–992. Nakamura, M., Gattass, R., Desimone, R. and Ungerleider, L.G. (1993) The modular organization of projections from areas V1 and V2 to areas V4 and TEO in macaques. Journal of Neuroscience, 13, 3681–3691. Nealey, T.A. and Maunsell, J.H.R. (1994) Magnocellular and parvocellular contributions to the responses of neurons in macaque striate cortex. Journal of Neuroscience, 14, 2069–2079. Nowak, L.G., Munk, M.H., James, A.C., Girard, P. and Bullier, J. (1999) Cross-correlation study of the temporal interactions between areas V1 and V2 of the macaque monkey. Journal of Neurophysiology, 81, 1057–1074. Oram, M.W., Perrett, D.I. and Hietanen, J.K. (1993) Directional tuning of motion-sensitive cells in the anterior superior temporal polysensory area of the macaque. Experimental Brain Research, 97, 274–294. Orban, G.A. (1997) Visual Processing in Macaque Area MT/V5 and its Satellites (MSTd and MSTv). In: K.S. Rockland, J.H. Kaas and A. Peters (eds), Cerebral Cortex Vol. 12: Extrastriate Visual Cortex in Primates. New York: Plenum, pp. 359–434. Orban, G.A., Kennedy, H. and Bullier, J. (1986) Velocity and direction selectivity of neurons in areas V1 and V2 of the monkey: influence of eccentricity. Journal of Neurophysiology, 56, 462–480. Perrett, D.I., Harries, M.H., Chitty, A.J. and Mistlin, A.J. (1990) Three stages in the classification of body movements by visual neurons. In: H.B. Barlow, C. Blakemore and M. Weston-Smith (eds), Images and Understanding. Cambridge, UK: Cambridge University Press, pp. 94–107. Perrett, D.I., Hietanen, J.K., Oram, M.W. and Benson, P.J. (1992) Organization and functions of cells responsive to faces in the temporal cortex. Philosophical Transactions of the Royal Society of London, series B, 335, 23–30. Perrett, D.I., Oram, M.W., Harries, M.H., Bevan, R., Hietanen, J.K., Benson, P.J. and Thomas, S. (1991) Viewer-centred and object-centred coding of heads in the macaque temporal cortex. Experimental Brain Research, 86, 159–173. Peterhans, E. and von der Heydt, R. (1993) Functional organization of area V2 in the alert macaque. European Journal of Neuroscience, 5, 509–524. Pollen, D.A. (1999) On the neural correlates of visual perception. Cerebral Cortex, 9, 4–19. Posner, M.I. (1980) Orienting of attention. Quarterly Journal of Experimental Psychology, 32, 3–25. Purves, D., Shimpi, A. and Lotto, R.B. (1999) An empirical explanation of the cornsweet effect. Journal of Neuroscience, 19, 8542–8551. Ramachandran, S. and Gregory, R.L. (1991) Perceptual filling in of artificially induced scotomas in human vision. Nature (London), 350, 699–702. Reynolds, J.H., Chelazzi, L. and Desimone, R. (1999) Competitive mechanisms subserve attention in macaque areas V2 and V4. Journal of Neuroscience, 19, 1736–1753. Rizzolatti, G., Riggio, L., Dascola, I. and Umilta, C. (1987) Reorienting attention across the horizontal and vertical meridians: evidence in favor of a premotor theory of attention. Neuropsychologia, 25, 31–40. Rockland, K.S., Saleem, K.S. and Tanaka, K. (1994) Divergent feedback conections from areas V4 and TEO in the macaque. Visual Neuroscience, 11, 579–600. Rodman, H.R. and Albright, T.D. (1989) Single-unit analysis of pattern-motion selective properties in the middle temporal visual area. Experimental Brain Research, 75, 53–64. Roe, A.W. and Ts’o, D.Y. (1995) Visual topography in primate V2: multiple representation across functional stripes. Journal of Neuroscience, 15, 3689–3715. Roe, A.W. and Ts’o, D.Y. (1997) The Functional Architecture of Area V2 in the Macaque Monkey. In: K.S. Rockland, J.H. Kaas and A. Peters (eds), Cerebral Cortex Vol. 12: Extrastriate Visual Cortex in Primates. New York: Plenum Press, pp. 295–333. Roe, A.W. and Ts’o, D.Y. (1999) Specificity of color connectivity between primate V1 and V2. Journal of Neurophysiology, 82, 2719–2730. Sakata, H., Taira, M., Kusunoki, M., Murata, A., Tanaka, Y. and Tsutsui, K. (1998) Neural coding of 3D features of objects for hand action in the parietal cortex of the monkey. Philosophical Transactions of the Royal Society of London, series B, 353, 1363–1373.
© 2002 Taylor & Francis
420
Stewart Shipp
Sawatari, A. and Callaway, E.M. (1996) Convergence of magno- and parvocellular pathways in layer 4B of macaque primary visual cortex. Nature (London), 380, 442–446. Schillen, T.B. and König, P. (1994) Binding by temporal structure in multiple feature domains of an oscillatory neuronal network. Biological Cybernetics, 70, 397–405. Schiller, P.H. and Colby, C.L. (1983) The responses of single cells in the lateral geniculate nucleus of the rhesus monkey to color and luminance contrast. Vision Research, 23, 1631–1641. Schiller, P.H., Logothetis, N.K. and Charles E.R. (1990) Role of colour-opponent and broad-band channels in vision. Visual Neuroscience, 5, 321–346. Schiller, P.H. and Malpeli, J.G. (1978) Functional specificity of lateral geniculate nucleus laminae of the rhesus monkey. Journal of Neurophysiology, 41, 788–797. Schmahmann, J.D. and Pandya, D.N. (1990) Anatomical investigation of projections from thalamus to posterior parietal cortex in the rhesus monkey: a WGA-HRP and fluorescent tracer study. Journal of Comparative Neurology, 237, 408–426. Seltzer, B. and Pandya, D.N. (1978) Afferent cortical connections and architectonics of the superior temporal sulcus and surrounding cortex in the rhesus monkey. Brain Research, 149, 1–24. Seltzer, B. and Pandya, D.N. (1994) Parietal, temporal, and occipital projections to cortex of the superior temporal sulcus in the rhesus monkey: a retrograde tracer study. Journal of Comparative Neurology, 343, 445–463. Sereno, A.B. and Maunsell, J.H. (1998) Shape selectivity in primate lateral intraparietal cortex. Nature (London), 395, 500–503. Shapley, R.M., Kaplan, E. and Soodak, R. (1981) Spatial summation and contrast sensitivity of X and Y cells in the lateral geniculate nucleus of the macaque. Nature (London), 292, 543–545. Sheliga, B.M., Riggio, L. and Rizzolatti, G. (1995) Spatial attention and eye movements. Experimental Brain Research, 105, 261–275. Sherman, S.M. and Guillery, R.W. (1996) Functional organization of thalamocortical relays. Journal of Neurophysiology, 76, 1367–1395. Shipp, S., Blanton, M. and Zeki, S. (1998) A visuo-somatomotor pathway through superior parietal cortex in the macaque monkey: cortical connections of areas V6 and V6A. European Journal of Neuroscience, 10, 3171–3193. Shipp, S. and Zeki, S. (1985) Segregation of pathways leading from area V2 to areas V4 and V5 of macaque monkey visual cortex. Nature (London), 315, 322–325. Shipp, S. and Zeki, S. (1989a) The organization of connections between areas V5 and V1 in macaque monkey visual cortex. European Journal of Neuroscience, 1, 309–332. Shipp, S. and Zeki, S. (1989b) The organization of connections between areas V5 and V2 in macaque monkey visual cortex. European Journal of Neuroscience, 1, 333–354. Shipp, S. and Zeki, S. (1995) Segregation and convergence of specialised pathways in macaque monkey visual cortex. Journal of Anatomy, 187, 547–562. Somers, D.C., Dale, A.M., Seiffert, A.E. and Tootell, R.B. (1999) Functional MRI reveals spatially specific attentional modulation in human primary visual cortex. Proceedings of the National Academy of Sciences of the USA, 96, 1663–1668. Sparr, S.A., Jay, M., Drislane, F.W. and Venna, N. (1991) A historic case of visual agnosia revisited after 40 years. Brain, 114, 789–800. Tamura, H., Sato, H., Katsuyama, N., Hata, Y. and Tsumoto, T. (1996) Less segregated processing of visual information in V2 than V1 of the monkey visual cortex. European Journal of Neuroscience, 8, 300–309. Tanaka, K., Saito, H., Fukada, Y. and Moriya, M. (1991) Coding visual images of objects in the inferotemporal cortex of the macaque monkey. Journal of Neurophysiology, 66, 170–189. Thorell, L.G., Devalois, R.L. and Albrecht, D.G. (1984) Spatial mapping of monkey V1 cells with pure color and luminance stimuli. Vision Research, 24, 751–769. Tononi, G., Sporns, O. and Edelman, G.M. (1992) Reentry and the problem of integrating multiple cortical areas: simulation of dynamic integration in the visual system. Cerebral Cortex, 2, 310–335. Tootell, R.B.H. and Born, R.T. (1991) Spatial frequency tuning of units in macaque supragranular striate cortex. Proceedings of the National Academy of Sciences of the USA, 88, 7066–7070. Tootell, R.B.H. and Hamilton, S.L. (1989) Functional anatomy of the second visual area (V2) in the macaque. Journal of Neuroscience, 9, 2620–2644. Tootell, R.B.H., Hamilton, S.L. and Switkes, E. (1988a) Functional anatomy of macaque striate cortex. IV. Contrast and magno parvo streams. Journal of Neuroscience, 8, 1594–1609. Tootell, R.B.H., Silverman, M.S., Hamilton, S.L., DeValois, R.L. and Switkes, E. (1988b) Functional anatomy of macaque striate cortex. III. Color. Journal of Neuroscience, 8, 1569–1593. Tootell, R.B.H., Silverman, M.S., Hamilton, S.L., Switkes, E. and DeValois, R.L. (1988c) Functional anatomy of macaque striate cortex. V. Spatial frequency. Journal of Neuroscience, 8, 1610–1624. Treisman, A. (1996) The binding problem. Current Opinion in Neurobiology, 6, 171–178. Treisman, A. (1998) Feature binding, attention and object perception. Philosophical Transactions of the Royal Society of London, series B, 353, 1295–1306.
© 2002 Taylor & Francis
Fundamentals of Association Cortex
421
Treue, S. and Martinez Trujillo, J.C. (1999) Feature-based attention influences motion processing gain in macaque visual cortex. Nature (London), 399, 575–579. Treue, S. and Maunsell, J.H. (1999) Effects of attention on the processing of motion in macaque middle temporal and medial superior temporal visual cortical areas. Journal of Neuroscience, 19, 7591–7602. Ts’o, D.Y. and Gilbert, C.D. (1988) The organization of chromatic and spatial interactions in the primate striate cortex. Journal of Neuroscience, 8, 1712–1727. Ungerleider, L.G. and Desimone, R. (1986) Cortical connections of visual area MT in the macaque. Journal of Comparative Neurology, 248, 190–222. Van Essen, D.C., Felleman, D.J., DeYoe, E.A., Olavarria, J. and Knierim, J. (1990) Modular and hierarchical organization of extrastriate visual cortex in the macaque monkey. Cold Spring Harbor Symposia on Quantitative Biology, 55, 679–696. Van Essen, D.C. and Maunsell, J.H.R. (1983) Hierarchical Organization and functional streams in the visual cortex. Trends in Neurosciences, 6, 370–375. Ware, C. and Cowan, W.B. (1983) The chromatic Cornsweet effect. Vision Research, 10, 1075–1077. Webster, M.J., Bachevalier, J. and Ungerleider, L.G. (1994) Connections of inferior temporal areas TEO and TE with parietal and frontal cortex in macaque monkeys. Cerebral Cortex, 4, 470–483. Westheimer, G. (1979) Coperative neural processes involved in stereoscopic acuity. Experimental Brain Research, 36, 585–597. Wiesel, T.N. and Hubel, D.H. (1966) Spatial and chromatic interactions in the lateral geniculate body of the rhesus monkey. Journal of Neurophysiology, 29, 1115–1156. Wong-Riley, M.T., Hevner, R.F., Cutlan, R., Earnest, M., Egan, R., Frost, J. and Nguyen,T. (1993) Cytochrome oxidase in the human visual cortex: distribution in the developing and the adult brain. Visual Neuroscience, 10, 41–58. Xiao, Y., Zych, A. and Felleman, D.J. (1999) Segregation and convergence of functionally defined V2 thin stripe and interstripe compartment projections to area V4 of macaques. Cerebral Cortex, 9, 792–804. Yoshioka, T., Blasdel, G.G., Levitt, J.B. and Lund, J.S. (1996) Relation between patterns of intrinsic lateral connectivity, ocular dominance, and cytochrome oxidase-reactive regions in macaque monkey striate cortex. Cerebral Cortex, 6, 297–310. Young, M.P. (1992) Objective analysis of the topological organization of the primate visual system. Nature (London), 358, 152–154. Zeki, S. (1983) Colour coding in the cerebral cortex: the reaction of cells in monkey visual cortex to wavelengths and colours. Neuroscience, 9, 741–765. Zeki, S. (1993) A Vision of the Brain. Oxford: Blackwell. Zeki, S. and Shipp, S. (1988) The functional logic of cortical connections. Nature, 335, 311–317. Zeki, S. and Shipp, S. (1989) Modular connections between areas V2 and V4 of macaque monkey visual cortex. European Journal of Neuroscience, 1, 494–506. Zeki, S.M. (1974) Functional organization of a visual area in the posterior bank of the superior temporal sulcus of the rhesus monkey. Journal of Physiology (London), 236, 549–573. Zheng, D., LaMantia, A.S. and Purves, D. (1991) Specialized vascularization of the primate visual cortex. Journal of Neuroscience, 11, 2622–2629.
© 2002 Taylor & Francis
18 Wheels within Wheels: Circuits for Integration of Neural Assemblies on Small and Large Scales Robert Miller Otago Centre for Theoretical Studies in Psychiatry and Neuroscience, c/o Department of Anatomy and Structural Biology, School of Medical Science, University of Otago, P.O. Box 913, Dunedin, New Zealand Tel: 0064-3-4797357; FAX: 0064-3-479-7254; e-mail:
[email protected]
The environments in which we live include an infinite variety of possible combinations of information, and yet, within this, contain considerable redundancy. The mammalian forebrain has developed a system of staggering complexity and beauty for representing such environments. Within the cerebral cortex, the basic repository of information is a network with rather low levels of activity, so that the tendency to uncontrolled “explosions” is reduced to safe levels. Nevertheless, priming of this rather quiet “library” by more active parts of the forebrain (e.g. cortical lamina V, and thalamic projection neurones) can, in the waking state, set into activity assemblies of nerve cells which represent the fine spatial and temporal structure of the environment. By interaction between cortex and basal ganglia the temporal structure on a larger scale can be represented as sequences, when exact temporal representation is not possible. Ambiguities of representation, which are inherent in the cortical network can be resolved by cortico-hippocampal interplay, given that a global representation of the current environment has been established. Two sorts of cell assemblies are needed, representing respectively spatial and temporal structure. They can both form in a realistic cortical network, but cannot both operate at the same time in such a network. This may be the reason for hemispheric specialization, and should apply throughout the mammalian kingdom. “Omniconnection” as a substrate for consciousness is not strictly realised (although it is approximated). In view of the redundancy of information in the environment, strict omniconnection is not necessary. KEYWORDS: axonal conduction, cell assemblies, hemispheric specialization, cortico-basal ganglionic loops, exact timing, neocortical laminae, cortico-hippocampal interplay, “omniconnection”, sequencing, thalamo-cortical relations
1. INTRODUCTION The mammalian cerebral cortex is a vast network of interconnecting neurones, in which about 89% of synaptic links are excitatory (Braitenberg and Schüz, 1991, 1998). The view is gaining increasing support that the basic principle for functional organization of the cerebral cortex is the cell assembly (or neural assembly). This concept was first advanced by Hebb (1949) and has been developed in many ways subsequently (Braitenberg, 1978; Palm, 1982; Miller, 1991, 1996a,b,c; Miller and Wickens, 1991; Sakurai, 1996; Wickens and Miller, 1997). In essence, a cell assembly is a distributed group of cortical neurones, with stronger functional connections amongst themselves than with the surrounding majority of other neurones. The strong connections required to form a cell assembly are envisaged to arise by 423 © 2002 Taylor & Francis
424
Robert Miller
synaptic strengthening, according to the well-known rule, also originally proposed by Hebb (1949). Once formed, cell assemblies constitute a static store of information coded in the configuration of very many strengthened connections. That information may be brought to an active form only if the cell assembly is “ignited” (Braitenberg, 1978). This occurs when member neurones of the assembly are activated from extrinsic sources above a threshold level, at which point reverberating activity can spread to all members of the assembly (Wickens and Miller, 1997). Since the member neurones which constitute an assembly may be widely distributed, any one neurone can be part of several different assemblies, and assemblies can represent meaningful information in a very versatile manner; and since the unit of information storage is the strengthened synapse rather than the neurone, this means of storage is a very economical use of the anatomically-defined cortical network. However, there is a serious problem with the cell assembly theory, which has at present been resolved only partially. In a network consisting mainly of excitatory connections, there is always a danger that excitation will spread well beyond the boundaries of a specific cell assembly. Once activity in the network as a whole exceeds a certain level, overall activity levels would tend to increase in a rapidly-accelerating fashion, until an “explosion” of maximal activity occurs, limited only by pathological effects such as neuronal fatigue. If this occurs, the possibility of selective representation of specific categories of information would be totally lost. The fact that various forms of epileptic seizure can occur in the cortex indicates that this is not just a theoretical possibility in the cortex, but an ever-present danger, requiring powerful safeguards if normal cortical functioning is to prevail. There are several possible processes within the cortex which could serve to limit the uncontrolled spread of excitation. Most obviously, excitatory links are not the only way in which cortical neurones can interact. There are many inhibitory interneurones in the cortex, utilising GABA as a transmitter, and it is estimated that approximately 11% of cortical synapses are GABAergic (Braitenberg and Schüz, 1998). However, another aspect of the design of the cortex to allow effective operation of neural assemblies has rarely been considered: the layers of the cortical mantle where most of the cortico-cortical connections have their origin and termination, and which are thus dominant for neural assembly function, are relatively quiescent. Under many circumstances, the amount of neural traffic carried by neurones in these layers is rather small. Perhaps, therefore the problem of stable operation of cell assemblies should be viewed from the opposite perspective, captured by the following questions: How can such relatively inert layers of cortical network tissue ever be activated sufficiently to ignite cell assemblies? How can they be activated to such a degree that cell assemblies form in the first place? The aim of this chapter is to present evidence and arguments that there is in fact a series of interlocking circuits, some very local and small scale, others more global and all-encompassing, whose role is to bring selected member neurones of the cortical network to the level of activity at which cell assemblies can form and perform their informational functions. The scale of the mechanisms varies, and as it does so, the scale of integration achieved by cell assemblies also varies, from the very local to the global.
2. DIFFERENCES BETWEEN ACTIVITY LEVELS ACROSS CORTICAL LAMINAE A crucial fact in elucidating the operation of cell assemblies in the cortex is the laminar differences in firing rate (discussed also in Miller, 1996a). According to Swadlow (1988,
© 2002 Taylor & Francis
Neural Assemblies on Small and Large Scales
425
1989, 1994) in the awake rabbit the firing rate of pyramidal neurones in laminae II and III of various cortical areas ranges from 0.1–2 Hz. Many additional neurones in these laminae may be essentially silent, and so escape detection. Due to such a sampling bias (discussed by Miller, 1996c) the true range of firing rates may be even lower than in the samples observed by Swadlow. According to Swadlow, pyramidal neurones in lamina VI also have such low levels of spontaneous activity. In lamina V, however, the median rate of pyramidal cell firing is considerably higher. This is a common observation in anaesthetized preparations, and is also found in the waking state. In the waking rabbits studied by Swadlow, spontaneous firing rate in such neurones ranged between 4 and 8 Hz. Revishchin (1985) also finds that spontaneous firing rates are much higher for neurones in lamina V of rabbit visual cortex, than for laminae II and III. From older data there are indications that these conclusions apply to other species, although there are hints that there is some variation in the finding. Schiller et al. (1976a) found, in monkey striate cortex that one class of neurone (i.e. those whose excitatory and inhibitory field areas were coextensive) commonly had higher rates of spontaneous firing if they were located in lamina V or VI than in laminae II to IV. Gilbert (1977) studying cat primary visual cortex found two bands of high spontaneous activity, namely lower lamina III and lamina V. Mangini and Pearlman (1980) and Lemmon and Pearlman (1981) show, in mouse, that corticotectal units (which are located exclusively in lamina V) have higher spontaneous firing rates than other classes of neurone. Some studies also document the fact that pyramidal neurones in laminae II and III are less able to sustain high firing rates in response to sensory stimuli than those in lamina V. McKenna et al. (1984) made recordings from superficial laminae of the cat primary somatosensory cortex. Although the evidence is confined to upper laminae, their comparison with other studies suggests that many neurones in these laminae have response properties different from those in deep laminae of the same column. In particular, in responding to repeated stimuli, neurones in superficial laminae prefer infrequent repetition (<0.5/s stimulation): repetition at slow rates leads to enhancement of the response, while repetition at fast rates leads to a decrease of the response, and then to silencing of spontaneous activity. Similarly, Chapin (1986), finds that neurones in laminae II, III and VI in S1 cortex of the rat responded more slowly and weakly to sensory stimuli than those in lamina IV. (No specific statement is made about those in lamina V). In addition, Swadlow (1990) finds for the primary somatosensory cortex of awake rabbits that a lower proportion of neurones in laminae II, III or VI than in lamina V could show a sustained response.
3. IMPLICATIONS FOR CELL ASSEMBLY IGNITION, MAINTENANCE AND FORMATION What are the implications of such findings for cell assembly function? Envisage the circumstances in which a sensory stimulus activates the cortex, and there is the possibility of an already-existing assembly being ignited. What determines whether it will actually do so? Computations of this were conducted by Wickens and Miller (1998) based on assumptions which were empirically plausible for laminae II and III of the cortex, and referring to a small (1 mm3) block of cortical tissue. The assumptions included those for cell assembly size, connectivity ratios, neuronal firing rate, EPSP size, threshold, and hence convergence ratios needed to produce suprathreshold excitation. It was shown that ignition of a localized neural assembly from a small fraction of its members could be achieved over a wide range
© 2002 Taylor & Francis
426
Robert Miller
of plausible parameter values. For instance, with the probability of connection between neighbouring pyramidal cells assigned the value of 0.1, assembly size of 1000, and convergence ratio of 10 (i.e. near-maximally strengthened synapses), it required only about 50 of the 1000 members of the assembly to be activated by a stimulus, in order for excitation to spread throughout the assembly (Wickens and Miller, 1998). It is quite plausible that a stimulus could activate this number of neurones in the assembly. For networks with weaker synapses, larger numbers of neurones would need to be activated, which may not be achieved in practice. However, when one considers the conditions for maintenance of assemblies (that is the maintenance of sufficient synaptic strength in the connections between member neurones), a severe problem arises. According to recent views on synaptic plasticity (Bienenstock et al., 1982; Dudek and Bear, 1992), conjunction of incident EPSPs is not in itself adequate to ensure synaptic strengthening. The conjunctions themselves must occur close enough in time—that is within about 100 msec of each other—if strengthening is to occur. For conjunctions further separated in time than 100 msec synaptic depression occurs. Using plausible assumptions about the firing rate in maximally-activated neurones in laminae II and III (obtained from Swadlow, 1989, 1994), the probability of interconjunction intervals less than 100 msec was computed by Wickens and Miller (1998). It was found to be far too low for synaptic strengthening to outweigh synaptic depression. To put it another way, average firing rate of activated assembly neurones would need to be several times what is actually observed, if the assembly is to maintain the synaptic strength in its interconnections. Overall, if one considers laminae II and III in isolation one can conclude that pyramidal neurones in these laminae may have activity levels high enough to permit cell assembly ignition, but can neither maintain the integrity of cell assembly structure over time nor form new assemblies from the “uncommitted” state of the neuropil.
4. LOCAL SPATIOTEMPORAL INTEGRATION OF CORTICAL ACTIVITY 4.1. Scheme for Interplay between Laminae II/III and Lamina V The above calculations were based on the connectivity ratios and physiological properties of pyramidal neurones in laminae II and III. Pyramidal neurones in lamina V receive inputs from the local collaterals of lamina II/III pyramidal cells (by synaptic contacts either on their apical dendrites as they penetrate the superficial laminae, or on their basal dendrites with terminals from descending collaterals of superficial cells). In addition, lamina V pyramidal cells regularly give local axon collaterals ascending to the superficial laminae, where they are likely to make synaptic contact with pyramidal cells (discussed in Miller, 1996a). Thus, in morphological terms, lamina V pyramidal cells can be regarded as intermediate stations in indirect pathways from one superficial pyramidal cell to another. We can represent this as a series of triangular configurations, with single lamina V cells at the apex of each triangle (see Figure 18.1). There are no quantitative data on the probability of connection between superficial pyramidal cells and those in lamina V, although qualitative studies (Burkhalter, 1989 [their Figures 4–6 and 9]) suggest they make up a substantial proportion of all local connections derived from lamina II/III. Thus each lamina V pyramidal cell presumably receives synapses from a variety of superficial cells, and also gives ascending collaterals back to a number of superficial cells. Thus, it is likely that each
© 2002 Taylor & Francis
Neural Assemblies on Small and Large Scales
427
Figure 18.1. Triangular configurations of neurones, with “base” as a direct link between two neurones in lamina II/III, and “apex” as a neurone in lamina V, receiving from one of the neurones in lamina II/III and transmitting to the other. (A) One lamina V neurone can be the apex of many triangles based in lamina II/III. (B) One pair of lamina II/III neurones can form the base for many triangle with different apices in lamina V.
lamina V pyramidal cell could in principle serve as an intermediate station between the members of a large number of pairs of superficial cells, thus forming the apex for a large number of such “interlaminar triangles” (see Figure 18.1A). In terms of anatomical connectivity, it is also likely that a single direct synaptic link between two lamina II/III cells could form the “base” of a number of triangular configurations involving, as their apices, different neurones in lamina V (see Figure 18.1B). Furthermore, as noted above, lamina V pyramidal cells have on-going impulse frequency one or two orders of magnitude higher than in pyramidal cells in laminae II and III. The higher levels of activity in lamina V pyramidal cells, compared with those in other laminae suggests that in the waking state the lamina V cells have membrane potentials
© 2002 Taylor & Francis
428
Robert Miller
held closer to threshold than those in other laminae. Swadlow (1992) has produced more direct evidence that this is actually the case. Cells were identified by antidromic stimulation from various distant sites, and, in addition, current pulses could be delivered by the recording electrode in the vicinity of the neuronal soma. With such juxta-somal current pulses, all neurones could be made to respond directly, at similar threshold current intensities, regardless of the lamina, presumably as a result of direct activation of the neuronal membrane. However, many neurones in lamina V also gave responses of a different type, with longer latency and greater latency “jitter”, superimposed on the shock artefact. These responses were interpreted as indirect responses, produced by synaptic activation of the recorded cell rather than by direct somal activation. With near-threshold current pulses, such responses were found in 20% of lamina V neurones, but extremely rarely in neurones in lamina II, III and VI. With more intense current pulses (10 µA) 80% of lamina V cells showed indirect responses, while only 5% of those in the other lamina did so. Clearly in the situation of normal waking, lamina V cells are held much closer to threshold for synaptic excitation than those in the superficial lamina. It is also known that in urethane-anaesthetized animals the membrane potential of cortical pyramidal cells is usually bistable, and can undergo a state transition from a “down state” with membrane potentials in the region –65 to –70 mV to an “up” state with membrane potentials between –50 and –55 mV (Metherate and Ashe, 1993). Cowan and Wilson (1994) and Stern et al. (1997) also document a bistability for corticostriatal neurones in lamina V in urethanized rats, between a “down state” and “up state”. Illustrations show that, in the up-state, membrane potential fluctuates by less than 5 mV, and is within 5 mV of threshold. Similar behaviour is illustrated by Inubushi et al., 1978 [their Figure 3]), in unanaesthetized brain preparations, at a time when the EEG showed slowwave activity and spindle bursts. Scrutiny of figures in this and later papers by this group (Ezure and Oshima, 1981a,b), reveals that electrographic arousal was accompanied by excitation of neurones, with membrane potential undergoing depolarization of 5–10 mV, and becoming stabilized within a few mV of spike threshold. At present it is uncertain whether such bistability of membrane potential applies equally to pyramidal cells in all cortical laminae, or shows laminar differences. The implication of the higher firing rate in lamina V cells than in superficial pyramidal cells may be that the former more commonly hold the “up” state than the latter. However, detailed information on laminar differences in this feature are not available. Given that lamina V cells are often held closer to firing threshold than those in laminae II/III, one can regard the indirect pathway between two lamina II/III cells (via one or more lamina V cells) as a more secure route than the direct pathway. Whereas convergence from many co-active synapses is required to activate a neurone in lamina II or III, activation of a lamina V neurone should require only a low degree of convergence, or even just one or two unitary synaptic activations. Since we are considering only local interactions (defined above as a 1 mm3 block of cortical tissue), synaptic and conduction delays will not be sufficient to produce temporal dispersion beyond the neuronal integration time. All events are thus effectively instantaneous as far as integration within single neurones is concerned. Given this, every time one lamina II/III neurone elicits an EPSP directly in another neurone in the same laminae, it is likely that there will be a number of other closely-coinciding EPSPs generated indirectly via lamina V neurones in the same recipient neurone in lamina II/III. The direct synaptic connection by itself can only produce subthreshold activation; but when combined with indirectly-generated EPSPs via several
© 2002 Taylor & Francis
Neural Assemblies on Small and Large Scales
429
interlaminar triangles, activation above threshold may occur. The indirect pathways can then be seen as “priming” the direct pathway, adding synaptic activations to the recipient neurone in lamina II/III to bring its membrane potential above threshold for firing (Figure 18.1B). There is actually some evidence for such a relationship: Hess et al. (1996), using slice preparations, found that stimulation of horizontal pathways in lamina II/III would not normally produce long term potentiation, unless the tonic GABA activity was eliminated with a GABAa blocking agent (bicucculine). However, LTP could be induced in lamina II/ III neurones, without bicucculine, if vertical pathways ascending to the recording site were tetanized at the same time as the horizontal ones in lamina II/III. In this scheme, each lamina V cell could perform its priming function for a large number of pairs of superficial cells (Figure 18.1A), and each pair of superficial cells would be primed by a number of lamina V cells (Figure 18.1B). Each lamina V cell would thus not be critical in defining the information content of links between lamina II/III cells, this being specified by the direct link. Indeed, indirect disynaptic links between two cortical pyramidal cells are inherently ambiguous in information content, for reasons considered in section 7, dealing with the role of the hippocampus. Nevertheless, when the superficial laminae operate in association with the more excitable lamina V, it may become possible to overcome the problem defined above, arising from the relative inexcitability of the superficial lamina. This may enable local cell assemblies (defined principally by local lamina II/III connections) not only to ignite on appropriate occasions, but also to form from the uncommitted neural network and maintain synaptic strength, given stimulation at empirically-plausible levels. 4.2. Receptive Field Evidence for Interlaminar Interplay The schema outlined above cannot yet be expressed in rigorous formal terms, because a number of quantitative data are needed, such as the convergence ratio needed to activate each class of cells and the probability of synaptic connections being made in either direction between pyramidal cells in laminae II/III and lamina V. Such data are not yet available. However, other empirical evidence provides support for this scheme, in the absence of formal theoretical reasoning. If the above scheme of laminar interaction is correct, one would expect that the pyramidal cells in lamina V involved in cell assembly operation would have a facilitating role with respect to many pairs of superficial cells, and therefore for many different assemblies. One would therefore expect lamina V pyramidal cells to have lower information selectivity than those in superficial laminae. In many areas of cortex, the precise implications of this are difficult to specify. However, in sensory areas, one would predict that lamina V cells would have lower selectivity of sensory responsiveness than those in laminae II and III. From the rich evidence available on receptive field properties of sensory cortical neurones, there is much evidence supporting this prediction. The paragraphs below give some of the clearest examples. Most of the evidence comes from the primary visual cortex, although there are several relevant papers from somatosensory areas. The best evidence comes from lower mammals rather than from primate species. (It is becoming clear that the primary sensory—especially visual—cortex in primates is rather specialized, not only in comparison with other cortical areas, but in relation to sensory areas of lower species.) The prediction that lamina V neurones have low selectivity for sensory features might be capable of expansion beyond the examples given below, but all the details of sensory
© 2002 Taylor & Francis
430
Robert Miller
convergence upon neurones in lamina V cannot be specified without a better understanding of which are the basic features extracted in the visual cortex, and which are derived by combination. In the primary visual cortex, neuronal receptive fields are generally larger (i.e. spatially less specific) in lamina V than in other laminae. This has been found in rodents by Mangini and Pearlman (1980), Lemmon and Pearlman (1981), Swadlow (1988), and by Metin et al. (1988). In cats, Palmer and Rosenquist (1974) found that identified corticotectal cells (which are located in lamina V) typically had large fields. Schiller et al. (1976a) made a distinction between cells with spatially separate excitatory and inhibitory subfields, and those where the excitatory and inhibitory areas were coextensive. The former showed little laminar difference in field size or spontaneous activity, but the latter included a portion with larger field sizes and higher spontaneous rates in laminae V/VI than in laminae II-IV. Leventhal and Hirsch (1978) found that both classes of visual cortical cells as defined by Schiller et al. (1976a) tended to include ones with larger receptive fields in laminae V/VI than in laminae II-IV. Gilbert (1977) found, for “standard complex” cells (that is, ones showing summation of response as stimulus length was increased) cells in lamina V had a greater receptive field area than those in lamina II-IV, while those in lamina VI had receptive field areas greater even than in lamina V. In monkeys, however, the low spatial selectivity in lamina V is not found. Snodderly and Gur (1995) found that the width of receptive fields for laminae V cells was small, comparable to those of laminae II/III cells and lower than those of lamina IVa and IVc cells. Spontaneous activity in lamina V units was also as low as in units in laminae II/III, and much lower than in IVa, or IVc. Many studies show that units in lamina V of the visual cortex generally have a lower degree of specificity for one or other eye (i.e. they more commonly have a binocular input), than those in other laminae. This was found in rodents by Metin et al. (1988) and Swadlow (1988), in cat by Palmer and Rosenquist (1974), Schiller et al. (1976b), Gilbert (1977), and Ferster (1981), in the mink by LeVay et al. (1987), and in a noctural primate, the bush baby, by DeBruyn et al. (1993). Leventhal and Hirsch (1978) found that the preponderance of binocular cells in lamina V was restricted to those cells whose excitatory and inhibitory fields were co-extensive, while Berman et al. (1982) failed to finding any excess of binocular cells in lamina V. Several studies show that orientation tuning is less selective for lamina V cells than for those in other laminae. This was found in rodents by Metin et al. (1988) and Swadlow (1988), although this was not found by Revishchin (1985). Mangini and Pearlman (1980) found in mouse visual cortex that a non-oriented class of cells was located exclusively in lamina V. Similar findings have been reported in cats by Leventhal and Hirsch (1978, for cells with co-extensive excitatory and inhibitory fields), in the bush baby by DeBruyn et al. (1993), but not by Schiller et al. (1976b), or Gilbert (1977) in cats. Ferster (1981), in the cat, finds that lamina V cells in area 17 are almost all insensitive to ocular disparity, whereas in other laminae (II-IV and VI) cells form a variety of classes with differing disparity sensitivities. DeBruyn et al. (1993) finds that lamina V cells in the bush baby have the least specific spatial frequency tuning of all layers. In these papers, a variety of other parameters of visual responsiveness are reported to vary between laminae, such as direction sensitivity, optimal spatial frequency selectivity, optimal temporal frequency and temporal frequency cut-off, contrast sensitivity, peak velocity sensitivities and high-velocity cut-off.
© 2002 Taylor & Francis
Neural Assemblies on Small and Large Scales
431
In the somatosensory areas of cortex, several studies find, as in the visual cortex, that receptive fields are larger in lamina V than in other layers. In rodents, this was found by Ito (1985), Chapin (1986) and Swadlow (1990, 1991, 1994), in cat by McKenna et al. (1984), but the finding was not confirmed in monkeys (Sur et al., 1985). Swadlow (1990) also found, in rabbits, that when units responding to deep and superficial cutaneous stimuli were recorded in the same laminae in the same electrode penetration, the receptive fields of units for “deep” and “superficial” sensory modalities were likely to be spatially congruent for units in lamina II/III and VI, but could be far from congruent for units in lamina V. While the exact interpretation of this is uncertain, the result suggests that the pyramidal cells in lamina V sample a wider extent of the sensory surface than do those in other laminae. Uhr and Chapin (1983) confirmed that lamina V cells in the barrel cortex have extended vibrissal receptive fields, and investigated the interaction by which lamina V cells came to have larger receptive fields than those in other laminae. In the barrel cortex of anaesthetized rats, intracortical stimulation at sites were identified which could produce activations of recorded neurones across a distance of a few mm. When such stimulation sites were destroyed with an electrolytic microlesion, the extended vibrissal receptive fields of lamina V cells were reduced in size, losing input from the vibrissae represented in the region destroyed. While this experiment does not positively identify inputs from superficial laminae as providing the input, it does show that cortico-cortical inputs converging from cortex some distance from the lamina V neurone provide it with its extended receptive field. Despite the interpretation of some of the above findings being uncertain, there is considerable evidence supporting the prediction that lamina V cell have lower selectivity for sensory parameters than those in superficial laminae. It is plausible to suggest that this laminar difference reflects the fact that lamina V cells receive convergence from cortical cells in other laminae with a variety of different selectivities, and so have themselves a low selectivity.
5. SPATIOTEMPORAL INTEGRATION OF ACTIVITY IN MORE WIDESPREAD ASSEMBLIES 5.1. Constraints on Non-Local Operation of Cell Assemblies The original intuition leading to the cell assembly hypothesis envisaged that the member neurones of a cell assembly could be widely spread over the cortical mantle. For instance, Hebb (1949) considered assemblies with member neurones in each of several visual areas of the cortex. However, in terms of real networks of cortical nerve cells, integration of neuronal activity, such as is proposed for cell assemblies, becomes more problematical both in the spatial and temporal dimensions when that integration is non-local. The reasons for this are easily explained. In the spatial dimension, the probability of connection between two neurones is maximal –0.8—for nearest neighbours, falls to a value of 0.1 and below for more distantly-related pairs of neurones within a 1 mm3 block (Braitenberg and Schüz, 1998; Hellwig, 2000), and falls even further for more distantly located pairs of neurones. Thus one neurone may be able to make direct synaptic contact with about 10% of neurones within a distance of a few hundred microns (Braitenberg and Schüz, 1991, 1998). However, if one considers an area of cortex of dimensions 10 × 10 mm, centred on a chosen neurone, there are of the order of 10 million pyramidal cells within this area, giving an overall ratio for direct connectivity of less than 1/1000.
© 2002 Taylor & Francis
432
Robert Miller
In the temporal dimension, for local interactions, axonal conduction time could be ignored, because it was never likely to be more than the neuronal integration time. When assemblies are distributed over and between cytoarchitectonic areas, even if morphological connectivity is compatible with effective convergence, the probability of functionally-effective convergence would fall due to temporal dispersion of signals. For instance, suppose that five-fold convergence of afferent activity from a transmitting cortical locus was required to activate a recipient neurone in another locus. If all afferents to the neurone in question had conduction times less than 10 msec (an assumed value for the neuronal integration time), loss of convergence due to temporal dispersion would be insignificant. However, for distances of axonal conduction of 10 mm, conduction time could range from 3 to 33 msec (corresponding to conduction velocities of 3 to 0.3 m/sec, a range of values corresponding to those found in Swadlow’s studies) (see also Swadlow, 2000). Longer conduction times would be expected for axonal trajectories longer than this (which certainly exist). This would lead to a reduction of at least 3-fold (and more, for the longer trajectories) in the degree of convergence occurring in a typical recipient neurone. Such reduction of amplification appears to be a severe constraint on large-scale integration of cortical information processing. Overall, despite the fact that there is a very rich array of long cortico-cortical connections, a neurone can only synapse with a tiny fraction of those located in other cortical areas, and the possibility of suprathreshold convergence is also reduced by temporal dispersion of signals. However, this constraint may be overcome if recent data and conceptual developments about cortical connectivity and corticothalamic interaction are taken into account. There appear to be mechanisms for “concentration” both in space and time, of the influences of sufficient single units, to make suprathreshold activation more probable than would be expected on the basis of random spatiotemporal connectivity alone. 5.2. Cortical Connectivity and Spatial Concentration of Neural Influences In the spatial dimension, there is much evidence that long cortico-cortical connections are not randomly distributed within the territory they innervate. Instead they are distributed in patches with high local connection density, with intervening regions having few connections (e.g. Rockland and Lund, 1983; Amir et al., 1993; Levitt et al., 1993, 1994; review: Malach, 1994). In a projection from one cortical area to another, connections arising in adjacent loci may each have patchy distribution of terminals in the recipient area, without overlap of the respective patches. It has been pointed out by Schüz (1994), that this increases the probability of convergence for long-distance connectivity. This principle applies not only within an individual cytoarchitectonic area, but also over much longer distances, since a patchy distribution of connections is also found for such long connections. In spatial terms (i.e. strictly anatomical aspects of connectivity), the cortex as a whole is likely to be integrated better in this way, than with more uniform random connectivity. However, the patchy distribution does mean that some associative links across the cortex are not direct monosynaptic ones, but polysynaptic. This raises further problems for global cortical integration, considered in section 7, on the role of the hippocampus. In addition, in functional terms, the scheme of long cortico-cortical connections just described may nevertheless still be insecure, especially if we consider the reduction of temporal convergence due to temporal dispersion along long pathways. However, when we consider circuits between cortex and thalamus additional mechanisms can be suggested which may promote convergence in recipient cortical loci, not only in terms of spatial
© 2002 Taylor & Francis
Neural Assemblies on Small and Large Scales
433
aspects of connectivity, but also with respect to temporal aspects. Together, these mechanisms may permit long-distance connectivity to be functionally effective in building widelydistributed cell assemblies.
5.3. The Role of Corticothalamic Interplay in Spatiotemporal Integration on the Medium Scale 5.3.1. Evidence constraining ideas of corticothalamic interplay The simplest, and most often-repeated account of the thalamus is that it is a relay station on the way to the cerebral cortex. This account arises from the strong emphasis in past decades on sensory processing in the cortex. However, it ignores several secure facts: Morphologically identified synapses upon thalamic projection neurones are of two main types, different in size and in other features. Quantitatively, the number of morphologicallydefined inputs to these neurones from the cortex (identified as small round synaptic profiles) outnumber by far those ascending in sensory pathways from the brain stem, or other systems of the brain (identified as larger synaptic profiles) (Tseng and Royce, 1986; Sawyer et al., 1991; Wilson et al., 1984; Liu et al., 1995). Some nuclei of the thalamus have no major “ascending” inputs, and so receive both types of synapse from corticothalamic inputs (Mathers, 1972; Schwartz et al., 1991; Kuroda et al., 1992). In functional terms, impulse activity in thalamic principle neurones is under the control of cortical activity, as well that of pathways ascending from lower parts of the neuraxis. This statement is true both for the “spontaneous activity” in thalamic neurones (Bures et al., 1963; Waller and Feldman, 1967; Albe-Fessard et al., 1983; Kayama et al., 1984; Villa et al., 1991) and also for activity induced in response to sensory stimuli, since sensory response properties of single thalamic neurones change in subtle ways when the corresponding region of cortex is inactivated or ablated (Schmielau and Singer, 1977; Chow et al., 1978; Vidyasagar and Urbas, 1982; Molotchnikoff et al., 1984; Varela and Singer, 1987; Gulyas et al., 1990; Funke and Eysel, 1992; Villa, 2000; Ghazanfar and Nicolelis, 2000). Several clues are available to help define this interplay. First one should consider the spatial distribution of cortico-thalamic and thalamo-cortical connections. It has traditionally been believed that these two sets of connections have a strictly reciprocal distribution. This assumption was taken to apply generally in a recent comprehensive analysis of thalamo-cortical connections in cat (Scannell et al., 1999). However, this conclusion has been derived from separate studies of the two pathways in different animals, and averaging the resultant maps across animals (inevitably with low spatial resolution). Reciprocality generally referred to a relatively large scale (cortical area to thalamic nucleus and vice versa) rather than within subdivisions of individual areas and nuclei. In addition, precise evaluation of the concept of reciprocality was not possible until it was recognized that cortico-thalamic connections are of two types, with rather different synapses, laminae of origin and local terminal distribution (Steriade et al., 1990). It is now becoming clear that regions of termination of cortico-thalamic axons do not correspond exactly with regions of origin of thalamo-cortical connections returning to the corresponding cortical region. Winer and Larne (1987) injected axonal tracers into the auditory cortex of the rat, and thus produced small regions of both anterogradely labelled terminals and retrogradely labelled projection cell bodies within the medial geniculate nucleus. Regions of cell body and terminal labelling were not entirely co-extensive, the cortico-thalamic terminal areas being
© 2002 Taylor & Francis
434
Robert Miller
generally more extensive than the zones of retrograde neuronal labelling. This principle has more recently been shown to apply in cats, for a subnucleus of the VPL thalamic complex and for the pulvinar, using a cocktail cortical injection including both an anterograde and a retrograde tracer (Darian-Smith et al., 1999). Since both sets of connections were labelled in the same animal, it was possible to show that, on a fine scale, there was substantial non-reciprocality of connections, especially for the fine calibre cortico-thalamic connections arising in cortical lamina VI. Sherman and Guillery (1996) also raise the possibility that thalamic nuclei may be important in determining transmission from one cortical area to another, and rather similar possibilities are implied by Deschenes et al. (1998). Non-reciprocality of connections at the cortical side of cortico-thalamic loops has at present only fragmentary support. However, an electrophysiological study of cortico-thalamic projections to the lateral geniculate nucleus of the cat by Lindstrom and Wrobel (1990) included, as an “unpublished observation” the remark that “individual principal cells receive convergent excitation from cortico-geniculate neurones in a larger area of cortex than the termination zone of their axons.” While the generality of such findings is uncertain at present, they suggest that each thalamic nucleus is not so much a relay station on the way to the cortex, but a “junction” where signals originating in one cortical region can be redistributed not only to that region, but also to other regions. The degree of divergence possible in such a scheme can only be guessed at this stage; but since non-reciprocality seems to apply to the connections of the pulvinar, which has widespread, but diffuse projections to the cortical mantle, it is probable that some thalamic nuclei could re-route signals in a manner permitting wide divergence across the cortex. In addition, from the wide-ranging recent analysis of thalamo-cortical connectivity of Scannell et al. (1999), it is clear that most thalamic nuclei project to several (or many cortical areas). If thalamic neurones projecting to different areas were closely intermingled within a nucleus, the possibility of re-routing cortico-thalamic signals to other areas of cortex could be very great. The second clue to thalamo-cortical interplay is the fact that one class of corticothalamic axonal projections—those originating in lamina VI—have quite slow conduction velocities (Harvey, 1980; Swadlow and Weyand, 1981, 1987; Ferster and Lindström, 1983; Swadlow, 1988, 1990, 1991, 1994). This conclusion has been corroborated in crosscorrelation studies of interlinked pairs of neurones in thalamic and cortex (Tsumoto et al., 1978; Johnson and Alloway, 1994). As a result, conduction time from cortex to thalamus in single axons can be very long, (up to several tens of msec in rabbits, according to Swadlow) and can span quite a wide range of delays, across a population of such axons, from just a few msec up to as much as 50 msec. Consequently, these cortico-thalamic axons are likely to impose a degree of temporal dispersion on a signal originating in a cortical locus matched only by that in the long cortico-cortical connections. The third clue to thalamo-cortical interplay is that, during waking, thalamic projection cells appear to have membrane potentials poised close below threshold. The evidence for this is admittedly somewhat indirect because of the technical and ethical difficulties of recording intracellularly from structures deep in the brain in waking animals. Coenen and Vendrik (1972) made quasi-intracellular records from the visual thalamus of paralysed cats during transitions from drowsiness to waking. The ratio of spikes to EPSPs was as high as 0.9–1.0 during waking, implying membrane potentials close to threshold, but was substantially lower during sleep. Hirsch et al. (1983) recorded intracellularly from cat visual thalamus during rapid eye movement sleep (a state akin, in many ways, to waking). They
© 2002 Taylor & Francis
Neural Assemblies on Small and Large Scales
435
illustrate an impaled neurone with stable membrane potential upon which are superimposed small irregularly-timed EPSPs, almost all of which give rise to single action potentials. Intracellular records from thalamic nucleus VL have been published more recently by Contreras and Steriade (1997), which appear similar to those of Hirsch et al. (1983), although recorded from cats anaesthetized with zylazine/ketamine combinations. Such records were obtained at times when the electrocorticogram displayed desynchronization similar to that during waking. Single projection neurones were depolarized to between 55 and 60 mV, and showed irregular non-bursty, single spikes, each related to small EPSPs. Sometimes such spikes were locked to fast (gamma range) EEG rhythms (Steriade, 1997). Deepening of anaesthesia led to increased negativity in the membrane potential of single cells, and to rhythmic activity in them, and in the electrocorticogram. The authors write “the high sensitivity of the EEG pattern to the fluctuations in the degree of cellular correlation and rhythmic behaviour leads us to believe that essentially the same basic principle underlies intercellular relations during the changes in the EEG that accompany shifts in natural states of vigilance.” 5.3.2. Hypothesis of Medium-scale Spatiotemporal Integration by Corticothalamic Interplay The implication seems to be that thalamic projection neurones, like cortical pyramidal cells of lamina V, have periods when membrane potential is stabilized just below threshold, so that only one incident EPSP (or only a very few coincident ones) are required to produce an action potential. Given that these projection cells are junction points in twoway cortico-thalamic relays, the relation between them and active cortical assemblies appears in some ways similar to that between lamina V cortical cells and lamina II/III cells (which, it was suggested, are the primary substrate for cell assembly formation) (see Figure 18.2A, B). By analogy with the latter arrangement (and with preceding arguments), one can suggest that the indirect cortico-thalamo-cortical pathway is more secure than the direct cortico-cortical pathway between distant cortical loci; and thus two-way corticothalamic interplay can “prime” activity in direct, albeit long, cortico-cortical connections (see Miller, 1996b). There are however some important differences between these two sets of inter-neuronal relationship, arising from the differences in scales of integration, in both the spatial and the temporal dimension. In the spatial dimension, use of the indirect (cortico-thalamocortical) pathway to ensure security of transmission in the direct (long cortico-cortical) pathway depends on the condition that indirect thalamo-cortical pathways activated by a transmitting cortical locus have sufficiently dense projections to the same cortical loci as to receive direct cortical connections from the transmitting cortical locus. Evidence compatible with this is that thalamo-cortical projections, from at least some thalamic nuclei, have a patchy distribution in the cortical target region (e.g. Giguere and Goldman-Rakic, 1988). More incisive evidence comes from Baleydier and Maugiere (1987) for relationships between parietal cortical area 7a, cingulate cortical area 23, and medial pulvinar. Within the patchy distribution of long connections between the two cortical areas, distant loci which were reciprocally connected also had thalamic projections from common thalamic loci in the pulvinar; whereas non-connected cortical loci in the same areas received thalamic projections from separated loci in the pulvinar. In itself, this finding does not show that a cortical locus which projects directly to another distant cortical locus also sends
© 2002 Taylor & Francis
436
Robert Miller
Figure 18.2. Schemes for thalamocortical interplay. (A) Two cortical loci (each containing sets of triangular configurations, as in Figure 18.1) are connected by long connections (dashed lines). Such connections are supported by equivalent indirect connections via a thalamic nucleus which receives signals from one area and transmits them to the other. (B) Larger version of Figure 18.2A, in which assemblies—spread across several cortical areas—are integrated with the support of several thalamic nuclei. Note that in this scheme each thalamic nucleus has reciprocal connections with one area, but also connects to another area, thus allowing it to serve as a junction point between two areas.
supporting connections to the same locus indirectly via the thalamus. This is not explicitly proven because the thalamic loci were identified only by simultaneous retrograde transport from the two cortical areas, not by combined retrograde/anterograde injections. However, from Winer and Larne (1987) and from Darian-Smith et al. (1999), it is clear that the
© 2002 Taylor & Francis
Neural Assemblies on Small and Large Scales
437
distribution of fine cortico-thalamic terminals is quite extensive and includes both the thalamic regions which give reciprocal thalamo-cortical connections and other surrounding regions which do not. Hence, in the experiments of Baleydier and Maugiere (1987), it is likely that the thalamic loci which project in common to the two directly-interconnected cortical loci also receive connections from each of these loci, and thus constitute junction points for indirect relay from one cortical locus to the other. Thus, one has some tentative evidence that distributed subsystems exist, in which dense direct cortico-cortical projections are matched by a correspondingly dense indirect projections via the thalamus. This seems to imply that the density of the combined connectivity in a recipient cortical locus received directly from a transmitting locus and indirectly from such a locus via the thalamus may be much higher than would be expected in a randomly-connnected cortical network. It may even approach the connection density found locally within each cortical locus. If so, the main problem becomes that of the temporal dispersion of signals transmitted from one cortical locus to a distant one. However, since cortico-thalamic connections appear to have the similar long conduction times, and the similar dispersion of conduction times across a populations of axons as the long cortico-cortical connections, it is plausible to suggest that any cortical neurone which receives indirect, and delayed synaptic influences from a transmitting cortical locus is likely to receive indirect synaptic influences via the thalamus, some of which have delays matching those of the direct influences. If so, the direct and indirect synaptic influences may coincide within the same neuronal integration times, and can therefore summate. Hence, despite the temporal dispersion of signals due to the distance between the two cortical loci, the indirect pathway can still act to support or prime the direct cortico-cortical pathway. Recent evidence, while not directly proving such a relationship, is readily explained by this hypothesis, and is otherwise rather difficult to explain. Simultaneous recording of spike trains from several cortical neurones at the same time shows precise temporal structure in the time relations between spikes in different neurones. In early demonstrations of this (e.g. Abeles, 1981), the different neurones were recorded very close to each other (from the same electrode, or from separate electrodes on a single electrode stem). Therefore, the temporal structure so demonstrated was explained in terms of a model relying on strictly local interactions (Abeles, 1991). However, more recent work has demonstrated temporal structure across multiple spike trains recorded further apart in the cortex, from spatially-separate electrodes (Villa, 2000). Thus temporal structure of cortical neuronal firing is not just a local phenomenon. Even more striking is the demonstration of temporal structure across multiple spike trains recorded from thalamic projection neurones (in the same, or sometimes in different nuclei) (Villa and Abeles, 1990; Villa, 2000). This result is particularly dramatic, because thalamic projection neurones have no direct collateral connections (Steriade et al., 1990), and so cannot be influencing each other directly. The most likely explanation of these results is that the temporal structure is determined by thalamo-cortico-thalamic connections, with the precision and delays inherent in the temporal structure being determined mainly by conduction delays in cortico-thalamic and/or cortico-cortical axonal connections. Once again the detailed theory of the interaction proposed cannot be given, for lack of quantitative data on connectivity ratios, convergence ratios, etc. It is possible that the scheme outlined above, if taken in isolation, will still fail for lack of sufficient signal amplification. However, there are yet other tiers to be added to the interactions described, at a yet larger scale, which may confer greater operational robustness on the patterns of interlaminar and cortico-thalamic interplay described above.
© 2002 Taylor & Francis
438
Robert Miller
Given the analogous relations outlined above, on the one hand between neurones in lamina V and those in lamina II/III, and on the other between cortical assemblies and thalamic projection neurones, there are implications for mechanisms of widespread stability control in the cortex. The essential neurones in both such arrangements—the lamina V neurones, and the thalamic projection neurones—become critical points at which global control of brain dynamics can be exerted. Quite apart from detailed informational inputs to these neurones (e.g. sensory inputs) excitatory controlling signals exerted upon such neurones can be expected to have powerful global influences on activity levels and stability of neural activity in cortical networks.
6. CORTICO-BASAL GANGLIONIC LOOPS: SELECTIVE ATTENTION, AND SEQUENCING WITHOUT EXACT TIMING So far this chapter has presented ideas about the function of cell assemblies which represent concepts and percepts either containing no temporal structure (being effectively synchronous), or having a detailed temporal structure which can be coded in a brain, with an exact temporal metric determined by axonal conduction time. There is a third alternative to consider— that the brain represents relationships between successive concepts or percepts in terms of sequence, but not of exact timing. The environment contains many examples of such sequential, but inexactly-timed relationships, which undoubtedly the brain can come to represent. The relationship between lightning and thunder is a good example of sequence without accurate timing. In mammalian psychology, lightning creates an expectation of thunder, without specifying the time of the thunder. Likewise, in many examples of instrumental behaviour, the relationship between an emitted piece of behaviour and the perception of its consequences is not precisely timed, although it is a predictable sequence. How are such sequential relationships represented in the brain? Several sorts of neural activity are known which code for the expectation or anticipation of an event which is forthcoming a short time ahead (up to a few seconds). The “contingent negative variation” (CNV: Walter et al., 1964) is a potential recorded from the scalp (especially in frontal regions) which grows in magnitude during the interval between signals in typical “delay” tasks. The “Bereitschaftspotential” or Readiness Potential (RP: Kornhuber and Deecke, 1965; Deecke et al., 1969) is a similar negative potential change which appears in frontal regions in anticipation of spontaneous (untriggered) voluntary movements, and can also be seen in anticipation of well-attended stimuli which require no response. In typical delayed response tasks, single units can maintain firing throughout the delay between a warning (or informational) signal, and the imperative signal a few seconds later which triggers an actual response (Fuster, 1980; Komatsu, 1982; Joseph and Barone, 1987; Okano and Tanji, 1987). Such maintained firing during a delay is probably the neural event at the single unit level whose signature on the mass level is detected as the CNV or RP. In such examples, neural activity represents relationships of sequence rather than accurate timing. (In fact in typical delayed response tasks, training is accomplished by starting with a short delay, and then moving progressively to longer delays, for which successful task performance would be impossible without prior training on the easier short-delay version of the task). The fact that such neural activity is maintained during a delay, when there is no explicit stimulus which can directly sustain the activity suggests that some sort of recursive loop,
© 2002 Taylor & Francis
Neural Assemblies on Small and Large Scales
439
involving positive feedback is involved. The predominant location of anticipatory neural activity in frontal regions gives an indication that the circuitry of the basal ganglia might be involved, since the cortical connections of these structures are established mainly with anterior cortical regions. It is well known that the basal ganglia are involved, with the anterior regions of cortex, in complex arrays of connectional loops (Alexander et al., 1986; Joel and Weiner, 2000). The simplest of these loops involve a cortico-striatopallido-thalamo-cortical circuit, but there are others involving in addition the subthalamus and pars reticulata of the substantia nigra. The basal ganglia are also well known to be involved in aspects of motor control, and in reward-mediated learning. It is appropriate that the latter function is dealt with in circuitry more generally concerned with expectation of events occurring in sequence, since (as mentioned above) the relation between emitted behaviour and subsequent rewarding consequences is an important variety of such patterns of events. Pharmacological and clinical evidence, especially that obtained from parkinsonian patients supports the view that the basal ganglia are critical in generating anticipatory activity in the cortex. For instance, the RP and CNV are reduced in amplitude in Parkinson’s disease (Deecke et al., 1977; Simpson and Khurabait, 1986; Dick et al., 1987, 1989); and their amplitude is restored towards normal during L-DOPA treatment (Amabile et al., 1986; Dick et al., 1987). It is likely that the repetition of contingencies of sequence which give rise to expectation are acquired by synaptic modification in the basal ganglia. Probably dopamine-mediated strengthening of critical synapses in the striatum, which is thought to underlie reward-mediated learning (Miller, 1981; Wickens, 1993; Wickens et al., 1996), also allows the formation of functionally-effective loops of connections, in which activity can circulate, to maintain activity in cortical and other parts of the loops, during delay tasks. One possibility which is still sub judice, is that, in striatal neurones, neural activity which leads to behavioural output leaves its trace, as a “state of readiness” for a short period after the activity itself has subsided (Miller, 1981; Wickens, 1990). According to this hypothesis, dopaminergic reinforcement occurring while the state of readiness still endures can achieve strengthening of the synapses which led to the neural activity. With such a scheme, the slightly-delayed consequences of behaviour become linked with the initial stimulus which provoked the piece of behaviour (and corresponding neural activity), and thus the latter can set up anticipation of the former. Two features of the circuitry by which the basal ganglia influence the cortex deserve particular attention. First, the pathway through striatum, pallidum (or pars reticulata of substantia nigra) to thalamus involved two inhibitory synapses in sequence (Carpenter, 1981) producing a net excitatory (or strictly a disinhibitory) effect (Deniau and Chevalier, 1985) (see Figure 18.3). Thus the pathway from cortex through the basal ganglia to the thalamus and back to the cortex is, as expected, a positive feedback loop, but, significantly this is achieved not by arranging that all links be excitatory, but by having an even number (2) of inhibitory links. Therefore, the signal transmitted from striatum to the cortex which activates behaviour (or other hidden cognitive processes) consists of release from tonic neural inhibition, rather than actual excitation. Secondly, there are more complex components of this circuitry involving sequences of three inhibitory synapses. This might involve either an additional inhibitory link within the striatum, via collaterals of its inhibitory principal neurones, or it might involve an additional inhibitory link in the subthalamus (Carpenter, 1981). (The details of operation in this circuitry are not fully understood.) These relationships have two larger-scale implications: the fact that the final effect on the thalamic projection neurones (the critical “switch” for cell assemblies) is inhibition rather
© 2002 Taylor & Francis
440
Robert Miller
Figure 18.3. Depiction of interplay between neocortex, basal ganglia and thalamus. The cortex (above) contains a number of active loci, symbolized as triangular configurations (see Figure 18.1) and these are in twoway interaction with thalamic nuclei (thal: lower right). Basal ganglia (including striatum [str], pallidum [pal] and substantia nigra [sn]) are depicted in lower left. Inhibitory links in the basal ganglia are shown as filled circles, other connections (excitatory) depicted as arrow heads. The main positive-feedback circuit through the basal ganglia is shown as bold connections.
than excitation suggests that the combined cell assembly activity of cortex and thalamus is not limited by inadequate global levels of neural activity, but (sometimes at least) by a tendency to overactivity. It therefore needs on-going restraint, at least in its anterior portions, with periodic release from restraint when executive decisions are made. Secondly, the fact that there can be both positive feedback loops (with 2 inhibitory links) and negative feedback loops (with 3 inhibitory links) allows selective control over cortical activity—with enhancement of one focus of activity and inhibition of others. In itself, this could underlie (in part) the psychological fact of selective attention—amplification of activity in one assembly, and suppression of activity in the intermingled neurones (see discussion in Miller and Wickens, 1991). More germane to the present chapter, these facts have implications for the dynamics of the combination of cortex and thalamus, upon which the basal ganglia exert their influence. In section 3 of this chapter, it was suggested that the cortical laminae which are most essential for cell assembly function are constrained by inadequate amplification of neural activity. However, the facts just mentioned suggest that the additional mechanisms considered (interlaminar interplay, cortico-thalamic interplay, and global control mechanisms exerted at critical points of each set of circuits) may already have overcome this problem. As a result, cortex and thalamus together exhibit more versatile
© 2002 Taylor & Francis
Neural Assemblies on Small and Large Scales
441
dynamics, where there is a large region of “state-space” which is relatively stable, without uncontrolled departure from the stable region—either uncontrolled excesses or cessation of activity. Within this region, bidirectional control of global activity levels is both necessary and possible, to ensure efficient information processing. Sometime this involves activation of specific cell assemblies, but at other times (for instance, control of thalamus and cortex via the basal ganglia) it involves inhibitory control.
7. ROLE OF HIPPOCAMPUS So far we have considered the forebrain with the cerebral neocortex in interaction with the thalamus and the basal ganglia, but without the hippocampus. On the basis of this model, we can account for cell assembly operation despite the fact that the principal cortical laminae in which assemblies are housed have activity levels too low to be effective by themselves. We can account (plausibly, if not conclusively) for cell assembly operation in the spatial sense, despite constraints on connection probability which would apply in a randomly connected network; and we can account for assembly operation in the temporal sense despite constraints set by likely temporal dispersion of signals in long axonal projections. We have a forebrain which can represent associations in space, and in exactly-measured time for intervals up to a few hundred msec; and this model of the forebrain can also represent relations of sequence, including those in reward-mediated learning, which occur, without an exact temporal metric, over intervals of time rather longer than are possible with exact temporal measuring. We appear to have modelled (in informal, qualitative terms, if not in formal quantitative terms) a brain with a profile of psychological capabilities in which any stimulus configuration has the potential to elicit any response pattern, and the animal can acquire the programs for this, given appropriate contingencies of reinforcement of spontaneously emitted fragments of behaviour performed in the corresponding sensory circumstances. The profile of psychological capabilities, we have modelled, seems in some ways similar to that described in the monograph of O’Keefe and Nadel (1978) for behaviour of animals (usually rats) in which the hippocampus has been destroyed or damaged on both sides. The common theme in many of the features of these animals is that behaviour is dominated by immediately-present local stimuli as opposed to aspects of the situation derived by analysis of it over a longer time period or from a wider sensory perspective (including aspects such as familiarity/novelty of a stimulus, or the place of its presentation rather than its intrinsic nature). In addition, given constant stimuli, behaviour tends to consist of stereotyped units, with loss of the normal internally-generated variability. For instance, in their review the following conclusions are reached about animals with hippocampal damage: in discrimination learning there is a tendency for animals to perseverate, or, in spatial discriminations, for responses to be determined by the item or cue presented, regardless of its place. Maze learning tends to rely on an inefficient strategy, based on learning response sequences (e.g. LRL sequences of turning) rather than on the more efficient strategy of guidance by place. In reinforcement-mediated learning, reinforcing effects are not fundamentally abnormal. However, in Skinnerian operant conditioning, response patterns for lever pressing are more predictable (less variable), but more sensitive to abrupt shifts in reward contingencies. In “differential reinforcement of low rates” schedule (where an animal is not reinforced if it responds too frequently), hippocampus-lesioned
© 2002 Taylor & Francis
442
Robert Miller
rats respond at lower than normal rates, except when the end of each delay is signalled by an explicit cue. Likewise alternation between two levers cannot easily be learned as an overall pattern, but can be performed well when cues are used at each step as guidance. There are problems with reversal of spatial discrimination (due to perseveration in responses) but not of nonspatial discrimination (i.e. that based on a stimulus itself rather than its position). There is, overall, an integrity of responses to threatening items, but not to threatening places. There is an excess of nonexploratory stimulus-elicited motor activity, which does not habituate over time. There is little true exploration of novel items in the environment, and apparently little awareness of novelty, and with these, there is a reduction in the duration of interference of ongoing behaviour by novel stimuli distracting from the main task (defined in stimulus–response terms). Behaviour may be characterised by microstereotypes, rather than true exploration. In two-choice mazes there is a tendency to reiterate whichever response initially occurs more commonly, rather than alternation of responses. The animal without either of its hippocampi resembles to some extent the brain we have modelled thus far, and is also similar to the simplified profile for mammalian behavioural considered earlier this century under the heading of “stimulus-response theory” (e.g. Hull, 1943). However, in that early work it became clear, after critical examination, that stimulusresponse theory was inadequate as a full description of mammalian behaviour (Tolman, 1932; Miller, 1991). Stimuli do not dictate responses unconditionally, unless the overall environmental and motivational context has also been specified. Potentially, any stimulus can call forth a wide variety of responses, and any response can be called forth by a wide variety of stimuli. Many overlapping stimulus–response relationships can coexist as part of an adult animal’s behavioural repertoire, and which one is utilized on a particular occasion depends critically on the overall context in which the animal finds itself. But what is meant by the word “context” in the above paragraph? One meaning of “context” is a configuration of information from the environment (and the animal’s relation to it), which is essentially a global aspect of the animal and its environment. However, when we look at the cerebral neocortex and related brain structures we find that “context” can come to have a quite separate (but parallel) definition, referring, again globally, to aspects of forebrain dynamics, and their relation to information processing. To gain this perspective, we must remind ourselves of the connectional nature of the neocortex, which is the primary repository of the behavioural programs upon which an animal can call. Every pyramidal neurone has of the order of 5000 input and output connections in mouse, (and probably far more in primates, according to Braitenberg and Schüz, 1998) and so is potentially linked to several thousand other cortical pyramidal cells on both its afferent and its efferent side. Given this, pathways for signal transmission through the cortex have a very high degree of inherent ambiguity once those pathways have traversed more than a single synaptic relay. As an example, consider Figure 18.4A. Level “A” can be thought of as a set of neurones in the cortex which is primarily responsible for receiving signals in the cortex. Level “C” is related disynaptically to level “A”, all pathways between the two levels passing through the neurone at level “B”. Level “C” could thus be considered as a set of output neurones. (Specifically neurones at level “A” may be assumed to receive sensory signals from the outside world, those at level “C” to be those controlling behaviour, although such specific assumptions are not necessary in a more general account.) Figure 18.4A can be thought of as an example of the long connections possible in the cortex: a transmitting cortical locus (A) projects to distant neurones in a directly-linked “patch” of terminal distribution (B), and this in turn projects to a locus
© 2002 Taylor & Francis
Neural Assemblies on Small and Large Scales
443
Figure 18.4. Cortico-hippocampal interplay: (A) Microarchitecture of the role of the hippocampus in the neocortex. The neurone “B” is a junction point, receiving several inputs (“A1–4”) and transmitting to several outputs (“C1–4”). All neurones require multiple convergence before they will fire; and they receive many other synapses than those shown, which are the ones critical for defining information processing. These other inputs include ones conveying theta-rhythmic activity from the hippocampus. The ambiguous role of this network in signal transmission can be resolved if specific combinations of input and output neurones can be selected by matching of the active phase in their respective theta rhythms imposed by the hippocampus. This A1 and C4 have connections from a theta rhythm with period 125 msec, and the disynaptic connection from one to the other, via B imposes a delay of 62.5 msec, which matches the difference in theta phase in the two loci. (B) (reproduced from Psychobiology, vol. 17, pp. 115–128, with permission of the Psychonomic Society Inc.) Global scheme for self-organization of phase-locked loops between hippocampus and neocortex. Master oscillator (hippocampus) has afferent and efferent links with various neocortical loci, all of which include lines with a range of delays. (Conduction time [ms] for each axonal link shown by numbers adjacent to each axon.) Selected loops (solid lines) are selected to have total loop time of 150 msec, the same as the concurrent theta period. Different neocortical loci have different phase relations to the hippocampal rhythm.
© 2002 Taylor & Francis
444
Robert Miller
near to “B” which does not itself receive direct connections from “A”. In such a simple scheme, contingencies of reinforcement might be directed, essentially, at relations between input and output, that is between neurones active in levels “A” and “C”. Under different contextual cirumstances, secure transmission may be needed between different combinations of specific neurones in “A” and “C”. However, given the possibility of great convergence and divergence in the intermediate neurone “B”, there is in this model (in anatomical terms at least), no definite, unambiguous pathway between any neurone in “A” and any in “C”. This problem has been considered in extenso in a previous work (Miller, 1991). The solution proposed involves interplay between the cerebral neocortex and the hippocampus. Such interplay, it is suggested, provides a mechanism by which a wide range of specific multisynaptic “subnetwork” pathways through the cortex can be activated selectively, each appropriate to the overall context which obtains. A critical feature of this hypothetical interaction is the theta rhythm. This rhythm is the most regular of all electrographic rhythms, and is generated in the hippocampus, with the medial septal nucleus acting as a pacemaker under most circumstances. Its frequency (in rats) ranges from 5–12 Hz, or, equivalently, has oscillation periods ranging from about 80–200 msec. Another critical feature of the relation between neocortex and hippocampus is a rich array of reciprocal axonal connections, linking the hippocampus to the neocortex, usually multisynaptically, many regions of neocortex being involved, with preference for the associational areas rather than the primary receiving areas. While not explicitly proven, it is likely that these reciprocal connections are of fine calibre, and therefore act as slow-conducting delay lines, including a wide range of delays between the hippocampus and any neocortical locus, as is known to be the case for other long cortico-cortical connections. Given this, it is proposed that neural activity underlying the theta rhythm can occur not only in the hippocampus, but can impinge on the neocortex, which may then become entrained in activity at the theta frequency. Such entrainment depends on the conjecture that multisynaptic loops of connections, starting in the hippocampus, relaying in neocortical loci, and returning to the hippocampus can have total “round-trip” conduction times which match the period of the concurrent theta rhythm. Such connectional loops, it is envisaged, are selected from a much larger repertoire of possible connectional loops (with a variety of conduction times) by the requirement that only when “round-trip” conduction time matches the theta period can synaptic strengthening occur (i.e. in the next excitatory phase of the theta rhythm in the hippocampus) (see Figure 18.4B). In different external contexts, different sets of cortical neurones may be set into activity, thus forming the points of “reflection” in the neocortex for cortico-hippocampal loops. Diverse sets of loops, each matched to its external context, are possible, even if the theta frequency is identical in the different contexts. However, frequency may also vary sometimes, from one context to another (as depicted in Figure 18.4A, different oscillation periods are transmitted to “A4/C1” and “A1/C4”). Thus in each situational context, different sets of activated neurones, firing in precise temporal relationship to each other, provide a cerebral representation of the context. Referring to Figure 18.4A, one such context may (for example) increase the activation in neurones A1 and C4 at the activated phase of a 5 Hz theta rhythm, another in A4 and C1 at the activated phase of an 8 Hz theta rhythm, and so on. Thus different combinations of A and C neurones are brought closer to firing threshold in different contexts, with the result that different pathways between neurones A and C can be facilitated under different circumstances. Overall, because of the role of the hippocampus in providing repeated signals to the neocortex, phase-locked to the underlying theta rhythm, many large-scale
© 2002 Taylor & Francis
Neural Assemblies on Small and Large Scales
445
spatiotemporal regimes of activity are set up in the neocortex, each specifying a different context. These define a wide variety of exact operational modes for “interpreting” cortical neural activity in a neocortical network which is certainly, in itself, multifunctional, and uncommitted, and thus highly ambiguous with regard to the modes which might obtain at any one time. Each of the operational modes is a global pattern of organization of activity, pervading the whole of the neocortex, and therefore, indirectly, the whole of the forebrain. Since periods of regular theta rhythm may last several (even many) seconds, the entrainment of the neocortex in rhythmic activity can define the operational mode for similar lengths of time. Such entrainment thus constitutes another aspect of the multifaceted psychological concept of “selective attention”. From animal experiments, it is clear that the times when entrainment of the neocortex is most vigorous is not when an animal is performing firmly-established learned behaviour, but during the phase of most active learning (e.g. Adey et al., 1960; Grastyan et al., 1966; review: Miller, 1991). Thus it appears that, for overlearned tasks, the neocortex by itself does not need to resonate with the hippocampus at the theta frequency: this may indicate that, in this situation, the neocortex can reconstruct the necessary spatiotemporal regimes without the theta rhythm to “synchronize” it, just as a well-rehearsed orchestra can play without its conductor, although the conductor is needed at rehearsals. However, there is an alternative interpretation: many tasks, learned by extensive repetition of stimulus–responsereinforcement combinations, can be acquired by animals without hippocampi. These are sometimes distinguished as “habits” rather than “memories”, the form of learning being referred to as “procedural” rather than “declarative”. In this circumstance, the program is presumably so strongly stamped into the cortical network, that the subtleties of an exact spatiotemporal regime set up by the hippocampus are unnecessary. It is conceivable that, for other tasks, finely-tuned cortical dynamics supervised by the hippocampus are essential at an early stage of learning but not later when performance becomes an ingrained habit. The respective importance of these two interpretations is at present difficult to assess either from neurodynamic theory or from experiment. This theory of cortico-hippocampal interplay was developed from evidence on the theta rhythm obtained from animals. Comparable evidence from humans is fragmentary, largely because the recording of activity from hippocampus, which is a deep structure, is impossible with scalp electrodes, and is seldom attempted in humans with depth electrodes. In the neocortex, an obvious theta rhythm (such as could be recognized in single electrographic traces recorded from the scalp) is not usually detectable, even when the hippocampus shows a vigorous theta rhythm. This failure is probably due to the fact that scalp electrodes average activity across many cortical loci. Thus the different loci which are points of reflection of an on-going theta rhythm would not be identifiable, due to averaging of phase-dispersed rhythms from different loci. Despite these difficulties, a number of recent electroencephalographic studies in humans, using power spectral analyses of the EEG, have demonstrated that theta activity in the neocortex occurs most abundantly in behavioural and cognitive epochs when (from animal experiments) entrainment at the theta frequency between hippocampus and neocortex would be expected. Klimesch et al. (1997) report that EEG theta power increases during an episodic memory task only for words which are subsequently remembered correctly. Yordanova and Kolev (1998) show phase-locking to stimuli of theta activity recorded from scalp electrodes as single traces, during an auditory oddball task. Doppelmayr et al. (2000) showed that such phase locking occurs best in subjects with good episodic memory performance. All these findings would
© 2002 Taylor & Francis
446
Robert Miller
be predicted from the theory of cortico-hippocampal interplay, but are not specific to that theory. An alternative view of cortico-hippocampal interplay, compatible with the above data, was put forward by Bibbig et al., 1995 (see also Wennekers and Palm, 2000), on the basis of simulations. In this simulation, regular phases of excitation transmitted from the hippocampus provided “a source of coherent input to the involved sub-assemblies that will, in general, reside in different and perhaps only weakly-connected cortical areas.” This “increases the probability for synchronous firing events in and between those areas, and thereby the chance for the Hebbian strengthening of synapses between the sub-assemblies.” However, in their simulations, theta rhythms transmitted to the neocortex from the hippocampus do not set up any activity in the neocortex entrained, via phase-locked loops, to the hippocampal rhythm, and thus do not necessitate exact phase-locked timing between unit firing in hippocampus and neocortex. The predictions for unit firing patterns derived from the model of Bibbig et al. (1995) are thus different from those advocated here, in that precise temporal relations between hippocampal and neocortical neural activity need not occur. Results of recent experiments by Villa (2000) show that precise temporal structure across the cortex can have definite correlations to on-going behaviour. In this study, the behaviours in which temporal patterns in multispike trains are seen most frequently are reminiscent of the times during various learning experiments when animals show theta activity in the hippocampus (although the hippocampal EEG was not recorded in these experiments). For instance, temporal spike patterns were more common in Villa’s experiments during the delay in a delayed-response task or a GO-NOGO task, preceding correct rather than incorrect task performance, and prior to cues needed for task performance. From other experiments (reviewed in Miller, 1991) all of these are circumstances when theta activity is prominent, and in some of these cases there is further evidence compatible with extension of the theta rhythm to other structures such as the neocortex. The most incisive test of the theory of cortico-hippocampal interplay requires simultaneous recording of unit activity in the neocortex, and of the hippocampal EEG, in free-moving animals. It is predicted that spike trains recorded from single neurones in neocortical loci should show a tendency to fire at specific precise phases of the concurrent hippocampal theta rhythm, the phase of firing probably varying greatly from locus to locus. Such phaselocked unit firing would form part of the known temporal structure seen amongst spike trains recorded from the neocortex, although its phase-locking to the hippocampal theta rhythm could not be detected if that is not recorded. This critical experiment, involving simultaneous recording of the hippocampal theta rhythm and neocortical unit activity has recently been undertaken in rats (Siapas et al., 2000). These authors present evidence that “the firing of prefrontal cortical neurons is phase-locked to hippocampal theta oscillations, with prefrontal neurons firing at a preferred phase of the hippocampal theta rhythm, even in the absence of local cortical theta oscillations.” These preliminary reports appear to favour the original theory of cortico-hippocampal interplay Miller, 1991, rather than the model of Bibbig et al. (1995).
8. BALANCE BETWEEN HEMISPHERES IN HUMANS AND ANIMALS So far this chapter has dealt with generic aspects of the dynamics of the mammalian forebrain, but has not touched on an aspect of forebrain functional organization which is usually thought to be specific to humans. This is the topic of cerebral lateralization of
© 2002 Taylor & Francis
Neural Assemblies on Small and Large Scales
447
function. However, there is a small number of morphological papers which document asymmetries between the hemispheres in animals, for instance in size of the hemisphere or thickness of the cortex (Diamond et al., 1981; Kolb et al., 1982). More interesting from a functional point of view is a large body of Russian work from the late V.L. Bianki and coworkers, little known to Western scientists, which shows many functional asymmetries in the animal brain. Asymmetries demonstrated in rats using the technique of unilateral spreading depression to inactivate temporarily one or other hemisphere reveal functional asymmetries which (mutatis mutandis) are in many ways similar to those seen in humans (Bianki, 1988, 1993), although it is sometimes necessary to study gender effects at the same time before asymmetry is clearly revealed (Bianki and Filippova, 2000). Thus cerebral lateralization may also be a generic property of the mammalian forebrain, rather than specific to humans. Some comments on this topic are therefore appropriate in the present account of forebrain function. Issues of cerebral laterality cannot be related to those of brain dynamics at the neuronal level unless the psychological evidence for cerebral lateralization is related to a theory at the neuronal level. A recent work (Miller, 1996c) put forward such a theory, in which differences between the hemispheres in distributions of conduction times in corticocortical axons were proposed to underlie the psychological and presumed cybernetic differences between the hemispheres. Specifically, it was proposed that the right hemisphere, which appears to function best in processing of data all of whose components occur close together in time, does so because it has a preponderance of rapidly conducting axonal links; while the left hemisphere, which appears better suited for dealing with patterns of information which are spread in time over brief intervals (up to a few hundred msec), does so because its axonal links are overall more slowly-conducting, giving greater temporal dispersion of signals. The direct test of this central hypothesis by cytological examination of subcortical white matter in the electron microscope is not possible in humans, because adequately fixed tissue is extremely difficult to obtain. If, as seems probable, cerebral laterality of similar nature applies to small experimental animals, such experiments may be possible in animals. Other indirect tests of the central hypothesis (some at the biological level, many at the psychological level) are discussed by Miller (1996c) giving substantial, support to the hypothesis, although the conclusive tests have yet to be conducted. From foregoing sections of this chapter, cell assemblies appear to operate according to two quite different types of dynamic. On the one hand, in small cortical regions, assemblies can form which sustain activity without detailed temporal structure between the firing of neurones. Presumably such assemblies represent static entities in the environment (Gestalts) which also lack temporal structure. On the other hand, assemblies which form across wider spans of cortical tissue necessarily have temporal structure in the firing of neurones, and thus have the potential to represent events with temporal structure. From the hypothesis just mentioned about cerebral laterality, the first version of cell assembly operation should apply more strongly to the right hemisphere (and probably, in that hemisphere, for cell assemblies spread well beyond the local domain considered earlier in this chapter), while the latter should be more characteristic of the left hemisphere. (These two modes of operation are schematized in Figure 18.5.) Having said this, however, it is likely that left and right hemispheres differ only in degree, not in kind (i.e. not in sharp categorical terms). This conclusion follows from the fact that either hemisphere has to some extent the ability to perform the operations more characteristic of the other hemisphere, though less efficiently than the more specialized hemisphere. Thus, in terms of the hypothesis about
© 2002 Taylor & Francis
448
Robert Miller
Figure 18.5. Schematized “space-time” diagram of the differences between assemblies characteristic of the left- and right-hemispheres. For both the upper and lower staves of this diagram, neurones and their connections are laid out as a geometrically-repeating pattern along the time axis. In spatial terms, the density of neurones and their connections is the same in each, as is the convergence ratio of connections in the neurones of each stave (i.e. three-fold). The difference between the two is that connections in the upper stave (“left hemisphere”) have longer delays (i.e. greater length in horizontal “time” axis) than those in the lower stave (“right hemisphere”). Activation of neurones in the upper stave thus depends more on temporal convergence, that in the lower stave on spatial convergence.
axonal conduction times, it is likely that subcortical white matter in either hemisphere has a rich mixture of both slow- and fast-conducting axons, the difference between the hemispheres being an overall bias to the slow or fast part of the conduction velocity distribution in left and right hemispheres, rather than a complete absence of one or other type. Given this, the following question arises: can the two dynamic modes of cell assembly operation, characteristic respectively of left and right hemispheres, function together at the same time in the same slab of neocortical tissue. The answer is probably “No”, for the following reason: Neural activity in hypothetical left hemisphere-type networks is subject to loss of amplification by temporal dispersion. This does not apply to hypothetical right hemisphere-type networks. Thus left hemisphere-type networks require high overall levels of activity before activity in a particular assembly/chain can be stably maintained over time. Right hemisphere-type networks on the other hand can function effectively in their characteristic manner at lower overall activity levels. Such assemblies can burst quickly into effective activity, and can then subside equally quickly. Even given that a cortical network has a rich mixture of both slow- and fast-conducting axonal links, the conditions required for these two dynamic modes (i.e. suitable overall activity levels) are mutually exclusive. This may be the real reason for hemispheric specialization. If so, the argument just presented should apply to the mammalian cortex generally, not just to humans, and cerebral asymmetry is to be expected in subhuman animals as well as in humans.
© 2002 Taylor & Francis
Neural Assemblies on Small and Large Scales
449
If one accepts that cerebral asymmetry arises from cellular differences between the cortex on the two sides (e.g. distribution of calibres of axonal projections) other questions arise. Do other forebrain structures such as the basal ganglia and hippocampus exhibit any additional right/left asymmetry? This is a largely unexplored question, and difficult to answer definitively, since asymmetry arising primarily in the cortex could lead in many ways to secondary asymmetry in subcortical structures, even though no such asymmetry is really intrinsic to these structures. While this question is at present incapable of a certain answer, the foregoing theories of forebrain dynamics lead to a few suggestions about how the cortex may engage in varieties of interaction with these subcortical structures which differ between right and left side. Both the basal ganglia and the hippocampus appear to be involved in sustaining patterns of activity in the cortex for periods of seconds or more (longer than the exactly-timed chains of activity considered so far). In terms of large-scale psychological concepts, both therefore contribute to short-term (or “working”) memory. Both are involved in recursive loops, providing re-entrant influences upon cortical activity, based on activity efferent from the cortex which activated the loop at a slightly earlier stage. However, on the left side, hypothetically, exactly-timed chains of activity are typical of the alert hemisphere, while on the right, sequences of Hebbian cell assemblies which are continuously active without exact temporal chaining of activity predominate. Given this scenario, one can envisage that the subcortical structures can co-ordinate the chaining together of exactly-timed sequences of activity in the left hemisphere whose temporal length is outside the capability of the left neocortex by itself (e.g. sentences, musical phrases). The subcortical structures on the right side, on the other hand, may be important for chaining together activity in assemblies which themselves do not have exact temporal structure, generically a set of sequences of Gestalt representations. The theory behind lateralized cortico-subcortical interactions merits much further development. However, it is a complex topic, because, to test such hemisphere-specific interactions, it is usually necessary to use material (e.g. verbal vs spatial) for which one or other hemisphere is specialized, although the real issue is not about hemisphere-specific material, but about hemisphere-specific styles of information processing. Thus, memory deficits after hippocampal damage which are selective to verbal tests for left-sided damage and to visuospatial tests after right-sided damage do not go beyond what is predicted from wider knowledge of cerebral asymmetry. To devise experimental designs for comparing cortico-subcortical interactions between hemispheres in tests which are adequately matched on all features except the style of information processing is very difficult; and therefore these issues are only at preliminary stages of investigation at present sent. For convincing demonstration of the different cortico-subcortical interactions in the two hemispheres, one needs to find examples where the laterality effect expected from the test material itself using a control experimental design is reversed when used in a design with specific additional requirements for information processing. Although this sort of experiment has seldom been considered, two published examples of such laterality effects selective for the style of information processing (rather than the material used) can be given. In higher aspects of language processing, for example in appreciation of metaphors, indirect inferences, and jokes, right hemisphere lesions produce more severe impairment than left hemisphere ones (Winer and Gardner, 1977; Wapner et al., 1981; Brownell et al., 1986). These appear to be right hemisphere-preferred functions, even though the component items, being verbal, should give a preferential left
© 2002 Taylor & Francis
450
Robert Miller
hemisphere representation. This task, although exemplifying laterality based on processing style, does not provide evidence for a distinct role of any subcortical structure in such laterality. Another example of processing-style-specific asymmetry is a PET study by Fuji et al. (1997). For verbal memory tasks, activation of the parahippocampal gyrus in a nonmatching version of the task, was, as expected, greater on the left side; but when the task was a matching one using the same material, the right parahippocampal gyrus was activated. It is uncertain what the basis of this reversal of asymmetry can be. It could rely either on a distinct type of asymmetry in the hippocampus itself, or just on the fact that the hippocampus (assumed to be symmetrical) can better handle certain styles of information processing, and thus can produce selective improvements on the side with relatively lower performance. Full interpretation of these complex results requires further development of the underlying theory of brain dynamics, and new experimental designs will be required to adequately test that theory.
9. THE “OMNICONNECTION PRINCIPLE” IN SPACE AND TIME: APPROXIMATION IN REALITY TO THE METAPHYSICAL IDEAL OF “CONSCIOUSNESS” In an earlier work (Miller, 1981) the concept of consciousness was analysed in terms of what was called “the omniconnection principle”. This term referred to an information-processing network in which all nodes are connected to all others. This concept was developed from a philosophical perspective, to explain the mysterious “unity” of consciousness. Initially omniconnection referred to a network which was conceived as completely connected spatially—an assumption used in very many computer simulations since 1981, albeit for small-scale networks, rather than the brain as a whole. However, the concept of omniconnection was also applied in the temporal dimension: since different cortical loci are interlinked by slow conducting axons which can function as “delay lines”, and since there may be a rich repertoire of delays between any two loci, one can consider that pairs of loci may be completely connected in the temporal dimension as well as in the spatial one. In other words, sequential activation of the any two loci with any intervening time interval can be matched by the delay in at least one of the axonal connections linking the two loci. Of course, this notion of omniconnection is an ideal, like the frictionless surface, or the infinitely efficient heat engine, postulated by physicists to aid their thinking. It thus has some value in conceptualizing consciousness in philosophical or metaphysical terms. However, it is far removed from the real physical substrate for information processing in the brain, and psychological analysis also shows that the ideal of “all pieces of information having access to all others” is not strictly realized. Nevertheless, it is useful to compare the idealized omniconnected network of the 1981 formulation with the various levels of structural organization and cybernetic operation of the forebrain outlined in this chapter. At the level of local processing in the hypothetical block of cortical tissue of dimensions 1 mm3, the ideal of omniconnection is almost realized. In the temporal dimension, local transmission of signals never takes them outside the integration interval of the principal neurones in that locale. All signals therefore occur effectively in the same instant of time, in an “eternal present”, which, within the local block, cannot be linked with other times. In the spatial dimension, the probability of connection between pyramidal cells in laminae II and III, even within the limits of the 1 mm3 domain is much less than unity (A value of 0.8
© 2002 Taylor & Francis
Neural Assemblies on Small and Large Scales
451
was assumed above for immediatley-adjacent pyramidal cells, but this probability falls of sharply within a few hundred microns). This is a necessary consequence of the fact that each pyramidal cell has only a few thousand locally-distributed synapses, while the number of neurones in the territory of distribution approaches 100 thousand. However, disynaptic connectivity ratios are the square of monosynaptic connectivity ratios. Thus, with disynaptic connections, it is, in principal, possible for every neurone to be in synaptic connection with every other. Nevertheless, disynaptic connections (e.g. via a neurone in lamina V) are in themselves ambiguous, unless disambiguated by the direct connections of the triangular configurations of Figure 18.1. In this respect, omniconnection does not apply in a local cortical domain. However, the omniconnection principle does not specify that the “nodes” in such a scheme are actually single cortical neurones: there may well be some redundancy at this level, in which case omniconnection might be better applied to cortical loci or columns, rather than single cells. For more distant cortico-cortical connections there appears to be a complex mosaic of strongly interconnected bands or patches of cortical tissue, arranged over the whole neocortex. Probably neighbouring cortical loci belong to different mosaics which overlap little with each other. In addition, there are hints that cortical bands which have strong direct links may be supported by parallel patchy connections received indirectly via thalamic relays. In any case, it is clear that not every cortical locus has links to every other. Nevertheless, it is plausible to suggest that every locus has links to the general region of every other locus, the finer details of connectivity then being left to local connections in the recipient region. In this sense, cortical connectivity approximates to an omniconnected network. However, the patchy distribution of long cortico-cortical connections implies that multisynaptic links are needed for every locus to be in connection with every other. As a result, associations between one arbitrary cortical locus and any other cannot be made unambiguously and unconditionally. The dictum “neurones that fire together become strongly connected together” thus cannot apply in a strict sense, if one considers just the connectivity pattern of the neocortex. Both the long cortico-cortical connections and cortico-thalamic connections provide a wide range of conduction times. This gives some measure of temporal omniconnectedness, well beyond the neuronal integration time, and perhaps up to 100 or 200 msec in the human brain (see Miller, 1996c, where this value is taken as the approximate time limit of monosynaptic connections). For intervals greater than this, direct delay-specific association between cortical loci is likely to break down, and is replaced by representation of sequence rather than of exact timing, probably achieved by interplay between anterior regions of the cortex and the basal ganglia. Both in the local cortical domain and for long-distance links across the cortex, the approximation to the ideal of omniconnectedness which is actually realized involves disynaptic or multisynaptic connections. This leads to inherent ambiguity of the network, so that different neurones or loci which repeatedly fire together cannot be assured of becoming strongly interconnected. However, the theory of cortico-hippocampal interplay (Miller, 1991), which has now survived a number of attempts to test it, provides a mechanism where the information from a global context in the environment can be used to define large-scale regimes of preferred pathways for transcortical interaction. In fact, the information structure of the environments we inhabit includes a great deal of redundancy. Some items of information are likely to be associated repeatedly, others will never occur together. In view of this, a strictly omniconnected network, even if possible, would be
© 2002 Taylor & Francis
452
Robert Miller
Figure 18.6. Schematic diagram of major interactions in the forebrain co-ordinating the operation of neural assemblies in the neocortex. The central block represents the neocortex, containing a number of local foci of activity (depicted as clusters of interlaminar triangles). Overall, these local segments of activity are co-ordinated by interplay with the hippocampus (“hipp”), such that time delays in transmission from one segment to another become parts of the phase-locked loops between hippocampus (master oscillator) and neocortex. Interplay between neocortex and thalamus can provide further support for the spatiotemporal structure of cortical neural activity, using reciprocal connections and corticothalamic axons as delay lines. Interaction between parts of the neocortex and the basal ganglia can enable recursive loops to form, which encode expectancy and sequence, even when the exact temporal relations are indeterminate.
unnecessary. The role of cortico-hippocampal interplay in development of representation of each distinct environment allows the multipotent neocortex to use selected subnetworks, each of which represents all the associations that can occur in the corresponding environment. Figure 18.6 attempts to depict the interactions possible within the cerebral neocortex, as organized and co-ordinated by interactions with the hippocampus, the thalamus and the basal ganglia. In terms of cell assembly theory, two sorts of cell assembly are capable of stable operation in this complex spatiotemporal network: first there are those which lack intrinsic temporal structure, whose neuronal components are linked together by axons having conduction times less than the neuronal integration time, and therefore incapable of contributing
© 2002 Taylor & Francis
Neural Assemblies on Small and Large Scales
453
temporal information to assembly activity. Such assemblies would be very useful for representing Gestalts, and other configurations of information, all of whose elements occur very close together in time, and would sustain activity without representing accurate timing of events. The second type of assembly is that which does have intrinsic temporal structure, extended for up to several hundred milliseconds. The elements of such an assembly are linked by slow-conducting axons (“delay lines”). Interplay with the hippocampus may be specially important for ensuring secure transmission along such extended chains of activation. These chains are likely to be important in representing patterns of information with exact (although only briefly extended) temporal structure. With the help of the hippocampus, such chains may be extended further. The vast population of long cortico-cortical axons which make up subcortical white matter are a mixture of slow- and fast-conducting axons, in either hemisphere. Thus, potentially, either hemisphere can operate with either class of assembly. However, the two types of assembly operate best at different levels of overall cortical arousal, so that they cannot both operate effectively at the same time. It is likely that the left hemisphere has a richer repertoire of slow-conducting delay lines, the right hemisphere having a richer array of fastconducting axons. This, it is suggested, is the basis for the differences in psychological processing between right and left hemispheres. The fact that the two sorts of assembly cannot operate simultaneously in the same cortical network, may be real the reason for hemispheric specialization; and if so, such specialization should apply throughout the mammalian kingdom, not just in humans.
REFERENCES Abeles, M. (1981) Local cortical circuits. Studies in Brain Function series, Springer Verlag, Heidelberg. Abeles, M. (1991) Corticonics: Neural circuits of the cerebral cortex. Cambridge University Press, Cambridge, U.K. Adey, W.R., Dunlop, C.W. and Hendrix, C.E. (1960) Hippocampal slow waves. Distribution and phase relationships in the course of approach learning. Archives of Neurology, 3, 74–90. Albe-Fessard, D., Condes-Lara, M. and Sanderson, P. (1983) The focal tonic cortical control of intralaminar thalamic neurons may involve a cortico-thalamic loop. Acta Morphologica Hungarica, 31, 9–26. Alexander, G.E., DeLong, M.R. and Strick, P.L. (1986) Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience, 9, 357–381. Amabile, G., Fattaposta, F., Pozzessere, G., Albani, G., Sanarelli, L., Rizzo, P.A. and Morocutti, C. (1986) Parkinson’s disease: electrophysiological (CNV) analysis related to pharmacological treatment. Electroencephalography and Clinical Neurophysiology, 64, 521–524. Amir, Y., Harel, M. and Malach, R. (1993) Cortical hierarchy reflected in the organization of intrinsic connections in macaque monkey visual cortex. Journal of Comparative Neurology, 334, 19–46. Balaydier, C. and Maugiere, F. (1987) Network organization of the connectivity between parietal area 7, posterior cingulate cortex and medial pulvinar nucleus: a double fluorescent tracer study in monkey. Experimental Brain Research, 66, 385–393. Berman, N., Payne, B.R., Labar, D.R. and Murphy, E.H. (1982) Functional organization of neurons in cat striate cortex: variations in ocular dominance and receptive-field type with cortical laminae and location in visual field. Journal of Neurophysiology, 48, 1362–1377. Bianki, V.L. (1988) The right and left hemispheres of the animal brain. Cerebral lateralization of function. Gordon and Breach, New York and London. Bianki, V.L. (1993) The mechanisms of brain lateralization. Gordon and Breach, New York, and London. Bianki, V.L. and Filippova, E.B. (2000) Sex differences in lateralization in the animal brain. (T. Ganf: trans; R. Miller: ed.), Conceptual Advances in Brain Research series, Harwood Academic Publishers. Bibbig, A, Wennekers, T. and Palm, G. (1995) A neural network model of corticohippocampal interplay and the representation of context. Behavioural and Brain Research, 66, 169–175. Bienenstock, E.L., Cooper, L.N. and Munro, P.W. (1982) Theory for the development of neuron selectivity. Journal of Neuroscience, 2, 32–48.
© 2002 Taylor & Francis
454
Robert Miller
Braitenberg, V. (1978) Cell assemblies in the cerebral cortex. In: R. Heim and G. Palm (eds), Lecture notes in Biomathematics Vol. 21: Theoretical approaches to complex systems. Heidelberg, Springer Verlag, pp. 171–188. Braitenberg, V. and Schüz, A. (1991) Anatomy of the cortex: statistics and geometry. Studies on brain function No. 18. Heidelberg, Springer Verlag. Braitenberg, V. and Schüz, A. (1998) Cortex: statistics and geometry of neuronal connectivity (Second, revised edition of Braitenberg and Schüz, 1991). Heidelberg, Springer. Brownell, H.H., Potter, H.H. and Bihrle, A.M. (1986) Inference deficits in right brain-damaged patients. Brain and Language, 27, 310–321. Bures, J., Buresova, O., Weiss, T. and Fifkova, E. (1963) Excitability changes in non-specific thalamic nuclei during cortical spreading depression in the rat. Electroencephalography and Clinical Neurophysiology, 15, 73–83. Burkhalter, A. (1989) Intrinsic connections of rat primary visual cortex: laminar organization of axonal projections. Journal of Comparative Neurology, 279, 171–186. Carpenter, M.B. (1981) Anatomy of the corpus striatum and brain stem integrating systems. Handbook of Physiology, The Nervous System II, Motor control Part 2. American Physiological Society, Bethesda MD, pp. 947–995. Chapin, J.K. (1986) Laminar differences in sizes, shapes and response profiles of cutaneous receptive fields in the rat S1 cortex. Experimental Brain Research, 62, 549–559. Chow, K.L., Brumback, H.D. and Glanzman, D.L. (1978) Abnormal development of lateral geniculate neurons in rabbits subjected to either eyelid closure or corticofugal paroxysmal discharge. Brain Research, 146, 151–158. Coenen, A.M.L. and Vendrik, A.J.H. (1972) Determination of the transfer ratio of cat’s geniculate neurons through quasi-intracellular recordings and the relation with level of alertness. Experimental Brain Research, 14, 227–242. Contreras, D. and Steriade, M. (1997) State-dependent fluctuations of low frequency rhythms in corticothalamic networks. Neuroscience, 76, 25–38. Cowan, R.L. and Wilson, C.J. (1994) Spontaneous firing patterns and axonal projections of single corticostriatal neurons in the rat medial agranular cortex. Journal of Neurophysiology, 71, 17–32. Darian-Smith, C., Tan, A. and Edwards, S. (1999) Comparing thalamocortical and corticothalamic microstructure and spatial reciprocity in the macaque ventral posterolateral nucleus (VPLc) and medial pulvinar. Journal of Comparative Neurology, 410, 211–234. DeBruyn, E.J., Casagrande, V.A., Beck, P.D. and Bonds, A.B. (1993) Visual resolution and sensitivity of single cells in the primary visual cortex (V1) of a noctural primate (bush baby): correlations with cortical layers and cytochrome oxidase patterns. Journal of Neurophysiology, 69, 3–18. Deecke, L., Scheid, P. and Kornhuber, H.H. (1969) Distribution of readiness potential, pre-motion positivity and motor potential of the human cerebral cortex preceding voluntary finger movements. Experimental Brain Research, 7, 158–168. Deecke, L., Englitz, H.G., Kornhuber, H.H. and Schmitt, G. (1977) Cerebral potentials preceding voluntary movement in patients with bilateral or unilateral Parkinson akinesia. In: J.E. Desmedt (ed.), Attention, voluntary contraction and event-related cerebral potentials. (Progress in Clinical Neurophysiology, Vol. 1). Karger, Basel, pp. 151–163. Deniau, J.M. and Chevalier, G. (1985) Disinhibition as a basic process in the expression of striatal functions. II. The striato-nigral influence on thalamocortical cells of the ventromedial thalamic nucleus. Brain Research, 334, 227–233. Deschenes, M., Veinante, P. and Zhang, Z.-W. (1998) The organization of corticothalamic projections. Reciprocity versus parity. Brain Research Reviews, 28, 286–308. Diamond, M.C., Dowling, G.A. and Johnson, R.E. (1981) Morphologic asymmetry in male female rats. Experimental Neurology, 71, 261–268. Dick, J.P.R., Cantello, R., Buruma, O., Gioux, M., Benecke, R., Day, B.L., Rothwell, J.C., Thompson, P.D. and Marsden, C.D. (1987) The Bereitschaftspotential, L-DOPA and Parkinson’s disease. Electroencephalography and Clinical Neurophysiology, 66, 263–274. Dick, J.P.R., Rothwell, J.C., Day, B.L., Cantello, R., Buruma, O., Gioux, M., Benecke, R., Berardelli, A., Thompson, P.D. and Marsaden, C.D. (1989) The Bereitschaftspotential is abnormal in Parkinson’s disease. Brain, 112, 233–244. Doppelmayr, M., Klimesch, W., Schwaiger, J., Stadler, W. and Rohm, D. (2000) The time locked theta response reflects interindividual differences in human memory performance. Neuroscience Letters, 278, 141–144. Dudek, S.M. and Bear, M.F. (1992) Homosynaptic long-term depression in area CA1 of hippocampus and effects of N-methyl-D-aspartate receptor blockade. Proceedings of the National Academy of Sciences, U.S.A., 89, 4363–4367. Ezure, K. and Oshima, T. (1981a) Dual activity patterns of fast pyramidal tract cells and their family neurones during EEG arousal in the cat. Japanese Journal of Physiology, 31, 717–736.
© 2002 Taylor & Francis
Neural Assemblies on Small and Large Scales
455
Ezure, K. and Oshima, T. (1981b) Excitation of slow pyramidal tract cells and their family neurones during phasic and tonic phases of EEG arousal. Japanese Journal of Physiology, 31, 737–748. Ferster, D. (1981) A comparison of binocular depth mechanisms in areas 17 and 18 of the cat visual cortex. Journal of Physiology (London), 311, 623–655. Ferster, D. and Lindström, S. (1983) An intracellular analysis of geniculo-cortical connectivity in area 17 of the cat. Journal of Physiology (London), 342, 181–215. Fuji, T., Okuda, J., Kawashima, R., Yamadori, A., Fukatsu, R., Suzuki, K., Ito, M., Goto, R. and Fukuda, H. (1997) Different roles of the left and right parahippocampal regions in verbal recognition: a PET study. NeuroReport, 8, 1113–1117. Funke, K. and Eysel, U.T. (1992) EEG-dependent modulation of response dynamics of cat dLGN relay cells and the contribution of corticogeniculate feedback. Brain Research, 573, 217–227. Fuster, J.M. (1980) The prefrontal cortex. Anatomy, physiology and neuropsychology of the frontal lobe. Raven Press, New York. Ghazanfar, A.A. and Nicolelis, M.A.L. (2000) The space-time continuum in mammalian sensory pathways. In: R. Miller (ed.), Time and the brain. Conceptual Advances in Brain Research series. Harwood Academic Publishers, Amsterdam. Giguere, M. and Goldman-Rakic, P.S. (1988) Mediodorsal nucleus: areal, laminar and tangential distribution of afferents and efferents in the frontal lobe of rhesus monkeys. Journal of Comparative Neurology, 277, 195–213. Gilbert, C.D. (1977) Laminar differences in receptive field properties of cells in cat primary visual cortex. Journal of Physiology (London), 268, 391–421. Grastyan, E., Karmos, G., Vereczkey, L. and Kellenyi, L. (1966) The hippocampal electrical correlates of the homeostatic regulation of motivation. Electroencephalography and Clinical Neurophysiology, 21, 35–53. Gulyas, B., Lagae, L., Eysel, U. and Orban, G.A. (1990) Corticofugal feedback influences the responses of geniculate neurons to moving stimuli. Experimental Brain Research, 79, 441–446. Harvey, A.R. (1980) A physiological analysis of subcortical and commissural projections of areas 17 and 18 of the cat. Journal of Physiology (London), 302, 507–534. Hebb, D.O. (1949) The organization of behavior: a neuropsychological theory. Wiley, New York. Hellwig, B. (2000) A quantitative analysis of the local connectivity between pyramidal neurons in layers 2/3 of the rat visual cortex. Biological Cybernetics, 82, 111–121. Hess, G., Aizenman, C.D. and Donghue, J.P. (1996) Conditions for the induction of long-term potentiation in layer II/III horizontal connections of the rat motor cortex. Journal of Neurophysiology, 75, 1765–1778. Hirsch, J.C., Fourment, A. and Marc, M.E. (1983) Sleep-related variations of membrane potenital in the lateral geniculate body relay neurons of the cat. Brain Research, 259, 308–312. Hull, C.L. (1943) Principles of behavior. New York: Appleton-Century-Crofts. Inubushi, S., Kobayashi, T., Oshima, T. and Torii, S. (1978) Intracellular recordings from the motor cortex during EEG arousal in unanaesthetized brain preparations of the cat. Japanese Journal of Physiology, 28, 669–688. Ito, M. (1985) Processing of vibrissa sensory information within the rat neocortex. Journal of Neurophysiology, 54, 479–490. Joel, D. and Weiner, I. (2000) Striatal contention scheduling and the split-circuit scheme of basal gangliathalamocortical circuitry: from anatomy to behaviour. In: R. Miller and J.R. Wickens (eds), Brain dynamics and the striatal complex. Conceptual Advances in Brain Research series. Harwood Academic Publishers, Amsterdam. Johnson, M.J. and Alloway, K.D. (1994) Sensory modulation of synchronous thalamocortical interactions in the somatosensory system of the cat. Experimental Brain Research, 102, 181–197. Joseph, J.P. and Barone, P. (1987) Prefrontal unit activity during a delayed oculomotor task in the monkey. Experimental Brain Research, 67, 460–468. Kayama, Y., Shosaku, A. and Doty, R.W. (1984) Cryogenic blockade of the visual cortico-thalamic projection in the rat. Experimental Brain Research, 54, 157–165. Klimesch, W., Doppelmayr, M., Schimke, H. and Ripper, B. (1997) Theta synchronization and alpha desynchronization in a memory task. Psychophysiology, 34, 169–176. Kolb, B., Sutherland, R.Y., Nonneman, A.Y. and Whishaw, J.G. (1982) Asymmetry in the cerebral hemispheres of the rat, mouse, rabbit and cat: the right hemisphere is larger. Experimental Neurology, 78, 348–359. Komatsu, H. (1982) Prefrontal unit activity during a color discrimination task with GO and NO-GO responses in the monkey. Brain Research, 244, 269–277. Kornhuber, H.H. and Deecke, L. (1965) Hirnpotentialänderungen bei Wilkürbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale, Pflügers Archiv für Gesamte Physiologie, 284, 1–17. Kuroda, M., Murakami, K., Kishi, K. and Price, J.L. (1992) Distribution of the piriform cortical terminals to cells in the central segment of the mediodorsal thalamic nucleus of the rat. Brain Research, 595, 159–163.
© 2002 Taylor & Francis
456
Robert Miller
Lemmon, V. and Pearlman, A.L. (1981) Does laminar position determine the receptive field properties of cortical neurons? A study of corticotectal cells in area 17 of the normal mouse and the reeler mouse. Journal of Neuroscience, 1, 83–93. LeVay, S., McConnell, S.K. and Luskin, M.B. (1987) Functional organization of primary visual cortex in the mink (Mustela vison) and a comparison with the cat. Journal of Comparative Neurology, 257, 422–441. Leventhal, A.G. and Hirsch, H.V.B. (1978) Receptive-field properties of neurons in different laminae of visual cortex of the cat. Journal of Neurophysiology, 41, 948–962. Levitt, J.B., Lewis, D.A., Yoshioka, T. and Lund, J.S. (1993) Topography of pyramidal neuron intrinsic connections in macaque monkey profrontal cortex (areas 9 and 46). Journal of Comparative Neurology, 338, 360–376. Levitt, J.B., Yoshioka, T. and Lund, J.S. (1994) Intrinsic cortical connections in macaque visual area V2: evidence for interraction between different functional streams. Journal of Comparative Neurology, 342, 551–570. Lindstrom, S. and Wrobel, A. (1990) Frequency dependant corticofugal excitation of principal cells in the cat’s dorsal lateral geniculate nucleus. Experimental Brain Research, 79, 313–318. Liu, X.-B., Honda, C.N. and Jones, E.G. (1995) Distribution of four types of synapse on physiologically identified relay neurons in the ventral posterior thalamic nucleus of the cat. Journal of Comparative Neurology, 352, 69–91. Malach, R. (1994) Cortical columns as devices for maximizing neuronal diversity. Trends in Neuroscience, 17, 101–104. McKenna, T.M., Light, A.R. and Whitsel, B.L. (1984) Neurons with unusual response and receptive-field properties in upper laminae of cat S1 cortex. Journal of Neurophysiology, 51, 1055–1076. Mangini, N.J. and Pearlman, A.I. (1980) Laminar distribution of receptive field properties in the primary visual cortex of the mouse. Journal of Comparative Neurology, 193, 203–222. Mathers, L.H. (1972) The synaptic organization of the cortical projection to the pulvinar of the squirrel monkey. Journal of Comparative Neurology, 146, 43–60. Metherate, R. and Ashe, J.H. (1993) Ionic flux contributions to the neocortical slow waves and nucleus basalismediated activation: whole-cell recordings in vivo. Journal of Neuroscience, 13, 5312–5323. Metin, C., Godement, P. and Imbert, M. (1988) The primary visual cortex in the mouse: receptive field properties and functional organization. Experimental Brain Research, 69, 594–612. Miller, R. (1981) Meaning and purpose in the intact brain. Oxford University Press, Oxford. Miller, R. (1991) Cortico-hippocampal interplay and the representation of contexts in the brain. Springer Verlag, Heidelberg. Miller, R. (1996a) Neural assemblies and laminar interaction in the cerebral cortex. Biological Cybernetics, 75, 253–275. Miller, R. (1996b) Cortico-thalamic interplay and the security of operation of neural assemblies and temporal chains in the cerebral cortex. Biological Cybernetics, 75, 263–275. Miller, R. (1996c) Axonal conduction time and human cerebral laterality: A psychobiological theory. Harwood Academic Publishers, Amsterdam. Miller, R. and Wickens, J.R. (1991) Corticostriatal cell assemblies in selective attention and in representation of predictable and controllable events: a general statement of corticostriatal interplay and the role of striatal dopamine. Concepts in Neuroscience, 2, 65–95. Molotchnikoff, S., Tremblay, F. and Lepore, F. (1984) The role of the visual cortex in response properties of lateral geniculate cells in rats. Experimental Brain Research, 53, 223–232. Okano, K. and Tanji, J. (1987) Neuronal activities in the primate motor fields of the agranular frontal cortex preceding visually triggered and self-paced movement. Experimental Brain Research, 66, 155–166. O’Keefe, J. and Nadel, L. (1978) The hippocampus as a cognitive map. Clarendon Press, Oxford. Palm, G. (1982) Neural assemblies. Springer Verlag. Palmer, L.A. and Rosenquist, A.C. (1974) Visual receptive fields of single striate cortical units projecting to the superior colliculus in the cat. Brain Research, 67, 27–42. Revishchin, A.V. (1985) Laminar distribution of neurons with different types of receptive fields in the visual cortex of the rabbit. Neirofiziologiia, 17, 19–27. Rockland, K.S. and Lund, J.S. (1983) Intrinsic laminar lattice connections in primate visual cortex. Journal of Comparative Neurology, 216, 303–318. Sakurai, Y. (1996) Population coding by cell assemblies—what it really is in the brain. Neuroscience Research, 26, 1–16. Sawyer, S.F., Martone, M.E. and Groves, P.M. (1991) A GABA immunocytochemical study of rat motor thalamus: light and electron microscopic observations. Neuroscience, 42, 103–124. Scannell, J.W., Burns, G.A.P.C., Hilgetag, C.C., O’Neill, M.A. and Young, M.P. (1999) The connectional organization of the cortico-thalamic system of the cat. Cerebral Cortex, 9, 277–299. Schiller, P., Finlay, B.L. and Volman, S.F. (1976a) Quantitative studies of single-cell properties in monkey striate cortex. I. Spatiotemporal organization of receptive fields. Journal of Neurophysiology, 39, 1288–1319.
© 2002 Taylor & Francis
Neural Assemblies on Small and Large Scales
457
Schiller, P., Finlay, B.L. and Volman, S.F. (1976b) Quantitative studies of single-cell properties in monkey striate cortex. II. Orientation specificity and ocular dominance. Journal of Neurophysiology, 36, 1320–1333. Schüz, A. (1994) Patchiness as a means to get the message across. Trends in Neuroscience, 17, 365. Schmielau, F. and Singer, W. (1977) The role of visual cortex for binocular interactions in the cat lateral geniculate nucleus. Brain Research, 120, 354–361. Schwartz, M.L., Dekker, J.J. and Goldman-Rakic, P.S. (1991) Dual modes of corticothalamic synaptic termination in the medio-dorsal nucleus of rhesus monkey. Journal of Comparative Neurology, 309, 289–304. Sherman, S.M. and Guillery, R.W. (1996) Functional organization of thalamocortical relays. Journal of Neurophysiology, 76, 1367–1395. Siapas, A.G., Lee, A.K., Lubenov, E.V. and Wilson, M.A. (2000) Prefrontal phase-locking to hippocampal theta oscillations. Society for Neuroscience Abstracts, 26, 467–1. Simpson, J.A. and Khurabait, A.J. (1986) Readiness potential of cortical area 6 in Parkinson’s disease. Evidence for a dopaminergic striatal control of postural set involving supplementary motor area. Journal of Neurology Neurosurgery and Psychiatry, 49, 475. Snodderly, D.M. and Gur, M. (1995) Organization of striate cortex of alert, trained monkeys (Macaca fascicularis): on-going activity, stimulus selectivity, and widths of receptive field activating regions. Journal of Neurophysiology, 74, 2100–2125. Steriade, M., Jones, E.G. and Llinas, R.R. (1990) Thalamic oscillations and signaling. New York: Wiley Interscience. Steriade, M. (1997) Synchronized activities of coupled oscillators in the cerebral cortex and thalamus at different levels of vigilance. Cerebral Cortex, 7, 583–604. Stern, E.A., Kincaid, A.E. and Wilson, C.J. (1997) Spontaneous subthreshold membrane potential fluctuations and action potential variability of rat corticostriatal and striatal neurons in vivo. Journal of Neurophysiology, 77, 1697–1715. Sur, M., Garraghty, P.E. and Bruce, C.J. (1985) Somatosensory cortex in macaque monkeys: laminar differences in receptive field size in areas 3b and 1. Brain Research, 342, 391–395. Swadlow, H.A. (1988) Efferent neurons and suspected interneurons in binocular visual cortex of the awake rabbit: receptive fields and binocular properties. Journal of Neurophysiology, 59, 1162–1187. Swadlow, H.A. (1989) Efferent neurons and suspected interneurons in S-1 vibrissa cortex of the awake rabbit: receptive field and axonal properties. Journal of Neurophysiology, 62, 288–308. Swadlow, H.A. (1990) Efferent neurons and suspected interneurons in S-1 forelimb representation of the awake rabbit: receptive fields and axonal properties. Journal of Neurophysiology, 63, 1477–1498. Swadlow, H.A. (1991) Efferent neurons and suspected interneurons in second somatosensory cortex of the awake rabbit: receptive fields and axonal properties. Journal of Neurophysiology, 66, 1392–1409. Swadlow, H.A. (1992) Monitoring the excitability of neocortical efferent neurons to direct activation by extracellular current pulses. Journal of Neurophysiology, 68, 605–619. Swadlow, H.A. (1994) Efferent neurons and suspected interneurons in motor cortex of the awake rabbit: axonal properties, sensory receptive fields and subthreshold synaptic inputs. Journal of Neurophysiology, 71, 437–453. Swadlow, H.A. (2000) Information flow along neocortical axons. In: R. Miller (ed.), Time and the brain. Conceptual Advances in Brain Research series. Harwood Academic Publishers, Amsterdam, pp. 131–155. Swadlow, H.A. and Weyand, T.G. (1981) Efferent systems of the rabbit visual cortex: laminar distribution of the cells of origin, axonal conduction velocities and identification of axonal branches. Journal of Comparative Neurology, 203, 799–822. Swadlow, H.A. and Weyand, T.G. (1987) Corticogeniculate, corticotectal neurons and suspected interneurons in visual cortex of awake rabbits: receptive field properties, axonal properties and effects of EEG arousal. Journal of Neurophysiology, 57, 977–1001. Tolman, E.C. (1932) Purposive behaviour in man and animals. Appleton-Century-Crofts, New York. Tseng, G.-F. and Royce, G.J. (1986) A Golgi and ultrastructural analysis of the centromedian nucleus of the cat. Journal of Comparative Neurology, 245, 359–378. Tsumoto, T., Creutzfeldt, O.D. and Legendy, C.R. (1978) Functional organization of the corticofugal system from visual cortex to lateral geniculate nucleus in the cat (with an appendix on geniculo-cortical monosynaptic connections). Experimental Brain Research, 32, 345–364. Uhr, J.L. and Chapin, J.K. (1983) Contribution of thalamo-cortical and cortico-cortical connections to receptive field properties in rat S1 cortex. Abstracts of the Society for Neuroscience, 74, 10. Varela, F.J. and Singer, W. (1987) Neuronal dynamics in the visual corticothalamic pathway revealed through binocular rivalry. Experimental Brain Research, 66, 10–20. Vidyasagar, T.R. and Urbas, J.V. (1982) Orientation sensitivity of cat LGN neurones with and without inputs from visual cortical areas 17 and 18. Experimental Brain Research, 46, 157–190. Villa, A.E.P. (2000) Empirical Evidence about Temporal Structure in Multi-unit recordings. In: R. Miller (ed.), Time and the brain. Conceptual Advances in Brain Research series. Harwood Academic Publishers, Amsterdam, pp. 1–51.
© 2002 Taylor & Francis
458
Robert Miller
Villa, A.E. and Abeles, M. (1990) Evidence for spatiotemporal firing patterns within the auditory thalamus of the cat. Brain Research, 509, 325–327. Villa, A.E.P., Rouiller, E.M., Simm, G.M., Zurita, P., de Ripaupierre, Y. and de Ripaupierre, F. (1991) Corticofugal modulation of the information processing in the auditory thalamus of the cat. Experimental Brain Research, 86, 506–517. Waller, H.J. and Feldman, S.M. (1967) Somatosensory thalamic neurons: effects of cortical depression. Science, 157, 1074–1077. Walter, W.G., Cooper, R., Aldridge, V.J., McCallum, W.C. and Winter, A.L. (1964) Contingent negative variation: an electric sign of sensorimotor association and expectancy in the human brain. Nature, 203, 380–384. Wapner, W., Hanby, S. and Gardner, H. (1981) The role of the right hemisphere in the apprehension of complex linguistic material. Brain and Language, 14, 15–33. Wennekers, T. and Palm, G. (2000) Cell assemblies, associative memory and temporal structure in brain signals. In: R. Miller (ed.), Time and the brain. Conceptual Advances in Brain Research series. Harwood Academic Publishers, Amsterdam, pp. 251–273. Wickens, J.R. (1990) Striatal dopamine in motor activation and reward-mediated learning: steps towards a unifying model. Journal of Neural Transmission (General Section), 80, 9–31. Wickens, J.R. (1993) A theory of the mammalian striatum. Pergamon Press, Oxford. Wickens, J.R., Begg, A.J. and Arbuthnott, G.W.(1996) Dopamine reverses the depression of rat cortico-striatal synapses which normally follows high-frequency stimulation of cortex in vitro. Neuroscience, 70, 1–5. Wickens, J.R. and Miller, R. (1997) A formalization of the neural assembly concept: constraints on neural assembly size. Biological Cybernetics, 77, 351–358. Wickens, J.R. and Miller, R. (1998) Requirements for neural assembly formation, ignition and maintenance: Formalization of traditional concepts. In: N. Kasabov (ed.), Progress in Connectionist-based information systems. Proceedings of 1997 International Conference on Neural Information Processing and Intelligent Information Systems. Heidelberg, Springer Verlag, pp. 117–120. Wilson, J.R., Friedlander, M.J. and Sherman, S.M. (1984). Fine structural morphology of identified X- and Y-cells in the lateral geniculate nucleus. Proceedings of the Royal Society, Series B, 221, 411–436. Winer, E. and Gardner, H. (1977) The comprehension of metaphor in brain-damaged patients. Brain, 100, 716–727. Winer, J.A. and Larne, D.T. (1987) Patterns of reciprocity in auditory thalamocortical and corticothalamic connections: study with horseradish peroxidase and autoradiographic methods in the rat medial geniculate body. Journal of Comparative Neurology, 257, 282–315. Yordanova, J. and Kolev, V. (1998) Single sweep analysis of the theta frequency band during an auditory oddball task. Psychophysiology, 35, 116–120.
© 2002 Taylor & Francis
19 Discussion Section Robert Miller and Almut Schüz
This chapter arose in the following way: when chapters had been submitted, we listed a set of issues for each chapter which would merit further discussion amongst the contributors. Following this, we drafted individual letters to chapter authors, posing one (or several) questions raised by their chapters, and invited authors’ comments. From the responses received, we have compiled the chapter that follows. For most of the issues we raise, we consider ourselves to be relatively impartial editors, drawing from the contributors their further insights into the questions we pose. Here we are identified as “Editors”. For a few topics, however, we are certainly not unbiased, but are protagonists in the debate. For such issues we identify ourselves as “Schüz” or “Miller”. We have broken the discussion into sections, starting with detail and finishing with broader scientific and philosophical issues.
1. SPECIFIC EXAMPLES WHERE CYTOARCHITECTURE MIGHT HAVE A BEARING ON CYBERNETICS OF CORTICAL TISSUE Editors: In the chapter of Hellwig, in a variety of areas, the derivation of myelin patterns from cytoarchitectonic data was elegant and convincing. However, the principles used concerned only the stripes of Baillarger (i.e. horizontal collaterals). We asked Hellwig if he had any suggestions about the fibre systems which could contribute to the other differences in myeloarchitectonics between areas, such as those in the total amount of myelin, or the less spectacular differences in the horizontal stripes higher up in the cortex (layers I and III). In particular, we asked if myelination in long cortico-cortical connections afferent to an area could also make a contribution to the observed pattern of myelination in an area. Hellwig: I think that the overall amount of myelin depends on both horizontal and vertical fibres, the latter consisting mainly of afferent and efferent cortico-cortical connections. As to myelin in upper layers (layer 1 or the stripe of Kaes-Bechterew in upper layer III), I simply do not know where it is derived from. Editors: We also had a somewhat technical question regarding the observer-independent cytoarchitectonic mapping of Amunts et al. In one of the papers cited in their chapter (Amunts et al., 1999), the statistical measure used to describe the laminar profile of each area, documented by automated methods, showed greater intersubject variability within an area than interarea variability across subjects. We asked Amunts/Zilles, if this is correct, and also asked if, in any way, it weakens the robustness of the automated definition of areas, and the borders between them. 459 © 2002 Taylor & Francis
460
Robert Miller and Almut Schüz
Amunts/Zilles: In some cases of non-primary cortical areas, we have found that intersubject variability in cytoarchitecture of one cortical area (e.g. areas 44) can be greater than the differences in cytoarchitecture between this and a neighbouring area of one and the same brain. This was shown for areas 44 and 45 which are very similar with respect to their cytoarchitecture in layers I-III and V-VI: both have large pyramidal cells in deep layer III, and large pyramidal cells in layer V (somewhat smaller in layer V than in layer III). Both show a pronounced columnar arrangement of cells. The major difference between them is that area 44 is a dysgranular region (i.e. layer IV is not clearly visible because pyramidal cells of layers III and V intermingle with granular cells of layer IV), whereas area 45 is a granular area with a distinct inner granular layer (layer IV). Our automated approach is sensitive enough to detect such subtle differences in cytoarchitecture. Thus, the automated detection of borders is robust. Despite the intersubject variability, the borders between areas 44 and 45 were found in all sections and brains which we have analysed. The variability in cytoarchitecture of one area across subjects may be considerable, but the difference to neighbouring areas is still visible and detectable. Such intersubject differences in cytoarchitecture may reflect not only “true”, biological variability, but are also influenced by the methodology (e.g. post mortem delay and conditions, histological proceeding, staining, etc.). All these sources of variability influence the cytoarchitecture of a single cortical area across subjects, but do not impair the detection of areal borders in a single brain. Editors: The chapter of Jacobs and Scheibel raised interesting questions about quantitative anatomical relationships within different cortical areas. In principle, the total length of the dendritic arbor of a neurone can vary either by increasing the length of dendrites or by making more branches. We asked whether the two measures covaried, in the areas these authors have investigated. Alternatively, did some of the areas stand out by having complex dendritic arbors, and others by loosely but widely ramifying dendritic trees? Jacobs: Overall dendritic length can increase by changes in length and/or in the number of branches. In the cortical areas we have examined, dendritic segment length and number do not reveal differential patterns for specific cortical regions. In the pyramidal cell populations we have examined, each subsequent branch order (up until the 5th) tends to increase by an average of about 20 µm in length, such that 1st order basal dendrites are around 20 µm in length, 2nd order average 40 µm, 3rd order average 60 µm, and so on. Fifth order and higher branches average approximately 80–100 µm. In terms of segment number, 1st order branches average around 5 per cell, 2nd order about 10, 3rd and 4th around 15, 5th decreasing to about 10, with subsequent decreases thereafter. This pattern appears to hold regardless of cortical region, with regional differences being essentially quantitative in nature (i.e. slightly longer and more numerous branches expressed consistently throughout the dendritic envelope) rather than qualitative (i.e. differential branching patterns). It may in fact be a general principle that there is an average length that dendrites achieve before bifurcation becomes a high probability (Lindsay and Scheibel, 1981). Nevertheless, other factors besides dendritic length or number, such as dendritic orientation, may show differential regional variation, as has been suggested by the research of Elston and Rosa (1998).
© 2002 Taylor & Francis
Discussion Section
461
Editors: Another of the questions concerns the relationship between the size of dendritic trees and spine number. Data presented in the chapter of Jacobs and Scheibel indicated that, as the size of dendritic trees increases, the number of spines on a dendritic tree increases more than dendritic length. We asked if this could be due, at least partly, to thicker dendrites. Did the authors have the impression that dendritic thickness also increases? It might be that the number of spines (and synapses) depends on size of membrane area rather than on dendritic length10. Admittedly, we are aware of the fact that the quantitative relationships in the neuropil do not leave much room for thicker dendrites: Thus, Bok (1959) has shown in a comparison in different species that the density of neurones decreases proportionally with increasing dendritic length, and may be explained entirely by this increase. Data presented in the chapter of Jacobs and Scheibel, on areas 10 and 18 in the human cortex, are well in accordance with this. However, a slightly larger proportional decrease in neuronal density than the increase in length does not completely exclude an additional slight increase in thickness. We invited Jacobs to comment. Jacobs: We cannot answer this directly. It is unclear at this point the extent to which dendritic thickness affects overall dendritic complexity. Thicker dendrites certainly might have more spines per unit length, but spine density itself is an independent variable, depending on a number of factors (e.g. synaptic density, distance from soma). Two additional issues need to be emphasised in this regard: (i) Dendritic thickness may be more of a factor in proximal (1st–3rd order) dendritic branches, which tend to be less spine dense, than in more distal segments. Thus, segment thickness may contribute differentially to overall dendritic complexity, depending on which portion of the arbor is being examined. (ii) Since one cannot clearly visualise spines directly above or below dendrites with light microscopy, most spine measures represent underestimates, especially for thicker dendrites (Horner and Arbuthnott, 1991). We suspect that such underestimates might be greatest in those regions exhibiting more complex (and perhaps thicker) dendritic systems. Thus, the observed regional differences we have observed in both dendritic and spine measures may actually be greater than typically reported. Further research is required to determine the relative contributions of dendritic thickness to overall neuronal surface area. Editors: We noted the suggestion of Jacobs and Scheibel that wider dendritic fields, and increased spine counts, in higher cortical areas might mean greater polymodal integration of information. We certainly agree that these features mean wider integration of inputs, but suggest that it need not imply greater polymodal integration. For instance, in their Figure 6.1, it is illustrated that in primary somatosensory cortex, the hand region has more complex dendritic arbors than other parts of this area. Presumably, it is integrating information in the same modalities as other parts of S1, though no doubt with a finergrained representation. Perhaps polymodal integration might be achieved in other ways, without increase in dendritic arbor size, for instance with organized batteries of cells, arranged hierarchically, which can achieve collectively a very wide integration. We invited Jacobs to comment on these issues.
10
In contrast to what we assumed in a former discussion on allometric relationships (Schüz and Demianenko, 1995).
© 2002 Taylor & Francis
462
Robert Miller and Almut Schüz
Jacobs: It is certainly possible that polymodal integration can be achieved by batteries of cells rather than by converging polymodal input on a specific dendritic array. Given the complexity of cortical neuropil, however, it seems highly probable that both types of polymodal integration obtain. Future research into convergence patterns is required to address this issue more directly and definitively. Editors: In sensory cortex, not all regions have callosal connections (a fact mentioned, for instance by Kaas), and we were interested whether this has implications for cytoarchitecture. We asked Kaas, as well as Amunts/Zilles, if the regions which have callosal connections have large and prominent pyramidal cells in lamina III, which could be a cytoarchitectonic detail with exact cybernetic meaning. (This was an idea suggested by one of us in an older paper [Miller, 1975]). Kaas: I agree completely that large pyramidal cells likely mark the borders of at least V1 (which have callosal connections) and possibly other visual areas. Amunts/Zilles: von Economo and Koskinas (1925) described a special subregion of OBg at the very beginning of area OB (otherwise known as area 18, or V2) which is characterised by very large pyramidal cells in deep sublayer IIIc. Clarke (1993) has found that this subregion may correspond to a callosal-rich region. She has identified degenerating myelinated axons by the Nauta technique. We could identify consistently OBg by our observerindependent approach in cytoarchitectonic preparations (Amunts et al., 2000). Editors: We were interested in the cybernetic (or other) role of myelination of axons in different cortical areas, as described by Hellwig. We took note of the role he suggested for myelin in “consolidating” an early memory trace, so that parts of the cortex (especially primary receiving areas) come to have dedicated functions for particular stereotyped forms of information processing. We cannot refute this idea. However, it is possible that myelination (and its consequence of rapid conduction) has an additional role in the cybernetics of nervous tissue. Hellwig’s argument depends on the assumption that conduction time in local axon collaterals is always small compared to the neuronal integration time, even when axons are unmyelinated, and therefore is unlikely to represent temporal information for the recipient neurones. However, there is recent evidence that conduction time may be longer (Bringuier et al., 1999; Gonzalez-Burgos et al., 2000), and neuronal integration time can sometimes be shorter (Carandini et al., 1996) than were apparently assumed. If so, it could be important from the cybernetic point of view whether axons are myelinated or not. We asked Hellwig for his comments. Hellwig: The answer is, of course, yes. In my chapter, I highlighted the idea that myelin may be important in consolidating early learning processes. But this does not, ofcourse, exclude the possibility that a broad spectrum of conduction times induced by myelination may be very useful. In this context, I would like to mention Abeles’ “synfire chains” (Abeles, 1982) which work only if spikes arrive at a postsynaptic neurone in precise temporal coincidence. It may be more likely to achieve this if neuronal activity at one cortical space is projected to another piece of cortex with a broad spectrum of different conduction velocities. Thus, the “good” conduction velocity which leads to temporal coincidence with other
© 2002 Taylor & Francis
Discussion Section
463
spikes may be amongst those represented. In a narrow spectrum of conduction velocities, this might not necessarily be the case. Editors: Jain et al. (1998) describe, in area 3b of monkeys that the cortical representation of the sensitive surface of each digit is highly myelinated, with myelin-light septa in between the representations of each digit. This myeloarchitectonic feature is not changed in the adult by deafferentation. Such a specialization may have both of the functions mentioned above—a cybernetic function (for rapid communication between parts of the surface of each digit), and a function of “consoldation” of a style of information processing, in a rather immutable form in the adult. We can also take another specific example—area MT—for discussion of the above point. It is a heavily myelinated area, and there is evidence that the projection to this area from V1 consists of large calibre axons (Rockland, 1995), which are myelinated (Anderson et al., 1998). In this context, Kaas referred in his chapter to the very large Meynert cells in V1 of primates. The large size of their cell bodies could be due not only to their having long axons to support, but to the fact that their projections to MT are fast, therefore requiring large-calibre axons. Valverde (1985, pp. 250–251) also discusses them, and mentions that “the axons of Meynert cells could never be followed . . . beyond the initial segment.” This suggests that the axons are myelinated. Kaas: Yes, ofcourse the axons of Meynert cells need not only to be long, but thick and myelinated in order to conduct rapidly. Editors: Does myelination of incoming fibres from V1 contribute to the overall heavy myelination in this area? Is the myelination in area MT confined to horizontal bands, or more uniformly spread (as might be expected if it was partly associated with incoming fibres)? Hellwig: I assume that vertical fibres contribute to the heavy myelination of MT. Among them are obviously incoming fibres from V1 considering the literature you quoted (Anderson et al., 1998). Kaas: The source of myelinations in MT appears to be intrinsic. Lesions of V1 remove V1 inputs to area MT, but do not alter the myelination in that region (J. Kaas et al., in preparation). Miller: From the cybernetic point of view, this area is concerned with detecting coherent motion. We ask whether a consistent story can be made of the fact that its inputs from lower visual cortical areas are rapidly conducting, and probably also myelinated. The unifying idea here is an informal model of motion processing in MT, dependent on rapid conduction in inputs, which therefore exhibit little temporal dispersion. This could be necessary for exact derivation of velocity in neurones which receive convergent input from a number of primary motion detectors (e.g. in V1). With slower-conducting axons, which have a wider spread of conduction time amongst a population, some temporal dispersion would occur of the signals from “primary motion detectors” in V1. Thus, the temporal accuracy of the information coming into MT from V1, by which coherent movement over a field is integrated, would be degraded.
© 2002 Taylor & Francis
464
Robert Miller and Almut Schüz
Shipp mentions that the V5/MT motion pathway is one of the most clearly segregated of all. We asked him if the sharp segregation of MT is necessary because its cybernetic requirements (fast, synchronized and very phasic processing) are very distinctive? Shipp: It certainly seems reasonable to suppose that the higher temporal resolution of signals arriving over fatter, faster-conducting axons might be of particular importance to an area specialised for processing motion (MT/V5). The anatomical evidence is broadly supportive here, as all three sources of ascending cortical input to V5 (layers 4B and 6 of V1, thick stripes of area V2 and area V3) as well as V5 itself, are relatively rich in a class of neurones picked out by the “CAT 301” antibody label (DeYoe et al., 1990; Levitt et al., 1994; Tootell and Taylor, 1995). This same label distinguishes magnocellular from parvocellular geniculate cells. It tags a structural element found in larger-than-average neurones, and thus demonstrates that these large neurones are a ubiquitous feature of a wider, cortical motion-processing circuitry. Thus, the CAT 301 data complements the other anatomical features (input from V1, heavy myelination) that distinguish V5 from the cortex immediately posterior, areas V4 and V4A. V5 is also notably different from these areas in its functional activities. The distinction with areas anterior to V5, such as MST and FST, which receive input from V5 and conduct higher forms of motion processing, is much less marked. On the other hand, slower forms of signal also make their way to V5/MT. Input from the parvocellular/tonic channel can be detected there (Maunsell et al., 1990; Seidemann et al., 1999) and, in human functional imaging studies, V5 can be shown to be activated by the chromatic channels, which convey sensations of motion in pure colour-contrast graphics (Wandell et al., 1999). Psychophysical approaches also identify fast and slow motion systems. One formulation invokes 1°, 2° and 3° mechanisms of motion detection (Lu and Sperling, 1995). The 3° mechanism is considerably slower than the other two (with a cutoff temporal frequency sensitivity of 3 Hz versus 12 Hz), and it is the only mechanism thought to be capable of signalling the motion of pure colour stimuli (Lu et al., 1999). Hence, combining these observations, the 3° mechanism should also feed V5, in addition to the 1° and 2° mechanisms. So far, it is unknown whether there is any segregation of these mechanisms within V5. Editors: In more general terms, there is recent evidence that latency of neuronal responses to visual flashes does not increase much as one passes sequentially from region to region in the “dorsal stream” of visual areas, but increases much more in the “ventral stream” areas (Schmolesky et al., 1998; Bullier and Nowak, 1995). Given this, is there any indication of greater myelination in “dorsal stream” areas than in those of the “ventral stream”? Hellwig: Higher myelination of dorsal regions is not strong enough to “leap to the eye”, when looking through a series of photographs of myelin-stained sections from a human brain. However, if one was to be sure, one would need to focus on this question in more detail. Editors: Another more general point, mentioned by Hellwig, is that primary sensory and motor areas are more heavily myelinated than association areas. This may serve the cybernetic function of preserving temporal patterns in the incoming sensory areas as faithful as possible to the actual stimulus. In the higher areas, slower conducting axons would allow
© 2002 Taylor & Francis
Discussion Section
465
temporal dispersion, and therefore association between components of the signal which arrived at slightly different times. Would this make sense? Hellwig: Yes, this makes sense to me. Strong myelination leads to high conduction velocities and short conduction times. This might be useful in the primary sensory areas in order to preserve incoming temporal patterns as faithfully as possible. In the higher association areas, it is less clear which temporal patterns are the useful ones. Therefore, there may be less myelination. The broader the spectrum of conduction velocities (in particular when slow conduction velocities are represented), the higher the temporal dispersion of neuronal signals. For the reasons mentioned above this may be useful. Amunts/Zilles: We would like to emphasise that the statement “heavily” or “less heavily myelinated” is rather sloppy and misleading regarding the anatomical situation. The diagnosis “heavy myelination” of a cortical area can be caused by relatively few and wider spaced axons with very thick myelin sheets (enabling fast conduction) or by relatively more and closely spaced axons with relatively thinner myelin sheets (enabling slower conduction). The diagnosis of the degree of myelination is normally performed at such a low microscopical magnification that the two conditions cannot be discriminated. Moreover, the diagnosis “heavy myelination” is often caused by the fact that the myelinated vertical fibre bundles ascend to the upper layer III in one cortical area, and thus, lead to the impression of a more intense myelination of this area compared to a neighbouring areas, where the fibre bundles terminate already in deeper layer III. The degree of myelination as diagnosed in histological sections at low magnification does not allow an unequivocal statement about conduction velocities. The present discussion of the criteria necessary for a more differentiated evaluation of myelin stained sections apparently does not take into consideration the sophisticated set of criteria already used by the Vogts (Vogt, 1911; Vogt and Vogt, 1919). Consideration of these papers would clearly improve our discussion about heavily and less heavily myelinated cortical areas.
2. PHYLOGENY IN RELATION TO LAMINAR AND AREAL SPECIALIZATION OF THE NEOCORTEX Editors: Valverde et al. provide some comparative data, with specific detail about the hedgehog. In this species the neocortex has a thick lamina I, an accentuated lamina II and absence of lamina IV. This finding might have a relation to the argument presented in the chapter of Miller and Maitra, where it is suggested that in the typical mammalian neocortex a slab of tissue is inserted into the more basic cortex, such as that found ventral to the rhinal fissure. This slab appears to make up lamina III (and perhaps adjacent tissue) in the adult, and thus splits the prominent superficial cell layer found ventral to the rhinal fissure, leading to the formation of the definitive neocortical laminae II and IV. This hypothesis might receive support from the similarity between cells in three locations: in lamina II ventral to the rhinal fissure (pyramidal cells with short apical dendrites), in lamina II of the neocortex (the same), and in lamina IV of the neocortex (spiny stellates or star pyramids). In addition, in the chapter of Hellwig, it is mentioned that cell density in laminae II and IV seems to covary across homotypical, agranular and granular cortices. As far as the hedgehog goes, the implication seems to be that the splitting fails to occur.
© 2002 Taylor & Francis
466
Robert Miller and Almut Schüz
We asked Valverde if he found this account convincing from a developmental or phylogenetic point of view. Valverde: The six layered fundamental pattern of cortical lamination for the neocortex was established by Brodmann. It was widely accepted as a tool for cortical parcellation, implying that it was present in all neocortical areas and in all mammals. In contrast to the six-layered neocortex, those parts extending ventral to the rhinal fissure (olfactory cortex) or those parts on the medial side corresponding to the hippocampal formation constituted the “older cortices”, i.e. paleocortex and archicortex. They do not show six layers and are considered to be representatives of a primitive type of cortex from which the neocortex evolved. Brodmann’s layering design was based on his belief that the six-layered schema derived from a developmental (ontogenetic and phylogenetic) conception, thus arriving at erroneous interpretations. Hence, if one is restricted to using only six numerals, one often finds baffling layering truncations such as “layer II-III”, or expansions, like “sublayers IVa, IVb and IVc”, not to mention the cases where even Greek characters are added to these. As emphatically noticed by Cajal, the fundamental pattern of cortical organization must be determined through the complete study of all cell varieties in specific cortical areas, using methods providing a complete picture of cells and fibres. The Nissl method is only the first step in this analysis (Lorente de Nó, 1949). The limits separating neocortical from allo- and paleocortical formations are not sharp and, several periallo- and peripaleocortical regions have been interpreted as transitional areas between both structures. This led Sanides (1970) to put forward a new concept of neocortical evolution, contemplating the entire neocortex as a set of concentric rings, or different waves of progressive differentiation extending from the most primitive cortical areas. This progressive differentiation takes place by thickening of the cortex, with the appearance of new layers, and eventually the appearance of granular cells (granularization). Pyramidal cells in primitive cortices (olfactory, hippocampal and fascia dentata) have dendrites projecting predominantly towards the pial surface. They correspond to the extraverted pyramidal cells of Sanides (1970) which make up most of Layer II, not only in the paleo- and archicortex, but also in transitional cortices bordering these areas. The persistence of these pyramidal cells in the neocortex of certain specimens of Erinaceinae, Chiroptera and Cetaceans indicates that the final stage of neocortical evolution has not been reached in these specimens. In addition, it also indicates that the major afferent input to these particular areas is made in superficial layers (I and II), where these cells extend their main dendrites. Miller and Maitra (this Volume) raised the interesting question whether the laminar pattern lateral to the rhinal fissure (i.e. towards the neocortex) and ventral to it (i.e. towards the olfactory cortex) is essentially the same. The hypothesis is largely based on, and adds to, the former study of Reiner (1991), who made a comparison of certain neurotransmitter- and neuropeptide-specific cell types in the dorsal cortex of the turtles with those present in mammalian neocortex. For reasons that are outside the present context, the dorsal cortex in modern reptiles has been considered a forerunner of the mammalian neocortex. Reiner interpreted his results by suggesting that the dorsal cortex of turtles lacks the neuronal populations characterising layers II-IV of the mammalian neocortex, suggesting also that the evolution of the mammalian neocortex was characterised by the addition of new neurones in these layers. Miller and Maitra went a step beyond, by showing that layer II of the transitional cortices is in continuity, not only with the homologous
© 2002 Taylor & Francis
Discussion Section
467
neocortical layer but also with the densely packed cells in Layers III and IV. Their images do not leave any doubt in this respect and, what is most definitive, cell markers that were found in specific neocortical layers are absent in equivalent regions in turtles, suggesting that cells composing Layers II-IV were added in the mammal. I do not want to question these observations, since the facts are there, clearly exposed and convincing, but, I do ask “Where the devil do these new cells come from?”. Neither Reiner, nor Miller and Maitra explain convincingly, why and how these new cells were added. It has been assumed that in an hypothetical mammalian ancestor, the afferent fibres ended predominantly in the most superficial cortical layers. Our data with Golgi studies in the hedgehog—an insectivore—(Valverde et al., 1986) as well as the studies carried out in Cetaceans (Glezer et al., 1988) have shown an extensive input of afferent fibres in the exceedingly thick layer I, and in the extraverted Layer II pyramidal cells, and this type of cortical input is reminiscent of the organization found in lower vertebrates. During some advanced stages of cortical evolution the course and site of termination of afferent fibres gradually changed to deeper cortical layers (layers III and IV), and this may have been a driving force for the development of intrinsic cells in these internal layers which finally became responsible for relaying afferent cortical fibres to pyramidal cells. Therefore, we consider that during phylogenetic evolution, at least during the time span we may reasonably consider, there are no new types of neurones added to the cortex, but all of them represent secondary modifications occurring in specific target cells. An example may be given here: when we described for the first time the “chandelier cells” in the visual cortex of the cat (Fairén and Valverde, 1980)—a type of cell making inhibitory contacts specifically on the initial axon segments of pyramidal cells—(although Somogyi [1977] had described a similar type of cell in the visual cortex of the rat), we, and many others, thought that we were dealing with an entirely new type of neurone which had no equivalent in any other species or cortical area. We soon became aware that exactly the same type of neurone was present not only in the visual cortex, but in other cortical areas, as well as in all mammalian species thus far studied, from insectivores to man. In the study of different forms of cells with spiny dendrites, one gets the impression that all of them may share a common phylogenetic origin, and that a continuum can be traced from cells in lower forms, to the most advanced mammal. In the neocortex of the hedgehog, a complete series of intermediate forms between the most extraverted pyramidal cells and typical pyramidal cells can always be found (Valverde, 1986). It is possible to think that, during evolution, some pyramidal cells lose their apical dendrites in layer I but have retained a thin apical branch tapering at some distance from the cell body. The cell becomes stellate in form, and the dendrites appear concentrated in more restricted volumes. This seems to be the case of stellate or “grain pyramids” in the barrel field of the somatosensory and visual cortices of some rodents. In the final elimination of the remnant of their apical dendrites, the cells turn into typical spiny stellate cells like those found in the visual cortex of the cat and monkey (Valverde, 1971). These stages of pyramidal cell evolution also involve variations in the axonal patterns, which change from long projecting neurones (hedgehog, rat, cat) to intrinsic cells with axons remaining inside the cortex (monkey). Following this reasoning we may assume that, in the hypothetical mammalian ancestor, the primitive pyramidal cells (sole inhabitants) accumulate genetic variations by analogy with the hypothetical creation of new genes from a redundant duplicate of an old gene, as proposed by Ohno (1970). The outcome is that evolution may have acted to select new connections that conferred a selective advantage upon those animals possessing them (Valverde, 1988).
© 2002 Taylor & Francis
468
Robert Miller and Almut Schüz
Editors: Another phylogenetic question we were also interested in concerned the so-called “association cortex”. Kaas (1987) wrote, “Generalising from cats and monkeys it appears that evolutionary advance in brain organization is marked by increases in the numbers of unimodal sensory fields, not by increases in multimodal association cortex as traditionally thought.” We asked if this still holds true, in the light of more recent findings. How does the prefrontal cortex fit into this scheme? Does it mean that evolutionary advance involves deeper analysis within major modalities, rather than more sophisticated combination of information from several modalities? Kaas: The statement can still be supported, but I’m not sure it is correct. Even V1 in some sense is multimodal, and this complicates answering the question, and much of prefrontal cortex may be multimodal. What is clearly true is that higher primates have more stations in their sensory systems so that processing involves more steps. Editors: We were also interested in the cases where long cortico-cortical connections spread uniformly across an areal boundary. In such cases, is there any indication that the two adjacent areas have an origin in a single area of lower species. Kaas: If connections spread uniformly across a boundary, my first impulse would be to question the boundary.
3. STATUS OF CORTICAL “MODULES” Editors: The chapter of Levitt and Lund, as well as evidence reviewed in some of the other chapters, speak in favour of some kind of columnar organization throughout the cortex. However, Levitt and Lund also hint at the limitations of too strict a modular concept. The question then is to strike the right balance between the concept of a structural parcellation of the cortex into a series of discrete modules on the one hand, and that of structural homogeneity as it is suggested by Nissl- and fibre stains, as well as by the homogeneous distribution of synapses (e.g. Figure 17 in Braitenberg and Schüz, 1998) on the other hand. We were interested in this question. In some cases, the discrete character of a column is clear (e.g. barrels, cytochrome oxidase blobs and stripes, ocular dominance columns, superficial part of entorhinal cortex), and there is also evidence for discreteness (as opposed to smooth shift) with respect to intra-areal axonal patches (Amir et al., 1993). However, one may wonder how many of the neuronal elements at a given place participate in columnar organization: does this organization affect all elements and layers in parallel, or are there just a few systems with modular structure superimposed onto an essentially homogeneous network. The columnar structure seems mainly to concern the axonal side: inputs from the thalamus (barrels, ocular dominance columns), cortico-cortical and callosal projections (Gilbert and Wiesel, 1989; Goldman-Rakic and Schwartz, 1982) and intra-areal axonal patches (as in the chapter of Levitt and Lund). The dendrites (with the exception of the barrels) seem usually not to participate (Malach, 1994). However, not all the axonal systems seem to participate in columnar organization, e.g. the diffuse feedback connections in the visual system, mentioned in the chapter by Shipp. Also, anterograde tracer injections usually show a rather large and dense homogeneous field of collaterals
© 2002 Taylor & Francis
Discussion Section
469
surrounding the injection site, suggesting that patchiness is mainly restricted to the farreaching axon collaterals, and—judging from the fibre density—each patch probably contributes only a small proportion of the synapses at a given place (comparable perhaps to the thalamic fibres). In this context, the chapter of Dinse and Schreiner includes the following quotation: “functional maps are overlaid so to ensure that many, if not all combinations of the different parameters are represented in cortex. Recent theoretical studies show that geometrical factors do not constrain the ability of the cortex to represent combinations of parameters in spatially superimposed maps of similar periodicity. Considerations of uniform coverage suggest an upper limit of six or seven maps. A higher limit, of about nine or ten, may be imposed by the numbers of neurones or minicolumns available to represent each feature within a given cortical microdomain (Swindale, 2000; Swindale et al., 2000). Thus, several feature dimensions can be expected to be represented across VI, AI, and SI. . . . From this point of view, each neurone and each location in VI, AI, and SI can be understood as representing a specific set of many independent variables in the sensory environment. Topographically, each location on the cortical surface corresponds to a specific intersection of several systematic maps.” This quotation could even be taken to imply that (outside special areas like barrels, CO blobs, etc.) modules are an artefact of studying only one set of axonal projections at once. We asked Levitt if he would agree to a “moderate modular concept” in which only a moderate proportion of the neuronal elements participate. In this connection, we were also interested to know how many different kinds of patches contribute to a column, and if patches of different sources tend to be in register or rather to ignore each other. Levitt: It has long been noted that the extent of visual cortex over which the preferred orientation of recorded neurones shifts is smaller than the diameter of a single pyramidal neurone’s dendritic tree. Moveover, data on the nonclassical receptive field surround indicate that orientation selectivity of a given cortical neurone can be modified dynamically by the context in which it is embedded. Hence one asks: “What does it mean for there to be fixed orientation columns when the tuning of individual cortical neurones can vary?” It is difficult to think of a hard edged “column” in the cortex, at least physiologically. The concept of a module is certainly more straightforward when defined anatomically. In addition, while it is also the case that not all projection systems have a columnar or modular organization (for example certain cortical feedback projections), we have some evidence that some of these pathways have a very precise columnar termination pattern when studied more carefully (for example the terminations of V3 projections to layer 4B in area V1). Moreover, one should remember that the evidence for the “less modular” connections comes from injections several hundred microns in diameter, labelling many neurones. The connections of single neurones might well be more precise. We feel compelled to agree with a more moderate concept of the module, if only because we remain unsure how best to define one. It is even more difficult to define a module than to define art; like art we have trouble defining a module, but unlike art we remain unsure if we would recognize one even if we saw it.
© 2002 Taylor & Francis
470
Robert Miller and Almut Schüz
We agree completely with Dinse and Schreiner, and Swindale, that our uncertainties as to the definition or rigidity of modules in the cortex may reflect simply the problems of studying one parameter at a time. It might be that if one studied the organization of cells coding several parameters at once, or defined by several anatomical constraints, one would find a stricter modular organization. However, pondering the analysis of clustering in a high-dimension space makes our brains spin. Thus, we are still left with the first problem we posed, that even for a single parameter the dimensions of a module can be smaller than the extent of a single neurone’s dendritic field. These viewpoints can be reconciled only by using newer data about the previously unappreciated biophysical complexities of the individual cortical neurone, suggesting that each cell may consist of multiple independent functional input domains throughout the dendritic tree. This makes for a somewhat different notion of a module.
4. “HIERARCHY” OF AREAS Editors: We raised with Young the general topic of “hierarchy”. It was clear from his chapter that there is some kind of hierarchy, but it appears to be a very mild one if there are thousands of possible solutions. Young: Well, no! This is an interesting point. If you compare the number of violations for optimised shuffled data (around 120), with the optimised real anatomical data, the number of violations for real data is astonishingly low. The regularities are really very strong indeed. Nature really cares about these relationships. So, the system is, surprisingly, strictly hierarchical. “Indeterminacy” (the result that millions of different solutions fit the anatomical data equally well), is not a “real” problem. It is inherited from qualitative anatomical data (since anatomists hate to count), via qualitative relational constraints (e.g. “higher-than”), via an integer cost function (number of violations of a candidate hierarchy of the relational constraints). It is abolished by quantitative data, as we have shown with Henry Kennedy’s data (Barone et al., 2000). So, there is a single optimal structure that can be discovered. It will be a bit complex, because there are subtle differences in the hierarchy between representations of the visual periphery and those for central vision, and there are asymmetries in the apparently reciprocal relationships between several stations—but I don’t believe there is any meaningful functional interpretation of “indeterminacy”. Editors: Obviously it is not very clear “who is above whom”. It seems to be a complex hierarchy, like in a research institute in which the director can be uppermost or very low in the hierarchy, depending on the problem to be solved (writing an article, repairing a broken water tube, or chasing a computer virus). Thus, an occasional visitor to this institute can come up with many different hierarchies if the criteria is the flow of information between people. Young: Perversely, notwithstanding my remark above, you’re right about that! The relationship between hierarchical connectivity and information flow (dynamics) is a complex one. Both complex dynamics (including simultaneous onset latencies) and hierarchical connectivity can be properties of the same system (Scannell et al., 1999). “Information
© 2002 Taylor & Francis
Discussion Section
471
flow and dynamics” is related to hierarchy, but in a complex manner. Hierarchy is only one property of the visual network. It also has topological and hodological properties, among several others, and these other aspects of organisation are also important in determining the system’s dynamics. The misapprehension in the recent suggestions that the visual system can’t be hierarchical because some dorsal stations respond simultaneously to flashes is that “hierarchical” equates to “serial”. So, at least three non-equivalent aspects of the anatomical organization of the visual system are known, each of which would be expected to be reflected in rather different aspects of the system’s function. First, laminar hierarchy relates to strong anatomical regularities in the laminar origins and terminations of projections, and might be expected to reflect itself in the elaboration of stimulus preferences in stations at different levels of the hierarchy. Laminar hierarchy does not specify the number of stations through which signals must pass to reach a particular area: there are very many connections that pass between areas on non-adjacent levels (Felleman and Van Essen, 1991). Nor is it related directly to differences in the area-to-area pattern of inputs to different structures. Consequently, it would not be expected that this aspect of organization should reflect itself directly in timing of activity onsets in different stations, nor directly in the similarity in function of different stations. Second, hodology relates to the number of stations, or processing steps, through which signals must pass to reach one structure from another. Hodology specifies the sets of relationships between stations, which are different from those specified by patterns of their laminar connectivity; and hodology is not related directly to differences in the area-to-area pattern of inputs to different structures. Consequently, it would not be expected that this aspect of organization should reflect itself directly in the elaboration of receptive field properties in different stations, nor directly in their similarity and dissimilarity in function. Third, the topology of the system arises from the patterns of area-to-area connectivity within the system. This aspect of organization need not be related simply to either laminar hierarchy (Young, 1992) or to hodology (Young et al., 1994). However, an area’s information processing functions are constrained by the inputs they receive, and the destinations of their outputs (e.g. Young et al., 2000b). It would hence be expected that the more similar is the set of areas giving rise to inputs to two structures, and the more similar is the set of stations to which they send outputs, the more similar should be their functional roles (Young et al., 2000). Hence, topology should be related more directly to the functional similarity of different stations, and less directly to the elaboration of receptive field size and selectivity, or to the onsets of activity in different stations. The term “hierarchical”, carries a special and precise meaning in the context of the organization of the visual system (Felleman and Van Essen, 1991; Hilgetag et al., 2000a). It refers to aspects of organization specified by regularities in the patterns of laminar origin and termination of projections. By contrast, the term “serial”, also carries a specific meaning, relating to a property of the area-to-area connection patterns that is apparent both in the system’s topology and hodology. “Hierarchical” does not imply “serial”, nor
© 2002 Taylor & Francis
472
Robert Miller and Almut Schüz
vice versa. Indeed, quite different architectures can be specified by the combinations of these terms. It is possible to envisage an architecture that is both hierarchical and serial, as for example in sensory cortex; an architecture that is serial but not hierarchical, as perhaps in prefrontal cortex; one that is hierarchical but not serial (perhaps the dorsal pathway in the visual system); and an architecture that is neither hierarchical nor serial (as perhaps in classical auto-associative nets, or the CA3 field of the hippocampus).
5. STATUS OF AREA 4 Editors: Within the overall topic of hierarchy of cortical areas, we were particularly interested in area 4 and its position in the level of cortical organization (loosely, in the “hierarchy”; but see the distinctions in the use of this term from Young [above]). It is interesting to compare the various anatomical criteria which might be used. Most of them seem to place area 4 at a low position in the hierarchy, but the situation is a bit equivocal: the large amount of myelin, as well as (according to the measures by Jacobs and Scheibel) the fact that area 4 does not have particularly large dendritic arbors in supragranular layers (though larger than in primary sensory areas) place it at a low position in cortical hierarchy. On the other hand, the low cell density (see chapter by Jacobs and Scheibel) gives area 4 a place at a very high position. Jacobs and Scheibel point out that the increased size of dendritic trees correlates with a decrease in the density of neurones. However, the decrease in cell density seems to be far more dramatic than can be explained by the size of dendritic arbors: area 4 belongs to the areas with very lowest cell density. Since a decrease in the density of neurones cannot be due to anything other than an increase in neuropil, this suggests that in area 4—in contrast to other areas—there is a more than proportionate increase in the axonal contribution to the neuropil, or perhaps to the abundance of large calibre myelinated axons in the neuropil. However, as far as we know, not enough data are available to decide if there is an overproportionate increase in axonal volume in area 4 which could explain the low cell density. Interestingly, the hierarchical position of area 4 is also somewhat equivocal from the axonal side, as mentioned in the chapter by Levitt and Lund. In the visual system it has been shown that, away from the primary sensory area, axonal patches tend to become larger, and this is even more so for the overall size of the patch field produced by a single tracer injection (Amir et al., 1993; Lund et al., 1993: see chapter of Levitt and Lund, this Volume). In area 4, however, the patch size is larger than in the primary somatosensory areas (as is the size of the dendritic trees) but the size of the total patch field is not (Levitt and Lund, this volume; and personal communication). Also, from the overall arrangement of the cortico-cortical connections between areas, Young suggests a low position in hierarchy. We invited several contributors to comment on this, and/or on the “location” of area 4 in cortical hierarchy in general. Jacobs: It would not be surprising if area 4 had a larger axonal input per unit area than areas 3, 1 and 2, especially since the motor cortex constitutes a kind of final common pathway out of the cortex. In that sense, it might well be thought of as a “higher hierarchical centre,” but in a somewhat different sense from those in the sensory-related realms. Most high hierarchical cortical centres have a good deal of re-entrance, that is, mutual feedback connections to other contributing systems. Motor cortex might first be thought of as not
© 2002 Taylor & Francis
Discussion Section
473
having this much feedback output (except to brain stem and spinal cord), but when one considers all the “afference copy” lines it must send to sensory areas to alert them of “what is about to happen”, it can be considered a higher cortical region as well. Levitt: We acknowledge the conflicting data concerning the classification of area 4 as a primary or higher-order cortical area. We also recognize that cell density in area 4 is among the lowest in the cortex, and note the question whether an overproportionate increase on the axonal side could explain the low cell density. However, our measurements did seem to indicate that the spacing of terminal patches from intrinsic connections was scaled to the basal dendritic field of superficial layer pyramidal neurones, as in all other areas examined, i.e. they are not more closely spaced than would be predicted by dendritic field size. However, it is conceivable that these patches might contain a higher density of boutons or ramifying fibres. To our knowledge, there are no counts of terminal fibres or synaptic boutons that would allow such a comparison of area 4 with other cortical areas, so we cannot comment on whether the lower cell density in that area is offset by other factors. The problem is that purely anatomical criteria for classification of areas, either singly or into a hierarchy, ultimately depend on some form of functional measurements for confirmation. So we turn the question back to students of motor function to ask if there is anything distinctive about motor cortex that might shed light on the anatomical anomalies. One representative study illustrating that we do not completely understand how to classify area 4 is that of Schieber and Hibbard (1993). These authors showed that single M1 neurones were active during movements of several fingers, and that regions of M1 active during movement of different fingers overlapped extensively. We tend to think of primary cortical areas as having a precise topographic representation, with higher areas having less precise topography (due to bigger receptive fields or genuinely degraded maps). If this were so in M1, we might expect single M1 neurones to fire only during movements of a particular finger, or for given regions devoted to a single digit. One interpretation of the data from this study is that M1, nominally a primary area, contains a map more like that of a higher order area. Young: The neuroinformatic analyses of connectivity definitely say that area 4 is low in the hierarchy, as you describe. That’s just the way the results are, so there’s not a lot I can say in mitigation. The internal histology might more readily be expected to be the anomaly, since area 4 is certainly a bit unlike sensory areas in a functional sense. Miller: One resolution to this issue is that area 4 is an exception to any general scheme, and is best considered together with the primary somatosensory cortex, as part of an integrated cortical “organ” in this region. One can suggest this because area 4 lacks lamina IV (a principle input lamina) while area 3 (part of the primary somatosensory cortex) has very few pyramidal cells in lamina V (the main output lamina). Thus, in this combined cortical organ, inputs would be dealt with in area 3, followed by direct transfer of information by short cortico-cortical links to area 4, which then elaborates the output. In this context, I note that Young writes: “One feature of the (somatosensory) system, however, that it is rather unlike either the visual or auditory systems, in that parts of the system comprise the cortical motor system, viz: the medial supplementary motor area (SMA), the premotor cortex (area 6), and the primary motor cortex (area 4). These motor areas are arranged in what appears to be a hierarchy, in which the primary motor area is associated with primary
© 2002 Taylor & Francis
474
Robert Miller and Almut Schüz
somatosensory areas, the premotor area with higher somatosensory areas, and SMA with still higher ones”. We also noted that Rademacher had referred to the “extremely low neuronal density in layer V” in area 3. As part of the evaluation of the above idea (of sensorimotor cortex as an “integrated cortical organ”), we asked him whether the low pyramidal cell density in lamina V of area 3 is typical of primary sensory cortex generally, or just of area 3 in the somatosensory cortex? Rademacher: It is difficult to set up rules, since individual variations are quite striking. However, for primary auditory cortex, cell density in layer V is approximately 10% lower than in layers II to IV (mean values from 10 brains). Jacobs: Given the patterns of interconnectivity that characterise primary somatosensory and motor cortex, your suggestion that these two regions together constitute a cortical module of complementary cytoarchitectonics is an intriguing one. Certainly, from a developmental perspective, these regions mature hand-in-hand with each other (Chugani et al., 1987). Functionally, area 4 works very closely with areas 3, 1 and 2 by synthesising various sources of input, including processed proprioceptive and tactile information from layer II and III pyramidal neurones in area 3, 1 and 2 (Porter, 1997), prior to initiating smooth voluntary movements. Thus, from cytoarchitectonic, developmental, and functional perspectives, these two regions may indeed seem to constitute a cortical module. Amunts/Zilles: The concept of cortical hierarchy is related to connectivity, and the amount of connectivity is reflected by the amount of neuropil. The cortex is roughly subdivided by layer IV into a supragranular and an infragranular part. Layer IV is the major input layer of thalamo-cortical afferents. Layers I-III are targets of intracortical connections. Thus, an increase in neuropil in layers I-III may indicate an increased complexity in intracortical connectivity and a relatively high position in the hierarchy of cortical areas. Layers V and VI are mainly output layers and, therefore, cannot be used as separate indicators for cortical hierarchy. Based on our previous quantitative cytoarchitectonic analyses, area 4 has the lowest cell packing density in the cortex, both in supra- and in infragranular layers i.e., the amount of neuropil of area 4 is high. Using this criterion, it follows that area 4 reaches a high hierarchical order. In addition, whereas areas 3, 17, 41 (etc.) receive strong sensory input, area 4 is a motor area beside all the converging input from cortical areas. As such, it handles information which is most complex, that is area 4 integrates information of different modalities and cortical regions (in other words, it is high in the hierarchy). Area 3 has a much higher packing density in supragranular layers and therefore, a low amount of neuropil, or connectivity (and so is low in the hierarchy). The two areas are thus (conceptually) at “opposite ends” of the cortex. There are only a few areas in the cortex which are so different in architecture as are these two areas. Finally, behaviour as defined by motor activity is controlled by area 4. Thus, area 4 is contributing as the last cortical instance to the ultimate goal of the brain, i.e. behaviour. Schüz: I would like to add here some personal communication by Braitenberg. The low density of neurones in area 4 may be due to the particularly high degree of myelination
© 2002 Taylor & Francis
Discussion Section
475
submerging the pattern of horizontal striation characteristic for other areas. In this sense, there may indeed by an overproportionate increase in axonal volume in this area.
6. INTERPLAY BETWEEN LAMINAE, AND BETWEEN CORTEX AND THALAMUS Editors: In the chapter of Miller (“Wheels within wheels”) a hypothesis was presented that pyramidal cells in lamina V could act to support or “prime” the activations of connections within laminae II/III of the cortex, this enabling cell assemblies to form which could be ignited, without this implying uncontrolled explosions of activity. Any cell in lamina V might serve its “priming function” for a variety of connections within laminae II and III, and thus could be part of a number of independent cell assemblies. In the chapter of Shipp, one part refers to integration by “OR” logic, as follows: “A successful stimulus need possess just one of the requisite features, and the form selectivities need not be concordant. This may seem a bizarre conception, but it may be closer to the experimental picture: the paper illustrates a superficial V2 neurone driven by either (a) rightward motion of an achromatic slit, or (b) flashed presentation of a relatively large, blue spot; the effect of amalgamating these features in a single stimulus is not mentioned (Tamura et al., 1996). It is perplexing to think how the ambiguity inherent in the signal from this cell might be interpreted by higher visual centres.” The chapter of Miller (Wheels within wheels) proposes that interplay between laminae II/III and V is involved in the functioning of cell assemblies. The role proposed for lamina V cells, as a primer for many assemblies, themselves located mainly in superficial laminae may correspond to what Shipp refers to as “OR” logic. From the cell assembly viewpoint it is not at all puzzling, though it may be if one imagines that single neurones are, in themselves, the vehicle for information representation. However, it is noted that Shipp places the “OR” logic cells in superficial laminae which is different from the hypothesis in “Wheels within wheels”. Shipp was invited for his comments. Shipp: My interpretation of the dual colour and motion selective cells (DCMS cell for short) was influenced by the ideas of Wolf Singer and colleagues involving bindingby-synchrony (Singer and Gray, 1995; Singer, 1998). However, having read your account I can see how the data also fits your perspective. The “AND” logic seems to be required for a DCMS cell that might be signalling a feature conjunction to a higher area. However our data suggests that colour cells in thick stripes of V2 are rarest in lamina III, which houses most of the cells projecting to higher areas, and where directionally-tuned cells are most common. Hence some other explanation for a DCMS cell is required. “OR” logic seems to fit the bill for a cell whose role is to link up a synchronised assembly of direction-specific cells and of colour specific cells (located external to thick stripes) that are all responding to the same object. The effect of synchrony is to let this assembly win a competition with other potential assemblies, responding to other coloured moving objects (perhaps corresponding to selective attention to the object in question). If I’m reading you correctly, your proposal is more to do with how any assembly gets ignited and keeps going, rather than with which one gets ignited. However, the DCMS cell
© 2002 Taylor & Francis
476
Robert Miller and Almut Schüz
is like the proposed lamina V cell with regard to receiving more indiscriminate input, resulting in its dual selectivity. Moreover, although there is, as far as I know, little knowledge about such a cell’s resting threshold, the “OR” property means that either colour or motion inputs acting alone can tip it over threshold. Thus, I can agree that a DCMS cell in some senses fits your idea about recruiting additional neurones into an active assembly, allowing it to achieve a critical mass. Although I didn’t emphasise it in my chapter, our V2 data does suggest that lamina V is less selective than lamina III. Our actual conclusion is that the distinctiveness of stripes (i.e. thick vs thin vs interstripes, measured in terms of % selectivity for direction, orientation, colour or length) is maximal in lamina III, and declines both in deeper layers, and more superficially. In our data, lamina VI is where stripes are least distinct; lamina II and V are intermediate, and lamina IIIB is where they are most distinct. Thinking liberally, I like to picture the less selective neurones as part of a non-committed population that the more committed neurones compete to recruit. (Apart from the outer layers of V2 [i.e. layers 2, 5 and 6], the non-committed neurones could be in the pulvinar, and/or the superior colliculus, or caudate nucleus or other structures.) The committed neurones need support from the non-committed neurones in order to sustain long-lasting activity of the kind that results in conscious awareness. Thus, the noncommitted neurones act as a limited central resource, equating to the restricted capacity of the focus of attention. Editors: That seems reminiscent of the relationship proposed between neurones in lamina II/III (“committed”) and lamina V (“uncommitted”). Shipp: This brings me to the reference in the article “Wheels within wheels” to corticothalamic connections. I have just completed a study of connections from cortical areas V4 and V5 to the pulvinar. The two areas maintain almost separate pulvinar fields. However, in our material, their mutual cortico-cortical connections are also meagre to non-existent. By contrast, both areas have strong connections with V3, and the pulvinar fields of both V4 and V5 overlap with that of V3. Hence, further evidence is provided that the pulvinar interlinks cortical zones that are themselves interconnected, as your article assumes. Furthermore, analysis of past literature makes it clear that the same is true for the whole string of areas constituting the ventral pathway (V1, V2, V4, TEO, TE).
7. DEVELOPMENTAL FORCES DETERMINING GYRIFICATION, AND CYTOARCHITECTONIC DIFFERENCES Editors: Rademacher introduced questions about the process of gyrification, including the following: “ . . . one may postulate that an ontogenetic process exists that can produce profound morphological shifts as determined by random environmental factors without much genetic change.” We asked if it could be that the nongenetic part of the determination of gyral and sulcal patterns (such as determines the difference between monozygotic twins) is actually just random (a purely statistical difference, emerging as part of a complex developmental process), rather than a random environmental effect? Rademacher: I agree with that suggestion.
© 2002 Taylor & Francis
Discussion Section
477
Miller: Rademacher also mentioned the “tension model of gyrus formation”, based on minimizing axonal length. This has much to recommend it. However, there is a possibility that conduction time in axons has an important significance in the cybernetic operations carried out by cortical tissue (e.g. Miller, 1991, 1996). Conduction time is determined by both axon calibre and axonal length. Thus a general rule that the gyri form to minimise total axon length may limit the available range of axonal conduction times. Indeed there are some axonal bundles (such as the fornix, or the arcuate fasciculus) which appear to take deliberately circuitous routes, as if to increase total axonal length and therefore conduction time. If these arguments are correct, the third alternative proposed by Rademacher for gyrus formation (a complex process of “gyrification”, the resultant of many subsidiary developmental processes) may sometimes apply. We invited his comments. Rademacher: As discussed in the main text, according to the “tension model”, minimizing axonal length is a fundamental process in gyrus formation. However, this may not be the only force which drives the final shaping of the convoluted brain. Since axon calibre, axonal length, and degree of myelination put structural constraints onto functional measures such as the computational speed of network processes, application of the tension model might limit the range of possible axonal conduction times (Miller, 1991, 1996). The latter may represent an important parameter conveying relevant information in the “fourth dimension”. Information theory surely provides a theoretical framework for addressing this question concerning the detailed nature of neural codes. Anatomical information also suggests that there is indeed a possibility that conduction time in axons plays a role. The fundamental question is whether such a mechanism limits a general principle supposed to regulate the range of fibre tract length; or, alternatively the relation between structure and “time” may be hardwired exclusively in distinct “centres” or networks, which analyse this modality specifically, by analogy with other cognitive operations such as language processing. It has been shown recently that there may be something like an “internal clock” in a distributed network, composed of the dorsolateral prefrontal cortex, the posterior part of the inferior parietal cortex, the posterior cingulate cortex and the basal ganglia (Onoe et al., 2001). How this system accomplishes a temporal monitoring process in time perception is not known to date. However, I would not expect that a flexible neural system could be hardwired to the degree that time is translated in a linear fashion into axonal length. Neuronal firing rate and interspike interval distribution provide patterned information which can be represented on much more variable time scales than the distribution of axonal length could ever permit. It is also known that neurones can modify their sensitivity towards incoming (afferent) signals which suggests that control strategies may change for different tasks. Thus, I would rather imagine some kind of gating mechanism, as has been described for the thalamus, as a relay centre controlling transformation from temporal to rate coding (Ahissar et al., 2000), with the hippocampus as a phase and interval comparator for oscillating signals (Glassman, 2000). The climbing fibre system of the cerebellum appears also to be involved with timing. Some insight into precisely how such timing processes may work is available for the primate visual system (Victor, 2000). In the human auditory system, the temporal “envelope” of sound is processed by distinct and hierarchically organized series of neural substrates (i.e. superior olivary complex, inferior colliculus, medial geniculate body and Heschl’s gyrus) (Giraud et al., 2000).
© 2002 Taylor & Francis
478
Robert Miller and Almut Schüz
Schüz: I agree that long conduction times play an important role for cortical functions and that they do exist (Miller, 1975). In spite of this, the tension model (van Essen, 1997) seems plausible to me. Gyrencephaly is a feature of large brains, so their sheer size may give enough room for long conduction times without any detours. Large brains may even run into trouble with respect to short conduction times: in order to make the human brain as fast as a small brain (by using thicker axons), one would have to blow it up to an impossible size (Ringo et al., 1994). Indeed, in large brains, only some axons seem to compensate for the longer conduction times by a correspondingly larger thickness (Jerison, 1991; Schüz and Preißl, 1996). Also, the system of long-range connections is arranged as if the cortex would strive to save axonal length (Scannell et al., 1995; Young, this Volume; Schüz and Braitenberg, this volume). Editors: In the cross-modal primary auditory cortex A1 (studied by Pallas), which has acquired some of the properties of the visual cortex, we were interested whether there is any evidence for the existence of cytochrome oxidase “blobs” typical of visual cortex, or for radial arrangement of columns of orientation selectivity? We ask this since a simple intracortical mechanism has been postulated which explains many of the features typical for the primary visual cortex, in particular orientation selectivity, and the radial arrangement of orientation columns (Braitenberg and Braitenberg, 1979; Braitenberg, 1985, 1992): inhibitory centres which—in primates—should be located in the cytochrome oxidase blobs. Pallas: Cytochrome oxidase is an activity marker, and is found preferentially in cells whose metabolic activity is high. The study of its distribution is thus a very indirect way of inferring the organization of functional pathways. I am not aware of any study reporting on cytochrome oxidase blobs or stripes in ferrets, rewired or otherwise. Cytochrome oxidase blobs have been well-described in primates, but until 1995 had not been found in other mammals. In 1995, Murphy et al. (1995) demonstrated blobs thought to be related to ocular dominance columns in cat primary visual cortex. Boyd and Matsubara (1996) showed that the blobs in cats were co-extensive with patches of inputs from the C-layers of the dLGN, indicating an origin from W and especially Y pathways. The work of Shoham et al. (1997) supports this interpretation. Although blobs have not been reported, eye-specific fields have been reported in cross-modal ferret MGN by Angelic et al. (1997). Our unpublished data suggest a related pattern in A1. Charm et al. (2000) have recently demonstrated “pinwheel” type radial arrangements of neurones with differing orientation tuning characteristics in cross-modal ferrets. They also show physiologically that similarly tuned neurones are interconnected, supporting our anatomical data on horizontal connections. These findings together show that modular organization, typical of visual cortex, can apparently be induced by early visual input in a foreign cortical region. Editors: Pallas also mentions the work of Dehay et al. This was also referred to in another chapter (Rademacher), as showing that early bilateral enucleation leads to reduction in the size of area 17 and corresponding increase in the size of area 18. We were interested whether the distinctive features of primary sensory cortex (especially the distinctive lamina IV) are dictated by patterned sensory input, with the neighbourhood relations possible in a one- or two-dimensional sensory surface.
© 2002 Taylor & Francis
Discussion Section
479
Pallas: The laboratories of Rakic and Kennedy argued some time ago about whether or not bilateral enucleation in foetal macaques could result in the creation of a new occipital cortical area, or an expanded area 18. (They agreed however that enucleation results in a smaller area 17) (Rakic, 1988; Dehay et al., 1991, 1996; Rakic et al., 1991). It is my opinion that this question remains unresolved, because the enucleation itself makes it difficult to define cortical areal borders. Bilateral enucleation is a severe deafferentation that causes massive cell death in the lateral geniculate nucleus when done early in development. Death of LGN cells leads to substantial excess cell death in primary visual cortex, particularly in layer 4 (Windrem and Finlay, 1991), in part because thalamic axons from other parts of thalamus tend not to sprout and innervate denervated cortex (Miller et al., 1991). Finlay and Slattery (1983) reported that the number of layer 4 cells in different cortical areas is inversely related to the amount of thalamo-cortical input, the area receives during normal development. This does not necessarily imply that patterned sensory input is necessary for creation of a distinctive layer 4, only that physical input of some sort is required. It is not surprising that bilateral enucleation of foetal macaques would lead to striking changes in visual cortical cytoarchitecture (such as the loss of layer 4, the primary identifying characteristic of area 17), and a concomitant shrinkage in its area, leading to changes in those features of extrastriate cortex dependent on input from 17. Ablation of one cortical region is also well-known to cause changes in the typical convolutions of neighbouring regions, as they expand to fill the empty space. Without layer 4, it is difficult to define the border between areas 17 and 18 precisely. Although it is possible that enucleation creates a new cortical area, it does not seem to be the most parsimonious explanation. This could be clarified by injecting tracers in, or recording from, the “area X” of Rakic (or the expanded “area 18”), but to my knowledge this has not been done. Editors: We were interested in whether the transmutability of primary sensory areas, shown well by Pallas, could also involve non-sensory areas. Could a non-sensory area be converted into a sensory area (with its distinctive lamina IV) or vice versa? In terms of an actual experiment, would it (for instance) be possible to persuade the mammilothalamic tract to innervate a sensory thalamic nucleus, and observe the effects on the cortical projection region? Pallas: As mentioned above, sensory cortex is typified by a thick layer 4. If non-sensory cortex received heavy thalamic input, it is conceivable, from what we know now, that a thick layer 4 would appear. Whether this would convert non-sensory to sensory cortex or vice versa is unknown but seems possible. The actual reason that we can “persuade” the retina to invade the medial geniculate nucleus is quite fortuitous: the optic tract passes by MGN on its way to its normal targets and presumably can read signals emanating from the deafferented MGN in cross-modal animals. Only if the mammilothalamic tract normally passed close to sensory thalamus would innervation be likely. Editors: Given that the thickness of lamina IV appears not to be a result of differential cell loss during development (Finlay and Slattery, 1983), and since rearing in total darkness does not affect neurone numbers in lamina IV (Borges and Berry, 1978; Gabbott et al., 1986), it is possible that the presence of a thick lamina IV in sensory regions reflects a pre-determined developmental program.
© 2002 Taylor & Francis
480
Robert Miller and Almut Schüz
Valverde: I entirely agree with the proposal that the specification and number of Layer IV cells in various sensory areas reflects (and depends on) a pre-established developmental program. This leads us directly to consider the paradigm of areal and cellular specification at relatively early stages of embryonic development, which do not depend on the interaction with specific thalamic, or other sensory, inputs. An instructive example is found in our recent studies in the “Pax-6” mutant mouse (Jiménez et al., 2000). For many years, it was considered that the development of the olfactory bulb depends on the arrival of olfactory fibres from the nasal placode. Absence of olfactory receptor cells results in animals that lack olfactory bulbs (as is the case in the homozygous mutant Pax-6 mice). However, we were able to demonstrate the presence in these mutant mice of an olfactory-bulb-like structure inside the brain, containing cells hodologically and phenotypically similar to the mitral cells of the olfactory bulb in wild type animals. This argues in favour of the occurrence of intrinsic subsets of functional domains that exist and develop—albeit abnormally—even when the establishment of the proper afferent connections was lacking. This links beautifully with the suggested existence of a “protomap” in the embryonic cerebral cortex, which may be responsible not only for areal specification but also for defining the identity and number of particular cell groups (Rakic, 1988). In fact, recent gene expression studies have demonstrated that, during embryonic development, cells appear to be committed to become specific phenotypes and layers well before they reach their final position in the cortical mantle (see Donoghue and Rakic, 1999 and references therein). Pallas: Rockel et al. (1980) proposed that the decision for determining between pyramidal or stellate morphology occurs after arrival of thalamic input. If no or little thalamic input arrives, cells which might have been destined to end up in lamina IV, as stellate cells, end up instead as pyramids in lamina II/III, and then they or other cells die off in excess, to produce the thinner cortex seen in non-primary areas. This idea was supported by Windrem and Finlay (1991). They showed that thalamic ablations on P1 produced an increased incidence of cell death in lamina II/III, an absence of lamina IV and its stellate cells, and a reduced thickness of the affected cortex. In relation to Valverde’s comment, the amount of thalamic input can be pre-determined in a sense and can thus determine the cytoarchitecture, but by information extrinsic to cortex (thalamo-cortical axons). There is also data which is relevant here, that lamina IV is twice as thick in binocular cortex as in monocular cortex (Beaulieu and Colonnier, 1983). Also supportive are the multiple studies using the DNA-alkylating agent methylazoxymethanol (MAM) to ablate a subpopulation of cortical precursors. If one kills those that would have given rise to deep layers, the system readjusts and nonetheless produces a cortex with all of its layers (Gillies and Price, 1993), again suggesting that laminar identity and possibly morphology can be regulated. Jacobs: To state the obvious, those of us who work in the cerebral cortex need to remember that the cortex does not exist apart from subcortical structures such as the thalamus, anymore than the brain itself functions independently of the body. As such, ultimate understanding of cortical cytoarchitectonics, hierarchies, and interconnections cannot be obtained without a complete understanding of subcortical contributions to information processing. Editors: We asked Pallas one further question about the processes involved in areal differentiation: is it possible to tell whether the sorts of activity-dependent plasticity
© 2002 Taylor & Francis
Discussion Section
481
which Pallas has shown rely entirely on local influences between cells (e.g. Hebbian processes) or might they involve also action at a distance (e.g. diffusion of chemical markers). Pallas: Our manipulation involves only a change in the spatiotemporal patterning of activity reaching MGN. We did not change the identity of the thalamocortical fibres carrying the patterned activity. If changes in the activity pattern lead to alterations in diffusible markers then yes, action at a distance is possible. At present we do not have data to address this point although we are currently performing experiments that would address it. Recent experiments by the Chapman and Katz labs (Chapman et al., 1999; Crowley and Katz, 1999; Chapman, 2000; Crowley and Katz, 2000) show that much of the initial modular organization of visual cortex in terms of ocular dominance and orientation columns is independent of activity, although activity is required for maintenance and plasticity of modularity.
8. SYNOPSIS OF CYTOARCHITECTONIC DIFFERENCES Editors: From reading several chapters submitted for this book (for example that of Kaas) we obtained the following tentative picture of the cybernetic implications of cytoarchitecture: (i) The basic cytoarchitecture of the cortex is rather uniform, as befits a network for performing association on information which has to be described multidimensionally. There are, admittedly, differences between areas in the width of the dendritic arbor of pyramidal cells as well as in the extent of local axonal ramifications (as documented in the chapters by Jacobs and Scheibel, and Levitt and Lund). These differences are associated with differences in packing density and size of cells, but overall the impact on Nissl cytoarchitecture is not very great. (ii) On the other hand, for primary sensory areas, the information is different, described by two spatial dimensions (or in the auditory cortex by the single dimension of pitch). In these cases much more specialized cybernetic operations may be needed, perhaps for relatively stereotyped pre-processing of sensory information. The cytoarchitectonic features that arise in the sensory parts of the cortex as a result may also be more highly specialized, perhaps with critical involvement of a distinctive lamina IV (packed with spiny stellate cells), and for the visual system several more complex arrangements, including those to process motion. (iii) Apart from these considerations, throughout the cortex, cell size may be determined largely by the length and calibre of axons to be supported. This scheme is perhaps a simple-minded summary of cytoarchitectonics. How much will it explain? Not all, no doubt. There may be developmental (or other) reasons why (for instance) area 4 has a much reduced lamina IV, and much of the prefrontal cortex is the same; why sensory cortex has a granular lamina IV; and why area 3 has very few cells in lamina V, perhaps again for developmental reasons. We invited Kaas to comment. Kaas: I agree with your brief overview.
© 2002 Taylor & Francis
482
Robert Miller and Almut Schüz
9. NEOCORTEX: UNIFORM OR BIPARTITE PROCESSING ALGORITHMS? Editors: Underlying these last questions (especially the second point, above) is another, more fundamental one: to what extent is the cerebral neocortex an organ for analysing the three main sensory inputs (visual, auditory and somatic sense), with the neighbourhood relations possible in a two- or one-dimensional sensory surface? To what extent is it a more generalised information processing organ, which can analyse information even when it does not have this inherent structure? Perhaps the cortex is a mixture of both of these. If so, we ask two questions: How do the special features of the primary sensory areas equip them for analysis of information already organized dimensionally? Are other areas of cortex equipped for a different style of information processing—perhaps an “allpurpose” associative processor, rather than one specialized for one- or two-dimensional arrays? The alternative is that the cortex has a more uniform style, similar across all areas, for its cybernetic operations. In this case, one could envisage that the distinctive lamina IV of primary sensory organs is more of an “amplifier” for specially-important input from the outside world, rather than a lamina with a categorically different cybernetic function. Dinse/Schreiner: In our view, the principles of processing discussed for primary sensory areas hold equally well for all other cortical areas. The fundamental processing step is assumed to consist of a local operation modified (contextually) by long-range connections. The only way to generate a local operation of this type is by combinations of excitatory and inhibitory mechanisms, quite as outlined in our chapter for sensory cortical areas. In this view, this would provide the basis for an uniform manner of processing. In addition, this processing is performed within a 2-dimensional sheet allowing the combination of local operations with a continuous representation of parameters (see also next question), maintaining (locally) neighbourhood relationships. While local processing obeys per se rules of proximity, the 2-dimensional sheet defines proximities of various kinds. For sensory areas, the “representation” of the outside world implicates both 2-dimensionality and proximities within the various types of physical worlds, i.e. dimensions derived by further processing, rather than implicit in spatial two-dimensionality. In this sense this scheme is highly intuitive for early cortical representation, where it is reasonably clear what is represented. However, this scheme has been shown to hold also for intermediate states: perfect examples are the highly specialized and detailed maps as described by Suga et al. (1984) in the bat. Because of the highly specialized ultrasound environment of bats it was possible to deduce, and then to find higher-order maps that contain ordered representations of echo frequency and echo delay. The main problem in higher cortical areas arises from the fact that the relevant parameter spaces are unknown and, in principle, are difficult to deduce. The nature and properties of such parameter spaces can (and need to) be determined to understand cortical processing outside the primary sensory or motor domains. For example, it is likely that there are highly abstract parameter spaces representing a profile of a face in terms of emotional expression. In any case, the basic principle is to compute and assemble behaviourally relevant aspects of proximity, or similarity and dissimilarity in the projected parameter space. This view can easily be extended to incorporate temporal aspects as well. It is conceivable that temporal proximity defines entire feature spaces (For instance, a movement is a complicated space-time pattern; even more complicated space-time or spectraltemporal patterns are possible in the auditory system). As already shown for primary
© 2002 Taylor & Francis
Discussion Section
483
visual cortex, several of such highly abstract parametric maps can be superimposed. Without a priori knowledge, this renders an analysis of what is being represented quite difficult. Editors: In the chapter of Dinse and Schreiner, there are hints of another formulation of this, where they contrast “parametric mapping” with “distributed representation”. Could it be that the former are limited to sensory areas, while the latter are more widely useful throughout the neocortex? Dinse/Schreiner: In our view, the same arguments are valid for the distinction between local and distributed representation of sensory, motor or even conceptional aspects: in principle, parametric mapping is identical to distributed processing. As outlined in our chapter, the difference is mainly methodological, and arises from different reconstruction algorithms (e.g. the optical imaging-based feature maps are just due to an “iceberg effect”, by ignoring 90% of neural activation). We agree, however, that “distributed representation” sounds a bit more general, or to put it the other way round, “parametric-mapping” is more intuitive by directly relating to sensory representations of known physical features. However, as outlined above, in principle we don’t see any systematic and general differences between these concepts. Accordingly, we believe that the concept of “distributed representation” better encapsulates what is going on in the brain, and this concept holds equally in lower (or early) and higher cortical areas. Miller: Closely related to this broad issue, is a much more detailed question. We were interested on the distinctive lamina IV of primary sensory areas of cortex. Usually it is crowded with spiny stellate cells or other pyramid-like cells which have either no apical dendrite, or a much reduced one. This could mean that this lamina is, to a degree, shielded from the full impact of long cortico-cortical input to a region of cortex. An extension of this theme is to suggest that the lamina IV of primary sensory areas is specialized as a preprocessor of sensory thalamic input, to analyse sensory input “as itself”, before that input mingles very much with the rest of the information coursing through the cortical network. Against this idea, Young pointed out the evidence that, even in sensory lamina IV, only about 5% of excitatory synapses are derived from the specific sensory thalamic input, implying that input from other regions, including that from other cortical areas is far more important than thalamic input in this layer. Kaas: Young’s statement is correct, but one has to be careful about what conclusions to draw. The retina provides only a small portion of the synapses in the LGN, but all of the neurones in the LGN depend on this input for activation. Other inputs are modulatory, and many connections are intrinsic. The functional roles as well as the numbers of connections need to be determined. Miller: It seems that several further factors have to be taken into account before one can evaluate the relative importance of specific thalamo-cortical and cortico-cortical inputs to lamina IV of sensory cortex: (i) There is evidence that thalamo-cortical inputs have greater efficacy than corticocortical ones. This conclusion has been reached both from studies in electrophysiological studies in slices (Stratford et al., 1996; Gil et al., 1999) and from
© 2002 Taylor & Francis
484
Robert Miller and Almut Schüz
cross-correlation studies in vivo (Toyama et al., 1981; Tanaka, 1983; Reid and Alonso, 1996) as well as from the anatomical observation that thalamo-cortical afferents tend to be more densely ramified than the axons of pyramidal cells (Braitenberg, 1978). (ii) According to Lund (1979), much of the axonal arborization of spiny stellate neurones in lamina IV of cat visual cortex is distributed horizontally in the same lamina. If this is the case, it might be that many of the excitatory synapses on lamina IV spiny stellates neurones are ones from other similar spiny stellate neurones, rather than from distant cortical areas, or other structures. If this were the case, lamina IV in primary sensory cortex could be specialized as an amplifier of thalamic input. (iii) This suggestion can also be derived from evidence that synapses between neighbouring spiny stellate cells are of specially high efficacy and reliability (Feldmeyer et al., 1999; Tarczy-Hornoch et al., 1999). (iv) Beyond the number and efficacy of synapses, is the actual firing frequency in afferent axons. It is probable that thalamic projection neurones have a substantially higher rate of firing in waking animals than do most cortico-cortical neurones. The data I draw on for thalamic projection neurones are: Steriade and Hobson, (1976); Mukhametov and Rizzolatti (1970); Sakakura (1968); Lamarre et al. (1971); Glenn and Steriade (1982). For cortico-cortical neurones I draw on several papers by Harvey Swadlow in Journal of Neurophysiology, in rabbit. In larger animals there is not much useful data, and what there is from striate cortex, which may not be typical. However, if the thalamic cells really do fire at substantially faster rates than the cortico-cortical ones, the chances of impulses in the several thalamo-cortical inputs to a spiny stellate neurone arriving sufficiently close together in time to summate and produce suprathreshold excitation may be much higher than that for the cortico-cortical inputs. (v) Even beyond primary sensory areas (e.g. in MT/V5 of primate) there is evidence that the “ascending” input from primary sensory areas has an advantage over that from the rest of the cortex. In area MT electronmicroscopy shows that synapses of projections from V1 are exceptionally large, (often completely surrounding the recipient dendritic spines) and are therefore probably of high efficacy (Anderson et al., 1998). There is also evidence that blockade of the magnocellular layers of LGN essentially eliminates driven activity in V5, apart from a few sites where P-driven responses can be detected (Maunsell et al., 1990). Background activity (presumed to be derived from non-sensory inputs) was less than a third of the peak rates of firing in response to a stimulus. Given these factors (for which, I admit, empirical evidence is not always completely compelling), could not the idea that lamina IV of sensory cortex (and perhaps of other regions) is a preprocessor and amplifier of sensory input, be more attractive than might otherwise be implied? We asked several contributors about this issue. Initially we asked Lund for up-to-date views on the extent of mutual connectivity between spiny stellate cells in lamina IV of sensory areas. Lund: It is clear that the spiny stellate neurones in layer 4 make a large contribution to their spiny stellate cell neighbours in the same layer (30% of their excitatory inputs according to Ahmed et al., 1994). However, numerical weight of synapses may be less important than their individual efficacy. Physiologically these lateral connections are less reliable than the thalamic inputs, even though far more numerous, as a drive to the cells.
© 2002 Taylor & Francis
Discussion Section
485
They may depend on timing of other inputs, or withdrawal of inhibition to show their full potential as an efficient drive or modulatory input. These axon terminals have type 1 morphology, and so are presumed to be excitatory. In work by Anderson et al. (1993) filling single neurones, spiny stellate cells in the monkey visual cortex had either extensive lateral intralaminar connections but no rising connections to other layers, or projections to other layers and only local axon arbors in the same layers. However, they had only a few filled examples. Yabuta and Callaway (1998) have fuller observations on the detail of these local versus interlaminar projections. Young: I emphasise the relatively small proportion of synapses in geniculo-recipent layers that are “owned” by geniculate afferents—while Miller emphasises the specialised and effective nature of these inputs. We are both right! These inputs are special, but there are still very many other inputs, arising from internal brain sources, on these cells and their local network neighbours. Our original discussion was in relation to the consequences of these arrangements for “amplification”. I don’t dispute that there is amplification, and indeed I suppose that this is the principal means by which the visual system is oriented computationally to make representations about the outside world. But amplification, in the sense implied by the traditional view, is a multiplication of the input by a gain factor. Maybe the high gain of these special synapses is part of that multiplication. I only note that—probably—the other sources of information washing over these cells from internal sources also play a part in the multiplication, and so in what is represented. In fact, the traditional view (that it’s all “bottom-up”) makes a very much stronger claim than mine: that the 95% of other inputs on these cells don’t have any effect. Seems unlikely! So, I’m presently comfortable with my position on this. Miller: I accept this accommodation of the two viewpoints, especially since there is evidence that inputs to lamina IV from lamina VI (which itself receives many inputs from distant cortical regions) tend to show considerable facilitation with stimuli repeated in close succession (Stratford et al., 1996; Tarczy-Hornoch et al., 1999), apparently an “amplifier” for inputs other than thalamo-cortical ones. To evaluate properly the relative importance of “internal” inputs and thalamo-cortical (“external”) inputs, one needs evidence about whether the prior probability of stimuli (represented by the input from distant regions of cortex) has a decisive role in the firing of lamina IV cells in visual cortex. Shipp: You ask whether the comparative isolation of the magnocellular (M) pathway from LGN to V5 via layers 4Ca and 4B of V1 could be characterised as serving “to analyse sensory input as itself”. There are a number of relevant factors here. Each has its uncertainties, but they combine to weigh against this viewpoint. Firstly, only layer 4C is equivalent to “layer 4” as cited above; layer 4B is probably not layer 4! The terminology is that of Brodmann, and it has stuck, but many who have deliberated over layer terminology in primate V1 prefer to refer to 4B as lower layer 3, i.e. layer IIIc (e.g. Casagrande and Kaas, 1994). Why does this matter? Well, layer 4B is unusual in receiving direct feedback from areas V5 (and from V3 and V2). In other areas (e.g. V2, etc.) such feedback normally avoids layer 4 and is concentrated in layers 1 and 6; it can spread from layer 1 into layers 2 and 3, but it is not focused on layer 3. Layer 4B is thus unusual in receiving highly focused feedback from V5 (that also targets layer 6 but avoids layers 2, 3 and 5 and only involves layer 1 in the peripheral field). The above
© 2002 Taylor & Francis
486
Robert Miller and Almut Schüz
debate concerns the relative efficacy in layer 4 of ascending and intrinsic inputs to neurones. In layer 4B there is the added component of descending input (and note that the ascending input is not thalamic, but is from layer 4Ca). Secondly, the “isolation” of this relay to V5 refers to its domination by magnocellular (M) input, and a relative lack of contamination by other retino-geniculate channels. There are no obvious intrinsic axonal relays of parvocellular (P) or koniocellular (K) inputs into 4B. Some V5-efferent neurones in layer 4B are stellate cells, so they could transmit untainted M signals. However, other V5-efferents are obviously pyramidal, and these demonstrably relay P signals through influences upon their apical dendrite. So here is at least one route of P signals to V5 (see my earlier comments; also see my chapter in this volume “Fundamentals . . . ” for references). P and K signals are mixed together in layers 2 and 3, where M inputs are also substantial, so the (mainly) M output from layer 4B to V5 is purer by comparison. But, obviously, isolation from rival geniculate input channels is not the same thing as isolation from other ongoing cortical processing. Finally, for academic purity, I have to note that the whole notion of magno-based direction processing in the 4B-V5 relay has recently been called into question. Physiological data on the temporal response properties of V1 neurones, allied to motion energy models of directional tuning, imply that direction selectivity depends on an obligatory combination of fast M and slower P inputs, in order to achieve the modelled arrangement of RF subunits in spatial and temporal quadrature (De Valois et al., 2000). This evidence is compelling in isolation, but awkward to reconcile with everything else. Miller: More generally, in my paragraphs above, I raised two possibilities about the distinctive lamina IV for primary sensory cortex, either that it might confer special cybernetic capabilities on sensory cortex, or that it serves as an “amplifier” for input to these regions. In the light of the discussion presented above, I tend to favour the second (less specific) of these hypotheses, as more clearly formulated, and supported by substantial evidence, while the idea of a special cybernetic role, generalized across sensory cortical areas, is difficult to formulate in a testable manner. However, the “amplification” need not apply just to sensory input. This in turn persuades me to think (in agreement with Dinse) of the neocortex as having a rather uniform type of cybernetic operation (though with local quantitative variations), rather than being a mixture of areas with two basically different types of information processing.
10. THE HISTORICAL DEBATE: LOCALIZATION VERSUS DISTRIBUTION OF FUNCTION, AND THE NATURE OF ORGANIC UNITY OF THE CORTEX Editors: The last issue, on which we invited the comments of Shipp and Seitz, was perhaps the underlying focal point for the whole of this book. Shipp wrote, in his introductory part: “localisation can be thought of as segregation, the idea that any structurally (or neurochemically, or genetically) distinct module of tissue is likely to have a corresponding functional identity, a specialisation that distinguishes it from neighbouring structures. To anyone willing to accept the label ‘neuroanatomist’, this notion is probably axiomatic.” Later he wrote: “Clearly, any global theory of ‘how the brain works’ has to match the functions to the anatomical structures”. Nevertheless he softened this view with the
© 2002 Taylor & Francis
Discussion Section
487
following remark: “Segregation and integration are the twin fundamentals of cortical organisation. In an elementary sense, the latter cannot exist without the former. It is easy to cast them as opponent processes—but this, surely, is to miss the evident reality that they must cooperate to subserve the goals of cortical processing.” In similar vein Malcolm Young writes, in his chapter: “V4 and MT exchange quite robust projections, but each is the home of neurones with very different stimulus selectivities.” The balance between segregation and integration of function in the cortex is a difficult, fundamental and profound issue. Obviously there must be some aspects of function which can, in some experimental designs, be correlated with areal differences in architecture, while other functions can only be derived from coordination of several or many areas. Which sorts of function can be matched with morphological areas, and which require integration of many areas? A number of neurologists in the early and middle years of the twentieth century, in several countries (Britain, Germany, France, Ruissia) opposed the implications of localizationist neurologists, and neuroanatomists such as Brodmann. To give a flavour of their way of thinking we quote below from Alexander Luria (The Working Brain, Penguin Books). Luria was impressed by the way in which animals and humans had recourse to many ways of solving a problem, and tended to solve it in different ways at different stages of maturation of their cogntive apparatus. He also emphasised that, in its actual function, the brain was in continual interaction with the external environment (including its social dimension), so that any functional system was not defined simply in the brain, but by continual interplay between the brain and the outer environment. As a result, he writes as follows: “ . . . all mental processes such as perception and memorizing, gnosis and praxis, speech and thinking, writing, reading and arithmetic, cannot be regarded as isolated or even indivisible ‘faculties’, which can be presumed to be the direct ‘function’ of limited cell groups, or to be ‘localized’ in particular areas of the brain . . . . the fundamental forms of conscious activity must be considered as complex functional systems; consequently the basic approach to their ‘localization’ in the cerebral cortex must be radically altered.” “ . . . mental functions . . . cannot be localized in narrow zones of the cortex, or in isolated cell groups, but must be organized in systems of concertedly working zones, each of which performs its role in the complex functional system, and which may be located in completely different and often far distant areas of the brain.” “It is accordingly our fundamental task not to ‘localize’ higher human psychological processes in limited areas of the cortex, but to ascertain by careful analysis which groups of concertedly working zones of the brain are responsible for the performance of complex mental activity; what contribution is made by each of these zones to the complex functional system; and how the relationship between these concertedly working parts of the brain in the performance of complex mental activity changes in the various stages of development.” [Luria’s italics, in the above quotations] We suspected that Shipp would not disagree with any of these remarks, and some of the modern proponents of functional imaging are certainly looking for the “contribution . . .
© 2002 Taylor & Francis
488
Robert Miller and Almut Schüz
made by each of these zones to the complex functional system.” We invited Shipp to comment on these quotations. Shipp: Quite so! As you predict, there is nothing in Luria’s comments to reject. They seem both accurately and expressively worded. Let me return to the introductory remark in my chapter that a global brain theory needs to match functions to structures (the “matching problem”). The list of structures (at a cortical level) is complete at the level of lobes, approaching completion at the level of areas (less so in humans than monkeys), but far from complete in details of sub-areal modules. The list of functions (“faculties” is a preferable term) tends to be one of everyday abilities, which we all understand intuitively, but which cognitive scientists (including psychophysicists, clinicians, etc.) have the task of systematising and “anatomising”, to give the everyday terms a workable scientific definition. The problem here is an inescapable one: Any function that is defined on a cognitive, or perceptual basis is almost certainly going to derive from a distributed brain system (as pointed out by Luria). This is as true for say, “colour vision”, as it is for reading or memory. So the function of individual lobes, areas modules (etc.) will never be perfectly summarised in these terms. At this point, it is vital (if also dull and pedantic) to avoid purely semantic confusions. So, for example, a statement like “V1 blobs are specialised for colour vision” has to be understood not to mean: (a) that other parts of the brain (e.g. thin stripes in V2), or other parts of V1 (e.g. layer 4Cb), are not also involved in colour vision; (b) that blobs are not also involved in a function other than colour vision (e.g. perhaps the signals they process also contribute, eventually, to something like “shape from shading”). Such qualifications (i.e. clarifying the usage of the term “specialisation”) apply even more strongly to the equally true statement “area V4 is specialised for colour vision”. In other words, the term “specialisation” is consistent with (does not necessarily exclude) the fact that the neural basis of cognitive functions may be distributed, although clearly the operative nub of the term is that the distribution is not pluripotent (i.e. some areas are more equal than others). Now let us view the matching problem from the opposite perspective. It is incontrovertible that different lobes (or areas or modules) have different physiology, different response properties and different neural wiring. Hence they cannot have identical functions. This is such a trite truism that it barely merits printing, except insofar as it leads to the following: if a function is to be satisfactorily localised to an individual structure, it must be described or defined in neural terms. Evidently this is largely impracticable at present, because neural processing is so incompletely understood. However, I try to give an example. Following David Marr, it is useful to think of neural processing at three different levels: a computational goal, an algorithm, and a hardware (i.e. neural) implementation of the algorithm. So, “colour vision” involves a number of abilities, one of which is the recognition of the same surface hue under variant illumination (“colour constancy”). The computational goal for colour constancy is to retrieve the spectral reflectance function of a surface. Algorithms for colour constancy have been devised, initially by Edwin Land, in which the first stage is a local centre-surround “differencing” operation, where centre and surround operate on the same part of the spectrum. Physiologically, this equates to the classical definition of a “double-opponent cell” found in goldfish retina and, to some extent in V1 blobs. Hence, this might be mooted as a function
© 2002 Taylor & Francis
Discussion Section
489
localised to V1 blobs—except that double opponent cells are also found elsewhere in primate brains (e.g. V2), so the ultimate definition of blob function has to be yet further specified. Further, and importantly, the end-product of this function may be not only colour constancy; once established, evolution may find another use for it. Seitz: I agree with the contention that conscious activity must be considered as a unitary functional system of the human brain. Conscious activity represents a state of complex human brain functions which cannot be subdivided into a diversity of different fundamental subfunctions. Nonetheless, attention provides a window limiting the amount of information that can be processed consciously in the human brain, at a certain point in time, this being operative within a predefined capacity. Thus, conscious activity accommodates graded levels of awareness for executive, perceptive and cognitive processes. Over time, these processes are channelled into foci of attention, depending on the situational circumstances. I agree with the idea that mental functions are organized in systems of concertedly working zones, each of which performs its role in complex functional subsystems. These systems include the different sensory modalities providing the brain with external information, the motor system allowing for physical activity, and the so-called higher cortical areas. Both unity and diversity of mental functions can be appreciated, for example, in object exploration. In this activity, the subject performing exploratory movements perceives the object’s characteristics, and constructs a mental image of the manipulated object, but does not notice the exploratory movements themselves; nor can he describe the finger movements performed. The diversity is conveyed by the involvement of the various subsystems described, while the unity is reflected by the top-down combination, in concert, of the exploratory movements that sample the somatosensory information optimally in terms of completeness and speed. The question of how the different areas of the cortex can have diverse functions, and yet are part of a united and unified cortical structure seems to me to reflect the level of observation rather than to be a contradiction. One obvious reason is that what appears unified in terms of histology proves to be quite heterogenous in terms of quantitative cytology, neuroreceptor distribution, interareal connectivity, and neuroelectric properties, as reflected, for example, by the receptive or executive field size or cortical columns. The question to be resolved, however, is how the different modalities of highly differentiated information processing are tied together to a conscious percept, and to the intimately linked selfexperience of the subject. It is the aim of human systems physiology to disentangle the contribution which is made by each of these zones to the complex functional system, and to come up with refined methodology to address the question of conscious behavior. Historically, the first approach to be adopted was the study of neurological patients by correlating brain lesions with clinical deficits, as well as by describing electrical stimulation effects during open brain surgery. The advent of computer technology provided means to design non-invasive neuroimaging and electrophysiological methods which allow investigators to unravel the temporal evolution and to identify the distributed topographic representations of intact human brain function. These tools bear the promise of also elucidating the processes mediating conscious information processing. We have learnt from neurological “syndromology” that circumscribed brain lesions induce partial deficits usually involving some specific functions, while other functions are preserved. Examples are hemineglect and anosognosia, conditions in which perception of
© 2002 Taylor & Francis
490
Robert Miller and Almut Schüz
the extrapersonal space, or of the personal “state” is impaired in the presence of an otherwise intact perception. However, often there is no specificity of the clinical deficits relating to a unique lesion topography. Thus, similar clinical symptoms may originate from a variety of lesion locations. For example, there is the “alien hand syndrome”. This condition renders a limb—and preferentially the hand—alien to the patient, such that he perceives a touch as foreign, and he does not know how to use the limb as his “agent”. The underlying lesions may be located in the fronto-mesial region but also in posterior parietal cortex. Conversely, patients may suffer from hallucinations or illusions, such that they perceive non-realistic events in the absence of objective phenomena, with these experiences often limited to one modality. Furthermore, in amputees, phantom limb sensations may fool the patients such that they have the feeling of a still-existing limb. Thus, damage or abnormalities of the nervous system may not only induce drop-outs from the entire repertoire of possible brain functions, but may also stimulate over-activity resulting in misperception. A further interesting aspect of brain lesions is that they may be “silent” when they are small and limited to the cortex. However, they become clinically apparent as soon as they extend over a certain cortical area, and into the underlying white matter. In these situations, damage is not restricted to the resident cortical neurones, but also affects, in addition, the descending efferent projections to the different subcortical relay nuclei including the cerebellum, the corticopetal afferents, and the cortico-cortical connections. In general, clinically apparent brain lesions damage a portion of the entire neuronal apparatus and its connections to other cortical areas and subcortical structures. Conversely, the size of the neuroreceptive or neuroexecutive fields, as well as the locally-inherent connectivity patterns, determine whether a lesion of a given volume becomes clinically apparent. Further, recent evidence suggests that the wealth of parallel projection systems in the human brain plays an important role in the cerebral reorganization mediating recovery from acute neurological deficits. In consequence, brain diseases exemplify the diversity of brain function. Activation studies appear to supplement lesion studies, in that single areas, or a few brain areas are activated in brain activation tasks. However, these methods of measuring brain activity have been operationalized such that they are based on mathematical operations including psychophysical task subtractions, signal averaging and multidimensional data processing. Topographic information is inherent in these measuring devices, it being given by the number of recording units in EEG or MEG, or by the pixel matrix in tomographic imaging representing the elements of maximal resolution. Results show that the more specific the hypothesized difference between the task and control conditions, the fewer and the more circumscribed the number of haemodynamic or metabolic activation areas. Similarly, dipole calculations pinpoint activity-locked, bioelectric changes to cortical foci at a certain time point following processing onset. At first glance, these data support the focussed interest in the diversity of brain function. It should be emphasized, however, that the unsubtracted tomographic images and the raw bioelectric recordings show that each experimental state shows some activity changes throughout the entire brain, reflecting the complexity and unity of the working human brain. Editors: How can the different areas of the cortex have diverse functions, and yet be part of a united and unified cortical structure? There are similar debates in other areas of brain research. For example, for Hebb’s cells assemblies, one can ask how the component
© 2002 Taylor & Francis
Discussion Section
491
neurones of an assembly can individually have different response properties, while at the same time (when they are activated together as part of a functional whole) the assembly can have its own unique and more sophisticated representation of information. Likewise, one can ask how left and right cerebral hemispheres can, at the same time, have very different capabilities, and be part of a unified forebrain. These are perhaps examples of a recurring problem in biology, concerning the nature of the organic unity of living things. A major source of confusion when trying to analyse these questions is that in the same sentence, in the same breath, it is very easy to juxtapose assumptions referring to two quite different levels of organization: the level in which one seeks exact correlations between structure and function within an organism, and the level of the function of a whole organism. The very word “function” is itself highly ambiguous, since it is so easily used without specifying whether it refers to cybernetic function, or to the functional role for an intact organism. Probably it is impossible to construct arguments by which the latter can be derived from the former. The insoluble “three-body problem”, forerunner of chaos theory (Peterson, 1993), seems elementary by comparison! Even so, both are legitimate and important objectives for study. Despite this, it may be that cerebral localization of function can be exactly defined only in terms of cybernetic function; or as Shipp writes: “if a function is to be satisfactorily localised to an individual structure, it must be described or defined in neural terms”.
REFERENCES Abeles, M. (1982) Local cortical circuits. (Studies in Brain Function, No. 6). Springer, Heidelberg. Ahissar, E., Sosnik, R. and Haidarliu, S. (2000) Transformation from temporal to rate coding in a somatosensory thalamocortical pathway. Nature (London), 406, 302–326. Ahmed, B., Anderson, J.C., Martin, K.A.C. and Nelson, J.C. (1994) Polyneuronal innervation of spiny stellate neurons in cat visual cortex. Journal of Comprative Neurology, 341, 39–49. Amir, Y., Harel, M. and Malach, R. (1993) Cortical hierarchy reflected in the organization of intrinsic connections in macaque monkey visual cortex. Journal of Comparative Neurology, 334, 19–46. Amunts, K., Schleicher, A., Bürgel, U., Mohlberg, H., Uylings, H.B.M. and Zilles, K. (1999) Broca’s region revisited: Cytoarchitecture and intersubject variability. Journal of Comparative Neurology, 412, 319–341. Amunts, K., Malikovic, A., Mohlberg, H., Schormann, T. and Zilles, K. (2000) Brodmann’s areas 17 and 18 brought into stereotaxic space—where and how variable? Neuroimage, 11, 66–84. Anderson, J.C., Martin, K.A.C. and Whitteridge, D. (1993) Form, function, and intracortical projections of neurons in the striate cortex of the monkey Macacus nemestrinus. Cerebral Cortex, 3, 412–420. Anderson, J.C., Binzegger, T., Martin, K.A.C. and Rockland, K.S. (1998) The connection from cortical area of V1 to V5: a light and electron microscopic study. Journal of Neuroscience, 18, 10525–10540. Angelucci, A., Clascá, F., Bricolo, E., Cramer, K.S. and Sur, M. (1997). Experimentally induced retinal projections to the ferret auditory thalamus: Development of clustered eye-specific patterns in a novel target. Journal of Neuroscience, 17, 2040–2055. Barone, P., Hilgetag, C.-C., Kennedy, H. and Young, M.P. (2000) A determinate hierarchy for macaque visual cortex derived from quantitative laminar data. Society for Neuroscience Abstracts, 26, 449.17. Beaulieu, C. and Colonnier, M. (1983) The number of neurons in the different laminae of the binocular and monocular regions of area 17 in the cat. Journal of Comparative Neurology, 217, 337–344. Bok, S.T. (1959) Histonomy of the cerebral cortex. Princeton, New Jersey, Van Nostrand-Reinhold. Borges, S. and Berry, M. (1978) The effects of dark rearing on the development of the visual cortex of the rat. Journal of Comparative Neurology, 180, 277–300. Boyd, J.D. and Matsubara, J.A. (1996). Laminar and columnar patterns of geniculocortical projections in the cat: Relationship to cytochrome oxidase. Journal of Comparative Neurology, 365, 659–682. Braitenberg (1978) Cortical architectonics: general and areal. In: M.A.B. Brazier and H. Petsche (eds), Architectonics of the cerebral cortex. New York: Raven Press, pp. 443–465. Braitenberg, V. and Braitenberg, C. (1979) Geometry of orientation columns in the visual cortex. Biological Cybernetics, 33, 179–186.
© 2002 Taylor & Francis
492
Robert Miller and Almut Schüz
Braitenberg, V. (1985) Charting the visual cortex. In: A. Peters and E.G. Jones (eds), Cerebral Cortex, Vol. 3, (Visual Cortex). New York, London: Plenum Publishing Corp, pp. 374–414. Braitenberg, V. (1992) How ideas survive evidence to the contrary: a comment on data display and modelling. In: A. Aertsen and V. Braitenberg (eds), Information processing in the cortex, experiments and theory. Berlin, Heidelberg, New York: Springer, pp. 447–450. Braitenberg, V. and Schüz, A. (1998) Cortex: Statistics and Geometry of Neuronal Connectivity. (Revised edition of Anatomy of the Cortex. Statistics and Geometry, 1991). Berlin, Heidelberg, New York: Springer. Bringuier, V., Chavane, F., Glaeser, L. and Fregnac, Y. (1999) Horizontal propagation of visual activity in the synaptic integration field of area 17 neurons. Science (New York), 283, 695–699. Bullier, J. and Nowak, L.G. (1995) Parallel versus serial processing: new vistas on the distributed organization of the visual system. Current Opinion in Neurobiology, 5, 497–503. Carandini, M., Mechler, F., Leonard, C.S. and Movshon, J.A. (1996) Spike train encoding by regular-spiking cells of the visual cortex. Journal of Neurophysiology, 76, 3425–3441. Casagrande, V.A. and Kaas, J.H. (1994) The afferent, intrinsic and efferent connections of primary visual cortex in primates. In: A. Peters and K.S. Rockland (eds), Primary Visual Cortex in Primates. New York: Plenum Press, pp. 210–259. Chapman, B. (2000) Necessity for afferent activity to maintain eye-specific segregation in ferret lateral geniculate nucleus. Science (New York), 287, 2479–2482. Chapman, B., Godecke, I. and Bonhoeffer, T. (1999) Development of Orientation Preference in the Mammalian Visual Cortex. Journal of Neurobiology, 41, 18–24. Chugani, H.T., Phelps, M.E. and Mazziotta, J.C. (1987) Positron emission tomography study of human brain functional development. Annals of Neurology, 22, 487–497. Clarke, S. (1993) Callosal connections and functional subdivision of the human occipital lobe. In: B. Gulyas, D. Ottoson and P. Roland (eds), Functional organization of the human visual cortex. Oxford: Pergamon Press, pp. 137–149. Crowley, J.C. and Katz, L.C. (1999) Development of ocular dominance columns in the absence of retinal input. Nature, Neuroscience, 2, 1125–1130. Crowley, J.C. and Katz, L.C. (2000) Early development of ocular dominance columns. Science (New York), 290, 1321–1324. Dehay, C., Giroud, P., Berland, M., Killackey, H. and Kennedy, H. (1996) Contribution of thalamic input to the specification of cytoarchitectonic cortical fields in the primate: Effects of bilateral enucleation in the fetal monkey on the boundaries, dimensions, and gyrification of striate and extrastriate cortex. Journal of Comparative Neurology, 367, 70–89. Dehay, C., Horsburgh, G., Berland, M., Killackey, H. and Kennedy, H. (1991) The effects of bilateral enucleation in the primate fetus on the parcellation of visual cortex. Developmental Brain Research, 62, 137–141. De Valois, R.L., Cottaris, N.P., Mahon, L.E., Elfar, S.D. and Wilson, J.A. (2000) Spatial and temporal receptive fields of geniculate and cortical cells and directional selectivity. Vision Research, 40, 3685–3702. DeYoe, E.A., Hockfield, S., Garren, H. and Van Essen, D.C. (1990) Antibody labelling of functional subdivisions in visual cortex: cat-301 immunoreactivity in striate and extrastriate cortex of the macaque monkey. Visual Neuroscience, 5, 67–81. Elston, G.N. and Rosa, M.G.P. (1998) Morphological variation of layer III pyramidal neurones in the occipitotemporal pathway of the macaque monkey visual cortex. Cerebral Cortex, 8, 278–294. Fairén, A. and Valverde, F. (1980) A specialized type of neuron in the visual cortex of cat: A Golgi and electron microscope study of chandelier cells. Journal of Comparative Neurology, 194, 761–779. Feldmeyer, D., Egger, V., Lubke, J. and Sakmann, B. (1999) Reliable synaptic connections between pairs of excitatory layer 4 neurones within a singel “barrel” of developing rat somatosensory cortex. Journal of Physiology, 521, 169–190. Felleman, D.J. and Van Essen, D.C. (1991) Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1–47. Finlay, B.L and Slattery, M. (1983) Local differences in the amount of early cell death in neocortex predict adult local specializations. Science (New York), 219, 1349–1351. Gabbott, P.L.A., Stewart, M.G. and Rose, S.P.R. (1986) The quantitative effects of dark-rearing and light exposure on the laminar composition and depth distribution of neurons and glia in the visual cortex (area 17) of the rat. Experimental Brain Research, 64, 225–232. Gil, Z., Connors, B.W. and Amitai, Y. (1999) Efficacy of thalamocortical and intracortical synaptic connections: quanta, innervation and reliability. Neuron, 23, 385–397. Gilbert, C.D. and Wiesel, T.N. (1989) Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex. Journal of Neuroscience, 9, 2432–2442. Gillies, K. and Price, D.J. (1993) The fates of cells in the developing cerebral cortex of normal and methylazoxymethanol acetate-lesioned mice. European Journal of Neuroscience, 5, 73–84.
© 2002 Taylor & Francis
Discussion Section
493
Glassman, R.B. (2000) A “theory of relativity” for cognitive elasticity of time and modality dimensions supporting constant working memory capacity: involvement of harmonics among ultradian clocks? Progress in Neuropsychopharmacology and Biological Psychiatry, 24, 163–182. Giraud, A.L., Lorenzi, C., Ashburner, J., Wable, J., Johnsrude, I., Frackowiak, R. and Kleinschmidt, A. (2000) Representation of the temporal envelope of sounds in the human brain. Journal of Neurophysiology, 84, 1588–1598. Glenn, L.L. and Steriade, M. (1982) Discharge rate and excitability of cortically projecting intralaminar thalamic neurons during waking and sleep states. Journal of Neuroscience, 2, 1387–1404. Glezer, I.I., Jacobs, M.S. and Morgane, P.J. (1988) Implications of the “initial brain” concept for brain evolution in Cetacea. Behavioral and Brain Sciences, 11, 75–116. Goldman-Rakic, P.S. and Schwartz, M.L. (1982) Interdigitation of contralateral and ipsilateral columnar projections to frontal association cortex in primates. Science (Washington), 216, 755–757. Gonzalez-Burgos, G., Barrionuevo, G. and Lewis, D.A. (2000) Horizontal synaptic connections in monkey prefrontal cortex: an in vitro electrophysiological study. Cerebral Cortex, 10, 82–92. Horner, C.H. and Arbuthnott, E. (1991) Methods of estimation of spine density: are spines evenly distributed throughout the dendritic field? Journal of Anatomy, 177, 179–184. Jain, N., Catania, K.C. and Kaas, J.H. (1998) A histologically visibled representation of the fingers and palm in primate area 3b and its immutability following long-term deafferentations. Cerebral Cortex, 8, 227–236. Jerison, H. (1991) Brain size and the evolution of mind. 59th James Arthur Lecture. New York: American Museum of Natural History. Jiménez, D., García, C., De Castro, F., Chédotal, A., Sotelo, C., De Carlos, J.A., Valverde, F. and LópezMascaraque, L. (2000) Evidence for intrinsic development of olfactory structures in Pax-6 mutant mice. Journal of Comparative Neurology, 428, 511–526. Kaas, J.H. (1987) The organization of neocortex in mammals: implications for theories of brain function. Annual Review of Psychology, 38, 129–152. Lamarre, Y., Filion, M. and Cordeau, J.P. (1971) Neuronal discharges of the ventrolateral nucleus of the thalamus during sleep and wakefulness in the cat. 1: Spontaneous activity. Experimental Brain Research, 12, 480–498. Levitt, J.B., Kiper, D.C. and Movshon J.A. (1994) Receptive fields and functional architecture of macaque V2. Journal of Neurophysiology, 71, 2517–2542. Lindsay, R.D. and Scheibel, A.B. (1981) Quantitative analysis of the dendritic branching pattern of granule cells from adult rat dentate gyrus. Experimental Neurology, 73, 286–297. Lorente de Nó, R. (1949) Cerebral cortex: Architecture, intracortical connections, motor projections. In Fulton’s Physiology of the Nervous System. London: Oxford University Press, pp. 288–313. Lu, Z.L., Lesmes, L.A. and Sperling, G. (1999) The mechanism of isoluminant chromatic motion perception. Proceedings of the National Academy of Sciences, U.S.A., 96, 8289–8294. Lu, Z.L. and Sperling, G. (1995) The functional architecture of human visual motion perception. Vision Research, 35, 2697–2722. Lund, J.S., Henry, G.H., Macqueeen, C.L. and Harvey, A.R. (1979) Anatomical organization of the primary visual cortex (area 17) of the cat. A comparison with area 17 of the macaque monkey. Journal of Comparative Neurology, 184, 599–616. Lund, J.S., Yoshioka, T. and Levitt, J.B. (1993) Comparison of intrinsic connectivity in different areas of macaque monkey cerebral cortex. Cerebral Cortex, 3, 148–162. Luria, A. (1973) The working brain. London: Penguin Books. Malach, R. (1994) Cortical columns as devices for maximizing neuronal diversity. Trends in Neuroscience, 17, 101–104. Maunsell, J.H.R., Nealey, T.A. and DePriest, D.D. (1990) Magnocellular and parvocellular contributions to responses in the middle temporal visual area (MT) of the macaque monkey. Journal of Neuroscience, 10, 3323–3334. Miller, B.M., Windrem, M.S. and Finlay, B.L. (1991) Thalamic ablations and neocortical development: Alterations in thalamic and callosal connectivity. Cerebral Cortex, 1, 241–261. Miller, R. (1975) Distribution and properties of commissural and other neurons in the cat sensorimotor cortex. Journal of Comparative Neurology, 164, 119–132. Miller, R. (1991) Cortico-hippocampal interplay. Berlin, Heidelberg: Springer Verlag. Miller, R. (1996) Axonal conduction time and human cerebral laterality. Amsterdam: Harwood Academic. Mukhametov, L.M. and Rizzolatti, G. (1970) The responses of lateral geniculate neurons to flashes of light during the sleep-waking cycle. Archives Italiennes de Biologie, 108, 348–368. Murphy, K.M., Jones, D.G. and Van Sluyters, R.C. (1995) Cytochrome-oxidase blobs in cat primary visual cortex. Journal of Neuroscience, 15, 4196–4208. Ohno, S. (1970) Evolution by gene duplication. Berlin, Heidelberg: Springer Verlag. Onoe, H., Komori, M., Onoe, K., Takechi, H., Tsukada, H. and Watanabe, Y. (2001) Cortical networks recruited for time perception: a monkey positron emission tomography (PET) study. Neuroimage, 13, 37–45.
© 2002 Taylor & Francis
494
Robert Miller and Almut Schüz
Peterson, I. (1993) Newton’s Clock: Chaos in the Solar System. New York: W.H. Freeman and Co. Porter, L.L. (1997) Morphological characterization of a cortico-cortical relay in the cat sensorimotor cortex. Cerebral Cortex, 7, 100–109. Rakic, P. (1988) Specification of cerebral cortical areas. Science (New York), 241, 170–176. Rakic, P., Suner, I. and Williams, R.W. (1991) A novel cytoarchitectonic area induced experimentally within the primate visual cortex. Proceedings of the National Academy of Sciences, U.S.A., 88, 2083–2087. Reid, R.C. and Alonso, J.-M. (1996) The processing and encoding of information in the visual cortex. Current Opinion in Neurobiology, 6, 475–480. Reiner, A. (1991) A comparison of neurotransmitter-specific and neuropeptide-specific neuronal cell types present in the Dorsal Cortex in turtles with those present in the isocortex in mammals: Implications for the evolution of Isocortex. Brain Behavior and Evolution, 38, 53–91. Ringo, J.L., Doty, R.W., Demeter, S. and Simard, P.Y. (1994) Time is of the essence: a conjecture that hemispheric specialization arises from interhemispheric conduction delay. Cerebral Cortex, 4, 331–243. Rockland, K.S. (1995) Morphology of individual axons projecting from area V2 to MT in the macaque. Journal of Comparative Neurology, 355, 15–26. Rockel, A.J., Hiorns, R.W. and Powell, T.P. (1980) The basic uniformity in structure of the neocortex. Brain, 103, 221–244. Sakakura, H. (1968) Spontaneous and evoked unitary activities of cat lateral geniculate neurons in sleep and wakefulness. Japanese Journal of Physiology, 18, 23–42. Sanides, F. (1970) Functional architecture of motor and sensory cortices in primates in the light of a new concept of neocortex evolution. In: C.R. Noback and W. Montagna (eds), The Primate Brain. New York: Appleton Century Crofts, pp. 137–208. Scannell, J.W., Blakemore, C. and Young M.P. (1995) Analysis of connectivity in the cat cerebral cortex. Journal of Neuroscience, 15, 1463–1483. Scannell, J.W., Hilgetag, C.C., Panzeri, S. and Young, M.P. (1999) Hierarchical organization and latency in the primate visual system. Society for Neuroscience Abstracts, 25, 821–14. Schieber, M.H. and Hibbard, L.S. (1993) How somatotopic is the motor cortex hand area? Science (Washington), 261, 489–492. Schmolesky, M.T., Wang, Y., Hanes, D.P., Thompson, K.G., Leutgeb, S., Schall, J.D. and Leventhal, A.G. (1998) Signal timing across the macaque visual system. Journal of Neurophysiology, 79, 3272–3278. Schüz, A. and Demianenko, G.P. (1995) Constancy and variability in cortical structure. A study on synapses and dendritic spines in hedgehog and monkey. Journal für Hirnforschung, 36, 113–122. Schüz, A. and Preißl, H. (1996) Basic connectivity of the cerebral cortex and some considerations on the corpus callosum. Neuroscience and Biobehavioral Reviews, 20, 567–570. Seidemann, E., Poirson, A.B., Wandell, B.A. and Newsome, W.T. (1999) Color signals in area MT of the macaque monkey. Neuron, 24, 911–917. Sharma, J., Angelucci, A. and Sur, M. (2000) Induction of visual orientation modules in auditory cortex. Nature (London), 404, 841–847. Shoham, D., Hübener, M., Schulze, S., Grinvald, A. and Bonhoeffer, T. (1997) Spatio-temporal frequency domains and their relation to cytochrome oxidase staining in cat visual cortex. Nature (London), 385, 529–533. Singer, W. and Gray, C.M. (1995) Visual feature integration and the temporal correlation hypothesis. Annual Review of Neuroscience, 18, 555–586. Singer, W. (1998) Consciousness and the structure of neuronal representations. Philosophical Transactions of the Royal Society of London, B, 353, 1829–1840. Somogyi, P. (1977) A specific ‘axo-axonal’ interneuron in the visual cortex of the rat. Brain Research, 136, 345–350. Steriade, M. and Hobson, J.A. (1976) Neuronal activity during the sleep-waking cycle. Progress in Neurobiology, 6, 155–376. Stratford, K.J., Tarczy-Hornoch, K., Martin, K.A.C., Bannister, N.J. and Jack, J.J.B. (1996) Excitatory synaptic inputs to spiny stellate cells in cat visual cortex. Nature (London), 382, 258–261. Suga, N. (1984) The extent to which biosonar information is represented in the bat auditory cortex. In: G.M. Edelman, W.E. Gall and W.M. Cowan (eds), Dynamic Aspects of Neocortical Function. New York: Wiley and Sons, pp. 315–373. Swindale, N.V. (2000) How many maps are there in visual cortex? Cerebral Cortex, 10, 633–643. Swindale, N.V., Shoham, D., Grinvald, A., Bonhoeffer, T. and Hübener, M. (2000) Visual cortex maps are optimized for uniform coverage. Nature Neuroscience, 3, 822–826. Tanaka, K. (1983) Cross-correlation analysis of geniculostriate neuronal relationships in cats. Journal of Neurophsyiology, 49, 1303–1318. Tarczy-Hornoch, K., Martin, K.A.C., Stratford, K.J. and Jack, J.J.B. (1999) Intracortical excitation of spiny neurons in layer 4 of cat striate cortex in vitro. Cerebral Cortex, 9, 833–843. Tootell, R.B.H. and Taylor, J.B. (1995) Anatomical evidence for MT and additional cortical visual areas in humans. Cerebral Cortex, 5, 39–55.
© 2002 Taylor & Francis
Discussion Section
495
Toyama, K., Kimura, M. and Tanaka, K. (1981) Cross-correlation analysis of interneuronal connectivity in cat visual cortex. Journal of Neurophysiology, 46, 191–201. Valverde, F. (1971) Short axon neuronal subsystems in the visual cortex of the monkey. International Journal of Neuroscience, 1, 181–197. Valverde, F. (1985) The organizing principles of the primary visual cortex in the monkey. In: A. Peters and E.G. Jones (eds), Cerebral Cortex, Vol. 3 (Visual Cortex). New York and London: Plenum Press, pp. 207–257. Valverde, F. (1986) Intrinsic neocortical organization: some comparative aspects. Neuroscience, 18, 1–23. Valverde, F. (1988) Competition for the sake of diversity. Behavioral and Brain Sciences, 11, 102–103. van Essen, D.C. (1997) A tension-based theory of morphogenesis and compact wiring in the central nervous system. Nature (London), 385, 313–318. Victor, J.D. (2000) How the brain uses time to represent and process visual information (1). Brain Research, 886, 33–46. Vogt, O. (1911) Die Myeloarchitektonik des Isocortex parietalis. Journal für Psychologiie und Neurologie, 18, 107–118. Vogt, C. and Vogt, O. (1919) Allgemeinere Ergebnisse unserer Hirnforschung. Journal für Psychologiie und Neurologie, 25, :292–398. von Economo, C. and Koskinas, G.N. (1925) Die Cytoarchitektonik der Hirnrinde des erwachsenen Menschen. Berlin: Springer. Wandell, B.A., Poirson, A.B., Newsome, W.T., Baseler, H.A., Boynton, G.M., Huk, A., Gandhi, S. and Sharpe, L.T. (1999) Color signals in human motion-selective cortex. Neuron, 24, 901–909. Windrem, M.S. and Finlay, B.L. (1991) Thalamic ablations and neocortical development: alterations of cortical cytoarchitecture and cell number. Cerebral Cortex, 1, 230–240. Yabuta, N.H. and Callaway, E.M. (1998) Functional streams and local connections of layer 4C neurons in the primary visual cortex of the macaque monkey. Journal of Neuroscience, 18, 9489–9499. Young, M.P. (1992) Objective analysis of the topological organization of the primate cortical visual system. Nature (London), 358, 152–155. Young, M.P., Scannell, J.W. and Burns, G. (1994) The Analysis of Cortical Connectivity. New York: R.G. Landes; Heidelberg: Springer. Young, M.P., Barone, P., Hilgetag, C.-C., Kennedy, H. and Falchier, A. (2000a) A determinate hierarchy for macaque visual cortex derived from quantitative laminar data. Society for Neuroscience, Abstracts, 26, 449.7. Young, M.P., Hilgetag, C.C. and Scannell, J.W. (2000b) On imputing function to structure from the behavioural effects of brain lesions. Philosophical Transactions of the Royal Society: Biological Sciences, 355, 147–161.
© 2002 Taylor & Francis
