Vision in Alzheimer's disease

Vision in Alzheimer’s Disease

Interdisciplinary Topics in Gerontology Vol. 34 Series Editors

Patrick R. Hof, New York, N.Y. Charles V. Mobbs, New York, N.Y.

Editorial Board

Constantin Bouras, Geneva Christine K. Cassel, Washington, D.C. Anthony Cerami, Manhasset, N.Y. H. Walter Ettinger, Winston-Salem, N.C. Caleb E. Finch, Los Angeles, Calif. Kevin Flurkey, Bar Harbor, Me. Laura Fratiglioni, Stockholm Terry Fulmer, New York, N.Y. Jack Guralnik, Bethesda, Md. Jeffrey H. Kordower, Chicago, Ill. Bruce S. McEwen, New York, N.Y. Diane Meier, New York, N.Y. Jean-Pierre Michel, Geneva John H. Morrison, New York, N.Y. Mark Moss, Boston, Mass. Nancy Nichols, Melbourne S. Jay Olshansky, Chicago, Ill. James L. Roberts, San Antonio, Tex. Jesse Roth, Baltimore, Md. Albert Siu, New York, N.Y. John Q. Trojanowski, Philadelphia, Pa. Bengt Winblad, Huddinge

Vision in Alzheimer’s Disease Volume Editors

Alice Cronin-Golomb, Boston, Mass. Patrick R. Hof, New York, N.Y.

46 figures and 18 tables, 2004

Basel · Freiburg · Paris · London · New York · Bangalore · Bangkok · Singapore · Tokyo · Sydney

Alice Cronin-Golomb, PhD

Patrick R. Hof, MD

Department of Psychology, Boston University, Boston, Mass., USA

Department of Neuroscience, Mount Sinai School of Medicine, New York, N.Y., USA

Library of Congress Cataloging-in-Publication Data Vision in Alzheimer’s disease / volume editors, Alice Cronin-Golomb, Patrick R. Hof. p. ; cm. – (Interdisciplinary topics in gerontology, ISSN 0074–1132 ; v. 34) Includes bibliographical references and indexes. ISBN 3–8055–7757–5 (hardcover) 1. Vision disorders in old age. 2. Geriatric ophthalmology. 3. Alzheimer’s disease–Complications. 4. Alzheimer’s disease–Patients–Health and hygiene. I. Cronin-Golomb, Alice. II. Hof, Patrick R. III. Series. [DNLM: 1. Vision Disorders–physiopathology–Aged. 2. Alzheimer Disease–complications–Aged. 3. Vision–physiology–Aged. WW 140 V8306 2004] HQ1060.I53 vol. 34 [RE48.2.A5] 618.97⬘6831–dc22 2004050749

Bibliographic Indices. This publication is listed in bibliographic services, including Current Contents® and Index Medicus. Drug Dosage. The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any change in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug. All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher. © Copyright 2004 by S. Karger AG, P.O. Box, CH–4009 Basel (Switzerland) www.karger.com Printed in Switzerland on acid-free paper by Reinhardt Druck, Basel ISSN 0074–1132 ISBN 3–8085–7757–5

Dedication

To Mark, Lucy, Olivia, and Gabriel, and in memory of my mother, Agnes Nelson Cronin (ACG) To Esther and Jonathan (PRH)

Contents

IX Introduction Cronin-Golomb, A. (Boston, Mass.); Hof, P.R. (New York, N.Y.) Structure and Function 1 The Anterior Visual System and Circadian Function with Reference to Alzheimer’s Disease Valenti, D. (Boston, Mass.) 30 Neuropathological Changes in Visuospatial Systems in Alzheimer’s Disease von Gunten, A. (Lausanne); Giannakopoulos, P. (Lausanne/Chêne-Bourg); Bouras, C. (Chêne-Bourg/New York, N.Y.); Hof, P.R. (New York, N.Y.) 62 Functional Imaging in Healthy Aging and Alzheimer’s Disease Anderson, N.D.; Grady, C.L. (Toronto) Abnormalities in Visual Behavior in AD and Related Disorders 96 Heterogeneity of Visual Presentation in Alzheimer’s Disease Cronin-Golomb, A. (Boston, Mass.) 112 Posterior Cortical Atrophy: A Visual Variant of Alzheimer’s Disease Mendez, M.F. (Los Angeles, Calif.) 126 Visual Hallucinations in Alzheimer’s Disease Holroyd, S. (Charlottesville, Va.)

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136 Similarities of Visual Deficits in Alzheimer’s Disease and Down Syndrome Rocco, F.J. (Fall River, Mass.) Visual Perception and Cognition 155 Visuospatial Disorientation in Alzheimer’s Disease: Impaired Spatiotemporal Integration in Visual Information Processing Duffy, C.J.; Cushman, L.; Kavcic, V. (Rochester, N.Y.) 173 Magnocellular Deficit Hypothesis in Alzheimer’s Disease Gilmore, G.C.; Morrison, S.R.; Groth, K.E. (Cleveland, Ohio) 199 Perceptual Organization in Alzheimer’s Disease Kurylo, D.D. (Brooklyn, N.Y.) 212 From Segmentation to Imagination: Testing the Integrity of the Ventral Visual Processing Pathway in Alzheimer’s Disease Tippett, L.J. (Auckland) 236 Reading and Visual Processing in Alzheimer’s Disease Glosser, G.; Grossman, M. (Philadelphia, Pa.) Visual Attention and Daily Function 248 Visual Attention and Visual Short-Term Memory in Alzheimer’s Disease Vecera, S.P.; Rizzo, M. (Iowa City, Iowa) 271 Visual Attention, Genetics and Alzheimer’s Disease Parasuraman, R.; Greenwood, P. (Washington, D.C.) 290 Closing the Window of Spatial Attention: Effects on Navigational Cue Use In Alzheimer’s Disease Mapstone, M. (Rochester, N.Y.); Weintraub, S. (Chicago, Ill.) 305 Improved Performance on Activities of Daily Living in Alzheimer’s Disease: Practical Applications of Vision Research Dunne, T. (Norwell, Mass.) 325 Author Index 326 Subject Index

Contents

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Introduction

Alzheimer’s disease (AD) is the most common cause of dementia in older adults. The Alzheimer’s Association estimates that 4.5 million individuals in the US currently are afflicted, and that by mid-century the prevalence in this country will be 11.3–16 million cases in the absence of a cure or prevention [1]. Understanding all behavioral, anatomical, and physiological aspects of this disease is obviously of utmost importance world-wide. AD is viewed as a disorder primarily of memory by patients, caregivers, and most health professionals. While agreeing that the memory deficit is usually the initial sign, researchers have long known that AD is characterized by impairments in several additional domains, including visual function. In recent years, although a consensus has been reached that even lower-level visual abilities are impaired in a large number of patients with AD, these findings have not yet appeared in the diagnostic guides consulted by healthcare professionals. For example, the most recent edition of the Diagnostic and Statistical Manual of Mental Disorders states that few sensory signs occur early in AD [2]. Less recent but widely used is the report on clinical criteria for AD diagnosis developed by the NINCDS-ADRDA work group [3], in which it is stated that sensory loss or visual field deficits make the diagnosis of probable AD ‘uncertain or unlikely’. This report acknowledged that our understanding of AD was still limited and that the proposed clinical criteria should therefore be considered tentative and subject to change. The current web sites of the Alzheimer’s Association [1] and the National Institute on Aging [4], agencies that fund research on AD and promulgate information on the disease, make no mention of sensory changes. In accordance with their own observations, and expectations, of the prominence of the memory impairment, as well as those of the clinical staffs with

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whom they come into contact, patients with AD report vision problems to their physicians less frequently than do healthy elderly individuals [5]. Nevertheless, visual dysfunction is prevalent in AD [6]. The neuropathology of this disorder affects several brain areas that are devoted to processing of low-level visual functions as well as higher-order visual cognition and attention. The neuropathological changes are especially prominent in the ‘visual variant’ of AD, also known as posterior cortical atrophy, but occur as well in the more typical AD case. Our goal in this volume is to provide information on current directions in AD vision research. While not exhaustive, the chapters represent several main foci of research including studies of structure, function, and behavior. The first section is on aspects of structure and function. Valenti describes the state of knowledge about the anterior visual pathways through the primary visual cortex, citing work that points to AD neuropathological change at multiple levels, including the lens of the eye, the retinal nerve fiber layer and optic nerve, and hypothalamic and thalamic nuclei. Attention to the anterior system leads to the identification of interventions that may improve visual input, as she documents. Intriguingly, Valenti also raises the possibility of disruption of the circadian system at the level of the retina and other higher structures, which may provide clues to sleep-wake abnormalities common in AD patients. Von Gunten, Giannakopoulos, Bouras and Hof extend the discussion of neuropathology to the cortex, from primary to association cortices, focusing on the long corticocortical projections that are subject to significant disruption in AD. In the domain of function, Anderson and Grady provide a summary of imaging studies in normal aging and AD with an emphasis on visual areas and their prefrontal targets. The second section introduces abnormalities in visual behavior in AD and related disorders. Cronin-Golomb supplies an overview of the prevalence of deficits in basic visual capacities in typical AD and considers the heterogeneity in the type and extent of presentations of visual signs. Mendez extends this discussion with his focus on the ‘visual variant’ of AD, or posterior cortical atrophy, including structure, function, and behavior. As described by Mendez and by von Gunten and colleagues, the characteristics of posterior cortical atrophy raise the question of whether typical AD and its visual variant fall on a neuropathological continuum or instead are distinct clinical and pathological entities. Visual hallucinations occur in some patients with AD, and Holroyd provides a careful consideration of the circumstances under which hallucinations arise and of their brain substrates, focusing on visual association cortex. As she reports, visual hallucinations are common in AD and may be the presenting symptom. Their association with more rapid cognitive decline, aggression, and premature institutionalization, as well as patient and caregiver distress, underscores the importance of research and treatment of these symptoms. Rocco points out the similarities in visual deficiencies in regard to structure, function, and behavior in Down

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syndrome, a disorder in which individuals develop AD-type neuropathology by the age of 35 or 40. He makes the case that further research on Down syndrome may offer clues to the etiology of the development of changes in visual behavior in AD. The chapters constituting this section together reinforce the idea that the study of variations in pathology and behavior will likely provide insights into typical AD as well as related conditions. The third section considers the state of visual perception and cognition in AD at a level beyond basic visual abilities. The dorsal, occipitoparietal visual pathway is the focus of work by Duffy, Cushman, and Kavcic, who describe visuospatial disorientation in AD. Part of the difficulty that patients have in spatial navigation arises from deficits in the perception of self-motion via optic flow, which contains information about heading direction and the threedimensional structure of the visual environment. Duffy and colleagues attribute these difficulties to disruption of corticocortical pathways such as described by von Gunten and colleagues in their chapter. Gilmore, Morrison, and Groth follow with an analysis of the magnocellular hypothesis of visual dysfunction in AD, a formative theory that proposes exceptional vulnerability of functions associated with the magnocellular pathway or, in cortex, the dorsal processing stream (see also Valenti, this volume). As is clear from their review, behaviors associated with these pathways are impaired in AD, as are functions dependent more on the parvocellular pathway and the ventral, occipitotemporal processing stream. The next three chapters complement the first two through their emphasis on the ventral pathway, which is specialized for object recognition. Kurylo discusses fundamental perceptual organization, which serves to organize the visual scene in preparation for object identification. He reports extensive heterogeneity in AD patient performance on several tests of organization such as stimulus-from-noise, proximity, and alignment, and links this level to a higher level by noting that those patients with AD who performed poorly on a test of face discrimination also showed impairments on all tests of perceptual organization. Building upon this link to higher-level function, Tippett describes new findings on object recognition in AD, including apperceptive and associative agnosia, that have emerged through use of sophisticated tests drawn from the cognitive neuroscience literature. Her chapter also examines current views on the integrity of visual imagery in AD. In the last chapter of the section, Glosser and Grossman consider three cognitive models of letter-by-letter reading and alexia in AD, including impairments in lexical processing, attentional control, and general visual perception. They suggest that bottom-up effects can lead to a situation whereby a disorder early in visual processing (letter identification) can affect analysis at a later stage (orthographic lexicon). That is, a deficit in visual perception may contribute to an erroneous impression that there is a core orthographic problem in some patients.

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The focus of the final section of the book is on visual attention and daily function. Vecera and Rizzo provide a summary of attentional deficits in AD and a novel interpretation of these deficits in terms of deficient visual short-term memory. Parasuraman and Greenwood further this discussion with additional studies of visual search and attentional shifting, then describe exciting new work linking attentional impairments in putatively healthy individuals to genetic risk for AD. Pulling together information from several of the preceding chapters, Mapstone and Weintraub analyze the size of the window of spatial attention in AD. Their work on driving behavior is of clear importance because of the premier role of driving in independent living, which must be balanced against the needs of society for protection against erratic drivers. The final chapter by Dunne considers the basic research reported in the previous sections on visual deficits in AD and uses it to propose modifications of the daily living environment to enhance quality of life. Dunne provides concrete suggestions for visual interventions that are designed for ease of implementation by caregivers in the home setting and by staff in long-term care facilities. She demonstrates how fairly simple changes in the visual environment can lead to increased food and liquid intake, ability to dress, and success in navigation. In summary, this volume spans the range of topics on vision in AD and associated disorders, from structure (retinal and cortical) to function (cortical activation) to behavior (basic vision, hallucinations, perception, cognition, attention, and everyday activities). We present these multiple aspects of basic research with an eye to developing interventions that will improve the lives of patients with AD and related disorders. Far from being atypical and therefore of limited utility in understanding the common presentation of AD, the visual disorders of Alzheimer’s original case and its cousins of the 21st century have much to teach us about the changing visual system in aging and age-related neurodegenerative disease. Alice Cronin-Golomb, Boston, Mass. Patrick R. Hof, New York, N.Y. References 1 2 3

4 5 6

http://www.alz.org/AboutAD/Statistics.htm Diagnostic and Statistical Manual of Mental Disorders, ed 4. Washington, American Psychiatric Association, 1994. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM: Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA work group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s disease. Neurology 1984;34:939–944. http://www.nia.nih.gov McCormack WC, Kukull WA, Van Belle G, Bowen JD, Teri L, Larson EB: Symptom patterns and comorbidity in the early stages of Alzheimer’s disease. J Am Geriatr Soc 1994;42:517–521. Mendola JD, Cronin-Golomb A, Corkin S, Growdon JH: Prevalence of visual deficits in Alzheimer’s disease. Optom Vis Sci 1995;72:155–167.

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Structure and Function Cronin-Golomb A, Hof PR (eds): Vision in Alzheimer’s Disease. Interdiscipl Top Gerontol. Basel, Karger, 2004, vol 34, pp 1–29

The Anterior Visual System and Circadian Function with Reference to Alzheimer’s Disease Denise Valenti Department of Psychology, Boston University, Boston, Mass., USA

Alzheimer’s disease (AD) impacts visual function early in the course of the disease, and functional losses correlate with cognitive losses. Several functional vision losses are frequently identified in AD. There is evidence for deficits in contrast sensitivity [1], motion perception [2, 3], performance on backward masking tests [1] and color discrimination of blue, short-wavelength hues [4]. Damage secondary to AD occurs in the visual association cortex and other higher cortical areas, as well as the primary visual cortex [5, 6]. Additional studies have reported deficits in the retinocalcarine pathway [7–10]. This pathway extends from the photoreceptors of the retina to the primary visual cortex. Other studies have shown no deficits in the retinocalcarine pathway [11, 12]. What have not been explored in AD are the unique retinal ganglion cells that innervate the suprachiasmatic nucleus [13, 14]. These cells do not participate in visual functions but respond to light in the short-wavelength blue range. The suprachiasmatic nucleus modulates circadian rhythm [14] and there are significant disruptions in circadian rhythm function in AD [15]. Another neurodegenerative disease, glaucoma, also impacts lower spatial frequencies in contrast sensitivity, and is associated with deficits in the blue short-wavelength color range, reductions in motion perception [16] and disruptions in the circadian rhythm patterns [17, 18]. When patients with AD also have glaucoma, the course of vision loss related to glaucoma is much more rapid and aggressive than in people with glaucoma, but without AD [19]. Glaucoma affects visual function at the initial site of neural activity, the retinal ganglion cells, and ultimately destroys their afferent axons at the nerve fiber layer in the retina. The axon loss eventually leads to additional atrophy farther up the visual pathway due to the eliminated neuronal input. AD, on the other

hand, impacts cells that may be considered terminal or intermediary in the visual pathway in the brain itself. The impact is also loss of nerve fiber connections and probable further atrophy along the visual pathway, again resulting from the elimination of neuronal input. When the two diseases exist in a single individual, the effect is greater destruction of the visual system [20]. AD and glaucoma impact the same visual pathways, but at different points. AD is a degenerative process starting relatively late in the visual system neural pathway, whereas glaucoma is a degenerative process starting at the beginning. When the two occur simultaneously, the loss of neuronal structures is cumulative and the functional loss in vision is greater. This chapter will discuss the impact of AD on the visual pathways and visual function. There will be further exploration of the similarities in visual pathway loss and functional loss between AD and glaucoma. Another disease with functional losses similar to those in AD and glaucoma, retinitis pigmentosa (RP), will also be discussed. RP affects the peripheral retina in the early stages of the disease, damaging the retina at the earliest point in the visual pathway, the rod receptors. The damage has similarities to that caused by glaucoma and AD, and results in losses of contrast sensitivity as well other losses. RP, even in early stages, affects contrast sensitivity [21], motion perception [22, 23], circadian rhythm [24] and blue short-wavelength color [25]. In addition to discussing the etiology of glaucoma as it relates to AD, this chapter will review current strategies for diagnosing, monitoring and managing glaucoma, and their application potential for AD will be considered.

Retinal Organization

The retina is organized in laminar groupings of cells, and is often categorized as having an ‘outer’ and ‘inner’ aspect, relative to the ocular globe. The outer retina includes the rod cells, cone cells, and the horizontal cells. The inner retina comprises layers of bipolar, amacrine, and ganglion cells. The photoreceptor cell responsible for regulating circadian function is hypothesized to be located in the inner retinal layer [26] (fig. 1). The Outer Retina Rod and cone cells have posterior attachments to the basement membrane of the ocular globe, as well as anterior attachments to the horizontal cells within the outer retina and anterior attachments to bipolar cells in the inner retina. Horizontal cells are lateral interneurons whose dendrites innervate the axon terminals of rod and cone photoreceptors, providing these cells with lateral communication and feedback loops. Horizontal cells form the lateral components

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Inner retina

Outer retina

Light enters through the pupil

To optic nerve Cone cell Bipolar cell Rod cell Ganglion cell

Amacrine cell

Horizontal cell

Basement membrane

Fig. 1. The retinal cell layers. Diagram by Thomas Laudate.

of the synaptic triad. The synaptic triad includes first the photoreceptors, then the horizontal cells which can either feed forward or feed laterally, and finally the inner retina cells. It is hypothesized that there are at least two types of horizontal cells – H1 and H2 cells. H1 and H2 cells process long and medium wavelengths of incoming light, whereas only H2 cells respond to short wavelengths. These H2 cells receive a significant amount of input from only a small percentage of cone cells – those 10% that process blue short wavelengths [27]. The Inner Retina The most posterior layer of the inner retina is made of bipolar cells. Midget bipolar cells have a single connection and a single cone axon terminal. Diffuse bipolar cells have connections to multiple cone and many other bipolar cells. Anterior to the bipolar cells are the amacrine cells, which laterally interconnect the neurons of the inner retina. It has been estimated that there may be as many as 40 distinct categories of amacrine cell populations [27]. Most anterior of the inner retina is the layer of retinal ganglion cells. Subtypes of ganglion cells include parasol, midget, and bistratified. Parasol ganglion cells have large cell bodies and project to magnocellular (M) layers of the lateral geniculate nucleus (LGN). Midget ganglion cells are in the central,

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mid-peripheral retina and to a lesser extent the extreme periphery, and project to parvocellular (P) layers of the brain. Midget ganglion cells are part of the spectrally-opponent P cell pathway. The traditional view is that M cells and pathways are responsible for achromatic gray scale information, while the P cells and pathway are responsible for processing chromatic information. However, this viewpoint is being challenged. There is hypothesized to be a category of midget ganglion cells existing in the peripheral retina that receive input from the receptive field centers of long- and medium-wavelength cone cells as well as their receptive field surrounds. This results in non-opponent light response for peripheral midget ganglion cells. In effect, spectrally-opponent cells can relay chromatic information by comparison of the center signal to the surround signal. A spectrally non-opponent signal relays all the information in both the center and the surround and is achromatic. Bistratified ganglion cells receive short-wavelength cone input, and have a ‘blue/on, yellow/off’ response. These cells were initially believed to project to the P layers of the LGN [27]. However, research now suggests that bistratified ganglion cells may have an independent pathway, projecting to a small layer of cells in the LGN that is below each P and M lamina. This pathway is referred to as the koniocellular (K) pathway [28, 29].

Visual System Pathways

The visual system has been traditionally thought to be made up of at least two parallel pathways. The pathways have individualized function. The P pathway has a role in processing color and form, and the M pathway is sensitive to motion. The M system has high contrast gain and saturates at relatively low contrasts. The P system has a low contrast gain and more linear contrast saturation [30]. The M and P pathways combine signals from long- and middle-wavelength-sensitive cone cells [31]. The P pathway goes from the midget ganglion cells to the P layers of the LGN, and from there the path proceeds to the upper layer IV of the primary visual cortex. The M pathway projects from the parasol ganglion cells to the M layers of the LGN and then to the lower layer IV of the primary visual cortex [28]. The M and P cells’ pathways overlap in their response to many visual stimuli. Pokorny and Smith [31] demonstrated that the M pathway adapts locally to a stimulus array, but the P pathway does not. A third pathway, the K pathway, is sensitive to short wavelengths [28, 29, 32]. To date, the majority of work that explores this pathway has been conducted on non-human primates. The K pathway is hypothesized to react by summing both long- and middle-wavelength signals and then subtracting the shortwavelength signals [28, 29, 31]. The function of K cells in the LGN appears to

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vary depending on the layer [29]. Cells in layer K3 have higher spatial resolution and lower temporal resolution than those in K1 or K2. This indicates there may be several classes of K cells in the visual system. In general, K cells’ contrast functions are more similar to M cells than P cells [29]. The K pathway was found to project from the LGN to layer III in the primary visual cortex [33, 34] and likely to layer I [34] and layer II [33]. The short-wavelength blue-sensitive pathway seems to be similar across a wide variety of non-human primate species. It does not differ significantly between bichromatic or trichromatic species [33]. The pathway is also well developed in the LGN of animals that are nocturnal with no color sensitivity [34]. This indicates that the role of the K pathway is not primarily chromatic. To date, there has been no published research investigating this pathway’s role in circadian function. The K pathway receptors, the short-wavelength cone cells, have been shown to be significantly influenced by the long-wavelength and a middle-wavelength cone receptors as well as rod receptors. In studies of humans who have only short-wavelength cone and rod cells due to a congenital defect, color perception of the short-wavelength cone cells can be modulated by rod input. It was found that when the retina of such individuals is stimulated by short-wavelength light of 450 nm, the rod cells reacted in an interfering manner and the shortwavelength cone cells’ response was substantially reduced in the perception of color of that wavelength [35]. In essence, the rod cells contributed to a blue defect in these individuals when stimulated by short-wavelength light. The conclusion made by researchers was that in normal individuals, the intact long- and middle-wavelength cone receptors stabilize the K pathway and make the receptors less resistant to rod input. In the absence of long-wavelength and middle receptors, the rod cells create signals that amount to noise, and the blue shortwavelength color defect is measurable [35]. Of note is that the stimulus contributing to the defect – short-wavelength light of ⬍450 nm – is similar to the wavelength of light stimulating the circadian rhythm system. Diseases such as AD, glaucoma, and RP are associated with blue shortwavelength color defects [4, 23, 25] and circadian rhythm abnormalities [15, 24], and individuals with RP and glaucoma frequently report glare problems that may actually reflect over-action of rod receptors [35]. One explanation for these problems may be defective input from long- and middle-wavelength cone receptors, allowing for defective response in the rod system. Another explanation is that the rod system has been damaged to disallow normal inhibitory responses. A combination of the two possible mechanisms is also conceivable. If the P pathway is responsible for long- and middle-wavelength color response, as some researchers theorize, then damage along this pathway could impact other chromatic functioning such as blue short-wavelength perception,

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by eliminating the modulating effect on rod cells. If damage occurs in the M pathway, the effect may be direct damage to the rod system and the elimination of inhibitory response by rod cells. Again the impact may be damage to the chromatic function in the blue short-wavelength system. AD appears to be associated with damage in areas that impact both the P pathway and the M pathway. Deficits specific to the M pathway have been identified in individuals with AD even in brain areas devoid of plaques and neurofibrillary tangles. The M pathway shows signs of significant cell loss in the primary visual cortex of AD individuals [6]. Amyloid plaques and neurofibrillary tangles have been identified in the cuneal and lingual gyri of individuals with AD and these correlate with the incidence of functional visual field loss [36]. In the LGN, the P layers have been shown to have plaques associated with AD, but such plaques are more prevalent in the M layers [37]. The interlaminar layers of LGN (area of K pathway) are affected in AD as well as the optic radiations and pregeniculate nuclei [37].

Non-Visual System Pathways

Berson et al. [14] identified a previously unknown photoreceptor that was dissimilar to both rod cells and cone cells. These cells are retinal ganglion cells that directly innervate the suprachiasmatic nucleus and are reactive to light even when all synaptic input from rod cells and cone cells is blocked. The newly identified retinal ganglion cell, unlike rod cells or cone cells, does not have a role in image formation. The cell’s response to light is such that it is believed to be the primary photoreceptor that synchronizes the circadian clock to environmental time. The cell may be the primary sensory cell involved in circadian rhythm. Melanopsin, the primary neural substrate in circadian function, has been identified in the ganglion and amacrine retinal cells of non-human primates [26]. Mice that have a total absence of melanopsin have been found to show non-visual photoreceptor activity. This indicates that rod and cone cells may have some contribution to non-visual functions [38]. Mice that were genetically engineered to have retinas that did not contain any cells involved in circadian rhythm function had an impairment of the pupillary reflex [39]. With the elimination of rod and cone cells, the retinal response matches that of the melanopsinexpressing retinal ganglion cells [40]. Lucas et al. [39] have concluded that the rod/cone system acting in conjunction with the melanopsin system account for the full range of pupillary response. The pineal gland secretes the melatonin. Blood plasma melatonin levels are used to indicate circadian patterns and response to light. The neural path

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continues on from the suprachiasmatic nucleus to the pineal gland. Melatonin secretion is high during the night and low during the day, with light suppressing its secretion. Brainard et al. [41] found that the wavelengths that produced the greatest suppression of melatonin in healthy human individuals were between 446 and 477 nm in the short-wavelength range. This compares with a finding of sensitivity to 484 nm short-wavelength range in the melanopsinexpressing ganglion cells of rats [14] and the 480 nm sensitivity identified by Menaker [42] in mice. These short-wavelength ranges are the blue wavelengths that are processed along the K pathway. In a study of blind humans, it was found that when the globe was intact with clear media, there was still suppression of melatonin despite flat electroretinograms, absence of light perception, and absent pupillary response. These same individuals had few complaints of sleep disturbance. Those blind individuals who did have sleep disturbances also had reduced suppression of melatonin and either had globes that were enucleated or had opaque media [43]. The suprachiasmatic nucleus is a small nucleus located above the optic chiasm on each side of the ventral third ventricle. It receives afferent input from the retinohypothalamic tract and the intergeniculate leaflet of the lateral geniculate complex [44]. It also receives efferent input from the intergeniculate leaflet [26, 40]. The suprachiasmatic nucleus is considered the primary region of the brain for circadian rhythm function. The intergeniculate leaflet, which also contributes to circadian rhythm function, is a small ventral portion of the thalamus and is part of the lateral geniculate complex [44]. The olivary pretectal nucleus is an integral structure for the pupillary reflex (light reflex) and receives input from circadian retinal cells [26, 40]. The olivary pretectal nucleus is located at the border between the mesencephalon and diencephalon. There is evidence of rod and cone cell interaction with the circadian retinal ganglion cells at both the suprachiasmatic nucleus level [45] and the olivary pretectal nucleus level [46]. The Edinger-Westphal nucleus is essential in pupillary reflex response, though its input from the circadian rhythm system is unclear. The Edinger-Westphal nucleus has been shown to contain significant amyloid plaques and neurofibrillary tangles in individuals with AD [47].

Eye and AD

Histopathology The degeneration of the retinal ganglion cells of patients with AD was identified histopathologically by several groups. The study by Blanks and colleagues [8] examined 16 individuals with AD with an age range of 76–93.

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There were 19 normal individuals with an age range of 55–91. They found that within the ganglion cell layer, the cells having the largest diameter may have been preferentially affected in AD. Hinton et al. [7] performed histopathologic studies on 10 individuals with AD and 10 age-matched normal individuals aged 76–89 years. This study found 8 of the 10 individuals with AD to have optic nerves that were significantly different in fiber count compared to the normal individuals. The level of AD severity or duration of illness was not documented. Sadun and Bassi [10] studied 3 eyes from AD individuals ranging in age from 76 to 89 years. In these samples, they found degeneration of the retinal ganglion cells. They also found axonal degeneration upon examining the retrobulbar optic nerves. There was a greater frequency of degeneration in the more posterior aspects of the nerve. The implication here is that retinal ganglion cell loss may be secondary to retrograde axonal degeneration. The above findings are in contrast to those of Curcio and Drucker [11] who examined 4 AD eyes with an age range of 67–86 years as well as the eyes from 4 age-matched normal individuals. The individuals with AD had the disease for at least 4 years and had severe dementia. There was no group difference in regard to the ganglion cells. Davies et al. [12] studied 9 AD and 7 normal eyes. They also reported that there was no histopathologic evidence of differences in the retinal ganglion cells between individuals with AD and agematched normal individuals. MT1 is a melatonin-1a receptor and is not only active in retinal function but is considered a potent antioxidative substance important in maintaining the integrity of photoreceptor cells [48]. In studies of normal human retinas, it was found that ganglion, amacrine and photoreceptor cells expressed MT1. The analysis of 2 AD individuals’ retinas showed significant differences in the level of MT1 compared to age-matched normal retinas, with the AD retinas having a significantly higher level of MT1 in ganglion and amacrine cells, and a lower level in photorecepter cells. In those with AD, greater amounts of MT1 were found in the central retinal arteries and veins as well as in the smaller retinal vessels, but not ciliary or choroidal vessels. The retinal arteries, veins and smaller retinal vessels supply the inner retinal layers while the ciliary and choroidal vessels supply the outer retinal layers [48]. A study was undertaken of 9 retinas from 9 individuals with age-related macular degeneration, the leading cause of irreversible vision loss in elderly. It was found that 4 of 9 retinas from those with age-related macular degeneration, but none of the 9 retinas from control individuals, had A␤ proteins in the retina. It was suggested that the presence of A␤ in lesions may correlate with the location of degenerating photoreceptors [49]. None of the individuals with age-related macular degeneration or the control individuals had a diagnosis of

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AD at death and there were no studies of brain tissue performed on either group. In another study, the lenses from 9 individuals with AD were compared to the lenses of 8 control individuals without AD. All lenses obtained from individuals with AD had unique equatorial supranuclear cataracts whereas none of the control lenses had them. A␤ protein was present in the cells of AD lenses but not control lenses. It was theorized that in AD, the A␤ proteins promoted regionally-specific lens protein aggregation and the formation of the cataracts [50]. Retinal Region Difference in Function: Right Visual Field/Left Visual Field and Superior Visual Field/Inferior Visual Field To date, analyses of retinal pathology in AD have not considered that retinal findings may be dependent on which brain hemisphere is more severely damaged. For example, an individual with AD may have predominantly lefthemispheric damage from plaques and tangles. This would potentially create subsequent retinal changes in only the left hemi-retinas of each eye (right visual fields). The right eye visual field would be impacted temporally (to the right) while the left eye would be impacted nasally (also to the right). If only one half of the retina is involved, smaller portions of the optic nerve and nerve fiber layer show losses. The left eye with affected temporal retina would show optic nerve damage in different parts of the nerve than the right eye with nasal retinal damage [51]. It is important that any studies of retina and optic nerve from individuals with AD specify which eye and the location in the retina or nerve under study. The left hemisphere of the brain may be dominant for cone-mediated photopic behavior and the right hemisphere may be dominant for rod-mediated scotopic behavior [52]. The retinal ganglion circadian cell may also have hemispheric laterality. There is a greater suppression of melatonin when the nasal retinas are exposed to light than when the temporal retinas are exposed [53]. Visser et al. [53] concluded that the suprachiasmatic nucleus may receive input only from fibers that cross over at the optic chiasm. A study undertaken by Glickman et al. [54] found that with light exposure, the inferior retina was associated with greater suppression of melatonin than the superior retina. In a study of contrast sensitivity in individuals with cerebral infarctions affecting either the primary visual cortex or the visual association cortex, Kobayashi et al. [55] found that those with unilateral medial occipital or occipitotemporal lesions had contrast sensitivity that was near normal despite having the greatest reductions in peripheral visual fields. Those with right lateral parieto-occipital lesions had significantly reduced contrast sensitivity. Those with left damage to the same areas showed only slight reductions. In evaluations

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of the foveal and perifoveal upper and lower hemi-retinal regions, it was found that the upper hemi-retinal areas were more sensitive to all spatial frequencies when compared to the lower area [56]. Any study of the optic nerve in individuals with AD should clearly identify which eye is under investigation and the region of the nerve. This is so that any hemispheric-specific losses and therefore regionally specific retrograde atrophy of the retinal neuronal layers and optic nerve can be investigated. Retinal Photography Photographic methods are used to analyze retinal ganglion cell loss and cell function. These methods measure the degeneration of the cell’s nerve fiber layer, which is an indirect method of assessing loss of cell function. The nerve fiber layer is made up of the individual cell axons. Histopathology methods rely solely on cell count with no mechanism to measure cell quality. Measuring the integrity of the cell fiber layer assesses loss in cell function that may actually occur prior to loss of the cell itself. Photographic methods also record the optic nerve parameters and the observer can readily determine which eye is under study and the orientation. All photographic studies have shown a difference in the nerve fiber layer in individuals with AD compared to normal individuals. Tsai et al. [57] photographed 22 AD individuals and 24 normal age-matched individuals. Five of the individuals with AD and none of the control individuals had nerve fiber loss. There was a significant correlation between the degree of segment optic nerve pallor and the duration of AD. Trends (0.05 ⬍ p ⬍ 0.10) were identified in the segment pallor and the mean score on the Alzheimer’s Disease Assessment Scale (cognitive, non-cognitive and total). Duration and severity of AD probably impact optic nerve findings. Studies that include only early-onset AD may result in conclusions that the optic nerve is not impacted (and therefore there is no change in the ganglion cells) because the sample studied has not had the progression of the disease to the point where the ganglion cells have been impacted. Hedges et al. [9] studied the retinal nerve fiber layer by photographs in individuals in various stages of AD. They found a greater amount of nerve fiber loss in the individuals with AD (n ⫽ 26) compared to age-matched normal individuals (n ⫽ 23). The ages ranged from 52 to 93. There was an indication of increasing nerve fiber layer abnormalities with progression and duration of the disease. However, it was not statistically significant. Nerve Fiber Layer Analysis There are three techniques currently in clinical use that image the optic disc and nerve fiber layers: optical coherence tomography (OCT), scanning laser polarimetry, and confocal scanning laser tomography.

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OCT has a fiber optic delivery system coupled with a slit biomicroscope. The test time is 1.5 s after initial focus and alignment. Parisi et al. [58] used OCT to assess the optic nerve fiber layer thickness in 17 AD individuals and 14 age-matched normal individuals and found a significant reduction in nerve fiber thickness in the AD individuals compared to the normal individuals. The ages ranged from 63 to 77 and included individuals with mild severity of cognitive impairment. OCT also has the ability to measure the thickness of the macular optic nerve fiber layer. No study of the macular layer thickness using optical imaging has been undertaken with individuals with AD. Scanning laser polarimetry (GDx Nerve Fiber Analyzer) assesses the nerve fiber layer by measuring changes in the polarization due to the birefringent properties of the nerve fiber layer. Once aligned, the image is acquired in 0.7 s. Kergoat et al. [59] used this technique and found no differences in the optic nerve fiber layer in 30 individuals with early-stage AD, compared to agematched normal individuals. Scanning laser tomography (Heidelberg Retinal Tomograph – HRT) uses confocal scanning lasers to provide real-time three-dimensional images and measurements of the optic disc and surrounding area. Parameters that can be measured include cup area, disc area, cup volume, rim volume, cup/disc area ratio and rim dim/disc area. Once aligned, images are obtained in 1.5 s. Kergoat et al. [60] used this technique and found no differences in the optic nerve fiber layer of individuals with early-stage AD, compared to age-matched normal individuals. Documentation of Macular Cell Loss and AD Two studies have shown decreases in the number of retinal ganglion cells of the maculae of individuals with AD compared to age-matched control individuals. One study showed that the loss varied as a function of the eccentricity or distance from the central macula or foveola [61]. It was found that AD individuals had lost 28% of the neurons from the RGC at 0–0.5 mm from the foveola, 24% at 0.5–1.0 mm out and 47% at 1.0–1.5 mm from the foveola. The reductions were consistently greater than those seen in age-matched control individuals. This same project found that while the age-matched control individuals had a high correlation of decrease in the cell count with age, no such correlation occurred with the individuals with AD. The cell count was consistently low over all ages. These findings were replicated by Blanks et al. [62] with an estimation that the loss at the distance of 0–2.0 mm from the foveola averaged 25% for individuals with AD. The reductions were in all quadrants but were greater in the temporal portion of the macula. The researchers concluded that the regional differences in the central, macular area of the retina differ from those of the peripheral retina, where the greatest losses were superior and then inferior.

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Regional Differences in the Eye with AD In a study of the count of retinal ganglion cells, not nerve fibers, Blanks et al. [63] found that in a sample of 16 AD retinas compared to retinas from agematched control individuals, there was a greater loss of retinal ganglion cells overall in the AD group, and the retinal ganglion cell dropout was greater in the inferior retina. Tsai et al. [57], in a photographic study of the optic nerves of AD individuals compared to age-matched control individuals, found a greater pallor and atrophy in the superior portion of the optic nerve in the AD group. The extent of the pallor was directly correlated with the duration of disease. In measurements of functional visual field, Trick et al. [64] compared 61 AD participants to 61 age-matched participants. There was a significant loss in the inferior visual fields of the AD participants compared to the control participants. Visual field loss in the inferior field corresponds structurally to superior optic nerve pallor or nerve fiber layer losses. The findings of Trick et al., are in agreement with the optic nerve imaging of Tsai et al., and the retinal cell count of the periphery findings of Blank et al. The more frequent loss of function in the inferior visual field also correlated with the frequency and density of senile plaques and neurofibrillary tangles found in the cuneal gyrus of the visual cortex [36]. Lesions in this region would produce inferior visual field defects. Studies of retinal function and the circadian rhythm system found that the inferior retina played a greater role in suppression of melatonin [54]. This does not correlate with the findings of greater loss of superior retina and therefore inferior visual field loss. However, the circadian rhythm system is not a function of vision and the losses upstream may not be reflected in tests of vision perception such as visual field measurements. To date, there have been no studies utilizing optic nerve fiber imaging to evaluate the integrity of the macula in AD individuals. Of the AD individuals tested for visual field loss by Trick et al. [64], 79.4% had visual field loss. Of these, 60% had losses in the central 15⬚, which indicates more central macular vision or foveal or parafoveal functional losses. This is in agreement with the ganglion cell losses in the macular region identified by Blank et al., in AD individuals compared to age-matched normal individuals.

Contrast Sensitivity and Other Pathology of the Visual System

As noted earlier, the pathways in the visual system have different contrast response properties. The M system has high contrast gain and saturates at relatively low contrasts. The P system has a low contrast gain and more linear contrast saturation [65]. The receptive centers of the ganglion cells contained in the M pathway sum input from a number of photoreceptors and have larger

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receptive fields. The M pathway has fewer ganglion cells. Both loss of ganglion cells and interruptions in the lateral communication can impact functional contrast. Losses posterior to the optic nerve can manifest themselves as retrograde dropout of innervations and such losses may eventually impact lateral communication in the M pathway. Disease in the retina itself and disease anywhere in the visual pathway may impact lateral communication in the M pathway [66]. Reductions in contrast sensitivity frequently accompany ocular pathology. While it is not currently a component in quantifying vision loss for disability or legal blindness, it does impact significantly function and creates disability [67, 68]. Diseases affecting central vision cause overall reductions in contrast sensitivity but the greatest decrease is at high spatial frequencies, with the higher spatial frequency loss generally correlating with acuity loss. The peripheral retina is affected in the initial stages of RP. It has been found that contrast sensitivity is a good predictor of mobility impairment in people with this disease [69, 70]. A study using the Pelli-Robson Contrast Sensitivity Chart (Clement Clarke Inc., Columbus, Ohio, USA), which primarily measures middle-low spatial frequencies, found significant correlations between deficits of contrast sensitivity and performances on a number of daily living tasks, including walking down steps, reading ingredient labels, writing, reading newspapers, recognizing faces, pouring liquids and using tools [70]. Reductions in contrast sensitivity can frequently be measured long before reductions in acuity in patients with RP.

Glaucoma, RP and Contrast Sensitivity

Reductions in contrast sensitivity secondary to glaucoma result from increasing internal eye pressure on the optic nerve. The compression of the nerve causes nerve fiber morbidity and eventually cell loss. It is believed that the M ganglion cells of the visual system are impacted to a greater extent than the P cells in glaucoma. M cells are fewer in number, have larger axon diameter and larger receptive fields [71]. Measurements of the visual field, measurements of intraocular pressure and observation of changes in the nerve fiber layer and optic disc are utilized to diagnosis and manage glaucoma. The location of optic nerve change and pallor corresponds to location and density of visual field loss [72]. Newer technologies that image losses in the nerve fiber layer may have the ability to detect glaucoma damage prior to the appearance of measurable visual field losses [72–75]. Contrast sensitivity function is frequently reduced in glaucoma [23, 71]. There has been found to be a high correlation of low spatial frequency contrast

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sensitivity loss and the mean visual field loss in glaucoma [76]. Studies of mobility and visual function found strong correlations between slower mobility and both contrast sensitivity deficits at the lower spatial frequencies and loss in visual fields [23]. In individuals with glaucoma compared to age-matched normal individuals, reductions were found in the overall contrast sensitivity curve. For individuals aged 20–35, the deficits were most significant at the low- and middle-frequency ranges [71]. Visual field deficits are a definitive sign of glaucoma, and these deficits correlate with reductions in contrast sensitivity. The appearance of optic nerve fiber loss either by observation or the newer technologies also correlates with visual field deficits. Glaucoma does not impact the vision receptors themselves. Even when up to 94% of the retinal ganglion cells are damaged, the cone cells are intact [77] and the rod cells are intact [78]. It was found early on that glaucoma does not impact all ganglion cell types equally and that the scotopic visual system was damaged earlier and was more susceptible to loss [79]. Traditional visual field testing may not detect glaucoma damage until up to 20–40% of the retinal ganglion cells are gone [80]. RP affects both contrast sensitivity and visual fields. Alexander et al. [65] found that there was a greater functional loss in the M channels compared to the P channels in individuals with RP. The M pathway has a high prevalence of large diameter fibers. Glaucoma also affects the large diameter fibers. Glaucoma and RP affect some of the same visual functions and visual pathways. Like glaucoma, RP affects the scotopic visual system earlier than the photopic system.

Glaucoma and AD

A recent study of nursing home residents found a higher rate of glaucoma among 112 residents with AD compared with 774 residents without AD. The diagnosis of glaucoma was based on visual field defects or optic nerve cupping. The rate was 25.9% for the AD patients and 5.2% for the control nursing home group [19]. The conclusion was that the optic nerve may be less resistant to elevated intraocular pressure levels with AD, because ocular hypertension with normal visual fields and normal optic discs were not found in the AD group. Ocular hypertension without visual field defects or optic nerve cupping was present at a rate of 7.8% in the control group. A retrospective review of records in a large glaucoma clinic found 7 patients diagnosed with glaucoma within 1 year of being diagnosed with AD. It was discovered that among these patients, there was a more severe progression of glaucomatous optic neuropathy compared to other glaucoma patients [20]. A third study likewise found a higher rate of glaucoma among AD patients compared to healthy individuals.

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Fig. 2. Photograph of the optic nerve of an individual with glaucoma. Glaucoma nerves have greater areas of pale tissue, appearing as white in the center of the circular disc. Healthy nerves have substantially less area of white with a thicker area of pink tissue around the rim of nerve. Photograph courtesy of Murray Fingeret, OD.

This study also found a higher rate of glaucoma among patients diagnosed with Parkinson’s disease [81]. In a case-control study investigating reported cause of death and associated diseases in 1,930,627 reports, it was found that 7,195 had dementia. In comparing those with dementia with non-demented cases it was found that glaucoma and blindness in general occurred at a significantly higher rate in the dementia cases [82] (fig. 2). In a study looking at records of non-compliant patients with glaucoma, it was found that untreated glaucoma progressed from early peripheral vision loss to end-stage in 14.4 years if the pressures were 21–25 mm Hg, 6.5 years for pressures between 25 and 30 mm Hg, and 2.9 years when pressures were ⬎30 mm Hg [83]. Mikelberg et al. [84], in a retrospective study of 45 individuals with chronic open-angle glaucoma, found that initially the overall area of visual field loss was small and gradual but the larger the area of loss, the more rapid was the subsequent loss of visual field. Janciauskiene and Krakau [85] found what might be link between glaucoma, exfoliation syndrome and AD. Exfoliation syndrome is a pathological flaking of the lens that can precipitate glaucoma by impairing aqueous fluid drainage. The researchers found AD-specific proteins in the aqueous humor of patients with glaucoma and exfoliation syndrome. Peptide A␤ was found in 38% of patients with exfoliation syndrome and 40% of all patients with

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glaucoma compared to 18% of the healthy control group. All but three of these exfoliation syndrome and glaucoma patients had both ␣1-antichymotrypsin or ␣1-antitrypsin or both. Comparisons of the neuronal axon damage in AD and glaucoma have been made by a number of researchers [86, 87]. The larger retinal ganglion cells damaged by glaucoma [88] contain neurofilament triplet proteins (NF triplet proteins) [89]. The presence of the NF triplet proteins is unique to the larger retinal cells [90]. The finding of amyloid plaques and neurofibrillary tangles upon autopsy is considered diagnostic of AD. Neurofibrillary tangles are composed of neuronal cytoskeletal proteins such as NF. Vickers et al. [89] found that NF triplet proteins occur in subsets of neurons, and may lead to an increased susceptibility of specific cortical cells to degenerate in AD [6, 89, 91–93]. One hypothesis comparing glaucoma and AD looks at mechanical compression as the initial trauma resulting in the altered proteins. Vickers et al. [89] postulated that the space-occupying lesions in amyloid plaques may be similar to the compression that occurs with increased intraocular pressure in glaucoma. The physical compression in both may trigger the altered protein and damage. Glaucoma, like AD, impacts the LGN. Some reports indicate loss only in the M layers of the LGN [94], whereas others report loss in the M and P layers [95] as well as the K layers [96]. There is shrinkage and change in the LGN neurons early on in the glaucoma disease process, which can be observed even before nerve fiber loss in the optic nerve [97, 98]. If there are two neurodegenerative processes such as glaucoma and AD affecting one of the major relay centers for visual function, it is easy to appreciate that the loss in function can be substantial. AD and Circadian Rhythm

Disruptions in the circadian rhythm have high prevalence in AD patients, with rates ranging from 12% [99] to 25% [15]. AD is associated with changes in the suprachiasmatic nucleus [100, 101]. The phenomenon of ‘sundowning’ may be secondary to disruptions in the circadian rhythm of AD patients. Sundowning is a general term that has been used to describe a variety of symptoms such as sleep disturbance, nocturnal delirium, disorientation at the onset of darkness, night-time activity and agitation [102]. Glaucoma, RP and Circadian Rhythm

The intraocular pressure of the eye follows a circadian rhythm with increases at night [103–108]. Sacca et al. [107] found greater fluctuation and

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range in intraocular pressure in individuals with glaucoma compared to healthy control individuals, as did Noel et al. [106]. A study by Liu et al. [105] also found that the larger the diurnal variation and fluctuation of intraocular pressure, the greater the risk for glaucoma. A possibility is that the fluctuation is not a risk factor, but actually an early symptom of glaucoma, as the disease impacts the optic nerve and the axons leading to brain structures processing circadian function. A study undertaken by Ionescu et al. [24] found that individuals with RP had a greater daytime sleepiness, reduced alertness and greater disturbances in night-time sleep compared to control individuals. They also found that the quality of sleep was poorer in individuals with RP compared to control individuals. They concluded that the retinal degeneration had an impact on circadian rhythm. Individuals with glaucoma and RP have differences in circadian function compared to individuals who have no ocular pathology. AD is associated with disruptions in circadian function. Understanding the similarities between glaucoma and RP and how the circadian function pathways are affected in these diseases will be of value in understanding circadian function in individuals with AD.

Pupillary Function and AD

There are numerous findings demonstrating an excessive mydriatic pupil response to dilute tropicamide, a pupil-dilating agent, in individuals with AD compared to control individuals [109–113]. While the response is indicative of AD, it is not specific enough to be considered diagnostic [110]. Studies using miotic drops that constrict the pupil, specifically pilocarpine, produced an exaggerated response of pupillary constriction in AD individuals compared to control individuals, suggesting a defective pupillary innervation response in AD [114]. Scinto et al. [47], in post-mortem studies of individuals with an excessive mydriatic response to dilute tropicamide, found a profound loss of neuronal structures in the Edinger-Westphal nucleus. The Edinger-Westphal nucleus is one of the primary structures involved in pupil reaction. The researchers observed amyloid plaques and neurofibrillary tangles in all of the individuals, some of whom had not shown clinical symptoms of AD. They concluded that the Edinger-Westphal nucleus is a specific and early target in AD. In earlier studies, Scinto et al. [113] estimated that healthy individuals who exhibit an exaggerated response to the dilute tropicamide have a risk of 3:1 for developing cognitive deficits that are early symptoms of AD. Circadian function pathways

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Right eye Test duration: 1:12

30˚

Fixation errors: False positive errors:

0/3 0/3

Fig. 3. Example of frequency doubling technology test in an individual with glaucoma in the right eye. Areas of black or stripes are deficits. Figure courtesy of Murray Fingeret, OD.

and pupillary function pathways may be linked as both involve the olivary pretectal nucleus.

Functional Testing of Glaucoma and Application Potential for AD

Tests traditionally used for glaucoma may have application in understanding ocular findings in AD. Two such tests are the frequency doubling technology (FDT) and short-wavelength automated perimetry (SWAP) (fig. 3). The FDT is portable, does not require a dilated eye and incorporates some of the principles of traditional visual field testing, but has a shorter test time. The FDT tests the central 20⬚ of the visual field. The targets presented are sinusoidal gratings presented in 1 of 17 test areas. Each presentation is made up of a monochrome sine-wave sinusoidal pattern of vertical gray stripes with a spatial frequency of 0.25 cycles/degree temporally modulated at 25 Hz. Contrast between the lighter and darker phases of the vertical stripes in each presented pattern is calculated automatically using a binary search procedure to establish thresholds for sensitivity across the visual field. The FDT is believed to have the capacity to isolate the subset of retinal ganglion cells in the M pathway. The FDT isolates this pathway by utilizing a low spatial frequency sinusoidal grating (⬍1 cycle/degree) that undergoes a high temporal frequency counterphase flicker at 15 Hz or greater. When this occurs, the gratings appear as if they are doubled. The combination of low spatial frequency and high temporal frequency characteristics of the instrument stimulates the M pathway [115]. Sample et al. [116] compared the FDT with stereophotographs and motionautomated perimetry in individuals with glaucomatous changes. They concluded

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Pupil diameter: 3.5mm Visual acuity: RX: ⫹0.00 DS DC X

Date: 12.03.1999 Time: 12:13 p.m. Age: 31

30

GHT Outside normal limits

Fig. 4. Traditional visual field of the right eye in an individual with glaucoma. Areas of black or dark gray are deficits. Figure courtesy of Murray Fingeret, OD.

that the FDT may allow easier detection of abnormality than stereophotographs. In tests of neuro-ophthalmic visual disorders, the FDT was compared to conventional automated perimetry and was found to have similar sensitivity and specificity [117] (fig. 4). FDT was a better predictor of progressive field loss as measured by standard automated perimetry than pattern electroretinography in a population of chronic open-angle glaucoma [118]. Since many of the functional losses in glaucoma are similar to AD, and the pathways affected are also similar, it is possible that the FDT will be a valuable tool in detection of functional losses in AD. SWAP tests peripheral visual field function using blue short-wavelength projected against a yellow background. Standard peripheral vision testing frequently uses a white projected light against a white background. SWAP testing utilizes narrow band of 440 nm short wavelength that falls in the peak sensitivity of blue cone cells against a yellow background of wavelength 530 nm. The test theoretically measures blue cone cells and their ganglion cell connections. By stimulating only specific types of cells, in this case the blue detections cells, there is a greater chance of identifying losses secondary to disease or pathology [118].

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SWAP visual field testing is utilized clinically for glaucoma and can provide earlier evidence and detection of the disease [119]. Glaucoma has been shown to damage the large diameter nerve fibers in its initial stage [88]. Measuring the blue chromatic function is becoming part of the diagnostic battery for glaucoma and likely has application in AD. Neither FTD nor SWAP technology has yet to be investigated for their application in the understanding of the functional losses in AD. Given that the functional issues in both glaucoma and AD include low spatial contrast sensitivity, blue specific color vision loss, peripheral field functional losses, optic nerve changes and probable circadian function changes, FTD and SWAP have potential to be applicable in the identification of AD-related visual losses.

Interventions

Yellow Filters Sakai et al. [120] applied yellow filters that block light in the blue shortwavelength range to 1 subject with AD. The researchers found that contrast sensitivity became enhanced at all frequencies and there was subjective improvement in visual function reported by the subject. Lenses that selectively filter specific wavelengths of light, and in particular those in the shortwavelength blue range, have been shown to subjectively increase function with pathology such as RP and glaucoma [121, 122]. In one study, reading speed in individuals with ocular pathology increased when filters blocking blue wavelength were used [121]. Reaction time to peripheral targets has been shown to improve with selective wavelength filters that reduce light in the shortwavelength blue range in normal individuals [123]. De Fez et al. [124] found that filters that cut off the short wavelengths in the blue spectrum did improve the contrast sensitivity of normal individuals. However, they also found that the filters decreased chromatic sensitivity. Filters that are designed to cut off the blue end of the visible spectrum usually eliminate wavelengths below 500 nm. The exact cutoff varies depending on the manufacturer. The yellow filters that eliminate blue short-wavelength light may in essence eliminate abnormal retinal rod responses or excess noise in the visual system. Haegerstrom-Portney and Verdon [35] showed that with the absence of viable long- and middle-wavelength cone receptors, the rod cells show an excess signal (noise) when stimulated by short-wavelength light. This results in defective visual perception of glare, brightness and color. Individuals with the absence of viable long- and middle-wavelength cone receptors also demonstrate reductions in all spatial frequencies in contrast sensitivity. AD causes damage to multiple areas of visual processing and it is likely that areas that process long- and

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middle-wavelength light are impacted. This results in reduced or lack of properly responding long- and middle-wavelength cone receptors. The yellow filters eliminate the short-wavelength light that stimulates the remaining cells to create defective visual perception of excess brightness, glare, and reduced contrast. Light Therapy Bright light therapy has been reported to improve function in AD individuals. Therapy consists of daily controlled exposure to light over specified periods (typically 1–2 h, protocols vary). Lyketsos et al. [125] found that bright light therapy improved sleep patterns, but it did not lead to improvement of agitated behaviors. Gasio et al. [126] also found that light treatment changed the circadian rhythms of sleep patterns by inducing a small advancement of the circadian rest-activity cycle and inducing an earlier onset of the most restful period of the night. They did not investigate agitation response. Van Someren et al. [127] not only looked at individuals with AD, but included those with severe vision loss or blindness. They found that light therapy changed the circadian rhythms of sleep patterns in AD individuals who had intact visual function, but there was no change in individuals with AD who also had severe vision loss or blindness. In another study of 5 individuals with AD, 3 had delusional symptoms prior to initiating light therapy and showed slight improvement, and 1 had no symptoms of delusions at onset and had no change with light therapy. The fifth had showed no delusional symptoms prior to treatment but then exhibited symptoms after treatment. The symptoms abated after the treatment was stopped [128]. The symptoms may have resulted from the stimulation of the defective light and dark detection system and the defective interactions with an intact visual system. Environmental Modifications Increasing contrast in the environment and decreasing visual clutter are beneficial in cases of vision loss [129]. Examples of this include the use of primary colors instead of muted tones which may blend from one object to another, and the elimination of glare surfaces and small objects which may result in visual confusion. For a more detailed discussion of environmental modifications, see Dunne, this volume. Geographic Issues The change in light quality towards dusk may also change the quality of short-wavelength light in the blue spectrum. The sky takes on a deeper color blue as the sun sets due to the contributions from the ozone layer [130]. Different regions on the globe may have different atmospheric portions of short wavelengths in the blue spectrum. There is a greater proportion of shorter blue-wavelength light in the spectral distribution of daylight in the southern

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hemisphere compared to that of the northern hemisphere, as measured in Australia and compared to standards of northern hemisphere regions including the United States, England and Canada [131]. A single report regarding subgroups of AD individuals with significant visuospatial deficits indicated a higher incidence of this subgroup in the southern part of the United States compared to other regions in the United States [132]. The response and symptoms due to defects in the circadian retinal ganglion cell and circadian rhythms may have regional differences based on atmospheric proportions of shortwavelength light in the blue spectrum. This would affect how any intervention is utilized, whether it is light therapy, lenses or environmental modifications.

Conclusions

The impact of AD on the visual system is complex and remains poorly understood. AD affects contrast sensitivity, motion perception, pupil reaction and color perception, among other functions. Neuritic plaques and neurofibrillary tangles have been found in the Edinger-Westphal nucleus, a principal structure in pupil reaction, in the LGN, a key area for M, P and K pathways, and in the primary visual cortex. Many of the visual deficits in AD have their origin in the brain’s higherorder processing regions. By contrast, the diseases glaucoma and RP affect the same visual functions but originate in the eye itself. Glaucoma affects structures at the axon level and RP at the receptor cell level. Through studies of all three diseases and animal and human models, we are beginning to obtain an understanding of the visual system. What is apparent is that damage at any level has implications for the entire visual system. What is less well understood are the complex interactions of cellular function, response, feedforward, feedlateral, and feedback connections. All three disease processes have greater functional deficits that involve short-wavelength light, such as blue chromatic color deficits, relative to the longer wavelengths. All three disease processes eventually impact upon the ganglion cells of the inner retina. Retinal ganglion cells in glaucoma have been shown to have NF triplet proteins similar to those cortical neurons prone to neurofibrillary tangle formation in AD. The amacrine retinal cells, which laterally interconnect the neurons of the inner retina, show unusual findings in AD, RP and glaucoma. In RP, the amacrine cells have shown neurite sprouting that may have similarities to neurofibrillary tangles. In AD, neuritic plaques and neurofibrillary tangles have been found in the suprachiasmatic nucleus, which processes circadian rhythm signals. Findings indicative of circadian rhythm defects such as changes in melatonin receptor activity in the amacrine cells of the retina have been identified in individuals

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with AD. Such changes in the retina are an indication that the retinal ganglion cells eventually are altered in individuals with AD. Glaucoma and RP also are associated with changes in circadian function, and the three diseases may impact function in similar ways. Understanding the decreases in visual function as a result of AD will not only benefit the patients, family members, and community affected by the disease, but will also serve to help us better understand the visual system as a whole and allow for more extensive understanding of other disease processes that affect vision.

Acknowledgements This work was made possible with support from NIH Disability Supplement to R01 1AG15361 (to Alice Cronin-Golomb, PhD) from the National Institute on Aging. I thank Tom Laudate, MA, for providing the diagram of the retinal layers, and Alice Cronin-Golomb, PhD and Tom Laudate, MA, for editorial suggestions. I also thank Murray Fingeret, OD, for providing several of the figures.

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Denise Valenti, OD, FAAO Department of Psychology, Boston University 648 Beacon St., 2nd floor, Boston, MA 02215-2013 (USA) Tel. ⫹1 617 358 3047, Fax ⫹1 617 358 1380, E-Mail [email protected]

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Cronin-Golomb A, Hof PR (eds): Vision in Alzheimer’s Disease. Interdiscipl Top Gerontol. Basel, Karger, 2004, vol 34, pp 30–61

Neuropathological Changes in Visuospatial Systems in Alzheimer’s Disease Armin von Guntena, Panteleimon Giannakopoulosa,b, Constantin Bourasb,c, Patrick R. Hof c,d,e a

Service Universitaire de Psychiatrie de l’Âge Avancé, Prilly, Lausanne, and HUG Belle-Idée, Service de Psychiatrie Gériatrique et Service de Neurospychiatrie, Département de Psychiatrie, Chêne-Bourg, Switzerland; Departments of cNeuroscience, dGeriatrics and Adult Development and e Ophthalmology, Mount Sinai School of Medicine, New York, N.Y., USA b

Cognition in typical Alzheimer’s disease (AD) is characterized by a progressive decline that affects first memory and later executive functions, language, and visuospatial skills. This sequence of cognitive deterioration is thought to reflect the stepwise invasion of cerebral cortex by two major pathological hallmarks of AD, neurofibrillary tangles (NFT) and senile plaques (SP). Detailed analyses of their distribution in the brains of AD patients have demonstrated that certain components of the neocortical and hippocampal circuits within the medial temporal lobe are particularly prone to degeneration [1–6; for a review, see 7]. Diagnosis of AD is, however, heavily weighted towards memory impairment as the central deficit and may, therefore, prevent inclusion of atypical cases [8]. Clearly, certain cases of dementia do not meet the accepted clinical and neuropathologic criteria for the definition of AD, yet they show the same histopathologic features [9]. These atypical AD cases may represent examples of clinicopathological subtypes of AD. Identifying the complete spectrum of clinical presentations of histologically proven AD appears to be an important issue. Clinical heterogeneity of patients diagnosed with AD has long been recognized [10, 11]. These patients usually have defined memory impairment according to the requirements of the diagnostic manuals in use [12, 13]. Thus, they may be different from those patients with possible or probable AD with

prominent and early deficits in language, musical skills and prosody, motor abilities, frontal and executive capacities, and visuospatial skills [14–24]. These reports have now been expanded to include anatomoclinical case studies confirming that many of these patients with atypical clinical presentation suffer from AD although they may not fit into neuropathological classification systems such as the one proposed by Braak and Braak [2]. As cognitive deterioration is thought to reflect the stepwise invasion of the cerebral cortex by NFT and SP, atypical clinical features suggest the presence of an unusual NFT or SP invasion of the cerebral cortex. Clinicopathologic case studies have generally found a strong correspondence between AD-type lesions and clinical features [4, 25–27] and suggest that etiologically unique and, therefore, independent neuropathological subtypes may underlie these atypical cases and correspond to patterns of neurodegeneration that are qualitatively different from usual AD [28–32]. Many reported cases with clinically atypical but neuropathologically confirmed AD died at an advanced stage of AD [8, 33]. However, if atypical cases correspond to specific patterns of neurodegeneration, the atypical NFT density distribution in these patients may still be related to the clinical features observed [32]. Indeed, the atypical lesion distribution suggesting specific corticocortical disconnections is still apparent at these advanced stages of dementia in spite of AD-type lesions that have spread over most or the entire cortex [33]. Atypical AD cases may even serve as excellent examples of clinicopathological correlations between NFT and SP distribution and clinical symptoms as the pathologic changes involve select neuronal types and degeneration of specific corticocortical projections [25, 27, 33–37]. The atypical lesion distribution is likely to correlate with clinical visuospatial features. In this chapter, we review NFT and SP changes in the central visual system of patients presenting clinically with visuospatial variants of AD.

Visuospatial Variants of AD

In recent years, several studies in humans have shown that many AD patients initially present with prominent visual and visuospatial symptoms. Since NFT and SP formation represent a reliable correlate of the disconnection of certain corticocortical pathways, the identifiable clinical deficits observed in such AD cases may be directly linked to the disconnection that has occurred. In these cases, the visual impairment affects specific aspects of visual function [38–41]. Although neuropathological confirmation was not provided for some of these patients, many of them may have had AD. Most of the recently described cases with posterior cortical alterations suffering from probable or

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confirmed AD display a complex clinical symptomatology and variable disturbances of visual function that include alexia both for words and music, anomia, agraphia, transcortical sensory aphasia, complete or partial Bálint’s syndrome (spatial disorder of attention, psychic paralysis of gaze or inability to shift gaze voluntarily to objects of interest despite unrestricted eye rotations, optic ataxia) and Gerstmann’s syndrome (left-right disorientation, finger agnosia, agraphia, acalculia) as well as cortical blindness, prosopagnosia, achromatopsia or hemiachromatopsia or other impairment of color perception, deficient contrast sensitivity, ocular dysmetria, and left or right hemineglect, apraxia, hemianopsia or dissociation between explicit and implicit processing of global visual information [14, 42–53]. The cortical atrophy and metabolic deficits, as demonstrated in some of these atypical cases by functional imaging or at autopsy, predominates in the parieto-temporo-occipital junction and occipital cortex. These cases are thus frequently referred to as posterior cortical atrophy [19, 26, 29, 30, 33, 39, 54–66] although others prefer dividing posterior cortical atrophy up in different entities such as progressive occipitotemporal or biparietal atrophy [41]. The topic on visual and spatial behavior in these cases is further explored by Mendez in this volume. Motion Perception and Target Tracing A particularly prominent feature in these cases with atypical visual presentation is a severe deficit in motion perception and target tracing [67–80]. The clinical symptomatology of AD patients with posterior cortical atrophy suggests that subsets of cortical pathways linking the primary visual occipital regions to the posterior parietal and cingulate visual association cortex are affected in the early stages of the dementia [26, 29, 33, 57, 81] whereas anterior spread may occur at later stages of the disease [82]. Similarly to AD, a visuospatial variant of mild cognitive impairment has been described. Based on visual motion-processing tasks, one study suggests that visuospatial impairment may develop as an independent precursor sign of neurodegeneration [83]. Bálint Syndrome As mentioned, several of the patients showing predominantly visual function impairment have symptoms comparable to the observation made in 1909 by the Hungarian physician Rezsö Bálint of a complex visual syndrome characterized by a simultagnosia, a cortical paralysis of visual fixation associated with an optic ataxia, and a severe disturbance of visual attention in patients with large bilateral watershed infarcts in the parieto-occipital region [84, 85]. The occurrence of a Bálint’s-like syndrome in AD patients was in fact first documented by Ernst Grünthal in 1928 [86] and subsequently by Ferdinand Morel in 1945 [87].

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Quantitatively, inordinately high densities of AD lesions are observed in the occipital regions of AD cases with posterior cortical atrophy and visuomotor impairment resembling Bálint’s syndrome with a gradient in NFT densities from area 17 to the visual association regions in the anterior occipital (area 19) and parietal cortex (area 7b), as well as in the posterior cingulate cortex (area 23) [19, 25, 26, 29, 30, 33, 51, 54, 55, 57, 58, 61, 62, 66, 88] (fig. 1). The division of these cases into dorsal parieto-occipital and ventral temporooccipital subtypes has been suggested [14, 41, 89]. However, not all of them may fit into these two categories and a primary visual subtype has also been suggested [8, 52]. This predominantly visuospatial symptomatology contrasts sharply with the memory impairment that is usually seen AD. Prosopagnosia and Visual Agnosia Patients with prosopagnosia and visual agnosia have also been reported [52, 90–93], but neuropathologically confirmed AD cases with these disorders are rare. One such study in these patients showed a comparable occipital displacement in the lesion distribution with a preferential localization of NFT and SP in the mid and inferior temporal cortex (areas 37, 20 and 21), instead of the inferior parietal and posterior cingulate cortex, demonstrating the specific involvement of the occipito-temporal visual pathway, consistent with the clinical symptomatology [57]. In typical AD, a statistically significant relation was found between NFT densities in Brodmann’s areas 18, 19, and 37 and associative visual agnosia, whereas NFT densities in the areas studied did not correlate with the presence of apperceptive visual agnosia [94]. In this context, it is worth noting that impairment in constructional tasks (i.e., constructional apraxia) in typical AD is correlated neuropathologically with the presence of neuritic changes in the occipital cortex, early in the course of the disease [95] and with hyperphosphorylated
protein [96]. Similarly, NFT densities in the superior parietal, posterior cingulate, and occipital cortex correlated with constructional apraxia [97]. Parietal Syndromes Cases of probable AD presenting with somatosensory and motor deficits, or parietal-lobe syndrome including visuospatial problems, apraxic aphasia, and difficulty with bimanual tasks have also been described [24, 41, 98, 99], possibly in combination with or mimicking corticobasal degeneration [31, 100]. In these cases too, the distribution of AD lesions was focal and more severe in the sensorimotor cortex and parietal cortex. The triad of visuospatial problems, agraphia, and impairment in bimanual tasks in AD appears to correlate with bilateral parietal atrophy and hypoperfusion [41]. Biopsy-proven AD has been reported in another case of slowly progressive apraxia [101], and particularly

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33

a

b

c

d

e

f Fig. 1. Distribution of neurofibrillary tangles in areas Brodmann’s areas 17 (a), 19 (b), 23 (c), 9 (d) in an AD case with posterior cortical atrophy. Note the very high numbers of lesions in all of the posterior cortical areas, and in particular in area 17, as well as the increase in lesion density in areas 19 and 23 compared to area 17. Furthermore, prefrontal area 9 exhibits numbers of neurofibrillary tangles comparable to those in area 17. Distribution of senile plaques in areas 17 (e), and MT ( f ) in an AD case with posterior cortical atrophy. Note the very high numbers of lesions in MT compared to area 17. Scale bar (shown in f )  200 m.

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extensive neuritic plaque and NFT formation was demonstrated in the parietal lobes in a patient with dyspraxia and related difficulties in writing as well as extremely poor performance on visuospatial tests and transitive and intransitive hand gestures [41]. A patient with confirmed AD with parietal involvement strikingly lateralized to the right hemisphere showed hemineglect, incomplete Bálint’s syndrome, and environmental agnosia, as well as left-sided motor symptoms including unskillful movement, dystonic postures, and myoclonus [88]. Similarly, demented cases presenting with predominant involvement of one hemisphere have been described. One case with marked right hemisphere atrophy had a massive deficit in all of the cognitive functions early in the course of dementia. NFT and SP were preferentially localized in the prefrontal, temporal and posterior parietal cortices in both hemispheres, whereas the hippocampal formation displayed lower lesion densities than neocortical areas, and significantly higher lesion densities were found in the more atrophic side, with a regional topography that matched the clinical symptoms [102]. Right-side neglect in AD with hypoperfusion in the left posterior regions has also been described although no neuropathological assessment was available in this patient [43], but cases of confirmed AD with predominant right-parietal AD-type lesion location showed left hemineglect among visual and motor signs [88] as well as progressive unilateral apraxia [28]. The alien hand sign has been observed in patients with lesions of the parietal lobes possibly due to AD [104]. Visual Hallucinations A number of reports suggest that visual hallucinations in AD may be caused by neuropathology of visual cortex [103, 105]. Increased NFT densities in the middle frontal gyrus, the superior temporal cortex and the inferior parietal lobule as opposed to the hippocampus and the entorhinal cortex in AD patients either with delusions or hallucinations has been found [106]. Surprisingly, little or no neuropathological demonstration of an association between visual hallucinations and visual centers has been made in AD, but a striking association has been found between high densities of temporal-lobe Lewy bodies, in particularly in the amygdala and the parahippocampal cortex, in patients having suffered from dementia with Lewy bodies in which visual hallucinations are a common feature. The topic on visual hallucinations is further explored by Holroyd in this volume. Other Visual Symptoms Recognition of higher-level visual stimuli is likely to depend on specific cortical regions. The fusiform face area seems to be highly specialized for face perception [107] whereas its neighboring ‘extrastriate body area’ may be

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specialized in the visual processing of the human body [108]. Similarly, there is evidence that the layout of the local visual environment is selectively represented by the ‘parahippocampal place area’ [109] whereas various other brain areas such as the parietal cortex, the parieto-occipital junction and/or the hippocampus have been proposed to be essential in topographical memory [110–113]. Most of these potential links between clinical presentation and cortical lesion in AD have yet to be demonstrated. However, spatial disorientation was found to correlate with the disruption of corticocortical connections between right areas 7 and 23 and the CA1 field of the hippocampus [114]. We have observed a patient with frontal-like symptoms and subsequent development of reduplicative paramnesia for places as well as some difficulty in recognizing familiar faces. In reduplicative paramnesia for places, one or more environments are believed to exist simultaneously in two or more locations. Patients with reduplicative paramnesia for places are able to recognize familiar faces and match facial expressions but they may have impairment of unfamiliar face matching and matching of disguised and undisguised faces as suggested by a case report [115]. A tendency to be captured by visual details of an object rather than the object as a whole and a lack of self-awareness could best account for another patient’s reduplicative paramnesia [116]. While the pathophysiology of reduplicative paramnesia is likely to be complex and is not well understood, visual disturbance may contribute to its pathogenesis. NFT distribution was very atypical in this patient. Although NFT densities are difficult to compare among studies (AD patients at different Braak and Braak stages, different techniques employed), NFT densities in this case were consistently lower than in typical AD cases in the entorhinal cortex [7, 117, 118], the hippocampus [7] as well as Brodmann’s areas 39 and 7b [27, 101, 115], and were consistently higher in Brodmann’s areas 8, 9, and 24 [7, 27, 102, 114, 117, 119]. Overall, this patient showed very high NFT densities in the frontal association cortex, high NFT densities in temporal association areas and extremely low densities in the hippocampus, entorhinal cortex, and parietal lobes. This pattern of NFT distribution contrasts with the one found in Bálint-like cases of AD where NFT pathology is prominent in the occipital cortex and the caudal portions of the temporal and parietal regions [4, 19, 25–27, 30, 98]. The gradient of the NFT load in this case may reflect the temporality of NFT invasion of the neocortex, in that it began in the frontal association cortex and invaded subsequently the temporal association cortex. It is interesting to note that the hippocampal and at least major parts of the parahippocampal and the parietal cortex were largely spared in this patient. This fact may corroborate our observation that he did not misidentify his home place, a frequent symptom in AD [120] and that he had no major topographical disorientation accompanying his reduplicative paramnesia for places. On the other hand, the massive pathological

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involvement of the temporal cortex may explain his difficulty recognizing familiar faces while he was still only slightly disoriented in time. Overall, these observations suggest that reduplicative paramnesia for places may be caused by NFT load in the temporal association cortices or corticocortical disconnection between the frontal lobes and temporal and parietal areas including anatomical links to visual centers.

Cortical Neuropathology Related to Visuospatial Processing in Typical AD Compared to Visuospatial Variants of AD

Atypical AD differs from typical AD, among other features, in that atypical AD begins with a clinical syndrome other than progressive memory impairment, such as a wide range of visuospatial features or linguistic and executive syndromes. This heterogeneity of clinical manifestations is often opposed to the apparent clinical homogeneity of typical AD which is suggested by the defining memory impairment particularly at the beginning of the disease [12, 13], the definition of a course of progressive involvement of other cognitive and also behavioral domains [12, 13], a normative approach of the functional staging of AD [121] and, likewise, averaging of neuropathologic data [2]. Although there is little doubt that this averaging approach is extremely useful in clinical settings, it may give the wrongful impression that typical AD is clinically homogeneous. The clinical heterogeneity of typical AD has long been demonstrated [10, 11] and confirmed by more recent clinical reports [122–125; see also Cronin-Golomb, this volume]. Heterogeneity of typical AD as demonstrated by rigorous clinical investigations suggests that better characterization of relationships between cognitive function and connectivity patterns in the human cortex may lead to a better understanding of the anatomical substrate and pathogenetic mechanisms underlying neurodegenerative disorders such as AD. In fact, several relationships between cognitive deficits and disruption of specific corticocortical circuits have been identified in typical AD cases in particular within the realm of visuospatial systems. Visual deficits independent of the aging process are quite common in AD and the original presenile case described by Alois Alzheimer [126] also had perceptual impairment and visual hallucinations. Visual deficits present mainly as reading difficulties, problems in shape and color discrimination, deficient contrast sensitivity, difficulties in visuospatial orientation and movement detection, visual field deficits, visual agnosia, and impaired processing of famous faces or impaired face discrimination, deficits in visual search strategies, and in some instances relationships between visuospatial and related deficits and

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disruption of visual pathways and corticocortical circuits have been identified [47, 61, 69, 94, 127–137]. Lesions in the retina, the visual pathways and subcortical visual centers such as the lateral geniculate nucleus, the lateral inferior pulvinar and the superior colliculus may play a role in visual disturbances in AD. Impairment of neuroretinal or retinocortical functioning has been suggested by some [138, 139] but not all clinical studies [140]. Similarly, reduction in the number of retinal ganglion cells and their axons have been found in neuropathological studies [141–143], but others have failed to confirm these data [144]. SP but no NFT were found in LGN whereas both SP and NFT were encountered in the superior colliculus [145]. Likewise, SP, NFT as well as amyloid and neuritic plaques and neuropil threads were found in the pulvinar [146]. See Valenti [this volume] for further considerations on the anterior visual pathway in AD. While visual association cortices contain high densities of SP and NFT in typical AD, area 17 may contain numerous SP, but relatively few NFT even at advanced stages of the disease [1, 25, 29, 35–37, 56, 147]. However, both high SP and NFT densities were found in the primary visual cortex of some AD patients [148]. Total neuron counts in area 17 do not differ between AD and control brains, unlike in frontal, parietal, and temporal areas [149], but a 30% neuronal loss in the primary area 17 has been reported [148] sparing, however, calcium-binding positive interneurons [150]. Contradictory findings may be partly explained by the possibility that specific visual symptoms may reflect differential pathology distribution in the primary visual cortex. Thus, densities of SP and NFT in the cuneus compared to the lingual gyrus of area 17 may contribute to the visual field defects in AD patients [127]. In nonagenarians and centenarians with either no dementia or very mild cognitive impairment [151], NFT were scarce in area 17 indicating that ageing per se had no major influence on NFT formation in the primary visual cortex of the oldest-old. NFT are present in area 18, but their laminar distribution differs from that in temporal, parietal and frontal cortices, as they are predominantly located within layer III, as opposed to layers IV–VI, and are far less numerous [25, 29, 35, 56]. Based on NFT distribution, areas 17 and 18 are relatively spared in AD as it usually presents, suggesting that the corticocortical outflow from area 17 to area 18 is far less affected than it is among temporal or parietal visual association areas, early in the course of the disease [26, 29, 33, 56, 81]. The apparent increasing degree of neuropathologic involvement along visual cortical hierarchies from the occipital cortex to the parietal and temporal cortex is consistent with many deficits in visual function observed in AD patients, although not all aspects of visual function are similarly affected [38, 65, 68, 74, 75, 77, 78, 135, 144, 152]. The variable impairment of different visuospatial and related functions in typical AD must be kept in mind in view of the clinical heterogeneity in various

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cognitive domains in patients with typical AD [10, 122–125]. A neuropathological study of 78 cases of AD aiming at identifying possible subtypes of the disease showed that the majority of cases fell into five major groups. One of these subtypes was a small group of patients who had higher densities of SP and NFT relative to other groups, especially in the parietal and occipital regions [153]. The clinical data of this study were not sufficiently detailed to determine whether the pathological subtypes represented clinical types of AD, but relationships between visuospatial and related deficits and disruption of corticocortical circuits have now been identified in typical AD cases: constructional apraxia is associated with NFT formation in left areas 7, 19, and 23 leading to damage of the occipitoparietal pathway [99], associative visual agnosia with NFT development in the left occipitotemporal visual pathway that link secondary visual area 18 and ventral area 19 to the high-order visual association area 37 [94], temporal and spatial disorientation with the disruption of corticocortical connections between right areas 7 and 23 and the CA1 field of the hippocampus [114], and impaired famous faces processing with bilateral damage of anterior cortical areas 9 and 24 involved in semantic memory and retrieval of information [132]. Taken together, these studies indicate that AD is a syndrome of cortical disconnection and that strong correlations exist between specific neuropsychological deficits and the progressive damage of defined sets of long projections. The distribution of SP and NFT in typical AD differs from their distribution in atypical cases of AD. Table 1 sums up clinical and neuropathological findings of atypical cases with posterior cortical atrophy. In comparison to AD cases with the usual presentation of dementia, areas 17, 18, and 19 contain a much higher density of NFT in the cases with posterior cortical atrophy. Differences are most marked in the occipital areas and were predominant in areas 17 and 18 with increases over conventional AD cases as high as 35-fold in cases with posterior cortical atrophy [25, 26, 29, 33]. Smaller differences from conventional AD exist in areas 19, 7b/7m and 23. NFT counts do not differ significantly in posterior cortical atrophy cases in areas 37, 20, and 21 compared to more typical AD cases, except in the case that presented with visual agnosia, in which regions of the inferior temporal cortex and occipitotemporal junction had increased densities of AD lesions compared to cases with the usual presentation of AD. In the prefrontal areas 9, 45, and 46 of posterior cortical atrophy cases, NFT densities are consistently lower than in conventional AD cases, supporting the notion of a shift of the lesion distribution into occipital regions that are usually not severely involved in AD [1, 25, 26, 29, 33, 56, 57, 81]. SP are also much more numerous in the occipital cortex of posterior cortical atrophy cases than usually seen in AD cases. Depending on the area, up to 5-fold increases in SP density occur in the posterior cortical atrophy cases, the largest differences being observed in areas 17 and 18.

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39

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Patient(s) described

1

1

1

1

Reference

Grünthal, 1928 [86]

Morel, 1945 [87]

Corsellis and Brierley, 1954 [187]

Faden and Townsend, 1976 [188]

Man; 69 years old at onset; progressive visual loss within 4 months to total cortical blindness; then progressive memory loss, myoclonus, disorientation, confusion, jargon aphasia

Woman; 56 years old at onset; initially ‘vacant air about her’, difficulty recognizing people; 6 years later memory problems, wandering about at night, dementia with spastic paralysis and paresis

Man; 55 years at onset; initially rapidly progressive memory impairment and dyslexia; motor disorders of vision, psychic paralysis of gaze, cortical blindness

Woman; 53 years old at onset; initially forgetfulness; spatial disorientation and Bálint syndrome; left-right disorientation, autotopoagnosia, reading difficulties

Clinical features

Minimal cerebral atrophy Diffuse neuronal loss; numerous SP and many NFT throughout cortex

Severe amyloid vascular change; NFT most marked in occipital cortex, absent in frontal cortex; SP apparently abundant and severe in all cortices

Brain atrophy more pronounced in both posterior segments; AD changes in ‘optopsychic’ area  ‘optomotor’ area  ‘optosensorial’ area Layer III V/VI; II/IV not affected

The highest amounts of SP and above all NFT in parietooccipital cortex, much less in occipital, frontal and temporal cortex

Cortical pathology

HE, cresyl violet, Mallory phosphotungstic acid-hematoxylin, Bodian, Bernhhold

Cresyl blue, hematoxylin and van Gieson, silver impregnation, congo red

Not specified

Not specified

Histological preparation

Ventricular dilation (encephalography)

Particularities/ Comments

Table 1. Summary of neuropathologically confirmed cases of AD in patients with predominantly visuospatial features at presentation

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1

1

Kobayashi et al., 1987 [189]

1

Cogan, 1985 [38]

Crystal et al., 1982 [98]

Man; 55 years old at onset; initially forgetfulness, disorientation for time and space; then gradually progressive dementia; when first seen at 60 years of age severe Bálint’s syndrome, constructional apraxia, myoclonus, muscle rigidity and hyperreflexia; paraphasisa and logoclonia, perseverations

First attended at 65 years; visuospatial disorientation, difficulty driving (poor vision), reading; left hemineglect; dressing difficulties, motor eye disturbance, visual and topographic agnosia, optic ataxia, left hemianopsia, prosopagnosia

Woman; 51 years at onset; initially numbness in left hand, difficulty in knowing arm position in space; later, astereognosis, agraphaestesia, of left hand; difficulty in figure construction; gegenhalten, choreoathetoid movements followed by progressive dementia

Diffuse atrophy of cerebrum most severe in occipital and temporal lobes Moderate to severe neuron loss and glial proliferation; many NFT in cortex and numerous SP throughout the cortex

Abundant NFT and plaques

Numerous SP; scattered NFT; mild reactive astrocytosis; focal amyloid angiopathy in right frontal lobe

Bodian, PAS, congo red, HE, Klüver-Barrera, Holzer

Electron microscopy; no specific preparation mentioned for NFT and plaques

Not specified

Diffuse brain atrophy (CT) AD in an Al-refiner; increased Al content in tangle-bearing neurons as compared with usual AD

Most parts of brain were discarded; only cortex material for electron microscopy research of slow virus was kept

Biopsy-proven AD; biopsy in right frontal lobe

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Patient(s) described

2

6

8

Reference

Kiyosawa et al., 1989 [73]

Hof et al., 1989 [29]

Hof et al., 1990 [25]

Mean age 80.3 years at autopsy; Bálint’s syndrome

Mean age 78.5 years at autopsy; Bálint’s syndrome; presence of memory defects and intellectual impairment

Simultagonsia, environmental visual agnosia, dyslexia, dysphasia with dysgraphia

Clinical features

Table 1 (continued)

Strikingly higher NFT counts in occipital visual areas 17/18/19 and superior colliculus, lower counts in frontal areas 9 and 45, equal in

Very high amount of NFT in area 17 that further increased in area 18, substantially lower densities in superior frontal cortex, approximately equal densities in parietal cortex as compared to typical AD cases; neuritic plaques more numerous in area 17/18 and inferior parietal cortex as compared to typical AD

Biopsy-proven AD with no specification

Cortical pathology

Quantitative NFT and NP assessment; Gallyas and Globus stains

Quantitative NFT and NP assessment; Gallyas silver stain and Globus stain, HE

Not specified

Histological preparation

No individual case histories

No individual case histories

Biopsy-proven AD cases without specification which 2 of the 8 patients described underwent a biopsy; reduced glucose metabolism in visual association cortex and inferior parietal cortex; cortical atrophy restricted to parieto-occipital areas (MRI)

Particularities/ Comments

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1

1

1

Jacquet et al., 1990 [58]

Ross et al., 1990 [190]

Berthier et al., 1991 [54]

Woman; 45 years old at onset; initially, slowly progressive visual loss with piecemeal perception and writing difficulties; later, reading and calculation deficits, spatial disorientation, decreased manual skills

Initial alexia and visual agnosia; subsequently Bálint’s and Gerstmann’s syndrome, and transcortical sensory aphasia

Woman; 58 years at onset; initially, difficulty reading and visual impairment; later, difficulty executing manual tasks, linguistic impairment, spatial disorientation, prosopagnosia; progressive more general cognitive decline

Right parietal biopsy showing mild neuronal loss and lipofucsin accumulation; abundant neuritic plaques, some immature NP, and occasional NFT

Many NFT and SP strongly concentrated in the occipital and parietal lobes; congophilic arteriopathy

Cerebral atrophy predominant in parietooccipital regions Substantial neuron loss in occipital cortex, very high number of SP, few NFT but many neuropil threads; some SP but many NFT in frontal cortex; high numbers of both SP and NFT in temporal cortex; globally, predominance of AD lesions in occipital lobe

posterior parietal cortex 7b as compared with typical AD; much higher incidence of neuritic plaques in visual occipital cortex and higher incidence in posterior parietal (area 7b) and posterior cingulate (area 23) as compared to typical AD; similar profiles in atypical and typical AD in the inferior temporal gyrus

HE, McMannus, congo red, Bielschowsky silver impregnation; monoclonal antibody against paired helical filament

Not specified

Masson’s trichrome, HE, congo red, Klüver-Barrera, immunostain PAP against amyloid  and
proteins

Biopsy-proven AD

2 non-AD patients with PCA are also reported Abstract available only

Cortical atrophy predominant in occipital and partly parietal regions (CT)

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Patient(s) described

1

2

Reference

Hof et al., 1991 [57]

Hof et al., 1993 [26] Cortical atrophy predominating in posterior parietal cortex and occipital lobe Numerous NFT and SP throughout cortex; high densities of NFT in visual cortex increasing from area 17 through 18 and 19/7b/23 both in supra- and infragranular layers. Area 20 had lower NFT densities than areas 19/7b/23. As above, but highest NFT densities in area 7b

Man; 55 years old at onset; rapidly progressive visual impairment, increasing difficulties in reading, progressive memory impairment

Cortical atrophy predominant in temporal and occipital lobes Dramatic increase of NFT and neuritic plaques in visual occipital areas 17/18/19 and visual temporal association area 21, lower NFT densities in frontal areas (9/45/46) and posterior parietal cortex (7b) as compared with typical AD

Cortical pathology

Woman; 50 years old at onset; complaint of visual impairment; difficulty reading and identifying objects; probable Bálint’s syndrome; limited and infrequent eye movements; modest memory deficit on formal testing; progressive decline

Woman; 78 years old at onset; initially apperceptive visual agnosia; slight memory impairment and disorientation; 2 years later, prosopagnosia, tactile agnosia; progressive evolution towards dementia over the following 9 years

Clinical features

Table 1 (continued)

As above

Modified thioflavine S, modified Globus, Bielchowsky, Holzer, and Campbell-Switzer-Martin techniques; anti-
antibodies and anti-amyloid antibodies

Gallyas silver, modified Globus silver-gold impregnation, HE, cresyl violet

Histological preparation

This second patient is identical to the one Morel had reported

Mild cerebral atrophy (CT)

Particularities/ Comments

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1

2

Ceccaldi et al., 1995 [28]

1

Victoroff et al., 1994 [66]

Levine et al., 1993 [30]

Man; 50 years old at onset; left-hand apraxia, topographical difficulties and visuo-constructive impairment, spatial dysgraphia, mild left hemineglect; evolving slowly towards dementia

Man; 58 years old at onset; progressive difficulty in reading, calculating and finding his way while driving, no memory impairment; subsequent development of full Gerstmann’s and Bálint’s syndrome

Man; approx. 57 years old at onset; reading and driving difficulties; visual agnosia; calculation impairment; steady decline over 12 years with appearance of more generalized cognitive impairment

Right frontal biopsy showing SP and NFT

Cerebral atrophy slightly more prominent in the occipital lobes Many NFT and neuritic plaques throughout the cortex; however, lowest densities in area 17 and anterior cingulate, intermediate densities in area 18 and parietal and temporal cortex and highest densities in posterior cingulated, inferior frontal and especially postcentral gyrus

Marked cerebral atrophy, particularly in parietooccipital regions Abundant NFT and SP; NFT density was higher in the occipitoparietal areas (18/19  17), posterior cingulate and temporal lobes than in the frontal areas; SP density was higher in visual association and frontal cortex than in primary visual cortex, plaques with amyloid cores and neuritic change were very high in both primary and association cortex and very low in frontal cortex

Not specified

Bielschowsky silver stain

Luxol-fast blue, HE, modified Bielschowsky’s silver, Congo red

Biopsy-proven AD Cortical atrophy predominant in retrorolandic regions (CT and MRI)

Mild diffuse atrophy (MRI)

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Patient(s) described

1

1

Reference

Green et al., 1995 [101]

Levy et al., 1995 [59]

Woman; 65 years old; slowly progressive loss of visual acuity and field; trouble playing golf and reading; later, relative left homonymous visual field defect;

Right occipital biopsy; large quantities of NFT and neuritic plaques; amyloid angiopathy

Numerous predominantly neuritic plaques and NFT in right middle and inferior temporal lobe

Diffuse moderate cerebral atrophy, more pronounced in retrorolandic regions, especially the parietal areas Diffuse neocortical neuron loss; numerous SP (mostly neuritic) in pyramidal layer of hippocampus and all associative cortices; numerous NFT all over the cortex, especially in the superior parietal cortex bilaterally

Woman; 60 years old at onset; left-hand apraxia, spatial dysgraphia and dyscalculia, constructive apraxia, severe left hemineglect

Man; 60 years old at onset; problems driving car, slowly progressive apraxia; features of alien hand sign; then writing and calculation difficulties, mild left-hand agraphesthesia and astereognosis

Cortical pathology

Clinical features

Table 1 (continued)

Thioflavine S and silver stains

Bielschowsky silver stain

HE, Luxol-fast blue PAS, Loyez, modified Bielschowsky; antibodies against amyloid  and
proteins

Histological preparation

Biopsy-proven AD case Normal CT scan, subsequently bilateral parietal and occipital lobe atrophy (MRI); avascular zone in distal right calcarine segment (arteriography)

Biopsy-proven AD case Generalized atrophy (MRI) Hypoperfusion in right frontal, parietal, temporal, and occipital regions (SPECT)

Cortico-subcortical atrophy predominant in retrorolandic areas (CT); right retrorolandic hypoperfusion (SPECT)

Particularities/ Comments

Neuropathological Changes in Visuospatial Systems in AD

47

Rogelet et al., 1996 [93]

Mackenzie Ross et al., 1996 [41]

2

1

Right frontal lobe biopsy showing abundant neuritic plaques and NFT; mild neuronal loss and gliosis

Right frontal lobe biopsy showing abundant neuritic plaques and NFT; slight neuronal loss and reactive gliosis

Woman; 51 years old at onset; initially, complaint of memory difficulties; over following 4 years, progressive impairment of visuospatial and perceptual capacities, alexia, environmental agnosia; then, Bálint’s syndrome, components of Gerstmann’s syndrome, worsening of memory and linguistic abilities

Moderate generalized atrophy with particular emphasis on both superior parietal lobules; severe nerve cell loss in parietal cortex, less in medial temporal areas; extensive neuritic plaques and NFT in archi- and neocortices with particularly severe parietal involvement

Man; 59 years old at onset; initially, slight memory impairment spatial disorientation with environmental agnosia, agraphia, alexia; 2 years after onset, prosopagnosia, color agnosia; over the following years, progressive deterioration of his mental state, Bálint’s syndrome and Gerstmann’s syndrome

Woman; 54 years old at onset; dyspraxia and related difficulty in writing, then severe visual disorientation, simultagnosia, extremely poor performances on visuospatial tests and both transitive and intransitive hand gestures; later, rapid and global decline in cognitive functioning

HE, PAS, Masson trichrome, Luxolfast blue, Congo red stain

HE, PAS, Masson trichrome, Luxolfast blue

Not specified

Initial CT normal; CT 4 years after first examination: bilateral symmetrical atrophy prominent in parieto-occipital regions

Mild atrophy, most pronounced in parieto-occipital areas on initial CT; 2 years after first examination, marked hypoperfusion in right parietotemporal region (SPECT)

Greatly decreased perfusion in superior parietal lobes bilaterally (SPECT)

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Patient(s) described

11

1

1

Reference

Hof et al., 1997 [33]

Bashir et al., 1998 [44]

Kaida et al., 1998 [88]

Woman; 46 years old at onset; mild forgetfulness remaining stable; at 52 years, clumsiness and myoclonus in left upper limb; severe visuo–constructional impairment, partial Bálint’s, dressing apraxia, left

Woman; 66 years old at onset; initially, motor signs; 8 months later, left homonymous hemianopsia, gradual deterioration including confusion, agitation, depression, and visual hallucinations

2 early-onset cases; 6 patients presenting with partial or complete Bálint’s syndrome and 5 cases developing this syndrome during their dementia; all cases eventually developed severe AD clinically

Clinical features

Table 1 (continued)

Right parietal and occipitoparietal biopsy showing numerous neuritic plaques and infrequent NFT

Mild atrophy of temporal lobes Mild to moderate neuronal loss, gliosis, diffuse plaques, neuritic-cored plaques; Lewy bodies in cortex Far more NFT in right superior and inferior temporal and right striate and peristriate cortices

Marked posterior cortical atrophy; numerous NFT and SP throughout the cortical mantle; clear gradient of NFT densities from area V1 to visual parietal association cortex, posterior cingulate cortex, and area MT; much fewer NFT in frontal areas (9/45/46); intermediate densities in inferior temporal cortex

Cortical pathology

Polyclonal antibodies to amyloid  and
proteins

HE, Sevier-Munger and modified Bielschowsky silver stain

Modified thioflavine S, modified Gallyas, Globus, Bielschowsky, Holzer, Campbell-Switzer-Martin, HE, cresyl violet; antibodies to
and amyloid  proteins

Histological preparation

Needle biopsy right parietal and occipitoparietal cortices Severe hypoperfusion in parieto-occipital regions, more marked on the right (SPECT);

Right parietal-occipital and inferior temporal hypoperfusion (SPECT); Diffuse Lewy body disease and possible AD; however, NFT pathology correlated with hemianopsia

One of the cases corresponds to Morel’s patient

Particularities/ Comments

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49

Galton et al., 2000 [8]

3

Moderate neuronal loss in hippocampus (CA1 pyramidal layer); high amount of SP in hippocampus, entorhinal cortex; high amount of NFT visual and parietal cortex, CA1, and entorhinal cortex, moderate amount in temporal, frontal and primary motor and sensory cortex Moderate cortical atrophy more so in the superior parietal lobules Severe neuronal loss in hippocampus (CA1 pyramidal layer), entorhinal cortex and area 7; high amount of SP in hippocampus, entorhinal cortex, parietal and occipital cortex, moderate amounts in temporal and frontal cortex; high amount of NFT in visual and parietal cortex, CA1, and entorhinal cortex, moderate amount in temporal, frontal and primary motor and sensory cortex

Woman; 65 years old at onset; poor vision, perceptual problems, severe visual disorientation; attention and severe episodic memory impairment

Man; 63 years old at onset; difficulties performing manual tasks and writing, visual disorientation; later generalized intellectual impairment, extreme impairment on all visuospatial tasks and severe apraxia. Further decline also of language

hemineglect, environmental agnosia

As above

HE; antibodies against abnormal, amyloid  peptide and ubiquitin

Reported clinically as case 3 in ref. 41 Biparietal reduction in perfusion (SPECT), gross posterior cortical atrophy especially of left parietal and temporal lobes

Bilateral reduction in occipital lobe perfusion (SPECT); enlarged calcarine fissure with posterior brain atrophy (CT)

generalized mild cortical atrophy accentuated in right parieto-occipital area (MRI)

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1

Lleo et al., 2002 [100]

Not specified in abstract

Abundant  A4 deposits and phosphorylated
accumulation in NP, NFT, and neuropil threads

Progressive ideomotor apraxia, hemi-inattention, unilateral limb dystonia, myoclonus

As above

Severe neuronal loss in hippocampus (CA1 pyramidal layer) and entorhinal cortex; high amount of SP in hippocampus, parietal, occipital and frontal cortex; high amount of NFT in CA1, parietal, temporal, and frontal cortex

Woman; 58 years old at presentation; dyspraxia, dysgraphia, and simultagnosia; severe visuospatial deficits on testing; moderate episodic memory impairment

Histological preparation

Cortical pathology

Clinical features

Abstract available only

Biparietal hypoperfusion (SPECT)

Particularities/ Comments

The cases are listed in chronological order as to the publication date. Neary et al. [21] also described 16 biopsy-proven AD patients of whom some had visual or linguistic difficulties that appeared to coincide or even to precede substantial memory change; however, further details are lacking.

Patient(s) described

Reference

Table 1 (continued)

The distribution of NFT and SP in the posterior cortical atrophy cases suggests that specific elements of visual corticocortical circuits are severely disrupted. In 1945, Morel [87] reported that AD-type changes (i.e. NFT) in his case of visual AD affected predominantly layer III followed by layers V and VI while these alterations were not seen in layers II and IV. According to more recent studies, the high density of NFT in layer III of area 17 indicates that the projection to area 18 is affected, and the high density of NFT in layer III of areas 18 and 19 similarly suggests that there are feedforward projections to higher-level regions in the occipitoparietal junction. In this context, it appears that specific portions of ventral and dorsal area 19 are severely affected in the posterior cortical atrophy cases. In reference to cortical parcellation schemes obtained in macaque monkeys as well as from neuropathological and functional imaging studies in human, it is possible that these particular areas of the occipital cortex represent the human equivalent of areas V4 and MT and areas in the intraparietal sulcus of monkeys [53, 154–167]. Decreased densities of larger neurons are observed in the AD cerebral cortex and in some subcortical nuclei such as the basal nucleus of Meynert, locus coeruleus, and dorsal raphe [7, 168–173]. Little is known as to neuron loss in visual variants of AD, but loss of Meynert cells in layer VI of area 17 has been observed in posterior cortical atrophy cases with Bálint-like syndrome suggesting that long forward projections to the area corresponding in the human brain to area MT are involved [25, 29]. Although cell death appears closely related to the presence of NFT among the pyramidal neurons of the hippocampal formation [174], in neocortical areas both NFT-related and NFT-unrelated neuronal loss may take place, in particular in very old patients [175–176]. The human MT area consistently contains higher NFT and SP densities than the surrounding occipital fields [33]. A recent study in human brains also demonstrated the presence of projections from the occipital cortex to the dorsal posterior parietal cortex and angular gyrus (areas 7b and 39) that may correspond to some of the visual association areas located in the parietal cortex of the monkey and specialized for visuomotor attention and visuospatial tasks [160]. Interestingly, the existence in humans of a separate region involved in kinetic contour detection has also been reported [163]. The inferoposterior and medial parietal areas 7b/7m that are generally considered to be involved in several aspects of motion processing and are severely affected in posterior cortical atrophy cases may include this region, which is distinct from area MT, and the consistent and severe involvement of area 23 in the posterior cortical atrophy cases is particularly relevant, since this cortical area is in fact an important component of the visuomotor system [177–180]. The high numbers of NFT in layers V and VI of area 18 indicates that feedback projections to area 17 are also affected. The disruption of feedback projections, which terminate in both supra- and infragranular layers, is well

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correlated to the high SP densities in all layers of area 17 in these cases. Also, increases in SP density in area 18 suggest an involvement of feedback projections from the area 19 complex, and of feedforward projections from area 17. The same logic can be applied to the very high SP densities in areas 7b/7m and 23, which may reflect the degeneration of feedforward connections from area 19 as well as to the prefrontal areas 9 and 46 that receive projections from the parietal visual areas [181]. Taken together, these data support the involvement of cortical networks linking the parietal, cingulate and occipital regions [180, 182–185] and posterior cortical atrophy cases. Of note, positron emission tomography studies have shown that the posterior cingulate and cinguloparietal cortices appear to be selectively and severely affected regions together with the entorhinal cortex in the very early stages of AD, stressing the role of these areas in learning and memory impairment [186].

Conclusion

Although subcortical structures can be altered, AD is clinically and pathologically largely a disease of the cerebral cortex. Certain components of the neocortical and hippocampal circuits within the medial temporal lobe are particularly prone to degeneration and this predominance may account for a heavily weighted diagnosis of AD towards memory impairment as the central deficit. Memory-weighted diagnosis may prevent the inclusion of cases considered to be atypical. These cases may be atypical as to the macroscopic lesion site but, just as in typical AD, it is the long corticocortical association pathways rather than the short projections from primary to secondary sensory areas that are primarily involved. The available data suggest the possibility of a continuum between typical and more atypical AD cases and that we may ultimately come up with a matrix linking specific clinical presentations to degeneration of specific forward and backward projections of the long corticocortical pathways. This hypothesis stresses the necessity for detailed and accurate cognitive, behavioral, psychopathological and functional testing, as well as for meticulous neuropathologic assessment of dementing syndromes with more extensive sampling of the cortex including the occipital cortex.

Acknowledgement Supported by a grant from the University of Lausanne to A.v.G. and NIH grants AG02219 and AG05138 to P.R.H. P.R.H. is the Regenstreif Professor of Neuroscience.

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144 Curcio CA, Drucker DN: Retinal ganglion cells in Alzheimer’s disease and aging. Ann Neurol 1993;33:248–257. 145 Leuba G, Saini K: Pathology of subcortical visual centres in relation to cortical degeneration in Alzheimer’s disease. Neuropathol Appl Neurobiol 1995;21:410–422. 146 Kuljis RO: Lesions in the pulvinar in patients with Alzheimer’s disease. J Neuropathol Exp Neurol 1994;53:202–211. 147 Braak H, Braak E: Cortical and subcortical argyrophilic grains characterize a disease associated with dementia. Neuropathol Appl Neurobiol 1989;15:13–26. 148 Leuba G, Kraftsik R: Visual cortex in Alzheimer’s disease: Occurrence of neuronal death and glial proliferation, and correlation with pathological hallmarks. Neurobiol Aging 1994;15: 29–43. 149 Mountjoy CQ, Roth M, Evans N, Evans HM: Cortical neuronal counts in normal elderly controls and demented patients. Neurobiol Aging 1983;4:1–11. 150 Leuba G, Kraftsik R, Saini K: Quantitative distribution of parvalbumin, calretinin and calbindin D-28k immunoreactive neurons in the visual cortex of normal and Alzheimer cases. Exp Neurol 1998;152:278–291. 151 Leuba G, Saini K, Zimmermann V, Giannakopoulos P, Bouras C: Mild amyloid pathology in the primary visual system of nonagenarians and centenarians. Dement Geriatr Cogn Disord 2001;12: 146–152. 152 Hinton DR, Sadun AA, Blanks JC, Miller CA: Optic-nerve degeneration in Alzheimer’s disease. N Engl J Med 1986;315:485–487. 153 Armstrong RA, Wood L: The identification of pathological subtypes of Alzheimer’s disease using cluster analysis. Acta Neuropathol 1994;88:60–66. 154 Clarke S, Miklossy J: Occipital cortex in man: Organization of callosal connections, related myelo- and cytoarchitecture, and putative boundaries of functional visual areas. J Comp Neurol 1990;298:188–214. 155 Felleman DJ, Van Essen DC: Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex 1991;1:1–47. 156 Clarke S: Callosal connections and functional subdivision of the human occipital cortex; in Gulyas B, Ottoson D, Roland PE (eds): Functional Organisation of the Human Visual Cortex. Oxford, Pergamon Press, 1993, pp 137–150. 157 Clarke S: Modular organization of human extrastriate visual cortex: Evidence from cytochrome oxidase pattern in normal and macular degeneration cases. Eur J Neurosci 1994;6: 725–736. 158 Clarke S: Association and intrinsic connections of human extrastriate visual cortex. Proc R Soc Lond Ser B 1995;257:87–92. 159 Watson JDG, Myers R, Frackowiak RSJ, Hajnal JV, Woods RP, Mazziotta JC, Shipp S, Zeki S: Area V5 of the human brain: Evidence from a combined study using positron emission tomography and magnetic resonance imaging. Cereb Cortex 1993;3:79–94. 160 Miklossy J: The geniculocalcarine pathway in man, and some putative rostral visual areas involved in visuo-spatial attention; in Gulyas B, Ottoson D, Roland PE (eds): Functional Organisation of the Human Visual Cortex. Oxford, Pergamon Press, 1993, pp 123–136. 161 Hof PR, Morrison JH: Neurofilament protein defines regional patterns of cortical organization in the macaque monkey visual system: A quantitative immunohistochemical analysis. J Comp Neurol 1995;352:161–186. 162 McCarthy G, Spicer M, Adrignolo A, Luby M, Gore J, Allison T: Brain activation associated with visual motion studied by functional magnetic resonance imaging in humans. Hum Brain Mapp 1995;2:234–243. 163 Orban GA, Dupont P, De Bruyn B, Vogels R, Vandenberghe R, Mortelmans L: A motion area in human visual cortex. Proc Natl Acad Sci USA 1995;92:993–997. 164 Tootell RBH, Reppas JB, Kwong KK, Malach R, Born RT, Brady TJ, Rosen BB, Belliveau JW: Functional analysis of human area MT and related visual cortical areas using magnetic resonance imaging. J Neurosci 1995;15:3215–3230. 165 Tootell RBH, Taylor JB: Anatomical evidence for MT and additional cortical visual areas in humans. Cereb Cortex 1995;5:39–55.

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166 DeYoe EA, Carman GJ, Bandettini P, Glickman S, Wieser J, Cox R, Miller D, Neitz J: Mapping striate and extrastriate visual areas in human cerebral cortex. Proc Natl Acad Sci USA 1996;93: 2382–2386. 167 Engel SA, Glover GH, Wandell BA: Retinotopic organization in human visual cortex and the spatial precision of functional MRI. Cereb Cortex 1997;7:181–192. 168 Aletrino MA, Vogels OJM, Van Domburg PHMF, Ten Donkelaar HJ: Cell loss in the nucleus raphe dorsalis in Alzheimer’s disease. Aging 1992;13:461–468. 169 Bondareff W, Mountjoy CQ, Roth M: Loss of neurons of origin of the adrenergic projection to cerebral cortex (nucleus locus coeruleus) in senile dementia. Neurology 1982;32:164–168. 170 Terry RD, Masliah E, Hansen LA: Structural basis of the cognitive alterations in Alzheimer disease; in Terry RD, Katzman R, Bick KL (eds): Alzheimer Disease. New York, Raven, 1994, pp 179–196. 171 Terry RD, Peck A, DeTeresa R, Schechter R, Horoupian DS: Some morphometric aspects of the brain in senile dementia of the Alzheimer type. Ann Neurol 1981;10:184–192. 172 Vogels OJM, Broere AJ, Ter Laak HJ, Ten Donkelaar HJ, Nieuwenhuys R, Schulte BPM: Cell loss and shrinkage in the nucleus basalis Meynert complex in Alzheimer’s disease. Neurobiol Aging 1990;11:3–13. 173 Whitehouse PJ, Price DL, Struble RG, Clark AW, Coyle JT, DeLong M: Alzheimer’s disease and senile dementia: Loss of neurons in the basal forebrain. Science 1982;215:1237–1239. 174 Gómez-Isla T, Price JL, McKeel DW Jr, Morris JC, Growdon JH, Hyman BT: Profound loss of layer II entorhinal cortex neurons occurs in very mild Alzheimer’s disease. J Neurosci 1996;16: 4491–4500. 175 Giannakopoulos P, Hof PR, Kövari E, Vallet PG, Bouras C: Distinct patterns of neuronal loss and Alzheimer’s disease lesion distribution in elderly individuals older than 90 years. J Neuropathol Exp Neurol 1996;55:1110–1120. 176 Terry RD, Hansen LA, DeTeresa R, Davies P, Tobias H, Katzman R: Senile dementia of the Alzheimer type without neocortical neurofibrillary tangle. J Neuropathol Exp Neurol 1987;46: 262–268. 177 Olson CR, Musil SY: Posterior cingulate cortex: Sensory and oculomotor properties of single neurons in behaving cat. Cereb Cortex 1992;2:485–502. 178 Olson CR, Musil SY: Topographic organization of cortical and subcortical projections to posterior cingulate cortex in the cat: Evidence for somatic, ocular and complex subregions. J Comp Neurol 1992;323:1–24. 179 Olson CR, Musil SY, Goldberg ME: Neurophysiology of posterior cingulate cortex in the behaving monkey; in Vogt BA, Gabriel M (eds): The Neurobiology of Cingulate Cortex and Limbic Thalamus. Boston, Birkhäuser, 1993, pp 366–380. 180 Vogt BA, Vogt L, Nimchinsky EA, Hof PR: Primate cingulate cortex chemoarchitecture and its disruption in Alzheimer’s disease; in Bloom FE, Björklund A, Hökfelt T (eds): Handbook of Chemical Neuroanatomy. The Primate Nervous System, Part I. Amsterdam, Elsevier, 1997, vol 13, pp 455–528. 181 Wilson FAW, Scalaidhe SP, Goldman-Rakic PS: Dissociation of object and spatial processing domains in primate prefrontal cortex. Science 1993;260:1955–1958. 182 Vogt BA, Van Hoesen GW, Vogt LJ: Laminar distribution of neuron degeneration in posterior cingulate cortex in Alzheimer’s disease. Acta Neuropathol 1990;80:581–589. 183 Vogt BA, Crino PB, Vogt LJ: Reorganization of cingulate cortex in Alzheimer’s disease: Neuron loss, neuritic plaques, and muscarinic receptor binding. Cereb Cortex 1992;2:526–535. 184 Minoshima S, Foster NL, Kuhl DE: Posterior cingulate cortex in Alzheimer’s disease. Lancet 1994;344:895. 185 Reiman EM, Caselli RJ, Yun LS, Chen K, Minoshima S, Thibodeau SN, Osborne D: Preclinical evidence of Alzheimer’s disease in persons homozygous for the E4 allele for apolipoprotein E. N Engl J Med 1996;334:752–758. 186 Minoshima S, Giordani B, Berent S, Frey KA, Foster NL, Kuhl DE: Metabolic reduction in the posterior cingulate cortex in very early Alzheimer’s disease. Ann Neurol 1997;42:85–94. 187 Corsellis JAN, Brierley JB: An unusual type of pre-senile dementia. Brain 1954;77:571–587. 188 Faden AI, Townsend JJ: Myoclonus in Alzheimer’s disease. Arch Neurol 1976;33:278.

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189 Kobayashi S, Hirota N, Saito K, Utsuyama M: Aluminum accumulation in tangle-bearing neurons of Alzheimer’s disease with Balint’s syndrome in a long-term aluminum refiner. Acta Neuropathol 1987;74:47–52. 190 Ross GW, Benson DF, Verity MA, Victoroff J: Posterior cortical atrophy: Neuropathological correlations. Neurology 1990;40(suppl 1):200.

Patrick R. Hof Department of Neuroscience Box 1065, Mount Sinai School of Medicine, One Gustave L. Levy Place New York, NY 10029 (USA) Tel. 1 212 659 5904, Fax 1 212 849 2510, E-Mail [email protected]

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Cronin-Golomb A, Hof PR (eds): Vision in Alzheimer’s Disease. Interdiscipl Top Gerontol. Basel, Karger, 2004, vol 34, pp 62–95

Functional Imaging in Healthy Aging and Alzheimer’s Disease Nicole D. Andersona, Cheryl L. Gradyb a Kunin-Lunenfeld Applied Research Unit, bRotman Research Institute, Baycrest Centre for Geriatric Care, Toronto, Ont., Canada

In this chapter we review the functional correlates of perception, attention, and memory deficits that occur over the course of normal aging, focusing primarily on activation in prefrontal and occipital cortex. Two general patterns of findings emerge from functional neuroimaging studies of cognitive aging. One is characterized by age-related declines in brain activation of functional networks important for cognition, and the other by age-related increases in brain activation in other brain regions that are not typically associated with the cognitive process under study. Age-related reductions in functional brain activity are more pronounced during demanding tasks that require self-initiated processes than during supportive tasks that guide appropriate information processing. Age-related increases in functional brain activity occur during both relatively simple perceptual tasks and during more complex, demanding memory tasks. Similar findings of increased and decreased activation have been reported in patients with Alzheimer’s disease compared to healthy elderly adults. In both age groups, the most prominent increases are in prefrontal cortex and these may reflect compensatory changes in response to less effective activation of other brain areas. Direct investigations of the relationship between brain activity and behavioral performance have provided support for the notion of compensation, but more work is needed to define the boundaries of this phenomenon and the conditions that promote it.

Introduction

Aging is associated with perceptual and cognitive changes that impinge on older adults’ quality of life, particularly among those living with neurodegenerative

diseases such as Alzheimer’s disease (AD). The neural mechanisms of cognitive aging are complex, and a number of theories have been derived in an attempt to explain them. Some investigators have postulated that healthy older adults’ cognitive impairments stem primarily from frontal-lobe dysfunction. This hypothesis is based on anatomical and imaging evidence that age-related brain atrophy may be exaggerated in anterior brain regions [1, 2] and on evidence that the types of attention and memory deficits experienced by older adults and patients with frontal-lobe damage are similar [3, 4]. On the other hand, age differences in brain areas outside prefrontal cortex (PFC) indicate that the picture is not so simple, and that functional changes in other regions, such as visual areas, as well as the interactions among brain areas must be taken into account. In AD, although the neuropathology is relatively restricted to the medial temporal lobes in the very early stages [e.g. 5–7], considerable disruptions can occur in neurocognitive networks distributed throughout the brain. Other theories of cognitive aging emphasize top-down versus bottom-up processing. Bottom-up influences on behavior occur when a stimulus or stimulus feature is particularly salient, or because it activates a highly practiced, automated process such as word reading. By contrast, top-down influences are intentional, or goal-directed, act to prevent interference from irrelevant stimuli or stimulus features, and require attentional control. Many of the reductions in cognitive function seen in healthy elderly individuals are thought to be the result of changes in top-down processes, whereas bottom-up influences are thought to remain relatively spared [8, 9]. In addition, AD patients are impaired on top-down processing [e.g. 10], although they also have impairments in perceptual functions that are more bottom-up in nature [e.g. 11]. In this review we will examine the changes found in brain activity in healthy elderly adults and patients with AD during cognitive tasks, and the implications that these have for our understanding of the cognitive alterations seen in these groups. In the section on ‘Age-Related Differences in Brain Activation during Non-Memory Tasks’, we review studies that have been conducted with healthy young and older adults during non-memory tasks. In the sections ‘AgeRelated Differences in Brain Activation during Working Memory Processes’ and ‘Age-Related Differences in Brain Activation during Episodic Memory Processes’, we cover the differences between young and old adults on tasks of working and episodic memory, respectively. In the section on ‘Differences in Brain Activation between AD Patients and Healthy Elderly Adults’, we review studies that have compared patients with AD to healthy elderly control participants. All of the studies we describe used either positron emission tomography or functional magnetic resonance imaging. Due to the focus of this volume on visual function, we have restricted the bulk of our review to visual cortex (including striate and extrastriate areas) and PFC. We include frontal areas

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because PFC activity is often increased in older adults, compared to young adults, and a similar increase has been reported in AD patients in a number of experiments. Occipital and frontal areas also are of interest in the context of ‘bottom-up’ and ‘top-down’ processing, respectively. The younger and older participants involved in the studies reviewed here were generally in the age ranges of 18–30 and 60–80, respectively. The healthy participants lived independently living and generally were well educated. The majority of studies screened for physical, neurological, or psychiatric conditions or medication use that might affect brain function.

Age-Related Differences in Brain Activation during Non-Memory Tasks

Face Processing Visual perceptual functions such as face perception are affected by age, mainly due to changes in contrast sensitivity [12, 13]. However, these changes are much smaller in magnitude than those seen for face memory in the elderly [e.g. 14]. Studies examining age effects on brain activity during perceptual tasks are summarized in table 1. Early studies of age-related differences in brain activity during face processing involved both face and location perception, to compare the effects of aging on the activation of the ventral and dorsal processing streams [15, 16]. These studies involved a match-to-sample task for faces or locations in which the sample and two choices were present simultaneously, thus eliminating any demands on memory. In both age groups, face matching activated primarily occipitotemporal cortex while location matching activated primarily occipitoparietal cortex, a finding that is consistent with the dual-path model of visual processing. In addition, however, the young adults showed greater activation in early visual processing regions (in prestriate cortex) and the older adults showed greater activation along the ventral or dorsal pathways (e.g., inferior parietal cortex) or outside of them, notably in left PFC. Subsequent network analysis of these data, using a technique that allows assessment of functional interactions among brain regions [17, 18], showed positive correlations between activity in ventral occipitotemporal cortex and activity in anterior temporal regions in both age groups. Anterior temporal regions showed functional interactions with inferior PFC in both groups. However, the older adults also showed a strong feedback influence from PFC to occipital cortex. These analyses suggest that although the network of ventral brain regions involved in face processing is similar in young and older adults, the magnitude of activity in these areas varies with age as do the functional interactions among them. In addition, higher-order visual processing areas such as the inferior temporal regions may

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Table 1. Face and word processing Group (first author)

Task

Task performance

Activation

Grady, 1994 [16], 2000 [19]

Face processing

Y⫽O

Occ (bilateral): Y ⬎ O PFC (left): O ⬎ Y

Grady, 2000 [19]

Degraded face processing

Y⬎O

Occ (bilateral): Y ⫽ O PFC (bilateral): Y ⫽ O

Iidaka, 2002 [21]

Gender discrimination of emotional faces

Y⫽O

Occ (bilateral): Y ⬎ O PFC (bilateral): Y ⫽ O

Madden, 1996 [32]

Word processing

Y⫽O

Degraded word processing

Y⬎O

Occ (left): Y ⬎ O PFC: not activated Occ (bilateral): Y ⫽ O PFC: not activated

Madden, 2002 [35]

Word processing

Y⬎O

Occ (left): Y ⬎ O PFC (left): Y ⫽ O

Johnson, 2001 [36]

Semantic processing (auditory)

Y⫽O

Phonological processing (auditory)

Y⫽O

Occ (midline): Y ⬎ O PFC (bilateral): Y ⫽ O Occ (right): Y ⬎ O PFC: not activated

Numbers in parentheses in this and subsequent tables refer to the number of each reference in the reference list.

have been recruited by older adults in response to less effective use of early visual areas. As we shall see, the greater engagement of PFC in older adults, which may indicate that there is an age-related increase in the reliance on frontally-mediated strategic monitoring of low-level processes, is a common finding in the literature for both non-memory and memory kinds of tasks. Another study [19] investigated age-related differences in brain activity during the perception of degraded and non-degraded faces. Degraded face matching should reduce the ability to rely on simple perceptual matching and should increase the need for strategic monitoring processes. Indeed, increasing levels of face degradation in both younger and older adults led to decreased activity in occipital regions and to increased activity in frontal regions, mainly in right anterior PFC. Brain activity during non-degraded face matching replicated the earlier findings. The younger adults had greater activity in prestriate regions, whereas the older adults had greater activity in left ventral PFC. To further examine the issue of greater prefrontal activation in older adults during face-processing tasks, the data from the degraded face perception experiment were combined with data from two memory experiments (one of

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episodic memory and another of working memory) and a meta-analysis was carried out across the three data sets [20]. Each experiment contained an easy face-matching condition and a more difficult processing condition. Young adults showed greater activity in bilateral ventrolateral and dorsolateral PFC during the memory tasks compared to face matching, but no difference in PFC activity between degraded and non-degraded perception. Older adults, on the other hand, had greater PFC activity in both memory and degraded perceptual tasks compared to matching. Interestingly, activity in both occipital and frontal regions was correlated with faster response times in young adults across all three tasks, whereas in older adults this was true only for the degraded face task. In the memory tasks, activity in these areas was correlated with slower responses in older adults. These results suggest that increased PFC activity is less task-specific in old adults, and may be a general response to increased cognitive effort or need for resources, which in turn can lead to slower responding. A more recent face-processing study examined the effect of emotional expressions in the face [21]. Pairs of faces with positive, negative or neutral expressions were presented and participants were instructed to indicate which of the two faces was a person of the target gender (either male or female). Both young and old adults showed increased activity in fusiform gyrus and inferior frontal cortex, as would be expected when individuals are engaged in processing faces. However, the young adults had more activation than did older adults in the amygdala during processing of negative faces. Activity in the amygdala is consistent with the role of this region in emotion [e.g. 22–24], particularly in the processing of emotional faces [e.g. 25, 26]. In addition, less amygdala activity in older adults is consistent with reports of reduced accuracy of older adults in identifying negative emotions from faces [27, 28]. Similar to the other studies of face processing, young adults also had more activity in prestriate cortex. PFC activity did not differ between young and old groups. As these activations were in ventral PFC, where older adults showed more activity on other faceprocessing tasks (see above), this suggests that activity in this region of PFC generally is not reduced with age, and may even be increased. Similar results have been reported in memory studies (see sections on ‘Age-Related Differences in Brain Activation during Working Memory Processes’ and ‘AgeRelated Differences in Brain Activation during Episodic Memory Processes’, and tables 3 and 5). Word Processing Processing of words, such as lexical decision and retrieval of semantic information, is well preserved with age, although it occurs more slowly [e.g. 29, 30]. Indeed some abilities such as vocabulary and world knowledge increase with age [for a review, see 31]. These results are consistent with the notion that

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well-learned, highly automated abilities initiate bottom-up processes that are spared by aging. Several studies have compared brain activity in young and old adults during the processing of single words (summarized in table 1). Madden et al. [32] examined age-related differences in brain activity during lexical decision tasks, which are presumed to involve semantic retrieval. Widespread activation in ventral occipital, lingual, and fusiform regions was seen bilaterally, but stronger in the left hemisphere. These activations were comparable in younger and older adults, except for an age-related decrease in activity in a small region situated in the left lingual gyrus. In another condition, the words and non-words were perceptually degraded by the inclusion of asterisks between each letter (e.g., H*O*R*S*E). In this condition, relative to the processing of nondegraded words, additional activity was found in the left lingual gyrus that did not differ between the two age groups, although the older adults performed more poorly on this task. There was no activation of left inferior PFC during these word-processing tasks as others have reported [e.g. 33, 34], probably because passive viewing of words and non-words was used as the baseline task, which might have initiated semantic processing. Nevertheless, the results from this study demonstrate that in very simple lexical tasks that activate welllearned highly automated processes such as word reading, age-related differences in brain activity are minimal. In a subsequent study, Madden et al. [35] modified their control task to avoid the problem of automatic semantic processing. In this study, the control task was to search for the letter ‘c’ in a string of Z’s and T’s. Using these tasks, both younger and older adults exhibited left occipitotemporal and left inferior PFC activation for the lexical decision task relative to the visual search baseline. Younger adults had more activity in striate cortex, whereas the older adults had more activity in left inferior temporal regions. No differences were seen in PFC regions. The reductions seen in early visual areas are similar to those reported by Grady et al. [16, 19] for face processing, as discussed above. Madden et al., interpreted their data similarly; they concluded that the greater activity in temporal cortex in older adults represents recruitment of higherorder visual cortex in response to less adequate activation of lower order visual areas (i.e., striate cortex). Finally, a recent study examined semantic and phonological processing of word-pairs that were presented auditorily rather than visually [36]. Participants made judgments about category congruence within the pairs (e.g., beveragemilk) or whether the pairs matched in terms of category and function (e.g., beverage-sip). The phonologic task consisted of matching pseudowords. Both young and older participants showed increased activity during the semantic tasks (compared to rest) in left temporal and bilateral inferior PFC, and increases

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in bilateral temporal regions during the phonological task. There were no significant group differences in PFC activity during either task, but the younger adults showed more activity in visual regions although no visual stimuli were presented, perhaps reflecting imagery processes. This interpretation is consistent with reports of age reductions in imagery during verbal memory tasks [e.g. 37], and may also explain reductions in visual areas during the visual processing of words, described above. Executive Functions Executive functions are those that are recruited to organize or monitor behavior [38, 39], and include attention, inhibition, and reasoning. Older adults show reductions in many aspects of selective attention, primarily because of distraction caused by irrelevant information in the environment. For example, relative to their younger counterparts, older adults are disproportionately slower to read words that have been perceptually degraded than intact words [40] and to locate objects or faces when they are presented amongst distractors versus in isolation [41, 42]. Older adults also show reductions in inhibitory function using other task paradigms such as negative priming or working memory [43, 44]. Age-related reductions on tests of reasoning, such as the Wisconsin Card Sort Test and Raven’s Progressive Matrices also are common [45–48], though this may also be related to deficits in working memory. Executive functions are thought to depend on the frontal lobes [e.g. 49], and, as the studies reviewed here indicate (summarized in table 2), dorsolateral PFC seems to play a critical role in many of them. A few investigations of age-related differences in brain activity during attention tasks have been conducted. Madden et al. [50] investigated simple and divided attention. Two letters were pre-assigned as target letters, and on each trial a grid of letters was presented and participants pressed one of two buttons depending on which target was present. In a ‘central condition’, a target letter appeared in the central grid position on every trial, and in the ‘divided attention’ condition, the location of the target in the grid varied from trial to trial. The divided condition, relative to the central condition, was associated with a larger increase of activity in occipital cortex bilaterally in the younger adults, compared to the older adults, and a larger increase in the right middle frontal gyrus in the older adults. When the divided attention condition was compared to a lower-level passive viewing baseline, the younger adults also had more occipital activation bilaterally, and older adults had more activation in the middle frontal gyrus, although it was in the left hemisphere. These findings suggest that while younger adults relied more on object processing to perform the divided attention task, the older adults relied more on strategic control and response monitoring.

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Table 2. Executive functions Group (first author)

Task

Task performance

Activation

Madden, 1997 [50]

Divided attention in visual search

Y⬎O

Occ (bilateral): Y ⬎ O PFC (bilateral): O ⬎ Y

Madden, 2002 [51]

Conjunction search

Y⬎O

Occ (right): Y ⬎ O PFC (right): Y ⫽ O

Esposito, 1999 [54]

Raven’s matrices

Y⬎O

Wisconsin Card Sort

Y⬎O

Occ (bilateral): Y ⬎ O PFC (left): O ⬎ Y Occ (left ventral): Y ⬎ O Occ (midline dorsal): O ⬎ Y PFC (left dorsolateral): Y ⬎ O PFC (bilateral anterior): O ⬎ Y

Nagahama, 1997 [57]

Wisconsin Card Sort

Y⬎O

Occ (left): Y ⬎ O PFC (left): Y ⬎ O

Milham, 2002 [58]

Stroop Color-Word Test

Y⫽O

Occ (bilateral): O ⬎ Y PFC (left dorsolateral): Y ⬎ O PFC (bilateral inferior): O ⬎ Y

Nielson, 2002 [60]

Go/No-Go

Y⬎O

Occ (right): Y ⬎ O PFC (right): Y ⬎ O PFC (left): O ⬎ Y

Somewhat different results were reported in a later experiment [51] that utilized several visual search conditions including a feature search, in which a target stimulus differed from distracters in both color and shape, and a conjunction search, in which half the distracters were the same color as the target, necessitating a wider search. During the conjunction task, compared to the feature search task, both young and old adults showed increased activity in middle frontal and superior parietal regions consistent with the attentional demands of the conjunction search [e.g. 52, 53]. In addition, the young group had greater activation in striate cortex and the fusiform gyrus than did the older group. There were no significant group differences in PFC activation and there also were no areas of greater activity in the old adults compared to the young adults. This result is similar to that reported in their earlier study in that young adults relied more on visual cortex to perform the task than did the older adults. However, the older adults in the second experiment did not show greater PFC activity, indicating that there is variability in the kinds of attentional conditions that elicit this recruitment. Esposito et al. [54] examined cerebral blood flow while participants performed Raven’s Progressive Matrices, a task that taps problem-solving and

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abstract reasoning. In this task, participants are shown a 3 ⫻ 3 matrix with one empty cell and must select the missing element from a number of alternatives displayed beneath the matrix. Relative to a control task matched for sensorimotor demands, activation during the Raven’s task in younger adults was found in ventral and dorsal visual cortex, parietal regions, and dorsolateral PFC in both hemispheres. Older adults showed less activation in a number of these regions, including the left inferior parietal lobule and ventral occipitotemporal regions. In addition, the younger adults showed reduced brain activity during the Raven’s test relative to the control task in bilateral frontal pole and the posterior cingulate. Older adults showed smaller reductions in these areas. Thus, there was less of a distinction in brain activity between the Raven’s test and the control task in the older adults (i.e., they had smaller activations and ‘de-activations’), which suggests that older adults were less sensitive to the demands of the task. Esposito et al. [54] also examined age differences on the Wisconsin Card Sorting Task (WCST), which involves executive functions such as attention, problem solving, and working memory [55, 56]. In this test, participants sort cards differing in the number, shape, and color of stimuli onto one of four piles. Participants must discover the sorting rule (e.g., color) and after they have successfully sorted a number of cards according to this rule, the rule is switched without notice (e.g., to number). The young adults activated bilateral occipital, parietal, and dorsolateral PFC regions. Age-related decreases in activation during the WCST occurred in dorsolateral PFC, inferior parietal lobule and ventral occipital cortex, all in the left hemisphere. Age-related increases in activation were found in dorsal extrastriate cortex, bilateral frontal poles, and right parahippocampal gyrus. Nagahama et al. [57] used a modified WCST, in which each stimulus card shared only one attribute with a reference card, thus making their task less demanding than the original WCST. They also reported age-related decreases in activation during the WCST in left dorsolateral PFC, the left inferior parietal lobule, and a number of posterior brain regions associated with visual processing, but they did not find any brain regions in which activation was greater in the older than younger adults. Thus both studies suggest that there is an age-related decrement in the ability to activate dorsolateral PFC involved in strategic processes such as problem solving and reasoning, which was accompanied by a reduced ability to shift categories and more perseverative errors in the older adults [57]. Finally, several recent studies have examined the effects of aging on brain activity during tasks of inhibition, another critical executive function. Milham et al. [58] examined inhibition in the context of the Stroop task [59]. In this task, participants are required to indicate the color of ink in which color words such as ‘red’ are printed, but the ink color and color word are incongruent (e.g., the word ‘red’ is printed in green ink). The prepotent response of reading the word

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must be inhibited so that the color of the ink can be correctly identified. When activity in the incongruent condition was compared to that during a control condition, both young and old adults showed increases in ventral prefrontal and superior parietal regions. Young adults had significantly more activation compared to older adults in left dorsolateral PFC and left parietal regions, whereas older adults had more activation in ventral PFC and extrastriate regions, particularly ventral extrastriate cortex (in both medial and lateral areas). Therefore, this is one of the few experiments reporting more activity in older adults in prestriate regions, as well as in more lateral regions of extrastriate cortex. Nielson et al. [60] studied brain activity during a Go/No-Go paradigm, in which participants viewed a series of rapidly presented letters and were instructed to respond to the letters X and Y in alternation. Non-alternating presentations of a target letter required withholding or inhibiting the response. Both young and old adults had significantly increased activity in prefrontal and parietal regions during the trials where they correctly inhibited their responses (No-Go trials). Young adults showed more activity during inhibition in right inferior frontal and fusiform gyri, whereas older adults had more activity in left inferior frontal and precentral gyri. This additional recruitment of left frontal cortex in older adults is similar to that reported in studies of working memory and memory retrieval discussed below, and indicates that this region may mediate some as yet poorly understood processes that are engaged to a greater extent in older adults, perhaps in a compensatory fashion. Summary of Non-Memory Processing As can be seen from table 1, even when older adults are performing relatively simple perceptual or word-processing tasks, activity in visual cortex is reduced compared to that seen in young adults, particularly in those areas that are early in the visual processing stream. In some cases, higher order extrastriate regions or frontal areas are recruited to a greater degree, which may be a response to less effective low-level visual analysis. These occipital reductions also could be due to differences in baseline activity in older adults. Madden et al. [51] have shown that older adults sometimes have higher levels of activity in visual cortex during low-level baseline tasks, so that when higher-level task conditions are contrasted with such baselines, no further increases in older adults are seen. This would suggest recruitment of these regions at a lower level of visual stimulation in older adults, which may not always increase as task demands increase. On the other hand, in more difficult tasks, such as degraded perceptual tasks, activity in occipital regions seems to be equivalent between young and old adults. Thus, these areas clearly can respond to increasing task difficulty in older adults, although the conditions under which this occurs are not yet clear. PFC activity generally does not distinguish old from young adults

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on these perceptual tasks, perhaps because they can be performed with relatively little involvement of top-down processes. On tasks of executive function that place higher demands on top-down function (table 2), older adults generally have reduced occipital activity similar to that seen on perceptual tasks. However, the picture in PFC is considerably more complex. On some tasks younger adults have more frontal activity and on other tasks the older adults have more activity in these areas. While some of this variability could be due to specific task demands, a closer look suggests the direction of the age difference may depend to some extent on the particular area of PFC in question. Young adults have more activity in dorsolateral PFC in several studies, whereas older adults have more activity in ventrolateral or anterior PFC. In one study [60], PFC activity in the right hemisphere was greater in young adults, but in older adults this was true for the left inferior PFC. These results suggest that when tasks specifically place demands on the executive functions that engage dorsolateral PFC, older adults are less able to recruit this area. Activation of other regions of PFC could indicate use of alternate strategies or functions, but given that the performance of older adults was poorer than that of their younger counterparts on almost all of these tasks, these alternatives may not be successful in maintaining function.

Age-Related Differences in Brain Activation during Working Memory Processes

Working memory, which refers to the active maintenance and manipulation of information held in mind [61], is associated with robust age-related deficits [e.g. 62, 63]. In neuroimaging studies with young adults, the inferior parietal lobule and PFC are consistently activated during working memory tasks [e.g. 64, 65]. In addition, inferior PFC is more activated during passive maintenance of information, while more dorsal PFC regions are activated during active manipulation of information [e.g. 66, 67]. Most neuroimaging studies of age-related differences in brain activation during working memory tasks (see table 3) have used what we will call an Encoding-Maintenance-Probe paradigm [68–74]. Participants encode stimuli and maintain it over an unfilled delay and then indicate whether or not a particular probe stimulus was presented. The number and type of stimuli presented, and the duration of each phase varied across studies. Two other studies used somewhat different paradigms. Smith et al. [75] tested operation span. Each trial presented an arithmetic operation and a word; participants first indicated whether or not the operation was correct, and then encoded the word into memory. After five such trials for the young adults, and four such trials for the

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Table 3. Working memory Group (first author)

Task

Task performance

Activation

Rypma, 2000 [68]

Letters, objects, and locations

Y ⫽ Oa

Occ: not reported PFC (bilateral inferior): Y ⫽ O PFC (bilateral dorsolateral): Y ⬎ O

Rypma, 2001 [69]

Letters

Y⬎O

Occ (left lingual): Y ⫽ O Occ (left middle): Y ⬎ O Occ (right cuneus): Y ⫽ O PFC (left inferior): Y ⫽ O PFC (left anterior): O ⬎ Y PFC (right dorsolateral): Y ⬎ O

Jonides, 2000 [70]

Letters

Y⬎O

Occ (bilateral): Y ⫽ O PFC (left inferior): Y ⬎ O

Reuter-Lorenz, 2000 [71]

Letters and locations

Y⬎O

Occ (left)–verbal: O ⬎ Y Occ (right)–spatial: O ⬎ Y PFCb: Y unilateral, O bilateral

Mitchell, 2000 [72]

Objects, locations, and object-location pairs

Y ⫽ O (objects and locations) Y ⬎ O (objectlocation pairs)

Occ (bilateral): Y ⫽ O

Bennett, 2001 [73]

Sine wave gratings

Y⫽O

Occ (right): O ⬎ Y PFC (left superior): O ⬎ Y

Grady, 1998 [74]

Faces

Y⬎O

Occ (anterior fusiform): Y ⫽ O Occ (posterior fusiform): O ⬎ Y PFC (left): O ⬎ Y PFC (right): Y ⬎ O

Smith, 2001 [75]

Operation span

Good Y ⬎ Poor Y ⫽ O

Occ (left): Y ⬎ O PFC (left dorsolateral): O and Poor Y ⬎ Good Young PFC (left dorsolateral): O ⬎ Y

Grossman, 2002 [76] Sentences aStatistics

Y⫽O

PFC (right): Y ⬎ O

provided for one of three studies. included premotor and supplementary motor cortex.

bRegions-of-interest

older adults, serial order memory for the words was tested. Grossman et al. [76] presented sentences that were either subject-relative or object-relative, and that had either a short or long subject-object gaps. Another point to note about the aging and working memory literature is that most of the investigators report only analyses targeted at very specific a priori questions. For example, Mitchell et al. [72] were interested in feature binding, and hence focused on brain areas that were significantly more activated when object-location pairs had to be held

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in working memory than when a single stimulus feature was required to be held. While this approach is more theoretically motivated, it does make ascertaining commonality across studies more difficult. Despite all of these procedural and analytical differences, two relatively common findings of age-related differences in PFC activation emerge, as shown in table 3. The first is an age-related equivalence or reduction in inferior PFC activation [68–70, 74]. By contrast, most studies report an age-related increase in dorsolateral PFC activation [71, 74–76, but see 68, 69]. The discrepancies may be due to procedural or analytical differences. Nevertheless, it appears that older adults rely more on effortful, strategic manipulation processes mediated by dorsolateral PFC regions to help offset an age-related decline in the maintenance of information. Smith et al. [75] compared brain activation in young adults with good or poor working memory performance to that of their group of healthy older adults. They reported increased dorsolateral PFC activation in the older adults and in the young adults with poor working memory performance. This result highlights the point that differences between young and old adults are due not to age per se, but due to the decline in cognitive functions that occurs with age. No clear pattern emerges regarding age-related differences in activation of visual cortex during working memory tasks (see table 3). As discussed above in the section ‘Summary of Non-Memory Processing’, Madden et al. [35] have found greater activation of visual areas during low-level baseline tasks in older than younger adults. The discrepancies in visual cortex activation across studies may be related to age-related differences in activation of occipital cortex in the control tasks.

Age-Related Differences in Brain Activation during Episodic Memory Processes

Numerous studies have demonstrated that episodic memory is quite vulnerable to aging [for reviews, see 77, 78]. Episodic memory refers to conscious memory for a specific past event including its contextual details such as when and where the event occurred [79]. It involves several stages of processing, including encoding, in which information is perceived, analyzed, and related to previously stored information; and retrieval, in which information is searched for (via an external or self-generated retrieval cue) and brought back into consciousness. Previous research has found that older adults suffer both encoding and retrieval deficits. Evidence in favor of an encoding deficit includes findings that older adults are less likely to engage in the types of operations that facilitate proper learning, such as mental imagery [80] or elaborative

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encoding to make the information semantically rich and distinctive [81]. However, when older adults are instructed to use these strategies, their memory performance improves. Evidence in favor of a retrieval deficit includes the fact that memory impairments are typically larger on tests of recall (free and cued recall) than on tests of recognition [82]. Variants of the top-down/bottom-up distinction have been formulated to explain age-related deficits in episodic memory. For example, Jennings and Jacoby [9] argued that conscious recollection of a past event is impaired by aging, but automatic familiarity-driven processes remain intact. More broadly, Craik [62] proposed that the greater the demands are for attention-demanding self-initiated operations during encoding, maintenance, or retrieval, the worse older adults perform, because the amount of attentional resources available for such operations declines with age. Conversely, the more the task stimuli or task environment guides appropriate encoding and retrieval operations (i.e., a bottom-up influence), the smaller the age-related deficit. Two consistent patterns emerge from studies of long-term memory and aging. One is an age-related reduction in brain activity, particularly within the left inferior frontal lobes during encoding. The other is an age-related increase in frontal lobe activity; this pattern is most evident in bilateral PFC activity in older adults compared to PFC activity restricted to one hemisphere in young adults. We begin by considering these studies of explicit memory, and focus first on encoding and then on retrieval. Episodic Encoding Some encoding studies employ a single encoding condition and others compare two or more different encoding conditions that vary in the degree to which they promoted deep semantic encoding. Some studies use incidental learning conditions (i.e., the participants were unaware that their memory would be tested) and others use intentional encoding conditions (i.e., the participants knew their memory would be tested). We previously noted that learning conditions that guide deep encoding operations appear to reduce age-related differences in memory performance and brain activity [83]. Specifically, in younger adults, ‘deep’ encoding conditions that guide semantic encoding also increase activity in left inferior PFC relative to ‘shallow’ or perceptually-based encoding conditions [e.g. 84]. Our observation that deep encoding conditions also appear to minimize age-related reductions in left PFC activity was based on only four studies that were available at that time [85–88]. The results of many new studies are consistent with this observation. As shown in table 4, three studies that found age-related equivalence (or an age-related increase) in left PFC activity employed incidental encoding tasks

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Table 4. Long-term memory encoding Group (first author)

Task

Memory performance

Activation

Madden, 1999 [88]

Words: deep (incidental)

Y⬎O

Daselaar, 2003 [89]

Words: deep (incidental)

Y/HiOld ⬎ LoOld

Morcom, 2003 [90]

Words: deep (incidental)

Y⬎O

Iidaka, 2001 [91]

Picture pairs: learn (intentional) Word pairs: learn (intentional)

Y⬎O

Occ: (left): O ⬎ Y PFC (left): O ⬎ Y PFC (right): O ⬎ Y Occ: (left): Y ⫽ O PFC (left): Y ⫽ O Occ (bilateral): Y ⫽ O PFC (left): Y ⫽ O PFC (right): O ⬎ Y Occ (right): Y ⬎ O PFC (left): Y ⫽ O Occ (left): Y ⬎ O Occ (right): O ⬎ Y PFC (left): Y ⬎ O Occ (bilateral): Y ⬎ O PFC (left): Y ⬎ O PFC (right): O ⬎ Y Occ: not reported PFC (left): Y ⬎ O PFC (right): Y ⫽ O PFC (left): O ⬎ Y PFC (right): Y ⫽ O Occ (bilateral): Y ⫽ O PFC (left): Y ⬎ O PFC (right): O ⬎ Y PFC (left): Y ⬎ O Occ: not reported PFC (left)–learn: Y ⬎ O PFC (left)–deep/ shallow: Y ⫽ O PFC (right)–deep: O ⬎ Y Occ: not reported PFC (left)–deep: HiOld/Y ⬎ LoOld PFC (right)–deep: HiOld ⬎ Y/LoOld Occ (bilateral)– pictures: Y ⫽ O PFC (left)–deep: Y ⬎ O PFC (right)–learn: Y ⬎ O PFC (bilateral)– Deep/learn pictures: Y ⫽ O

Cabeza, 1997 [86]

Y⫽O

Anderson, 2000 [85]

Word pairs: learn (intentional)

Y⬎O

Logan, 2002, Experiment 1 [92]

Words: learn (intentional)

Y⬎O

Faces: learn (intentional)

Y⬎O

Sperling, 2003 [93]

Face-name pairs: learn (intentional)

Y⬎O

Grady, 1995 [87] Logan, 2002, Experiment 2 [92]

Faces: learn (intentional) Words: learn (intentional) vs. shallow and deep (incidental)

Y⬎O Y⬎O

Rosen, 2002 [96]

Words: deep (incidental) vs. shallow (incidental)

Y ⬎ HiOld ⬎ LoOld

Grady, 1999 [98]

Pictures and words: learn (intentional) vs. deep and shallow (incidental)

Words: Y ⬎ O Pictures: Y ⫽ O

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Table 4 (continued) Group (first author)

Task

Memory performance

Activation

Grady, 2002 [97]

Faces: learn (intentional) vs. deep and shallow (incidental)

Y⬎O

Occ (left)–deep: Y ⫽ O PFC (bilateral)–deep: Y ⬎ O

Stebbins, 2002 [99]

Words: deep (incidental) vs. shallow (incidental)

Not tested

PFC (left)–deep: Y ⬎ O PFC (right)–deep: Y ⫽ O

that promoted deep, semantic processing of the to-be-remembered information [88–90]. Moreover, Daselaar et al. [89] reported that left inferior PFC activity during encoding of successfully remembered words (relative to a baseline task) was equivalent in younger adults and two groups of older adults, one whose memory performance was on par with the younger adults (HiOld) and one whose memory performance was a standard deviation or more below that of the younger adults (LoOld). Together, this provides strong support for the notion that incidental encoding conditions that guide deep, semantic learning operations also guide effective encoding-related activity in PFC. Of the studies that used intentional learning conditions [85–87, 91–93], all but two of them found an age-related reduction in left PFC activation during encoding. Iidaka et al. [91] reported no age-related differences in left inferior PFC activation during the encoding of picture pairs (relative to a control condition). Much previous behavioral research has found that relative to memory for words, older adults are generally as able as younger adults to remember pictures [e.g. 94, 95]. Hence, in this case it is possible that the stimuli, rather than the encoding task, guided the type of encoding operations necessary for effective learning. The other exception was the study by Logan et al. [92; Experiment 1] when faces were encoded. However, they focused on a region-of-interest in Brodmann areas 6/44 (close to premotor cortex), a region that is more posterior and dorsal than that typically found in studies of face encoding. One would expect studies employing two or more encoding tasks that vary in the degree of semantic processing to find that deep, relative to shallow encoding would reduce age-related differences in left PFC activation. Overall, this prediction is maintained. Three of the five studies [92, 96, 97] reported that deep relative to shallow encoding conditions increased left PFC activity, especially in older adults. Two studies [98, 99] reported that deep encoding increased activity in left PFC in both groups, but the effect was greater in the younger adults. In summary, deep encoding generally increases left PFC activity regardless of age, and sometimes it can reduce the age differences seen in this region.

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Much less focus has been placed on activity in visual areas during encoding, but on the whole, a similar depth of processing effect appears to affect age-related differences in activity in visual regions as in frontal lobe regions (see table 4). Studies that employed a single incidental task promoting deep encoding resulted in age-related equivalence or an age-related increase in striate and extrastriate activation [89–100]. In contrast, when participants were instructed to simply learn the information, activation in these regions was generally greater in younger than older adults [85, 86, 91, but see 93]. Again, one would expect this pattern to hold in studies that directly compared encoding tasks varying in the amount to which they guided deep encoding. Unfortunately, Logan et al. [92], Rosen et al. [96], and Stebbins et al. [99] analyzed only PFC regions-of-interest (i.e., they did not examine activity outside the frontal lobes). Grady et al. [98] examined encoding of pictures and words, and reported that during deep picture encoding, young and old adults showed similar amounts of extrastriate activity. A similar age-related equivalence in extrastriate activation was found during deep face encoding in a later study [97]. In summary, agerelated reductions in PFC and striate/extrastriate cerebral activation appear to be reversed by learning conditions that guide effective encoding operations. The second relatively consistent pattern that is evident in table 4 is that older adults often activate the right PFC during encoding. An important question is whether this additional encoding activation in the old is compensatory, that is, beneficial to memory performance, or indicative of less efficient processing, greater difficulty, or some other factor that would be negatively associated with performance. A few studies speak to this question. Grady et al. [97] examined how brain activity was correlated with memory for faces. For older adults, activity in both inferior and middle frontal gyri bilaterally was correlated with better memory. However, this correlation was found during both encoding and retrieval, and so it is unclear whether activation of these regions during encoding or during retrieval was most beneficial to memory performance. Stebbins et al. [99] did not test memory for the words encoded in their study. However, they did examine the relationship between encoding-related brain activity and performance on other measures of episodic (story recall) and working memory (listening span). For the young adults, there was no correlation between these measures and lateral PFC activation, but for the older adults, listening span was correlated with activity in left PFC and story recall was correlated with activity in right PFC. Morcom et al. [90] contrasted brain activity for words that were remembered versus words that were forgotten, thus producing a ‘subsequent memory effect’ pattern of brain activity. For the younger adults, this effect was lateralized to the left PFC whereas it was bilateral for the older adults. Finally, Rosen et al. [96] compared PFC activity associated with deep relative to shallow encoding in younger adults and in healthy older adults with high

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(HiOld) or low (LoOld) scores on standardized tests of memory. The LoOld showed reduced activity in left and right PFC relative to the young and HiOld. By contrast, encoding-related activity in the HiOld relative to their younger counterparts was equal in the left PFC but greater in the right PFC. Together, these results provide initial evidence that older adults’ increased tendency to activate right PFC during encoding can be compensatory, offsetting deleterious effects of aging. In addition, compensatory activity in older adults during encoding may also take the form of utilization of different areas of PFC compared to those utilized in younger adults. For example, a recent analysis of the Grady et al. [98] data comparing functional brain networks engaged by young and old adults during deep encoding of words and pictures [101] showed that the networks in young adults consisted of ventral PFC, ventral extrastriate areas and hippocampus. Older adults engaged dorsolateral prefrontal, parietal and hippocampal regions. Of greatest significance for the compensation idea was the finding that in both groups, those who engaged the age-specific network to the greatest degree during encoding also showed the best recognition memory. In summary, the existing data reveal that age-related reductions in encodingrelated brain activity in left PFC and in visual areas are mitigated or eliminated when the encoding task guides effective learning. Moreover, regardless of the encoding task, older adults often show additional activation of the right PFC or sometimes more dorsal regions of PFC that appear to be associated with better memory performance. Episodic Retrieval As noted in the introduction to this section, it has been proposed that the processing resources needed to engage in attention-demanding, self-initiated encoding and retrieval processes decline with age, but task environments that guide effective encoding or retrieval operations reduce the need to engage in these processes and hence minimize age-related differences in memory performance [e.g. 62]. According to these ideas, we would expect neuroimaging studies that employed memory tasks that guided effective retrieval processes would reveal smaller age-related decrements in retrieval-related brain activity than studies that employed memory tasks that demanded more self-initiated retrieval processes. In younger adults, when brain activity during retrieval is compared to brain activity during a baseline task or during encoding, the right PFC is typically activated [e.g. 102]. The left PFC is also activated in many circumstances, particularly if retrieval requires semantic generation [e.g. 103, 104]. Regardless, we would expect more supportive retrieval tasks such as recognition to lead to smaller age-related differences in PFC activation than more demanding retrieval tasks such as recall.

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By and large, the results of studies comparing younger and older adults’ retrieval-related brain activation are in agreement with this expectation. These results are shown in table 5, where it can be seen that most studies that employed a recognition task revealed equal activation of right PFC in younger and older adults [87–89, 97, 105]. In the face recognition study by Grady et al. [97], a dorsal area of right PFC was more activated by retrieval in younger than older adults. However, a more ventral area of the middle frontal gyrus (Brodmann area 10) showed equal retrieval-related activation in the two age groups; this area was nearly identical to that also found to be age-equivalent in a previous study [87]. Word stem cued recall can also be considered a relatively ‘supportive’ retrieval task, in that the first three letters of the studied words are provided as a retrieval cue. Bäckman et al. [106] found an age-related equivalence in right PFC activity during word stem cued recall, and Schacter et al. [107] reported an age-related decrement in right anterior PFC activation but an age-related increase in left inferior and right dorsolateral PFC after a shallow encoding task. There were no differences in PFC activity following deep encoding. These results support the conclusion that memory tasks that provide retrieval support minimize age-related decrements in retrieval-related brain activity. The remaining four studies in table 5 employed memory tasks that required greater degrees of self-initiated retrieval. Cabeza et al. [86] and Anderson et al. [85] used a cued recall task and both reported an age-related reduction in retrieval-related right PFC activation. Two other studies led by Cabeza [108, 109] used similar logic. Both compared brain activity during retrieval of item information versus retrieval of source or context information. In these studies, Cabeza et al. predicted that both kinds of retrieval would involve the right PFC, but that the involvement of right PFC would be greater during context retrieval, especially for the younger adults. Indeed, in both studies right PFC was significantly more activated in younger than older adults when examining context retrieval compared to item retrieval, although an anterior region (Brodmann area 10) yielded a different pattern, as discussed below. Hence, these results are consistent with the conclusion derived above that memory tasks that place greater demands on self-initiated retrieval operations reveal age-related decrements in retrieval-related activation of right PFC, but these decrements are eliminated by more supportive memory tasks that guide effective retrieval operations. The second common pattern evident in the retrieval data (see table 5) is an age-related increase in activation of left PFC [85, 86, 88, 97, 106, 107, 109]. That is, retrieval activations, like their encoding counterparts, tend to be more bilateral in older than younger adults, and the question again is whether this finding reflects compensation or deleterious effects of aging. Several findings speak to this question. Cabeza et al. [110] conducted structural equation

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Table 5. Long-term memory retrieval Group (first author)

Task

Memory performance

Activation

Grady, 1995 [87]

Face recognition

Y⬎O

Occ (right): Y ⬎ O PFC (right): Y ⫽ O

Grady, 2002 [97]

Face recognition

Y⬎O

Occ (left): Y ⬎ O PFC (right dorsolateral): Y ⬎ O PFC (right anterior): Y ⫽ O PFC (left dorsolateral): Y ⫽ O PFC (left inferior): O ⬎ Y

Schiavetto, 2002 [105]

Object and spatial recognition

Y⬎O

Occ (right): Y ⬎ O PFC (bilateral): Y ⫽ O

Madden, 1999 [88]

Word recognition

Y⬎O

Occ (right): Y ⫽ O PFC (right): Y ⫽ O PFC (left): O ⬎ Y

Daselaar, 2003 [89]

Word recognition

Y/HiOld ⬎ LoOld

Occ (bilateral): Y ⫽ O PFC (right): Y ⫽ O PFC (left inferior): O ⬎ Y PFC (left anterior): Y ⫽ O

Bäckman, 1997 [106]

Word stem cued recall

Y⬎O

Occ: none PFC (right): Y ⫽ O PFC (left): O ⬎ Y

Schacter, 1996 [107]

Word stem cued recall

Y⬎O

Occ: none PFC (bilateral anterior): Y ⬎ O PFC (left inferior): O ⬎ Y PFC (right dorsolateral): O ⬎ Y

Cabeza, 1997 [86]

Word cued recall and semantic recognition

Y⫽O

Occ (right): Y ⬎ O PFC (right): Y ⬎ O PFC (left): O ⬎ Y

Anderson, 2000 [85]

Word cued recall

Y⬎O

Occ (left cuneus): Y ⬎ O Occ (left middle): O ⬎ Y Occ (right striate): O ⬎ Y PFC (right): Y ⬎ O PFC (left anterior): Y ⬎ O PFC (left inferior): O ⬎ Y

Cabeza, 2000 [108]

Word recognition

Y⬎O

Word temporal order

Y⬎O

Occ: none PFC (left): O ⬎ Y Occ (medial): Y ⬎ O Occ (lateral): Y ⫽ O PFC (right): Y ⬎ O PFC (left): Y ⫽ O

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Table 5 (continued) Group (first author)

Task

Memory performance

Activation

Cabeza, 2002 [109]

Word cued recall

Y/HiOld ⬎ LoOld

Occ: not reported PFC (left dorsolateral): Y ⬎ O PFC (left inferior): Y/LoOld ⬎ HiOld PFC (right dorsolateral): Y ⬎ O

Word source recall

Y/HiOld ⬎ LoOld

Occ: not reported PFC (right anterior): O ⬎ Y PFC (left anterior): HiOld ⬎ LoOld/Y

modeling on the results from the Cabeza et al. [86] cued recall study, and found that left-right PFC connections were negative in the younger adults, but positive in the older adults. Cabeza et al. [109] compared PFC activity during word cued recall and word source recall in two groups of older adults that differed in their performance on a separate series of standardized memory tasks. The HiOld’s performance on these tasks was on par with that of the younger adults, and the LoOld’s performance on these tasks was significantly lower than the young adults and HiOld, but within normal limits for their age. Cabeza et al., reasoned that if the increased bilateral activation was compensatory for older adults, then it should be evident in the HiOld but not the LoOld, whereas if it reflected a negative consequence of aging, then it should be present in the LoOld but not the HiOld. Only the HiOld demonstrated bilateral PFC activation during word source recall (relative to word cued recall), providing strong support that bilateral PFC activation reflects processing that is beneficial to memory performance, thus compensating for age-related declines. A study by Daselaar et al. [89] found similar results. They defined HiOld and LoOld on the basis of the recognition test used in their functional magnetic resonance imaging study, rather than on a separate series of memory tests. Only the HiOld had bilateral PFC activity during correct rejections (saying ‘no’ to a non-studied word) relative to a non-memory baseline. Correct rejections require some degree of source memory, because one must recollect that although the nonstudied words are familiar and common, they were not presented on the study list. Hence, this finding of greater bilateral activation in the HiOld is consistent with the results of Cabeza et al. [109]. By contrast, analysis of hits (saying ‘yes’ to a studied word), relative to correct rejections, found greater activation of left inferior PFC (Brodmann area 47) in the LoOld. Correct recognitions (hits) can be made mainly on the basis of an automatic sense of familiarity [111]. This

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result thus suggests that evidence of increased activation in older adults is not necessarily compensatory, but may reflect the use of less strategic, more automatic cognitive processing. The results regarding age-related differences in activation of visual areas during retrieval (see table 5) are more mixed, with some studies reporting age-related decreases, particularly in right extrastriate regions [86, 87, 97, 105], others reporting age-related equivalence [89, 100], and one reporting mixed results [85]. The two studies reporting no difference in activation of visual processing regions during retrieval [89, 100] both employed learning conditions promoting deep semantic processing and word recognition tests; speculatively, such support at both encoding and retrieval could help guide higher-order perceptual processing of the visual test materials. In summary, retrieval-related brain activity in right PFC may be reduced in older relative to younger adults, and it appears that a similar reduction is present frequently in both right and left hemisphere extrastriate activity. However, memory tasks that help guide effective retrieval processes, especially when combined with learning tasks that guide deep, semantic processing, reduce or eliminate this age-related decrement. At the same time, there are age-related increases in left PFC activation during memory retrieval that in many cases appear to reflect processes that help compensate for the deleterious effects of aging on memory. It is nevertheless important to link brain activation data with performance data to help determine whether increased activation is compensatory or not.

Differences in Brain Activation between AD Patients and Healthy Elderly Adults

AD affects a variety of cognitive functions, primarily those that could be described as dependent on top-down processing, including attention, working memory, semantic processing, and episodic memory [112–115]. Of all of these, episodic memory is affected most dramatically, particularly for information recently acquired [116], but retrieval of remote memories also may be impaired [117]. Functional neuroimaging studies carried out in the resting state have shown that the parietal cortices important for memory function are affected relatively early in the course of AD [118–122]. In contrast, metabolic measures in frontal cortex usually reveal reductions later in the course of the disease [123, 124]. In addition, activity in specific brain areas known to participate in memory in healthy individuals [e.g. 125] is related to memory ability in AD patients. For example, semantic processing in AD patients is correlated with resting metabolic activity in left hemisphere lateral temporal, parietal, and prefrontal regions [126, 127], and episodic memory in AD patients is correlated

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Table 6. Alzheimer’s disease – non-memory tasks Group (first author)

Task

Task performance

Activation

Mentis, 1998 [11]

Passive visual stimulation

None

Occ (bilateral): C ⬎ AD

Pietrini, 2000 [132]

Passive audiovisual stimulation

None

Occ (bilateral): C ⬎ AD

Thulborn, 2000 [146]

Visually guided saccade task

Not reported

Occ: not activated PFC (bilateral): AD ⬎ Ca

Grady, 1993 [135]

Face processing

C ⬎ AD

Occ (bilateral): AD ⫽ C PFC (right): AD ⬎ C

Saykin, 1999 [148]

Semantic task (auditory)

C ⬎ AD

Occ (midline): C ⬎ AD PFC (left): C ⬎ AD

Prvulovic, 2002 [136]

Judgment of angle size

AD ⫽ C

Occ (bilateral striate): AD ⫽ C Occ (left fusiform): AD ⬎ C PFC (bilateral): AD ⫽ C

Grossman, 2003 [133]

Pleasantness (semantic) judgment of words

AD ⫽ C

Occ (left): C ⬎ AD PFC (left): C ⬎ AD

aActivity in AD patients was reported as more extensive but not compared statistically to control participants.

with activity in temporoparietal regions [121, 127, 128] and in medial temporal regions [127, 129]. Studies examining brain activity in AD patients during cognitive tasks are summarized in tables 6 and 7. In occipital cortex, resting metabolism is relatively spared in patients with AD [130] except in cases of AD with prominent visual symptoms [131]. However, when activity in visual areas is elicited by visual stimulation during non-memory types of task, the response in patients is usually less than that seen in healthy elderly control participants, even in mildly affected patients. For example, two studies using passive viewing of visual stimuli reported reduced activity in AD patients, particularly in more severely affected individuals [11, 132]. Reduced activity in visual cortex also has been reported during semantic tasks [133, 134]. In contrast, no differences in extrastriate activity between AD patients and control participants were found during face perception [135], and greater fusiform activity was reported in AD patients during judging the size of angles to be greater or less than 90⬚ [136]. In addition, when memory tasks are used, AD patients generally have equivalent or even greater activity in extrastriate regions, compared to control participants. For example there were no differences in extrastriate activity between patients and control participants on tasks of facename encoding [93], encoding of complex scenes or designs [137, 138] or encoding of words and pictures of objects [139]. One study found that AD patients had

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Table 7. Alzheimer’s disease – memory tasks Group (first author)

Task

Task performance

Activation

Becker, 1996 [143]

Auditory verbal memory

C ⬎ AD

Occ: not activated PFC (bilateral): AD ⬎ C*

Woodard, 1998 [147]

Verbal rehearsal

AD ⫽ C

Occ: not activated PFC (bilateral): AD ⬎ C

Bäckman, 1999 [144]

Word-stem cued recall

C ⬎ AD

Occ: not activated PFC (left): AD ⬎ C

Kato, 2001 [137]

Encoding geometric designs

C ⬎ AD (recall)

Occ (right): AD ⫽ C PFC (right): C ⬎ AD

Rombouts, 2000 [138]

Encoding scenes

C ⬎ AD (recognition)

Encoding line drawings

C ⬎ AD (recognition)

Occ (bilateral): AD ⫽ C PFC: not activated Occ (bilateral): AD ⫽ C PFC (left): AD ⫽ C

Novel face-name pairs Repeated face-name pairs

C ⫽ AD (Face recognition) C ⬎ AD (Name recall)

Occ (bilateral): AD ⫽ C PFC (right): C ⬎ AD PFC (left): AD ⬎ C Occ (right): C ⬎ AD PFC (bilateral): C ⬎ AD

Grady, 2001 [140]

Face working memory

C ⬎ AD

Occ (midline): AD ⬎ C PFC (bilateral): C ⬎ AD

Grady, 2003 [139]

Semantic processing

C ⬎ AD

Episodic memory

C ⬎ AD

Occ (left): AD ⫽ C PFC (left): AD ⫽ C Occ (bilateral): AD ⫽ C PFC (bilateral): AD ⫽ C

Sperling, 2003 [93]

*Activity in AD patients was reported as more extensive but not compared statistically to control participants.

more activation of occipital cortex during a delayed match-to-sample face memory task [140]. Thus, there is not a clear pattern of differences between AD patients and healthy older participants in how occipital regions respond to visual stimulation, although more complex tasks seem to engage these areas to an equivalent degree. It would appear that the visual areas are not entirely unaffected in AD, but can be activated normally under some conditions. Other areas related to cognitive function show reduced activation in AD patients, notably the hippocampal region during encoding of new visual stimuli [137, 138, 141, 142] and during verbal retrieval [143, 144]. These deficits are consistent with the presumed pathological changes that occur early in the course of AD in medial temporal regions [e.g. 5, 145]. However, like the

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studies reviewed above for normal aging, some neuroimaging studies of early AD have found increased prefrontal activity during cognitive tasks compared to older control participants. Several studies have found increased PFC activity during visual perceptual tasks, including a saccade task [146] and a facematching task [135]. In a study of visual word-stem cued recall, mildly demented patients showed activations in many of the areas activated in healthy elderly participants, but also showed increased activity in left ventral PFC [144]. Becker et al. [143] examined auditory-verbal recall and found that control participants activated anterior prefrontal regions whereas AD patients activated a much wider area of frontal cortex, primarily in dorsolateral regions. This additional increase in PFC activation in the patients was interpreted as a compensatory reallocation of cognitive resources during the memory task, although the patients were significantly impaired in performance on these word memory tasks. Other verbal tasks, such as overt verbal rehearsal of visually presented word lists [147] also are accompanied by greater utilization of PFC in patients compared to age-matched control participants. In the case of verbal rehearsal, both right and left hemisphere PFC regions were more active in the patients. A study of the encoding of novel pairs of faces and names found that an elderly control group had more activation in right PFC than did the AD patients, but the patients had greater activity in left dorsolateral PFC compared to control participants [93]. When the same face-name pairs were shown repeatedly, control participants had more activity in PFC regions bilaterally. Others also have reported reduced left PFC activity in AD patients compared to healthy elderly participants on semantic processing tasks [133, 148]. Finally, some experiments have found equivalent activity in PFC regions between AD patients and healthy elderly participants during semantic and episodic memory tasks [139], and during visual encoding [138]. These experiments suggest that early in the course of AD, prefrontal activation often is maintained at normal levels, and may even be increased above normal levels in some memory tasks. This additional recruitment of PFC in AD patients can be found in either hemisphere, and sometimes in both. However, this redistribution of cognitive resources is generally not sufficient to maintain performance at normal levels. Functional connectivity, or correlations among activated brain areas, may be altered in AD patients, even if mean levels of activity are not. Decreased correlations between PFC and extrastriate regions were found in AD patients compared to control participants during a perceptual matching task, but the patients also had increased correlations among regions in PFC [17]. A similar reduction in correlated activity between PFC and posterior areas was found in AD patients during a delayed match-to-sample task for faces [140]. In control participants, activity in right PFC was positively correlated with blood flow in left PFC, bilateral fusiform areas, and the right hippocampus. In the patients, activity in right PFC was

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correlated mainly with other PFC regions, but not with the hippocampus or fusiform regions. These results support the idea of a functional disconnection in AD between PFC and other brain areas, including visual cortex and hippocampus, and suggest that cognitive breakdown in early AD is related to a reduction in integrated activity within distributed networks that include these areas. In contrast to this evidence for a breakdown of functional connections, alterations in functional connectivity also can indicate the likely presence of compensatory activity. In one study [149], a network approach was used to examine brain activity in patients and healthy control participants during the performance of a serial verbal recognition task in which study list size (SLS) was adjusted so that each subject recognized words at 75% accuracy. In the healthy older adults, higher SLS was associated with the recruitment of a network of brain areas involving left anterior cingulate and anterior insula. In the majority of patients, higher SLS was associated with the recruitment of an alternate network that included left posterior temporal cortex and the posterior cingulate. Activity in this alternate network was unrelated to memory performance in the healthy elderly participants. A more recent study also focused on the behavioral consequences of altered functional connectivity in AD patients during semantic and episodic memory tasks [139]. Patients as well as healthy elderly participants had increased activity in left ventrolateral PFC and left extrastriate areas during the tasks, compared to a baseline task. However the functional connectivity of these two regions differed markedly between groups. Control participants recruited a left hemisphere network of regions, including PFC, temporal and extrastriate cortices in both the semantic and episodic tasks, whereas patients engaged a network involving bilateral dorsolateral PFC and posterior cortices. Of particular interest, it was found that activity in this network of regions was correlated with better performance of the patients on both the semantic and episodic tasks. That is, those patients who were able to engage bilateral frontal and occipitotemporal regions to a greater degree also were able to perform more accurately on the tasks. These studies provide evidence that AD patients can utilize additional neural resources and networks to compensate for losses due to the degenerative process of the disease. These networks can include PFC, and presumably the executive functions that are mediated by this area of cortex (see section on ‘Age-Related Differences in Brain Activation during Non-Memory Tasks’).

Conclusions

This review shows that healthy older adults and AD patients are both able to recruit occipital and prefrontal brain areas to a greater extent than their respective

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control groups, suggesting that this recruitment may be a general response of the brain to damage or reduction in processing effectiveness regardless of the cause of that reduction. It is not yet clear what conditions promote this additional recruitment, whether these changes in brain activity indicate that different cognitive strategies are being used to carry out the same task, or whether over time different brain areas come to be used for the same cognitive process. It also is apparent that activity in visual and frontal cortices can be lower in older adults or AD patients. It would seem prudent to conclude that cognitive changes associated with aging or dementia cannot be explained solely on the basis of dysfunction in either bottom-up (visual areas) or top-down processing (frontal areas), and may in fact be due to both. Another question is when in the lifespan or in the course of a dementing illness these changes occur. It is likely that the differences in brain activity seen in older adults and in AD patients develop gradually over time, but it is not known at what point these changes begin. Finally, the relation between activation differences in visual and prefrontal regions, or other brain areas, is not known. For example, it is conceivable that increased PFC activity in older adults or AD patients on visual tasks could arise in response to reduced activity in areas that mediate low-level visual processing. Clearly much more work needs to be done before we can understand the complex changes in brain activity that are seen in normal older adults and in those with AD, and how these changes are expressed in terms of cognitive difficulties.

Acknowledgements This work was supported in part by grants from the Natural Sciences and Engineering Council of Canada (N.D.A.) and the Canadian Institutes of Health Research (C.L.G.).

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145 Jack CR Jr, Petersen RC, Xu Y, O’Brien PC, Smith GE, Ivnik RJ, Boeve BF, Tangalos EG, Kokmen E: Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology 2000;55:484–489. 146 Thulborn KR, Martin C, Voyvodic JT: Functional MR imaging using a visually guided saccade paradigm for comparing activation patterns in patients with probable Alzheimer’s disease and in cognitively able elderly volunteers. Am J Neuroradiol 2000;21:524–531. 147 Woodard JL, Grafton ST, Votaw JR, Green RC, Dobraski ME, Hoffman JM: Compensatory recruitment of neural resources during overt rehearsal of word lists in Alzheimer’s disease. Neuropsychology 1998;12:491–504. 148 Saykin AJ, Flashman LA, Frutiger SA, Johnson SC, Mamourian AC, Moritz CH, O’Jile JR, Riordan HJ, Santulli RB, Smith CA, Weaver JB: Neuroanatomic substrates of semantic memory impairment in Alzheimer’s disease: Patterns of functional MRI activation. J Int Neuropsychol Soc 1999;5:377–392. 149 Stern Y, Moeller JR, Anderson KE, Luber B, Zubin NR, DiMauro AA, Park A, Campbell CE, Marder K, Bell K, Van Heertum R, Sackeim HA: Different brain networks mediate task performance in normal aging and AD: Defining compensation. Neurology 2000;55:1291–1297.

Dr. Nicole D. Anderson Kunin-Lunenfeld Applied Research Unit, Baycrest Centre for Geriatric Care Departments of Psychiatry and Psychology, University of Toronto Toronto, Ont M6A 2E1 (Canada) Tel. ⫹1 416 785 2500/ext 3366, Fax ⫹1 416 785 2862 E-Mail [email protected]

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Abnormalities in Visual Behavior in AD and Related Disorders Cronin-Golomb A, Hof PR (eds): Vision in Alzheimer’s Disease. Interdiscipl Top Gerontol. Basel, Karger, 2004, vol 34, pp 96–111

Heterogeneity of Visual Presentation in Alzheimer’s Disease Alice Cronin-Golomb Department of Psychology, Boston University, Boston, Mass., USA

Researchers who study Alzheimer’s disease (AD) are well acquainted with the heterogeneity of its presentation, and clinicians are challenged daily to apply research findings to the behavior of individuals with the disorder. With the growing consensus that visual deficits are common and significant in AD, as illustrated by the work described in the chapters of this volume, it becomes important to begin a discussion of the relevance of the group findings to the individual case. As is true for most aspects of cognitive and perceptual function in AD, the visual profile is characterized by heterogeneity. Our study of samples may indicate that most patients perform well on certain tests and poorly on others, but how well does this information describe what one is likely to see in an individual case? In order to address this question, this chapter summarizes some of the recent research on group differences in visual performance between patients with AD and healthy adults, focusing on studies that assessed a range of visual functions. Then, it examines the heterogeneity of performance in an attempt to identify demographic, neuropathological and other factors that are most salient to the visual profile of individual cases. Behavioral studies described here that use the term ‘AD’ were conducted with patients who had a diagnosis of probable or possible AD in the absence of neuropathological confirmation of the disease.

Deficits in Basic Vision in Patients with AD

The prevalence of visual deficits is high in AD, with impairments found in a wide range of visual functions in a large sample of participants that we assessed [1]. This study included 77 patients with probable AD and 111 healthy control

participants who received the following tests of basic visual function: contrast sensitivity, color discrimination, stereoacuity, critical flicker fusion, global motion detection, local speed discrimination, and letter identification with backward masking. For each test, the control group was matched to the AD group for age (range of means ⫽ 63–71 years) and education (range of means ⫽ 13–15 years). Patients were referred from the clinic irrespective of visual symptoms and were screened for neurological disorders besides AD and for other serious conditions, as well as for ophthalmologic abnormalities. The range of dementia severity was wide (scores of 2–38.5 on the Blessed Dementia Scale). For each test, we calculated the cut-off score that would be expected from only one control participant in 100. We used these values to determine the percentage of patients with AD who performed poorly. First, we noted that the prevalence of deficits ranged from 0 to 59%. Backward masking (letter identification) was by far the most sensitive predictor of group status, with 59% of AD patients falling below the cut-off score for pattern masking and 52% for homogeneous masking. A second group of tests elicited substantial, if less striking, prevalence values of 20–33%. These tests included, in decreasing order of prevalence, contrast sensitivity at low spatial frequencies (1 cycle per degree (cpd) and 0.5 cpd), color discrimination, and stereoacuity. A low prevalence of AD deficit (7% or lower) emerged from assessment of contrast sensitivity at higher spatial frequencies (2–14 cpd), local speed discrimination, global motion detection, and critical flicker fusion. The distribution of test scores is depicted in figure 1. Of note, we found in a companion study that the tests with the highest prevalence of AD deficit, backward masking and low-frequency contrast sensitivity, were the best predictors of performance on cognitive tests of object recognition [2]. Results of the prevalence study accord with findings from the few individual studies comparing performance of a substantial number of patients with AD (⬎40) and healthy elderly adults on a wide variety of tests of visual function. Two of these studies [2, 3] had sets of participants that overlapped in part with those of Mendola et al. [1], and the third was by Rizzo et al. [4]. The studies examined individuals who were similar in age (mean 69–72 years) and education level (mean 14 years) and used common screening procedures; the only difference of note was that our studies examined patients across a wide range of dementia severity whereas the study by Rizzo et al. restricted participation to those with relatively mild impairment. As summarized in a review article [5], we found significant deficits in AD patients relative to control participants on tests of color discrimination, stereoacuity, contrast sensitivity, and letter-identification masking. We found normal AD performance on a test of critical flicker fusion and two tests of motion perception, as well as normal static binocular acuity. Rizzo et al. likewise found AD deficits in color discrimination and contrast sensitivity, and normal performance on motion detection and static acuity.

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Fig. 1. Prevalence of AD deficit for each visual test (percent participants impaired). Pa ⫽ Pattern masking; Ho ⫽ homogeneous masking; cs ⫽ contrast sensitivity (followed by number of cycles per degree); CU ⫽ City University Colour Vision Test; Rand ⫽ Randot Stereoacuity; LocMo ⫽ local motion (speed discrimination); GloMo ⫽ global motion; Flick ⫽ critical flicker fusion threshold (data from Mendola et al. [1]; graph courtesy of Dr. Janine Mendola).

The similarities in results are striking in light of the fact that our samples differed in the range of dementia severity studied. Moreover, the similar results were elicited by different measures of the same visual functions. For example, our color discrimination measures included the City University Colour Vision test, the Farnsworth D-15, and the Lanthony New Color Test [see 6 for details], whereas Rizzo et al. [4] used the Standard Pseudoisochromatic Plates. We administered a computerized measure of contrast sensitivity as well as the Vistech charts, whereas Rizzo et al. employed the Pelli-Robson chart. The only visual function for which the studies’ results diverged was stereoacuity, for which we found an impairment in performance of patients with AD but Rizzo et al. reported

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no significant group difference. Of note, in the latter study there was a substantial difference between the group means but the variability in AD performance was quite high, reducing the power to detect group differences. The high variability may have been elicited by the test chosen for use (Titmus test), which presumably had different demand characteristics than the stereoacuity test that we used (Randot chart). Although the sample tested by Rizzo et al. was restricted to those AD patients with mild dementia, this aspect of the sample difference probably did not account for the difference in results on stereoacuity, because we found no correlation between stereoacuity level and dementia severity [1, 3]. The investigations above reported normal performance on some vision tests. In our study [3], no patient was unable to perform more than one test, and we found no significant correlation between scores on the various visual tests (except between pattern masking and homogeneous masking). Within particular tests, the AD patients did not show a general impairment but rather a different pattern of performance than did the control participants, including a specific deficit on the tritan axis for color discrimination [3, 6] and a more prevalent impairment in contrast sensitivity at lower than higher spatial frequencies [1, 3]. Scores on some tests on which patients with AD exhibited impaired performance were correlated with dementia severity (lower frequency contrast sensitivity) but others showed no such correlation (color discrimination, stereoacuity, higher frequency contrast sensitivity) [1, 3, 6]. The correlation of dementia severity with backward masking was significant in one study [3] but not the other [1] probably because of an unusual bimodal distribution of AD performance in the latter study (described in more detail below); it is important to note that there were no group differences in confidence ratios in the former study, indicating equally reliable thresholds. Taken together, these results emphasize that impaired AD performance on the several visual tests did not reflect a pervasive general deficit in motivation or cognition, including inability to understand task instructions, but rather support the idea that visual dysfunction in AD is selective. Having summarized differences in visual performance between patients with AD and normal elderly adults, as assessed by the largest studies conducted to date, we may now examine the heterogeneity of performance revealed by these and other studies.

Heterogeneity in the Integrity of Individual Visual Functions in AD

The heterogeneity of visual ability in AD extends from completely normal performance on all tests to severe general dysfunction. Between these extremes lies the case of most patients with AD, who have impairments on some measures

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but not others, to a greater or lesser extent. The goal of this section is to describe the range of visual deficits of patients with AD, including possible mechanisms underlying these various deficits. The focus is on the visual functions of central acuity, color discrimination, stereoacuity, contrast sensitivity, backward masking, and motion detection. Central Acuity In our own studies, though we have reported binocular central acuity to be within normal limits in AD, in fact it tends to be a bit lower in AD than in control groups. For example, in our recent study of 15 participants per group [7], mean acuity for patients with AD was 20/27 whereas mean acuity for the control group was 20/19. We found that acuity levels within the ‘good’ range of 20/20–20/40 can influence contrast sensitivity as assessed with standard chart measures, intimating that even fine-grained differences may have functional consequences. Specifically, using the standard Regan Low Contrast Letter Acuity Charts, we found impaired AD performance relative to that of healthy elderly adults, but when we adjusted for acuity differences through analysis of covariance, the group difference disappeared (fig. 2). In our prevalence study described above [1], the best performance (6/6 Snellen) was attained by 46/59 AD patients (78%) and 62/64 control participants (97%). The next best line of 6/9 Snellen was attained by 17% of the AD and 3% of the control group; the remaining 5% of the AD group fell at 6/12 Snellen (equivalent to 20/40). Although at the time we interpreted these results as showing good acuity for the AD group, 95% of whom had acuity of 6/9 or better, the more recent work suggests that a cautionary note is in order. Most other studies report ‘normal’ acuity in AD [4, 8–12] but as we pointed out, few studies account for acuity variability in their findings [7]. It is not clear what underlies the minor group differences we see in acuity. Recent work describes ␤-amyloid deposits in lenses from individuals with AD, which were associated with the presence of supranuclear cataracts [13]. Other studies report an elevated risk for glaucoma in AD [14, 15]. Use of new ophthalmologic techniques that are sensitive to abnormalities in the anterior visual structures may reveal subtle abnormalities of explanatory value [see Valenti, this volume]. In any case, we recommend that researchers match groups very closely on acuity or control statistically any differences in acuity between groups, even if slight and seemingly within the accepted normal range. Color Discrimination and Stereoacuity As mentioned above, we have found no correlation between dementia severity and either color discrimination or stereoacuity. Some mildly demented patients showed significant impairments on one or both of these functions,

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Fig. 2. Performance on the Regan Low Contrast Letter Acuity charts by patients with probable AD and healthy elderly control participants (EC). Performance for each contrast level is plotted as a function of chart thresholds (the numbers closest to the y-axis) and their Snellen equivalents. The bars on the open and shaded columns represent mean performance prior to adjustments for group acuity differences. The horizontal thick black lines represent the adjusted means derived from the analysis of covariance. These lines indicate the location of the group means after accounting for the variability associated with group acuity differences (from Neargarder et al. [7]).

whereas some severely demented individuals performed the tests normally. Task difficulty obviously cannot explain these results. They are rather in accord with descriptions of color discrimination and stereoacuity as being more modular than other visual functions such as contrast sensitivity, in that they are associated with relatively discrete areas of extrastriate visual cortex [e.g., 16]. Beyond medial temporal involvement common to most patients early in the course of AD, the idiosyncratic pattern of neuropathology from individual to individual [17–19] apparently underlies the observed broad range of functional loss in color discrimination and stereoacuity, with extrastriate cortex affected early in the disease process in some patients and later if at all in others. Contrast Sensitivity Spatial frequency contrast sensitivity is the most studied visual function in AD, presumably because of its association with deficits in daily function in older adults [20–22; Dunne, this volume] (fig. 3). Contrast sensitivity reflects the

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Fig. 3. The image on the left is of a kitchen under normal viewing conditions. The image on the right is the same scene passed through the ‘Alzheimer filter’. The Alzheimer filter simulates the spatial contrast sensitivity deficit of a person with AD. The contrast in the image is digitally reduced to reflect the proportional difference in spatial contrast sensitivity between a healthy young adult and a person with AD. The image illustrates how challenging the spatial environment can be for anyone with a contrast sensitivity deficit (images and filtering courtesy of Grover C. Gilmore, Cecil W. Thomas, and Sandy Neargarder).

minimum amount of contrast that an observer needs to resolve a stimulus of a given size. Stimuli that are commonly used in standard tests of contrast sensitivity are sinusoidally modulated gratings, the number (cycles) of which varies within a set overall width, conventionally one degree of visual angle. The measure of this spatial frequency is cycles per degree (cpd). For most people, contrast sensitivity is less acute at low and high relative to mid-range frequencies, producing the characteristic contrast sensitivity curve. Many investigators have reported contrast sensitivity deficits in AD patients [1–4, 23–28], but the extent of impairment varies from study to study. In our recent investigation [7], we examined the two main potential explanations for such variability in the degree of impairment besides sample differences: acuity differences between AD and control groups, discussed above, and choice of assessment methods. We gave the same four contrast sensitivity measures to the same group of AD patients and control participants to examine whether the differences in results from study to study reflected differential test sensitivity rather than heterogeneity of functional loss within and across samples. We found that the Vistech chart, the Pelli-Robson chart, and the Freiburg

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computer-based test all elicited significant differences in performance between the AD and control groups; the Regan Charts did not reveal a group difference once acuity was accounted for. Our results suggest that variability in the extent of group differences reported in studies of contrast sensitivity in AD may have arisen from differences in acuity or other characteristics of the samples tested rather than from use of different standard measures. Contrast Sensitivity Relation to Face Discrimination Deficits in spatial frequency contrast sensitivity result in increased difficulty in face discrimination in healthy elderly individuals [e.g., 29] and especially in patients with AD [24, 28]. As described above, the spatial frequency of conventional grating stimuli is measured in cpd. When the stimulus is a face, face-width is the measure of overall size, the unit of measure is cycles per face (cpf) rather than cpd, and we refer to the facial frequency. In our study of the relation of contrast sensitivity to face discrimination in AD [24], we varied the size of target faces to induce shifts in the sensitivity curve. The hypothesis was that we could use the natural frequency response of the visual system to enhance the perception of the low facial frequency content and thereby enhance discrimination of face stimuli in AD patients, who have poor sensitivity especially at low spatial frequencies, as described above [1–3]. We found as predicted that reducing face size and thereby enhancing contrast sensitivity at low facial frequencies resulted in normal face discrimination in AD for that face size only. The results reported for the 18 patients with probable AD and 18 healthy control participants do not tell the whole story, however. We had tested an additional 10 patients with AD who were not included in the final study because they were not as well matched to the control group on a number of factors [30]. This sample was somewhat older, less educated, slightly more demented (mean score of 19.7 on Mini-Mental State Exam, compared to 21.6 in the final reported group), and most were men (8 of 10, in contrast to 8 men and 10 women in the final group). Their contrast sensitivity at the high spatial frequency of 18 cpd was more deficient than was that of the final reported group (mean log sensitivity 0.54 and 0.75, respectively), as was Snellen acuity (median 20/30 and 20/20, respectively). This sample did not show the effect of low-frequency enhancement on face discrimination, performing equally poorly with all three tested face sizes. Their performance with large faces (mean score 11.3 correct of 17) and medium faces (mean score 10.1) was comparable to that of the final study sample (mean scores of 11.0 and 10.6 respectively), but unlike the final study sample that had normal scores on the small faces (mean 12.1), the group of 10 was substantially impaired with the small faces (mean score 9.3).

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The failure of this excluded sample to demonstrate the pattern of performance shown by the control-matched study sample indicates the probable interaction of potentially influential variables such as age, education, dementia severity, and male:female sample composition as well as general contrast sensitivity and acuity. As noted above, we reported that variability in acuity levels, even within what is usually considered the normal range of up to 20/40 Snellen, can predict performance on standard contrast sensitivity assessment [7]. It is likely that some constellations of demographic and sensory-based features permit while others may prevent the visually-mediated enhancement of cognitive performance through contrast manipulation. Letter-Identification Masking Masking was the visual test that showed the most prevalent deficit, with 59% of a large sample of AD patients performing below a strict cut-off score [1]. In this study, we identified a subgroup of 14 AD patients who performed exceptionally poorly on the masking task. These patients were significantly younger and had a shorter duration of AD than the other 42 patients who took the test. We examined numerous other factors but found none besides age and disease duration that predicted this bimodal distribution of performance. Because the masking test used letters as targets, we suggested that the impairment in the extreme subgroup may have reflected pathological change in left occipito-temporal cortex [31] and was consistent with the view that early-onset AD is associated with increased severity, possibly biased toward left hemisphere, language-related functions in some patients [32–37]. Other early-onset subgroups may show prominent visuoperceptual disorders, such as visual agnosia and impaired visuospatial abilities, early in the disease course [e.g., 38, 39]. Such subgroups presumably would show a different pattern of performance on masking and the other tests of visual function that we describe. The extreme example here would be what has been referred to as the ‘visual variant’ of AD [9] with a presentation similar to Balint’s syndrome [40], or posterior cortical atrophy [see Mendez, this volume]. Motion Perception It is quite difficult to establish factors driving AD performance deficits on motion detection because of the variety of stimulus parameters employed in the literature. For example, using superficially similar motion tasks, Gilmore et al. [41] and Trick and Silverman [42] found that AD patients had a higher threshold than healthy adults for discriminating motion, whereas we [1] and Rizzo et al. [4] reported no AD deficit (though in the latter study, AD variability in performance was quite high, reducing the power to detect group differences). The tasks used in these studies varied on potentially important factors such as

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stimulus size and speed of movement. Expanding the domain of motion perception under investigation, recent reports indicate that AD patients have deficits in the perception of optic flow [43–45; see Duffy et al., this volume]. This interesting new work, like the studies of planar motion, raises the question of what aspects of the stimulus display are most successful in eliciting group differences in performance, as well as what specific areas of dorsal-stream pathology are likely to be implicated. Besides stimulus parameters, another factor that is likely relevant to understanding AD performance on motion-detection tasks is male:female composition of the samples. Older women have more pronounced deficits in motion perception than older men and young men and women [46–48]. These differences in motion perception may be related to normal age-related changes in intraocular pressure that are especially sensitive to estrogen levels in the eye and may be exacerbated by molecular processes associated with AD. Sex steroids, including estrogen, influence both structural and functional aspects of the human eye. Estrogen receptors have been found in a wide variety of structures in the eye [49, 50]. Early menopause is associated with a higher risk of glaucoma, suggesting a protective role for female sex hormones; intraocular pressure is higher in postmenopausal women than in men of the same age [51]. The magnocellular fibers in the retinocalcarine pathway are vulnerable to ocular hypertension [e.g., 52–54], and those degenerative changes extend beyond the lateral geniculate nucleus to visual cortex [55]. Normal motion perception is critically dependent on the magnocellular pathway extending to cortical area MT [56]. These observations taken together raise the possibility that postmenopausal women may be exceptionally prone to deficits in motion perception that arise from high intraocular pressure or glaucoma. In addition, there is some evidence that AD is associated with increased risk for normal-pressure glaucoma as diagnosed by a characteristic pattern of visual field loss or an optic nerve head cup-to-disc ratio of 0.8 or greater; the lack of ocular hypertension suggests that the optic nerve fibers are less resistant to increased intraocular pressure levels in AD than in healthy elderly individuals [14, 15]. This literature leads us to the hypothesis that there are two possible mechanisms by which women with AD may have ocular hypertension and glaucomatous changes that would affect motion perception: age-related estrogen levels, and AD itself.

General Sources of Variance in AD Visual Function

The poor performance of an individual with AD on a particular test of visual function may arise from one or more of multiple sources, which may or may not coincide with the sources of similarly poor performance on the same

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test by another person with AD. The fact that dysfunction may occur at various levels warrants investigation of the source of an individual’s impairment. In the section above, we attended to possible sources of variability in performance for discrete visual functions. In this next section, we consider sources of variability that are probably common to multiple visual functions. Heterogeneity of AD Patients’ General Clinical Presentation AD is heterogeneous in several aspects of presentation, including age at disease onset, age at testing, education level, disease duration, and medication usage. Samples described in the literature differ in ratio of men to women and most provide no information on the percentage of non-Caucasian participants; such factors may be important for understanding heterogeneity of performance [see 46–48 on differences between elderly men and women on motion perception; and Valenti, this volume, on racial differences in anterior visual structures]. AD is also a heterogeneous disease in terms of other factors, such as family history, rate of progression, and presenting or predominant behavioral symptomatology. We have discussed a number of these aspects of heterogeneity in relation to individual visual functions but such factors may well be important to understanding multiple visual abilities. Neuropathology Underlying Impaired Visual Abilities in AD Most aspects of visual cognition are impaired in AD, including the abilities to recognize and discriminate objects, faces, and patterns [reviewed in 30, 57, 58]. Deficits in visual cognition arise from pathological changes in highorder association areas of the brain, but also from defective input from multiple lower-level visual processing areas. High-order visual processes build upon, and so depend upon, the integrity of visual processes at earlier stages. The visual deficits that are observed in many individuals with AD are likely to be of cortical origin though they may ultimately involve the retina. The distribution of neuropathological changes in the AD brain implies that the disease may cause retrograde degeneration within the visual system, from association to primary visual cortex [17, 19, 59–61] and also along cortico-geniculate and cortico-tectal routes [62]. Neuro-ophthalmological examination and electrophysiological testing yield mixed results in samples of AD patients who showed significant impairments on tests of basic vision [11, 63–67]. One recent report described ␤-amyloid deposits in lenses from individuals with AD, which were associated with the presence of supranuclear cataracts [13]. Some investigators have found evidence of degeneration of retinal ganglion cells in AD autopsy tissue [68–71], whereas others have not [72, 73; see Valenti, this volume, for a more comprehensive consideration of the anterior visual pathways in AD].

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In living patients, retinal nerve fiber layer changes with AD have not been found reliably [74, 75], but the null finding may indicate merely the lack of sensitivity of the measures used or confounds of visual inattention. Newer technologies may be more sensitive to glaucoma-type changes in visual fields and optic disc cupping, which would address recent reports of elevated glaucoma risk in AD [14, 15; see also Valenti, this volume]. There is a more extensive literature on neuropathological change in cortical areas than precortical structures that may underlie visual deficits in AD [76; see also von Gunten et al., this volume], with behavioral evidence for the implication of both the color-opponent and broad-band channels [2, 4, 30, 58, 77, 78].

Summary and Conclusion

This chapter has described group differences in visual function in AD and healthy older adults in an attempt to determine the substrates of the heterogeneity of AD performance. First, some patients may have AD-related disorders of precortical visual structures, such as the lens or retina. One consequence of such disorders may be impaired visual acuity, and poor acuity in turn impacts upon the integrity of other visual functions such as contrast sensitivity. Second, there may be dysfunction of specific visual abilities, pointing to significant neuropathology in certain brain regions or networks. Deficits in color discrimination and stereoacuity arise from lesions of extrastriate cortex, and impaired contrast sensitivity can be caused by malfunction at this or higher cortical levels [3]. Deficient contrast sensitivity is associated with impairments in face discrimination. The prevalent disorder in letter-identification masking may be tied to pathological change in occipito-temporal cortex, especially on the left side. Motion perception deficits implicate dorsal-stream areas and also lead to consideration of sex-hormone effects from the eye through visual cortex. Finally, we raise the important issue of heterogeneity of performance associated with demographic characteristics such as age, education level, sex, and race, and with disease-related characteristics such as age at onset, duration, medication usage, family history, rate of progression, and presenting or predominant behavioral symptoms. Although some inroads have been made into understanding the sources of heterogeneity of visual performance in AD, much still needs to be done before we will be able to examine visual abilities in the individual patient and from this information both infer key areas of neuropathology and predict future vision-related disease course. The value of prognosis in terms of visual behavior is that we will then be in a position to tailor our modification of the visual environment [see Dunne, this volume] to each individual patient’s particular challenges and residual abilities.

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Acknowledgements I am grateful to a number of colleagues for informative discussions that helped shape this chapter, including Grover C. Gilmore, Peggy Jennings, Sandy Neargarder, and Denise Valenti. Janine Mendola graciously supplied figure 1, which was adapted by Tom Laudate. The images and filtering of figure 3 were kindly provided by Grover C. Gilmore, Cecil W. Thomas, and Sandy Neargarder. Tom Laudate and Helen Tretiak-Carmichael gave expert technical assistance. This work was supported by the National Institute on Aging (5R01 AG15361).

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Alice Cronin-Golomb, PhD Department of Psychology, Boston University 648 Beacon St., 2nd floor, Boston, MA 02215-2013 (USA) Tel. ⫹1 617 353 3911, Fax ⫹1 617 358 1380, E-Mail [email protected]

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Cronin-Golomb A, Hof PR (eds): Vision in Alzheimer’s Disease. Interdiscipl Top Gerontol. Basel, Karger, 2004, vol 34, pp 112–125

Posterior Cortical Atrophy: A Visual Variant of Alzheimer’s Disease Mario F. Mendez Departments of Neurology and Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, The V.A. Greater Los Angeles Healthcare System, Los Angeles, Calif., USA

Posterior cortical atrophy (PCA) is an insidiously progressive cognitive disorder with associated atrophy of the occipital and occipitoparietal regions of the cerebral cortex [1–4]. Patients with PCA usually present with visual complaints, often to ophthalmologists or optometrists, due to deficits in complex visual processing [1–4]. Arnold Pick originally described PCA in 1902, but investigators have only recently begun to clarify the clinical and pathological aspects of this unique cognitive syndrome [2–5]. PCA most commonly begins in the 50s or 60s with equal involvement of men and women and a course of about 6–12 years [1–18]. When followed over time, PCA gradually evolves into a more generalized dementing illness. This chapter discusses the clinical and pathological aspects of PCA. It focuses on the range of visual processing and related deficits that can occur from this syndrome. On autopsy, PCA is most commonly a visual variant of Alzheimer’s disease (AD) resulting from a ‘posterior shift’ of the characteristic neuropathology of that disorder [14, 19]. This chapter presents the similarities and differences between PCA and typical clinical AD. Finally, it proposes management strategies for PCA and criteria that can be used to diagnose this syndrome.

Visual and Other Clinical Characteristics

Although PCA is most notable for complex visual disorders, there is evidence that PCA also impairs basic visual functions, similar to the impairments seen in AD [20–22]. PCA patients have normal visual acuity but mildto-moderate constriction of the peripheral visual fields [12], and AD patients

Table 1. Visual disorders reported among 104 patients with probable posterior cortical atrophya Visual disorderb

n

%

Alexia (out-of-proportion to oral language difficulty) Balint’s syndrome (partial or complete) Visual agnosia (primarily apperceptive) Environmental disorientation Dressing apraxia Prosopagnosia Color problemsc Visual field deficits

75 69 45 34 33 16 12 9

72.1 66.3 43.3 32.7 31.7 15.4 11.5 8.7

a Taken

from the following references: 1, 2, 4–19, 21–24, 29, 31–36, 38, 40, 43–48, 50, 52, 53, 63, 68–70. b The report of visual disorders depends on the thoroughness of the visual assessment and examination. The reported visual problems are undoubtedly underestimated because only the most salient symptoms were reported in many patients. Non-visual symptoms such as ideomotor apraxia and elements of Gerstmann’s syndrome were common but difficult to estimate from clinical reports. c Partial achromatopsia, hemiachromatopsia, color anomia, color agnosia, or abnormal color after-images.

may have visual field deficits on perimetry, especially in the inferior visual fields [25, 26]. There are reports of PCA patients with a left inferior quadrantanopia or a right homonymous hemianopia [23, 24]. PCA patients can have severely impaired contrast sensitivity for low spatial frequencies [12], and AD patients may also have impaired contrast sensitivities especially evident at low frequencies [20, 27]. Although not systematically studied, PCA patients probably have deficits in depth perception similar to those seen in typical AD [20, 21, 28], and they are impaired in the global processing as opposed to the local processing of stimuli [10, 29]. Visuospatial deficits such as Balint’s syndrome are among the most common manifestations of PCA (table 1). In addition to inability to copy or trace drawings (‘constructional apraxia’) [4, 24, 29, 30], PCA patients may evolve from difficulty in visually integrating whole scenes (‘ventral simultanagnosia’) to problems detecting two or more stimuli simultaneously [10, 29]. This latter condition, termed ‘dorsal simultanagnosia,’ is part of the triad of Balint’s syndrome, along with oculomotor apraxia, or inability to look towards a stimulus by visual guidance, and optic ataxia, or inability to reach for a stimulus by visual guidance [1, 4, 10, 12, 13, 18, 24, 30, 31]. Patients with PCA can present

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with dorsal simultanagnosia as the initial manifestation [32], and they can manifest other isolated elements of Balint’s syndrome during the course of their disease [10]. They may also have deficits in visuospatial attention resulting in right or left visual field extinction, hemi-inattention, or hemispatial neglect [6, 11, 30, 33]. PCA is associated with other visuospatial disturbances including general difficulty with spatial localization, environmental disorientation, dressing apraxia, and disturbed spatial cognition such as determining the orientation or axes of non-upright objects [1, 4, 5, 18, 30, 34–37]. Other complex visual symptoms of PCA are more perceptual than spatial (table 1). A common presentation is progressive visual agnosia, with impaired visual recognition in the presence of intact tactile recognition [13, 18, 23, 38]. Visual agnosia is usually of the apperceptive type given the presence of difficulty with figure-ground discrimination, visual synthesis, fragmented or degraded stimuli, matching and copying shapes, and vulnerability to ambient illumination [2, 8, 10, 12, 29, 34]. Some patients, however, have more of an associative visual agnosia, or difficulty recognizing visually presented objects in the absence of detectable perceptual problems [4, 39]. PCA can alter color perception and produce hemiachromatopsia, color anomia, or color agnosia [7, 18]. Similar to patients with AD, patients with PCA have deficits in the tritanomalous or blue range and often complain of difficulty distinguishing blue from black [20, 21]. Occasionally, these patients see objects as abnormally colored after prior exposure to a colored stimulus (abnormal color after-images) [40]. Specific problems with prosopagnosia, or the recognition of familiar faces, are less common in this disorder possibly because of sparing of the more anterior, inferior temporal lobes [4]. PCA patients complain of difficulty reading and writing [4, 6]. Alexia out of proportion to other language difficulties is the most common disturbance in PCA and can result from various mechanisms (table 1) [1, 11, 12, 34]. In addition to ‘pure alexia’ (alexia without agraphia) [6], visual disturbances such as simultanagnosia can play a role in producing alexia and in particular, letter-by-letter reading [10, 11, 12, 34, 41]. PCA can dissociate the phonological and lexical routes for reading. These patients may have phonological dyslexia with difficulty making grapheme-phoneme conversions, reading non-words, and reading stylized script or print in unusual fonts [34]. In contrast, the direct lexical route may be spared with retained reading of irregular words, access to visual word forms, and a word superiority effect for obscured words, even in the presence of visual agnosia and visuoperceptual impairments [34, 42]. When agraphia is present it too can result from various mechanisms. Writing disorders include not only linguistic agraphia but also spatial agraphia and agraphia predominantly of the apractic type [6, 44]. In Japanese, PCA has produced an agraphia for more ideographic writing (kanji) with relative sparing of more phonological writing (kana) [45].

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The range of clinical characteristics of PCA includes several other non-visual cognitive deficits. PCA patients with parietal involvement may develop ideomotor apraxia or inability to perform learned motor movements in the absence of underlying motor or sensory loss, Gerstmann’s syndrome, or transcortical sensory aphasia [1, 12, 16]. Limb apraxia is usually of the ideomotor type, but there can be more limited difficulty just in bimanual coordination [44]. Ideomotor apraxia can be a presenting symptom of PCA and may be confined to the non-dominant upper extremity [5, 16, 18, 23, 24, 30, 31, 35, 36]. There are elements of Gerstmann’s syndrome: linguistic agraphia, acalculia, right-left disorientation, and finger agnosia [1, 10–12, 24, 31]. Some patients have transcortical sensory aphasia: impaired auditory comprehension with intact verbal fluency and repetition [11, 16, 24]. Those PCA patients with elements of Balint’s syndrome, indicating parietal involvement, are particularly prone to Gerstmann’s syndrome and transcortical sensory aphasia [1]. Otherwise, in PCA there is early relatively spared language, insight, judgment, and executive abilities with the presence of only mild memory impairment [3, 5]. In particular, PCA patients have preserved awareness of their deficits, usually serve as their own historians, and may be very distressed over their visual decline. This retained insight and awareness of their visual deficits leads to severe depression in some patients. There are two major types of asymmetric variants of PCA. First, this disorder may differentially involve the dorsal ‘where’ or occipitoparietal visual pathway specialized for spatial perception, or the ventral ‘what’ or occipitotemporal pathway specialized for object perception [4, 5, 44]. The dorsal variant is responsible for Balint’s syndrome and other visuospatial deficits, and the ventral variant is responsible for visual agnosia and other visuoperceptual deficits [43, 44]. The segregation of deficits into dorsal and ventral streams, however, is not exact and most PCA patients have a mixture of disturbances from both. Second, early in the course, PCA may affect one hemisphere more than the other. Greater involvement of the left hemisphere has resulted in progressive visual agnosia, Gerstmann’s syndrome, right hemiachromatopsia, and pure alexia without agraphia [7, 11, 38]. Greater involvement of the right hemisphere PCA has resulted in left visual hemi-neglect, oculomotor apraxia, dressing apraxia, alexia for music, and spatial agraphia [6, 43, 46].

Disease Course

PCA patients have an insidiously progressive decline with the eventual development of other cognitive deficits or dementia. Unlike typical clinical AD, the visual cognitive deficits remain more prominent than memory, language,

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and other cognitive abnormalities until sometime later in the disease, indicating that PCA remains confined to posterior cortical areas until late in the illness [4, 11–13]. As noted, degeneration may develop asymmetrically progressing from one visual pathway to the other or from one hemisphere to the other [13, 30, 38, 47]. As PCA progresses, patients develop impairments in phonological aspects of language and in auditory-verbal short-term memory suggesting an anterior spread to perisylvian areas [44]. Greater memory loss and even frontal lobe involvement become evident as PCA continues to progress in a caudal to rostral direction [31]. Ultimately, these patients are severely demented with a mean disease duration of about 8 years, similar to that for early-onset AD [12, 34].

Laboratory Investigation

Routine laboratory tests are not helpful in the evaluation of PCA, but neuroimaging can be confirmatory of this syndrome. Magnetic resonance imaging (MRI) reveals disproportionate posterior cerebral atrophy and enlargement of the occipital horns in about half of PCA patients (fig. 1) [4]. Early in the course, this posterior atrophy can be asymmetrical [24, 47]. In contrast, MRI scans fail to show the prominent mesiotemporal atrophy that occurs in AD patients [4]. In PCA, functional imaging with either positron emission tomography (PET) or single photon emission tomography (SPECT) shows occipitoparietal hypometabolism or hypoperfusion (fig. 2). PET and SPECT do not show the typical temporoparietal changes of AD. PET and SPECT also demonstrate mild involvement of posterior temporal areas and relative sparing of the frontal lobes in PCA [4, 11, 13].

Neuropathology

The autopsied brains of PCA patients most commonly reveal the neuropathology of AD [2, 3, 9, 12, 19, 48–50]. In more than half of autopsied cases, PCA was proven to be a visual variant of AD with the usual temporoparietal pathology of AD shifted backwards to the occipitoparietal and posterior temporal regions [2, 12, 14, 15, 48–52]. Compared to typical AD, PCA cases with Alzheimer’s type pathology have a greater deposition of neurofibrillary tangles and neuritic plaques in primary visual and visual association areas and relatively fewer lesions in the prefrontal cortex and the inferior temporal region [12, 14, 19, 49, 53]. The relative sparing of the mesiotemporal region accounts for the better memory in PCA compared to typical clinical AD. In general, the symptoms of PCA may correlate most with the density of occipitoparietal

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Fig. 1. Magnetic resonance image (T1-weighted, sagittal) of a patient with posterior cortical atrophy demonstrating disproportionate atrophy of the posterior cerebrum with enlargement of the occipital horns [reprinted from 71].

tangles [12, 14, 19, 53]. For example, visual agnosia correlates with the density of neurofibrillary tangles in Brodmann’s areas 18, 19, and 37 [39], and Balint’s syndrome correlates with plaques and tangles in visual areas of the occipital and posterior parietal regions [3, 14, 53]. Some PCA patients may have a form of tangle predominant AD [52]. This AD pathology probably causes complex visual symptoms through a loss of specific corticocortical projections [14, 53]. The reason for this posterior shift of AD is unclear, but, at least in one instance, it may have occurred from heavy and sustained exposure to aluminum [52]. PCA is a clinical syndrome that can result from several other diseases [2, 12, 14, 15, 19, 48, 51]. The ‘Heidenhain variant’ of Creutzfeldt-Jakob disease presents with visual deficits from posterior cortical involvement [2, 9, 54]. These patients, however, have a more aggressive and fulminant course with other associated neurological abnormalities. In a few patients, PCA has resulted from subcortical gliosis or an unidentified familial illness [2, 24]. A subgroup with early prosopagnosia and visual agnosia have semantic dementia with right

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Fig. 2. Fluorodeoxyglucose positron emission tomography of a patient with posterior cortical atrophy showing bilateral posterior hypometabolism. There is a dramatic difference in the degree of metabolic activity in anterior areas, as indicated by the dark regions, as opposed to the posterior regions [reprinted from 71].

temporal involvement and inferior temporal lobe pathology consistent with frontotemporal lobar degeneration [55]. Clinicians must distinguish this ‘right temporal’ semantic dementia from the syndrome of PCA.

Similarities between PCA and AD

Similar risk factors and course further support the view that PCA is most commonly an early-onset, posterior-shifted AD. A matched pair comparison with early-onset AD patients (who met National Institutes of Neurological

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and Communication Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association criteria for Clinically Probable AD [56]) found no significant group differences in number of years of education, traumatic head injuries, vascular or metabolic illnesses, toxic exposures, or drug abuse [4]. Similar to typical clinical AD, 33–50% of patients with PCA had a positive family history of dementia [4, 24], and apolipoprotein (APOE) genotyping found the ␧4 allele, which increases the risk of developing AD, in about 25% of their alleles [4]. This frequency of the APOE ␧4 allele is more than expected for the general population and similar to that reported in early-onset AD [57]. The visual features of typical clinical AD also suggest the visual features of PCA. AD patients commonly have visual disturbances, which may occur even in the absence of semantic deficits [20, 58, 59]. AD patients are impaired in the visual evaluation of spatial locations, common objects, complex figures, and famous faces, and may have visual agnosia and hemispatial neglect [58, 60; see Tippett, this volume]. Similar to PCA, abnormal visual processing contributes to their alexia [61; see Glosser and Grossman, this volume]. Early-onset AD particularly resembles PCA and is more likely to have early, prominent visuospatial impairments compared to late-onset AD [62]. A significant subgroup of AD patients present with Balint’s syndrome and an early age of onset [14, 58, 63], and, similar to PCA, these patients have decreased sensitivity to low spatial frequencies [14, 63]. PET scans in mild-to-moderate AD show that visuospatial disturbances are related to parietal hypometabolism and that visuoperceptual disturbances are related to occipitotemporal as well as inferior parietal cortical hypometabolism with a relative sparing of inferior temporal, frontal, and limbic regions [21, 64, 65]. Neuropathological reports confirm that slowly progressive focal cortical syndromes resembling PCA can be a presentation for AD. This includes progressive visual dysfunction with severe occipitoparietal involvement in those with prominent visuospatial disorders [22]. It also includes a progressive parietal syndrome, which may be asymmetric or bilateral [22, 30]. AD with asymmetric parietal atrophy can resemble corticobasal degeneration, characterized by progressive unilateral motor signs including ideomotor apraxia and Gerstmann’s syndrome, as well as PCA [30]. Finally, in typical clinical AD, visual changes are more likely to be related to neuropathology in posterior cortical regions, as occurs in PCA, rather than to changes in the retina or optic nerve [66].

Differences between PCA and AD

PCA differs in several ways from typical clinical AD. Patients with PCA usually have a younger age of onset than those with typical clinical AD [4].

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12

PCA AD

* 10

8

* *

6

4

*

*

2

0 Fluency

Naming

Const

Calcul

Insight

Depress

Saving

Recog

Fig. 3. Neurobehavioral differences between PCA patients and age-matched, earlyonset AD patients. The results are plotted as raw scores on the y-axis. *Indicates that significant differences occurred in Insight, Depression, Savings Memory Score, and Recognition Memory Score. Fluency ⫽ verbal fluency for animals/minute; Naming ⫽ 15-item Boston Naming Test score; Const ⫽ constructions; Calcul ⫽ calculations; Insight ⫽ insight question; Depress ⫽ depression question; Saving ⫽ savings memory score; Recog ⫽ recognition memory score.

In PCA, patients have either normal or only mildly impaired memory and mild language difficulties, except for the occasional patient with transcortical sensory aphasia [4]. In contrast, patients with typical clinical AD have complex visual deficits later in the course of their disease when memory and other cognitive impairments are advanced [58]. Mendez et al. [4] studied 15 PCA patients who presented with progressive alexia, apperceptive visual agnosia, elements of Balint’s syndrome, dressing apraxia, environmental disorientation, or prosopagnosia (fig. 3). Compared to AD patients on neurobehavioral measures, these PCA patients had significantly more insight for their illness and more depression, consistent with sparing of frontal lobe perfusion observed on functional neuroimaging [4, 67]. This is consistent with the early preservation of frontal-executive functions among PCA patients in comparison to typical AD patients [4, 67]. The PCA patients also had better language (verbal fluency) and memory (savings and recognition memory scores) than the AD patients (fig. 3) [4]. Together, the similarities and differences between PCA and AD indicate a clinical syndrome of PCA that differs from typical clinical AD but is usually a variant of it.

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Table 2. Posterior cortical atrophy: proposed clinical diagnostic criteria [from 4, with permission] I. Core diagnostic features (all must be present) A. Insidious onset and gradual progression B. Presentation with visual complaints with intact primary visual functions C. Evidence of predominant complex visual disorder on examination Elements of Balint’s syndrome Visual agnosia Dressing apraxia Environmental disorientation D. Proportionally less impaired in memory and verbal fluency E. Relatively preserved insight with or without depression II. Supportive diagnostic features A. Presenile onset B. Alexia C. Elements of Gerstmann’s syndrome D. Ideomotor apraxia E. Physical examination within normal limits F. Investigations 1. Neuropsychology: predominantly impaired perceptual deficits 2. Brain imaging: predominant occipitoparietal abnormality (especially on functional neuroimaging) with relative sparing of frontal and mesiotemporal regions

Prevention and Management

Although there is no specific medication for PCA, a great deal can be done to alleviate the impact of this syndrome. Patients treated with acetylcholinesterase inhibitors such as donepezil may not have clinical improvement in visual deficits [4], but they may show improvement on more detailed neuropsychological tests and increased posterior cortical perfusion on SPECT [68]. Given the likelihood of Alzheimer pathology, acetylcholinesterase inhibitors may be used as part of the treatment of PCA. Other AD interventions may be considered for PCA including antioxidant therapy with vitamin E, the use of statins if indicated by blood lipids, and memantine, a blocker of neuroexcitatory receptors. These patients are more depressed than typical AD patients [4], and most PCA patients benefit from early intervention with antidepressant medications. The management of PCA further includes attention to methods to improve their visual impairment. PCA patients need referrals for eye examinations to maximize primary vision and for services that benefit the blind or visually impaired. Early in the course, their preserved insight makes them amenable to counseling or psychotherapy as well as to learning compensatory techniques

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for their visual impairments [see Dunne, this volume]. Furthermore, families can benefit from the services and support available to patients with dementia. In conclusion, the clinical syndrome of PCA is most commonly, but not exclusively, a rare presenile form of AD. PCA may correspond to the ‘visual variant’ of AD [12]. Their complex visual disorders result from visuospatial and visuoperceptual disturbances due to the neuropathology of AD in primary visual and visual association areas. The mechanism for the posterior shift in the location of AD pathology is one of the most important unsolved mysteries of this disorder. Finally, the patients reported in the literature suggest tentative criteria for the clinical syndrome of PCA (table 2). These criteria can help distinguish PCA from other causes of complex visual impairment and from other focal cortical atrophies such as semantic dementia and corticobasal degeneration. These criteria may also facilitate the more systematized research on PCA needed to adequately understand this syndrome. Acknowledgements The author acknowledges the invaluable support received from Jill S. Shapira, RN, PhD, Jeffrey L. Cummings, MD, and the UCLA Alzheimer’s Disease Center. They made it possible to see and care for the patients with posterior cortical atrophy.

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Mendez MF, Cherrier MM, Cymerman J: Hemispatial neglect on visual search tasks in Alzheimer’s disease. Neuropsychiatry Neuropsychol Behav Neurol 1997;10:203–208. Glosser G, Baker KM, de Vries JJ, Alavi A, Grossman M, Clark CM: Disturbed visual processing contributes to impaired reading in Alzheimer’s disease. Neuropsychologia 2002;40:902–909. Fujimori M, Imamura T, Yamashita H, Hirono N, Ikejiri Y, Shimomura T, Mori E: Age at onset and visuocognitive disturbances in Alzheimer disease. Alzheimer Dis Assoc Disord 1998;12:163–166. Mendez MF, Turner J, Gilmore GC, Remler B, Tomsak RL: Balint’s syndrome in Alzheimer’s disease: Visuospatial functions. Int J Neurosci 1990;54:339–346. Pietrini P, Furey ML, Graff-Radford N, Freo U, Alexander GE, Grady CL, Dani A, Mentis MJ, Schapiro MB: Preferential metabolic involvement of visual cortical areas in a subtype of Alzheimer’s disease: Clinical implications. Am J Psychiatry 1996;153:1261–1268. Fujimori M, Imamura T, Hirono N, Ishii K, Sasaki M, Mori E: Disturbances of spatial vision and object vision correlate differently with regional cerebral glucose metabolism in Alzheimer’s disease. Neuropsychologia 2000;38:1356–1361. Rizzo JF, Cronin-Golomb A, Growdon JH, Corkin S, Rosen TJ, Sandberg MA, Chiappa KH, Lessell S: Retinocalcarine function in Alzheimer’s disease: A clinical and electrophysiological study. Arch Neurol 1992;49;93–101. Craig AH, Cummings JL, Fairbanks L, Itti L, Miller BL, Li J, Mena I: Cerebral blood flow correlates of apathy in Alzheimer disease. Arch Neurol 1996;53:1116–1120. Warren S, Hier DB, Pavel D: Visual form of Alzheimer’s disease and its response to anticholinesterase therapy. J Neuroimaging 1998;8:249–252. Mangagi G, Bettoni L, Mazzucchi A: Lesioni atrofiche bioccipitali di incerta natura all’origine di una agnosia visiva. Riv Neurol 1982;52:137–148. Neary D, Snowden JS: Perceptuospatial disorders in Alzheimer’s disease. Semin Ophthalmol 1987; 2:151–158. Mendez MF, Cummings JL: Dementia: A Clinical Approach, ed 3. Philadelphia, ButterworthHeinemann and Elsevier, 2003, p 207.

M.F. Mendez Neurobehavior Unit (116AF) V.A. Greater Los Angeles Healthcare System 11301 Wilshire Blvd, Los Angeles, CA 90073 (USA) Tel. ⫹1 310 478 3711/ext 42696, Fax ⫹1 310 268 4181, E-Mail [email protected]

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Visual Hallucinations in Alzheimer’s Disease Suzanne Holroyd Department of Psychiatric Medicine, University of Virginia Health System, Charlottesville, Va., USA

Alzheimer’s disease (AD) is the most common cause of dementia affecting persons aged 65 and over [1]. Although the cognitive features of the illness have received the most study, increased attention has been paid to the behavioral and psychiatric features of this disorder. This chapter will review the current knowledge regarding visual hallucinations in AD with an overview to possible neuropathologic mechanisms resulting in visual hallucinations in AD.

Epidemiology

Hallucinations have been recognized and described in AD since Alois Alzheimer’s first description of a patient with not only memory loss and confusion, but delusions and hallucinations [2]. Psychotic symptoms such as hallucinations are common in AD and are part of the NINCDS-ADRDA criteria for clinical diagnosis of probable AD [3]. Hallucinations of all modalities occur in an estimated 12–53% of cross-sectional studies of patients with AD [4–8], with variation occurring depending on the severity or stage of illness of the sample studied and use of different methodology to define hallucinations. For example, some studies have not excluded patients with hallucinations due to concurrent delirium or other psychiatric illness. A recent report revealed a 3-year cumulative incidence of 51% for psychosis in AD [9], with post-mortem studies revealing cumulative incidences of 40–60% [10–12]. Since hallucinations and associated psychosis are associated with behavioral disturbances including aggression and institutionalization, research and treatment of these symptoms is important [13–15].

Across numerous studies, visual hallucinations are the most common hallucination in AD [4, 7, 8, 16, 17], occurring in 12–53%. Visual hallucinations may be the presenting symptom in AD [18] and are associated with more rapid cognitive decline and premature institutionalization [15, 19]. Visual hallucinations are reported most commonly of people (88.9%), then of animals (44.4%) and least commonly of objects (27.8%) [17]. Hallucinations occur daily in approximately one third, and occur less than daily, but more than once per month in two thirds of those who hallucinate [17]. An interesting observation regarding visual hallucinations in AD is that they do not occur evenly throughout in the course of AD, but rather are considered stage-specific phenomena. A study of 120 AD patients assessed as to stage of dementia using the Global Deterioration Scale revealed that no visual hallucinations occurred in stages 1–3, but occurred most often in stage 4, with only 6% occurring in stage 7 (end stage) [20, 21]. Another study, using the Clinical Dementia Rating Scale to rank severity of dementia, revealed that while psychotic symptoms occur throughout the course of AD, they are most common in moderate stages of dementia [8]. These studies demonstrate that hallucinations reach peak incidence in mid-stage disease, prior to the end stages of the illness. This suggests that while some deterioration is needed for development of visual hallucinations, severe deterioration can no longer support the existence of these symptoms [22]. On the other hand, since some hallucinations do occur in earlier or later stages, and because not all patients develop hallucinations despite passing through moderate stages of illness, there must be other risk factors to their development [22]. The issue of visual hallucinations in AD has become somewhat more complicated since the late 1990s, due to the discovery and report of patients with dementia of the Lewy body type. Dementia with Lewy bodies (DLB) is a relatively recently described dementia syndrome characterized by prominent visual hallucinations and shares many of the clinical diagnostic criteria of AD [23, 24]. In fact, prominent and recurrent visual hallucinations are one of the core features required for diagnosis, possibly calling into question some earlier research of ‘AD’ patients that included those with early and prominent visual features, which may have in fact included patients with DLB. Importantly, controversy continues regarding the validity of DLB and whether or not it is truly a separate disease entity [23]. Research is ongoing as to the differences between the two disorders but the current research suggests that DLB has more rapid cognitive decline, more spontaneous features of parkinsonism, more prominent visual hallucinations and fluctuating cognition as compared to AD. A study examining hallucinations among other symptoms in those with pathology-proven DLB or AD revealed that patients with mild cognitive impairment and prominent psychosis were more likely to have DLB

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than those with psychosis at later stages of dementia, who were more likely to have AD [25]. Two recent studies have validated the presence of visual hallucinations in AD [26, 27]. This and other research suggests that AD and DLB may be similar but distinct disorders, both with visual hallucinations as common and difficult symptoms worthy of further research.

Association of Visual Hallucinations and Visual Dysfunction in AD

In patients without AD, visual hallucinations have been reported in association with lesions throughout the visual system including the retina, optic nerve and chiasm, and occipital lobe [28–31]. Interestingly, 15% of patients with recent blindness have reported acute onset of visual hallucinations, correlating with the degree of visual loss [28, 32]. Of further interest, patients with homonymous field defects have hallucinations that are confined to the area of visual field loss, suggesting the hallucinations were ‘release’ phenomena rather than the results of an irritative lesion [30, 31]. Also of note was the recognition that because the calcarine area of occipital lobe was infarcted in many of these cases, it was not felt to be the origin of the visual hallucinations. Rather, the surrounding visual cortex was felt to be the likely origin [30]. Of interest, Foerster [33] electrically stimulated Brodmann’s areas 17 and 18 of the occipital lobe (primary and secondary visual cortex) which caused simple visual sensations, while stimulation of area 19 (higher-order visual association cortex) caused complex visual phenomena such as people, animals and figures, locating a possible region as responsible for the origin of complex visual hallucinations. AD is associated with a range of visual abnormalities. These include decreased contrast sensitivity, stereoacuity deficits, backward masking [34, 41], visual agnosia and deficits in blue-violet color discrimination [17, 35–37]. Another visual disorder, Balint’s syndrome, has been described in AD which is a disturbance of visual-spatial processing that includes simultanagnosia, optic ataxia, and ocular-motor apraxia that is hypothesized to occur due to disconnections to parietal and occipital pathways [38, 39, 40; see Mendez, this volume]. Studies examining the visual dysfunction in AD have suggested that pathology of visual association cortex [38, 41, 42] and primary visual cortex causes these symptoms, rather than pathology in the retina, optic nerve or retinocalcarine pathways [34, 42]. Specifically, neuropathology of Brodmann’s areas 17, 18 and 20 has been proposed as causing these deficits [34]. However, neuropathologic studies in AD have revealed that higher-order visual association cortices (areas 18 and 20) have significantly more neurofibrillary tangles

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and neuritic plaques than primary visual cortex (area 17) [43]. Thus, it has been proposed that visual dysfunction in AD may be related to neuropathology of visual association cortex. Because visual association cortex has been implicated by research in ophthalmologic disorders and by the electrical stimulation studies by Foerster [30, 31, 33], visual association cortex has also been suggested as the area of origin of visual hallucinations in AD, perhaps due in part to the dysfunction and neuropathology of the region. Other work has examined specific brain regions in association with visual hallucinations using functional imaging techniques. It is known than functional brain imaging of AD patients reveals fairly specific hypoperfusion of certain regions including temporal and parietal areas [44, 45]. However, because it has already been noted that visual hallucinations are largely a stage-specific phenomena, it is critical that studies comparing psychotic to nonpsychotic AD patients be matched on stage of illness to prevent false differences in groups due to stage of dementia, rather than differences due to psychosis. A study utilizing single photon emission computed tomography revealed that hallucinating patients had relative hypoperfusion in bilateral parietal lobes compared to cognitively matched nonhallucinating patients [46]. Other studies using functional imaging in matched samples have found hallucinations correlating with frontal hypometabolism [47] or frontal and temporoparietal hypometabolism [48]. A more recent study using positron emission tomography revealed that 2 patients with visual hallucinations had parietal as well as frontal and temporal lobe dysfunction compared to 5 AD patients without visual hallucinations [49]. In this study, the 2 hallucinating patients were not matched for cognition or stage of illness and given the small numbers; these results are at best preliminary. Taken as a whole, these functional studies suggest that hallucinations are associated with decreased function of cortical networks rather than localizing the origin of psychosis or visual hallucinations to individual specific brain regions. A neuroanatomic study using anatomic MRI compared 7 AD patients with visual hallucinations matched by cognitive score to 7 AD patients without visual hallucinations. The study revealed that the ratio of occipital volume to whole brain volume was significantly smaller for AD patients with visual hallucinations, suggesting that visual hallucinations in AD may be associated with greater neuropathology of occipital lobe [50]. Another neuroanatomic study examining white matter disease via MRI revealed that occipital white matter hyperintensities were actually associated with an absence of visual hallucinations and delusions [51]. This intriguing study may suggest that certain damage to visual cortex may prevent production of visual hallucinations. In addition to the association of visual hallucinations in AD and dysfunction of visual cortical regions, some research has examined the association of visual hallucinations to eye disease. The possible link of visual hallucinations

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in AD and eye disease was first suggested by a study examining patients with probable AD using NINCDS-ARDRA criteria, finding visual hallucinations in 18.4% of patients. Visual hallucinations were associated with four variables including female gender, older age, decreased visual acuity and presence of visual agnosia [17]. Of interest, three variables – older age, decreased visual acuity and presence of visual agnosia – correctly identified 91% of patients as visual hallucinators or non-hallucinators. This study not only linked visual hallucinations to sensory deprivation (decreased visual acuity) but also possibly to visual association cortex as visual agnosia is believed to arise from this brain region. Visual hallucinations and their association with decreased visual acuity were confirmed by a more recent study, which also noted that no AD patient without impaired visual acuity had visual hallucinations [52]. Such research suggests that improvement of visual acuity deficits may improve visual hallucinations in AD [52]. Indeed, a case report noted reduction of frequency of visual hallucinations in three cases of AD by utilizing optical aids [53]. However, follow-up of patients was limited to 9–15 weeks and no patient experienced elimination of visual hallucinations, just a decrease in frequency.

Genetics, Neurochemistry and Visual Hallucinations

In addition to visual deprivation, stage of illness and evidence for cortical dysfunction as linked to visual hallucinations in AD, some have examined genetic causes. A recent study found an increased familial risk for AD patients with a psychotic phenotype, compared to risk of AD without psychosis [54]. In addition, two genetic studies have found linkages to specific polymorphisms of the serotonin receptor in AD patients with visual hallucinations [55, 56]. This link is especially interesting as these polymorphisms have also been linked to visual and auditory hallucinations in schizophrenic patients. Exactly how this gene interacts with the illness of AD to produce psychosis is currently unknown. The relationship between cholinergic deficiency and hallucinations in AD has also been reported. Two case reports have suggested that cholinergic deficiency may contribute to delusions and visual hallucinations in AD. In one report, 2 AD patients, 1 with only delusions and 1 with delusions and visual hallucinations, improved after treatment with physostigmine, with a decrease in delusions and cessation of the visual hallucinations. The hypothesis was that restoring cholinergic function using physostigmine may have normalized or improved the cholinergic disturbances within the limbic system [57]. In another report, a patient experienced improvement of hallucinations during administration of physostigmine [58].

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Acetylcholinesterase-inhibiting drugs have become available to help restore cholinergic deficits in AD. Originally developed to improve cognition, research has suggested that some psychiatric and behavioral disturbances may also improve with these drugs. Interestingly, research has shown some improvement in neuropsychiatric symptoms particularly apathy and visual hallucinations, although there may be differences in different acetylcholinesterease-inhibiting drugs in their ability to affect these symptoms [59].

Proposed Neurophysiologic Mechanism of Visual Hallucinations in AD

The origin of visual hallucinations in AD is likely multifactorial, given the research cited above. However, these different areas of research may be linked together into a proposed hypothesis as to the neural mechanism of visual hallucinations in AD. A theory of hallucinations or psychosis in AD must account for several important observations. First, it must account for the stage-specific nature of psychosis in AD. Second, it must account for the fact that some patients experience certain psychosis (e.g., visual hallucinations) at one time but may experience other psychotic symptoms at another time in the course of the illness (e.g., paranoid delusions). In the proposed hypothesis, specific psychotic symptoms are thought to originate in specific brain regions as ‘release phenomena’ when other neuronal networks in the brain become dysfunctional or deteriorate so that they can no longer suppress or correctly modulate these specific brain regions. For visual hallucinations, it is proposed that they arise from discharges in visual association cortex and as the dementia advances, neuronal networks that normally suppress and modulate visual association cortex deteriorate or begin to function abnormally. As a result, the visual association cortex does not receive correct or adequate input, and produces release hallucinations. Support for this hypothesis comes from studies such as those using functional neuroimaging showing an association of hallucinations with dysfunction of frontal/temporal or parietal lobes, in that they reflect the neural system deterioration in other regions necessary for visual hallucinations to be released from visual association cortex. As well, research regarding the role of cholinergic drugs in hallucinations may reflect the improvement in neuronal-networks function that may occur with optimizing cholinergic function. This would normalize input to visual association cortex, thereby decreasing visual hallucinations. Similarly, research showing visual acuity to be related to visual hallucinations suggests that further sensory deprivation caused by decreased visual acuity may

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increase the risk for decreased input to visual association cortex, already at risk by deteriorating neuronal networks. This hypothesis takes into account the stage-specific nature of hallucinations, as a certain degree of deterioration is required for hallucinations to appear. Similarly, once a certain amount of deterioration has occurred in a specific brain region where the hallucination is originating, the visual hallucinations will end (as supported by research showing that vascular damage and white matter disease of the occipital lobes decreased visual hallucinations). The hypothesis also explains how patients could experience certain symptoms together or at different times, depending on which neuronal networks are deteriorating and affecting specific brain regions. It can also explain how some patients may not experience visual hallucinations, if they do not experience neuronal deterioration affecting their visual association cortex. Conclusions

Visual hallucinations in AD are common, distressing and complex phenomena. The study of visual hallucinations however can shed light on the neuroanatomy and neurophysiology of particular brain regions and may improve our understanding of visual hallucinations not only in AD but also in other disorders. After all, it is unlikely that visual hallucinations, occurring in so many psychiatric and neurological diseases, have different origins in different disorders. Rather, it is likely that visual hallucinations can originate in a variety of conditions as a common final pathway, caused by different neuronal abnormalities that may affect the regions of the brain used in visual processing. Further research is clearly warranted in understanding the mechanism of visual hallucinations and their relationship to visual system degeneration in AD. In doing so, it is possible that improved strategies to prevent or to treat these difficult symptoms may be developed. Acknowledgement This work was supported by grant R01 NS045008-01A1 from the National Institutes of Health.

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Suzanne Holroyd MD, Assoc. Prof. Department of Psychiatric Medicine University of Virginia Health System Box 800623, Charlottesville, VA 22908 (USA) Tel. ⫹1 434 924 2241, Fax ⫹1 424 924 5149 E-Mail [email protected]

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Similarities of Visual Deficits in Alzheimer’s Disease and Down Syndrome Frederick J. Rocco Counseling and Advisement Services, Bristol Community College, Fall River, Mass., USA

Down syndrome (DS) is a disease characterized by mental deficiency and over 100 physical and mental signs occurring at a rate of about 1 case per 600–700 live births [1]. Evidence demonstrating the development of Alzheimer’s disease (AD) in DS dates back to the early 20th century [2] and it is now widely accepted that AD neuropathology occurs in almost 100% of individuals with DS over the age of 40 [3–7]. Although a single causal factor for AD does not exist, there has been compelling evidence linking the disease to a gene that codes for amyloid precursor protein [8, 9] and a gene for a particular type of familial, early onset AD [10]. Both genes are located on chromosome 21, the chromosome implicated in DS [11, 12]. This particular protein is implicated in AD neuropathology [13]. The body of evidence describing the similarities between AD and DS is quite extensive, for neuropathological as well as for clinical features. The neuropathological similarities between AD and DS are striking [14]. The neurofibrillary tangles, neuritic plaques and nerve cell degeneration common to AD are also seen in individuals with DS over age 40 [6, 15]. The tangle formations present in DS are most common in the same cortical areas as in AD, namely areas connected with the hippocampus such as the entorhinal cortex [16, 17]. In turn, the clinical features of AD are also seen in DS, although the detection of dementia is more difficult in this population [18, 19]. Given the already low IQ scores that individuals with DS receive on standard cognitive assessments, it is often difficult to detect signs of dementia or cognitive decline. Nevertheless, research has demonstrated a decline in visual memory, object naming and immediate memory (digit span) in aging adults with DS [20]. These types of cognitive deficits are also associated with AD [21].

More recently, deficits in visual capacities have been numbered among the clinical features associated with AD. Several studies have shown dysfunction in patients with AD on tests of multiple visual capacities, including color discrimination, stereoacuity, and contrast sensitivity, among other visual functions. Color discrimination deficits, especially for blue hues, have been found in patients with AD [21–24]. Stereoacuity has been reported to be impaired in AD [22, 25, 26], as has spatial frequency contrast sensitivity [22, 25, 27–29]. While there is extensive research on ocular defects in DS [30–32], very little data exist on higher-order visual deficits. One exception is the report of Courage et al. [33], who found depressed contrast sensitivity function in infants with DS relative to age-matched infants with neither DS nor mentally retarded (MR).

Neuropathology of DS

The neuropathology of DS appears to involve the later developing parts of the brain and not the earlier developing parts [34]. Much of the developing DS brain appears normal at birth. Wisniewski and Schmidt-Sidor [35], for example, reported normal myelin formation in DS infants. Several neurochemical systems also appear normal at birth [36]. Of the later developing sections of the DS brain, the hippocampus and specific layers of the neocortex do not develop normally [34]. Animal models of DS demonstrate an immaturity of neurons and their connections [37].

Alzheimer-Type Pathology in DS

Autopsy on individuals with DS over the age of 40 virtually always confirms a diagnosis of AD, with the typical pattern of amyloid angiopathy, neurofibrillary tangles and senile plaques. Neuropathology is evident in neocortex, limbic system, and subcortical structures such as the thalamus, olfactory bulb, caudate, putamen and numerous subcortical nuclei [6, 15, 17, 38–42]. Neurotransmitter deficits closely resemble those seen in AD [43–46]. Progressive brain tissue loss in the temporal and temporoparietal cortices in DS is at least comparable in extent to that seen in AD without DS; ventricular enlargement may even be greater in the DS cases [47]. Further, Aylward et al. [48] have reported a decrease in mass of the amygdala in DS adults with AD. Dementia afflicts a large number of older DS patients. Dementia prevalence in one sample of 50 mildly retarded patients was 0% in 20- to 29-year-olds, 33% in 30- to 39-year-olds and 55% in 40- to 52-year-olds [49]. Over age 60, the

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prevalence may be 90% [7, 20] to 100% [19]. DS patients with AD show global cognitive deterioration. This global deterioration distinguishes old demented DS patients from old DS patients without dementia, who instead show a selective pattern of cognitive deficits relative to young DS patients [50]. However, there are discrepancies between the presence of AD-type neuropathology in DS and the presence of a clinically manifest dementia [51–55]. One possible explanation for the discrepancies is that many standard psychological tests yield ‘floor’ effects in the patient who is mentally retarded, making cognitive assessment of a superimposed dementia difficult [53, 56]. Another possibility is that the visual dysfunction documented in AD also occurs in DS patients, whose visual capacities are already limited by ocular pathology, resulting in poor cognitive performance and increased need for assistance in activities of daily living such as dressing, eating and toileting.

Neuropathology of the Visual Cortex and Visual Dysfunction in DS

Individuals with DS, unlike those of AD, frequently present with clinically obvious visual deficits. DS is associated with a variety of visual deficits of ocular origin [57–63]. The majority of ocular deficits in DS are present by age 14 [64]. Cataracts (congenital and acquired) occur in about 11% of the DS population [57]. Errors of refraction (myopia, hyperopia, astigmatism) occur in about 20–30% of people with DS [57] and are easily corrected with proper glasses. One study has shown that 46% of individuals with DS presented with amblyopia [31]. Many people with DS show strabismus, which is usually esotropi-convergent (cross-eyed) [32]. Caputo et al. [57] found strabismus in 57% of his sample of 187 DS participants. Nystagmus (involuntary rapid movement of the eyeball) has been shown to occur in about 29% of people with DS [57]. Keratoconus (conical cornea) is another, although rarer, ocular sign that occurs in fewer than 10% of DS cases [65, 66]. The occurrence of Brushfield spots in DS ranges from 38 to 90% of studied cases [67]. Brushfield spots consist of collagenous tissue in the anterior iris stroma and do not interfere with vision. DS is also associated with configuration changes of the eyelids and palpebral fissures, neither of which interferes with vision. In contrast to the detailed documentation of ocular signs in DS, little is known of visual function in these individuals. Sinson and Wetherick [68] found that on a delayed matching task involving Munsell colors in a seven-choice array, DS patients performed poorly, generally choosing the center of array. The authors postulated that the poor performance reflected a deficit in retention, rather than one in color discrimination. However, they also noted that use of

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artificial light, compared to daylight, resulted in a reduction in errors to green, red and purple, but not to purple-blue, yellow and yellow-red, suggesting that deficits in hue discrimination may be selective. In another study, Hewitt et al. [69] found an association between intellectual deterioration and deficient visual acuity; the method of acuity assessment was not reported. Courage et al. [33], in 1997, reported on depressed contrast sensitivity in infants and children with DS. The majority of studies of visual behavior in DS have not included an assessment of basic capacities such as color vision, stereoacuity, and contrast sensitivity. Instead, research has concentrated on ocular signs, visual retention deficits and the prevalence of AD in DS patients [4]. Significant research has also been reported on higher-order visuospatial dysfunction and other cognitive impairments in older adults with DS [39, 40, 70]. In contrast to the paucity of data on visual behavior in DS, several studies have shown dysfunction in patients with AD on tests of multiple visual capacities, including color discrimination, stereoacuity and contrast sensitivity. Bluehue color discrimination deficits have been found [21–24]. Individuals with AD have been reported to be impaired on measures of stereoacuity [22, 25, 26]. Deficits in spatial frequency contrast sensitivity have also been demonstrated in AD [22, 25, 27–29, 71]. We conducted a study in which adults with DS were compared to MR adults without DS on three visual tests (color discrimination, stereoacuity and contrast sensitivity) [72]. The tests had all been used successfully with even severely demented AD patients [21, 22]. A group of MR and DS participants who attended the same neurology and eye clinics were matched for age (18 years or older), binocular central acuity (20/50 or better with optical correction) and level of retardation (as measured on the WAIS-R). Twenty-two adults with DS (5 women, 17 men; mean age 45.5 years, SD 8.2) with phenotypic features of DS confirmed by chromosomal analyses were assessed and their data were compared to those of a control group of 18 MR adults without DS (2 women, 16 men; mean age 50.1 years; SD 9.3). All participants spoke English as their first language. DS and MR participants were excluded if they had any of the following medical conditions: cancer or serious chronic underlying medical illness (e.g. renal failure, pulmonary insufficiency, poorly controlled diabetes mellitus); uncontrolled hypo- or hyperthyroidism; uncontrolled seizures; serious cardiac disease, including arrhythmias, angina pectoris, and uncompensated congestive heart failure; use of antihypertensive medication; DSM-III-R diagnoses or neurological diagnoses other than DS. The MR group was matched to the DS group on the basis of ratio of men to women and age (t ⫽ 1.7, p ⫽ 0.11) as shown in table 1. The age range was 24–56 for the DS group (median 46) and 36–69 for the MR group (median 50).

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Table 1. Participant characteristics [from 72, with permission] Group

n (f/m)

Mean age years

SD

Mean IQ1 (WAIS-R)

SD

MR DS

18 (2/16) 22 (5/17)

50.1 45.5

9.3 8.2

57.9 39.4

10.4 12.5

1IQ scores were available for 14 MR and 16 DS participants.

Table 2. Ocular signs [from 72, with permission] Type of sign

DS, n

MR, n

Refractive errors (myopia, hyperopia, presbyopia) Astigmatism Strabismus (esotropia, exotropia) Mild or immature cataracts

12

9

9 6 8

2 3 5

Attempts were made to match groups on the level of mental retardation, but the DS group still had a significantly lower IQ than the MR group. IQ was found not to correlate with performance by either group on most of the measures used. IQ scores were not available for 4 MR and 6 DS participants. The medical records of these participants recorded that IQ was not measurable because of behavior problems or inability to perform reliably. All DS and MR participants underwent detailed neurological and ophthalmological assessments within 12 months of testing for this study. They were examined by a neurologist at the neurology clinic and by an ophthalmologist at the eye clinic of the E.K. Shriver Center for Mental Retardation in Waltham, Mass., USA. The ophthalmological examination included assessment of cataracts, glaucoma, macular degeneration, refractive errors, nystagmus, strabismus, astigmatism and central visual acuity. No participant showed significant, uncorrected pathology that could have impaired their performance (table 2). For each of the three tests (color discrimination, stereoacuity, contrast sensitivity), training tasks were given to assure that the participants could understand

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Table 3. Monocular and binocular visual acuity1 [from 72, with permission] Acuity

20/20 20/30 20/40 20/50

Number of participants right eye MR DS

left eye MR DS

binocular MR DS

3 11 3 1

2 11 3 22

3 12 3 0

0 11 8 3

1 12 4 53

0 14 7 1

1Snellen

H:O:T:V. 20/60; one 20/160. 3Two 20/60. 2One

the directions and carry out the necessary steps for the tasks. The training tasks are described in each test section below. Visual Acuity Monocular and binocular central acuity was assessed to a maximum of 20/20 (Snellen) using a H:O:T:V wall chart. The H:O:T:V procedure permits anomic or mentally impaired observers to match the chart letters visually to a hand-held match card consisting of four test letters: H, O, T, and V (Snellen optotypes). The DS and MR groups did not differ significantly on binocular acuity when comparing participants with 20/20 or 20/30 versus 20/40 or greater (␹2, 1 degree of freedom (d.f.) ⫽ 1.9, p ⫽ 0.17). Results for the binocular and monocular conditions are shown in table 3. Color Discrimination The City University Colour Vision Test [73] was used to assess the ability to discriminate similar hues. This test was chosen for its simplicity in response. It requires no naming or discrimination of complex forms and it identifies both blue-yellow and red-green deficits. It consists of 10 plates, each with five colored circles – a central color surrounded by four comparison colors on a black background. For the 10 test plates, the central color circle was surrounded by four comparison colors, which were not identical to the central color. Participants viewed the plates at a distance of approximately 0.5 m. Uniform illumination was provided by a True Daylight Color Illuminator for both training

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2.0

DS subjects make a disproportianate number of tritan errors compared to MR controls

MR (n ⫽ 17) DS (n ⫽21)

2.5

Mean number of errors

Mean number of errors

2.5

1.5

1.0

0.5

Deutan Color error type

1.0

Tritan

Protan

DS subjects show a higher stereoacuity threshold compared to MR controls

MR (n ⫽12) DS (n ⫽ 14)

400 350 Mean stereoacuity threshold (s of arc)

Mean stereoacuity threshold (s of arc)

300

1.5

0.0 Protan

350

EC (n ⫽18) AD (n⫽64)

0.5

0.0

400

2.0

AD subjects make a disproportianate number of tritan errors compared to EC controls

250 200 150 100 50

300

Deutan Tritan Color error type

AD subjects show a higher stereoacuity threshold compared to EC controls

EC (n ⫽18) AD (n⫽58)

250 200 150 100 50

0

0 MR

EC

DS Group

DS subjects show depressed contrast sensitivity across all spatial frequencies relative to MR controls 3.0

2.5 Mean log sensitivity

Mean log sensitivity

2.5

3.0

MR (n ⫽ 13) DS (n ⫽ 10)

2.0 1.5 1.0 0.5

AD Group

AD subjects show depressed contrast sensitivity across all spatial frequencies relative to EC controls

EC (n ⫽21) AD (n⫽47)

2.0 1.5 1.0 0.5

0.0

0.0 1.5

3

6

12

Spatial frequency (cpd)

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1.5 3 6 12 Spatial frequency (cpd)

18

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and testing. Participants were asked to point to the comparison color that ‘looked most like the color in the middle’ on the City University Test. Errors were categorized as protanomalous, deuteranomalous, or tritanomalous (red-, green-, or blue-deficient, respectively). For training, potential MR and DS participants were given a color-matching task, using the demonstration plate of the City University Test to show that they could match a central color to the identical color in a four-choice array. They also identified primary colors by name from a simple array. Seventeen MR (2 women, 15 men) and 21 DS participants (5 women, 16 men) were successfully tested. None of the participants had a diagnosis of congenital color blindness. To further ensure accuracy and reliability of the results, 7 MR and 7 DS participants were randomly chosen for re-testing at least 1 week but no more than 2 months later. Results were identical on test and re-test for all participants. The results were striking in that only 1 MR participant made any errors on the City University Test and in fact made only one (tritan) error. Age was not found to correlate significantly with performance for either group. IQ was correlated with performance by the DS group: Spearman’s r ⫽ 0.55 for protan and deutan, r ⫽ 0.63 for tritan errors (p ⬍ 0.05 in each case). IQ did not correlate with the number of tritan errors for the MR group. Age and IQ correlations involving those error types could not be calculated because MR participants made no protan or deutan errors. Forty-eight percent of participants in the DS group made one or more errors on the test. The distribution of error scores was found to be unequal, with the number of tritan errors, but not protan or deutan errors, occurring significantly more often than by chance (two-tailed binomial tests: protan, p ⫽ 0.11; deutan, p ⫽ 0.62; tritan, p ⫽ 0.015) (fig. 1). Commission of one error type (e.g., tritan) precluded the commission of other error types (protan, deutan) for a single trial, meaning that error types were not independent of each other. The number of each error type relative to chance level was assessed, which was defined as the sum of all errors divided by three, to reflect the three categories of protan, deutan and tritan error types. Of the DS participants making at least one error on this test, 50% made protan errors, 60% made deutan errors, and 80% made tritan errors (values do not sum to 100, because some participants made errors of multiple types). The color discrimination data were analyzed for DS and MR subgroups matched for IQ as well as for age, central acuity (20:40 or better, binocular), Fig. 1. Comparison of findings from mentally retarded (MR) and Down syndrome (DS) groups to findings from Alzheimer’s disease (AD) and elderly control (EC) groups on three vision tests. Data from the MR and DS groups are from Rocco et al. [72]. Graphs are reprinted with permission, Lippincott, Williams & Wilkins. Data from the AD and EC groups are from Cronin-Golomb et al. [77].

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and ocular pathology (two or fewer mild, functionally insignificant signs). Each subgroup included 6 participants: 5 men and 1 woman in the MR subgroup and 3 men and 3 women in the DS subgroup. The mean IQ was 51.3 for the MR subgroup (SD ⫽ 8.5) and 45.7 for the DS subgroup (SD ⫽ 13.8) (t ⫽ 0.86, d.f. ⫽ 10, p ⫽ 0.41). Mean age was 45.2 years for the MR subgroup (SD ⫽ 6.7) and 45.7 for the DS subgroup (SD ⫽ 4.5) (t ⫽ 0.15, d.f. ⫽ 10, p ⫽ 0.88). Onetailed binomial tests indicated that only tritan errors significantly exceeded chance level for the DS subgroup (p ⫽ 0.04), which committed 3 protan, 4 deutan, and 6 tritan errors. There was 1 tritan, no protan, and no deutan errors committed by the MR subgroup. Despite the similarity in demonstrated ability to name colors and colormatch, the DS participants showed markedly impaired performance on the color discrimination test compared to the MR group. Although DS participants made protan and deutan errors, only tritan errors occurred more often than predicted by chance in this group. Several explanations are possible for this finding. On first glance, it may appear that age or IQ influenced the findings. IQ, for example, was significantly lower in the DS than the MR group. One possibility was that commission of tritan errors was influenced by severity of retardation more so than for protan or deutan errors, but IQ did not correlate significantly with commission of any type of error on this test. Age was similarly ruled out as a contributing factor. Age did not correlate with performance, and the groups were matched for age. Further, the MR participants were slightly older than the DS participants. If age had been a factor, one would have expected the older MR participants to perform more poorly than the younger DS participants. Although optic nerve damage or retinopathy could have accounted for the poorer performance in the DS group, cases of optic nerve damage or retinopathy have been reported only rarely in the DS literature [67]. Retinal detachment is also uncommon, and occurs early in life among patients with DS. In one study [74], retinal detachment was directly related to trauma such as head banging, eye rubbing and corneal abrasions in young DS patients (mean age of 13 years). In our study, all DS and MR participants had ophthalmological exams within 1 year of testing, with negative results for retinal and optic nerve damage. A small subgroup of participants did present with mild cataracts or other mild ocular signs in both the MR and DS groups. For each participant, these signs were deemed by an ophthalmologist to be clinically insignificant. Having considered and rejected alternative explanations for the observed blue-hue deficit in DS, in light of the evidence presented it seems most likely that the observed blue-hue deficit in DS is associated with DS itself, and is similar to the pattern of performance seen in AD [21–24].

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Stereoacuity Participants were given the Randot Stereo Acuity Test in order to evaluate cortical processes mediating depth perception independent of cues provided by accommodation and convergence. The Randot has the advantage of providing participants with only one depth cue (binocular disparity of the image projected to the two eyes). Additionally, this test does not require a naming response, nor does it require the discrimination of complex figures. All participants wore polarizing lenses, over their own eyeglasses if used. The test card was held about 0.5 m from the participant and upright in order to maintain the proper axis of polarization. On the Randot test, participants pointed to one circle that appeared to stand out in depth in front of the plane of the other two circles in the array. They were asked, ‘Which circle looks different?’, or ‘Which one is floating up?’. Ten trials tested amounts of disparity ranging from 400 to 20 s of arc, beginning with the greatest disparity (which is easiest to perceive). The score reflected the smallest disparity at which the participant was able to point reliably to the correct circle. In some cases (5 DS and 8 MR participants), the Randot cartoon animals test was used at three levels of disparity (100, 200, and 400 s of arc). These participants had successfully performed the training task, but had difficulty with the circle discrimination, either because of inability or inattention. The participant pointed to the one animal of a five-choice array that ‘stood out’ in depth (or looked ‘different’). For training, participants were shown a variety of geometric figures and simply asked if they saw something or nothing (figures were at 500 and 250 s of arc, as well as blanks). Twelve MR (1 woman, 11 men) and 14 DS participants (4 women, 10 men) were successful on this part of the test. In addition, all 26 of these participants identified at least one of the geometric figures by name. To further ensure accuracy and reliability of the results, 7 MR and 5 DS participants were randomly chosen for re-testing at least 1 week but no more than 2 months later. Five of the MR and 3 of the DS participants gave identical results on test and re-test, and the others were within 1 standard deviation of their previous score. Participants’ data were grouped into two categories because scores on the Randot test are not continuous. Participants were categorized as either having a stereoacuity threshold of less than or equal to, or greater than, 200 s of arc. A ␹2 analysis revealed a significant difference in distribution (␹2 (1 d.f.) ⫽ 7.8, p ⬍ 0.005), with a higher stereoacuity threshold for the DS group (fig. 1). In order to match these two subgroups for age, the data from the youngest DS participant were eliminated and the results recalculated. A ␹2 analysis of the data from these 12 MR and 13 DS participants again revealed a group difference

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in distribution (␹2 (1 d.f.) ⫽ 9.8, p ⬍ 0.003), with a significantly higher stereoacuity threshold for the DS group. Group differences in stereoacuity were also assessed after eliminating data from participants with the following conditions: greater than one line difference in acuity for left and right eyes; any indication of astigmatism; any indication of strabismus. A ␹2 analysis of the data from the remaining 4 DS and 7 MR participants still revealed an unequal distribution of scores with a significantly higher stereoacuity threshold for the DS subgroup (␹2 (1 d.f.) ⫽ 4.1, p ⬍ 0.05). Finally, data were analyzed from subgroups of 5 MR (4 men, 1 woman) and 5 DS participants (3 men, 2 women), matched for age, IQ, binocular acuity (20/40 or better), and ocular pathology (two or fewer mild, functionally insignificant signs). Mean IQ was 51.8 for the MR subgroup (SD ⫽ 9.4) and 48.2 for the DS subgroup (SD ⫽ 13.8) (t ⫽ 0.48, d.f. ⫽ 8, p ⫽ 0.64). Mean age was 46.6 for the MR subgroup (SD ⫽ 6.3) and 46.0 for the DS subgroup (SD ⫽ 4.9) (t ⫽ 0.17, d.f. ⫽ 8, p ⫽ 0.87). Five MR participants and 1 DS participant had thresholds below 200 s of arc. No MR participant and 4 DS participants had thresholds at or above 200 s of arc. The difference in the distribution of scores was significant (␹2 (1 d.f.) ⫽ 6.67, p ⫽ 0.01). Correlations of performance on the Randot test with age and IQ were not significant for either group. The DS participants in this study showed elevated stereoacuity thresholds, relative to their age-matched MR counterparts. Neither age nor IQ correlated with performance on the Randot stereoacuity test. The groups were further matched for binocular central acuity and had similar distributions of mild ocular signs. It is unlikely therefore; that severity of retardation, ocular pathology, IQ and age contributed to these results as much as DS status itself. Cronin-Golomb et al. [21, 22] found that patients with AD typically showed a higher stereoacuity threshold than normal, age-matched control participants. Similar findings in AD patients have been documented by others as well [25, 26]. Contrast Sensitivity (CS) Given the variability of results using the typical measures of CS (observer adjusts contrast levels up or down until threshold is established), a forcedchoice format test was used. A forced-choice test was especially necessary given the cognitive limitations of the participants. The Vistech photographic wall chart was used. The chart depicted circles in a 9 ⫻ 5 array. The diameter of each circle subtended 1.4⬚ of visual angle. Mean luminance for each circle was approximately 150 cd/m2. Contrast decreased in 0.12 log unit steps from left to right across rows; down a column, sinusoidal gratings increased in spatial

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frequency, and included gratings of 1.5, 3.0, 6.0, 12.0, and 18.0 cycles per degree (cpd). Grating orientation was vertical, slanted 15⬚ to the right, or slanted 15⬚ to the left. The participants stood 10 ft from the chart (the same distance at which central acuity was assessed). For each row, participants were asked to describe the orientation of lines on each circle on the chart (by hand gesture or verbally) or by pointing to the same line orientation (or blank) on a hand-held sample thus indicating the minimal perceptible contrast level. For training, DS and MR participants were given a template card with the three line orientations and a blank circle. Participants were asked to match the circle on their sample template with the one the experimenter pointed to on the chart. Thirteen MR (1 woman, 12 men) and 10 DS participants (4 women, 6 men) were successful on this matching task. Seven MR and 4 DS participants were randomly chosen for re-testing at least 1 week but no more than 2 months later. Five of the MR participants and 3 of the DS participants gave identical results on test and re-test, and the others were within one unit of CS (i.e., one circle) of their previous score. The DS group showed depressed sensitivity compared to the MR group across all five spatial frequencies. This difference was significant for all but the lowest (1.5 cpd) and the highest (18.0 cpd) spatial frequencies, and there was a trend toward a group difference at the highest spatial frequency (p ⫽ 0.09). Mean sensitivity was significantly depressed in the DS group at 3.0 and 12.0 cpd (p ⬍ 0.05 each), and especially at 6.0 cpd (p ⬍ 0.01). The shape of the CS curves was similar for both groups (fig. 1). The pattern was maintained even when eliminating the data from participants with any indication of refractive error or cataract. The recalculated data showed the mean sensitivity of the DS group (n ⫽ 6) was lower than that of the MR group (n ⫽ 6) on all five spatial frequencies. Mean sensitivity at each frequency for this subgroup was as follows: 1.5 cpd; MR: 1.4, DS: 1.3; 3.0 cpd; MR: 1.8, DS: 1.5; 6.0 cpd; MR: 1.6, DS: 1.1; 12.0 cpd; MR: 1.4, DS: 0.6 (p ⬍ 0.05); 18.0 cpd; MR: 1.1, DS: 0.6. Correlations of performance on the CS test with IQ and age were not significant at any frequency, for either group. Nevertheless, subgroups matched for age, IQ, binocular acuity (20/40 or better), and ocular pathology (2 or fewer mild, functionally insignificant signs) were created and evaluated. Mean IQ was 54.0 for the MR subgroup (SD ⫽ 6.0) and 46.6 for the DS subgroup (SD ⫽ 15.2) (t ⫽ 1.01, d.f. ⫽ 8, p ⫽ 0.34). Mean age was 43.2 years for the MR subgroup (SD ⫽ 5.2) and 44.4 years for the DS subgroup (SD ⫽ 3.6) (t ⫽ 0.43, d.f. ⫽ 8, p ⫽ 0.68). Group differences (MR performance superior to DS) were significant at two of the three spatial frequencies that had elicited group differences in the larger groups. Mean log sensitivity at 6.0 cpd was 1.6 for the MR subgroup (SD ⫽ 0.2) and 0.8 for the DS subgroup (SD ⫽ 0.6)

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(t ⫽ 3.1, d.f. ⫽ 8, p ⬍ 0.02); at 12.0 cpd, 1.5 for the MR subgroup (SD ⫽ 0.3) and 0.5 for the DS subgroup (SD ⫽ 0.5) (t ⫽ 3.8, d.f. ⫽ 8, p ⬍ 0.01). For the third frequency, 3.0 cpd, the group difference was in the same direction, but did not reach a significant level, possibly because smaller group size resulted in reduced power. Mean MR sensitivity was 1.8 (SD ⫽ 0.2) and mean DS sensitivity was 1.5 (SD ⫽ 0.3) (t ⫽ 2.0, d.f. ⫽ 8, p ⬍ 0.08). The DS group’s performance was depressed relative to the MR group at all spatial frequencies, significantly so at all but the highest and lowest spatial frequencies. Perez-Carpinelli et al. [75] noted a substantial loss of sensitivity by DS participants at all frequencies on the Vistech chart relative to normal observers, although they did not have comparison data from a MR control group. A study by Courage et al. [33] also demonstrated depressed CS function in children with DS compared to non-DS control participants. Neither age nor IQ correlated with performance on CS at the five spatial frequencies, and ocular signs were insignificant, suggesting that DS itself is associated with impaired CS. Depressed CS has also been reported for participants with AD [22, 25, 27–29].

Comparison of DS and AD

The DS and MR data were compared to data previously collected on participants with AD who were administered the same visual tests [77]. The results from the DS group are strikingly similar to the AD results, as shown in figure 1. The graphs on the left side of the figure 1 represent DS and MR performance and the graphs on the right side represent AD and EC (elderly control) performance on the same measures (color discrimination, stereoacuity and CS). DS and AD participants showed a similar pattern of decreased sensitivity for blue hues, increased stereoacuity thresholds, and decreased CS. Given the extensive data indicating the prevalence of AD in adults with DS over age 40 [19, 20, 49], it seemed likely that there should be similarities in visual deficits between the two populations. This study confirmed that visual deficits similar to those seen in AD were found in DS participants: impaired color discrimination, especially for blue hues; increased threshold of stereoacuity, and decreased CS. The study sought to rule out all other possible explanations for these findings by matching participants on IQ, age and ocular pathology as much as possible. Additionally, all participants were given training tasks and tests to ensure that they could understand and follow the directions for each test. To establish reliability of results, a random sample of participants was re-tested on each task, all giving reliable results. Although the DS and MR groups were not matched for IQ, this variable did not correlate significantly with performance

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on any of the tests and subgroups matched for IQ, age, central acuity, and ocular pathology showed results that were very similar to those seen for the larger groups. To ensure that the ocular pathology frequently associated with DS was not a factor, all participants were given ophthalmological examinations within 1 year of testing and none showed significant, uncorrected pathology that could have impaired their performance. For all DS and MR participants in this study, the range and extent of ocular signs were determined by an ophthalmologist to be functionally insignificant and therefore not likely to influence results. In AD, functional loss increases over the course of disease progression. Because the neuropathology of AD occurs in DS generally after age 35 or 40, it was expected that older DS participants would be more likely to show the AD-like visual pattern than the younger members of the DS group. This did not turn out to be the case. The DS group ranged in age from 24 to 56 years. Some of the youngest DS participants (20–30 years old) showed greater impairment on the vision tests than some of the older DS participants; age and IQ were not significantly correlated (r ⫽ 0.10, p ⫽ 0.72). Of the 2 DS participants in their 20s, 1 had an IQ only slightly below the mean (36, with a mean of 39.4); IQ was not available for the other participant. Excluding these 2 young individuals, the range of ages in the DS group becomes 39–56 years, which is still a reasonably wide range though perhaps insufficiently wide to find age-associated deficits. None of the DS participants tested in the present study had been examined systematically for signs of dementia. The difficulty in documenting dementia in a MR group has been described elsewhere [51–55]. A review of the literature reveals that dementia prevalence in DS increases dramatically with age. Dementia prevalence in one sample of 50 mildly retarded patients was 0% in 20- to 29-year-olds, 33% in 30- to 39-year-olds, and 55% in 40- to 52-year-olds [49]; over age 60, prevalence may be 90% [20] to 100% [19]. DS patients with AD show global cognitive deterioration. This global deterioration distinguishes old demented DS patients from old DS patients without dementia, who instead show a selective pattern of cognitive deficits relative to young DS patients [50]. However, there are discrepancies between the presence of AD-type neuropathology in DS and the presence of a clinically manifest dementia [51–55]. AD-type pathology in DS does not always correlate with manifest dementia, because the average age of clinical dementia onset is in the early to mid-50s [52, 53, 55]. Diagnosing dementia in a MR population where cognitive tests may already yield floor effects [53, 56] is difficult at best. Constrained by these considerations, it can be inferred from the results of this study that visual deficits do not necessarily co-occur with dementia, and in fact may precede it in presentation. Assessing the performance of children and adolescents with DS

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might indicate just how early the AD-like visual profile emerges (e.g., see Courage et al. [33]).

Neuropathology and Visual Deficits in DS

Although a diagnosis of AD is sometimes difficult in younger people with DS, post-mortem examination almost always confirms the typical pattern of neurofibrillary tangles, senile plaques and amyloid angiopathy associated with AD, especially with individuals with DS over the age of 40. Neuropathology is evident in neocortex, the limbic system, and subcortical structures such as the thalamus, olfactory bulb, caudate, and putamen [6, 15, 17, 38–41]. Neurotransmitter deficits closely resemble those seen in AD [43–46, 76]. Progressive brain tissue loss in the temporal and temporo-parietal cortices in DS is at least comparable in extent to that seen in AD patients without DS. The similarity of neuropathology in DS and AD implies that the brain bases of visual dysfunction in AD and DS may also be similar. Research on AD supports the brain’s selective vulnerability to the disease. Certain archicortical areas such as the amygdala and hippocampus are severely affected, as are neocortical association areas, whereas the primary visual areas are left relatively intact [42, 48, 79–81]. Investigators have shown that the characteristic AD pathology of neurofibrillary tangles and neuritic plaques is evident in areas of the brain known to be involved in visual processing [79]. Deficits in visual behavior in participants with AD have been related to pathological findings in visual association and inferior parietal regions. AD participants who present with visual symptoms show more extensive pathological changes in Brodmann’s areas 17, 18, 19 and the superior colliculus than do participants with AD who do not present with visual symptoms [82]. Kiyosawa et al. [26] reported deficits in color discrimination and stereoacuity in patients with AD who also presented with decreased glucose metabolism in visual association cortex and inferior parietal cortex. In some individuals, visual symptomatology may precede other symptoms associated with AD [83; see Mendez, this volume]. The role of precortical pathology in visual dysfunction in AD is not yet understood [84–86; see Valenti, this volume], and we therefore favor the view that neuropathology of visual association cortices, especially para- and peristriate areas, may underlie the sometimes striking visual deficits that we observe [21, 22, 86]. The similarity in pattern of visual deficits demonstrated by DS and AD participants on the tests described here point to neuropathological change in parastriate and peristriate cortices, as is seen in AD. Further research on DS may offer insights into the etiology of the development of changes in visual behavior in AD.

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Acknowledgements This study was funded by a grant from the Sandoz Foundation for Gerontological Research. I thank my collaborators Alice Cronin-Golomb, PhD, and Florence Lai, MD, for their contributions to the research study discussed in this chapter.

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Frederick J. Rocco, PhD Counseling and Advisement Services, Bristol Community College 777 Elsbree Street, Fall River, MA 02720 (USA) Tel. ⫹1 508 678 2811/ext 2379, Fax ⫹1 508 730 3286 E-Mail: [email protected]

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Visual Perception and Cognition Cronin-Golomb A, Hof PR (eds): Vision in Alzheimer’s Disease. Interdiscipl Top Gerontol. Basel, Karger, 2004, vol 34, pp 155–172

Visuospatial Disorientation in Alzheimer’s Disease: Impaired Spatiotemporal Integration in Visual Information Processing Charles J. Duffy a, Laura Cushmanb, Voyko Kavcic a a

Departments of Neurology, Neurobiology and Anatomy, Ophthalmology, Brain and Cognitive Sciences, and b Physical and Rehabilitative Medicine and The Center for Visual Science, The University of Rochester Medical Center, Rochester, N.Y., USA

Visuospatial Orientation and Disorientation in Alzheimer’s Disease

Alzheimer’s disease (AD) causes visuospatial disorientation [1–3] that disables patients by interfering with safe driving, autonomous navigation, and independent living [4]. Normally, visuospatial orientation relies on the integration of location and self-movement cues. Location cues orient the observer by relating remembered landmarks to positions in a cognitive map. Self-movement cues orient the observer by path integration, the incremental updating of position relative to a landmark along the route. Visuospatial disorientation is often attributable to hippocampal damage [5, 6] that is typical of early AD [7, 8]. The role of the hippocampus in visuospatial orientation is well established [9] with neuronal place cell responses to specific locations in familiar environments [10–12]. This is consistent with human hippocampal [13] and parahippocampal [14, 15] activation in visuospatial tasks [16, 17]. Hippocampal damage might interfere particularly with landmark orientation by impairing memory for landmarks; a conventional view of disorientation in AD. Alternatively, hippocampal damage might disrupt a distributed system for spatial orientation that undermines the capacity to link landmarks with locations in cognitive maps that are maintained by hippocampal place neurons interacting with

Observer self-movement

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Fig. 1. The radial pattern of optic flow contains a focus of expansion (FOE) that indicates the observer’s heading. a During forward self-movement in the direction of gaze (left) the observer sees a symmetric, radial pattern of optic flow in which the FOE is at the fixation point (right). b During self-movement ahead-to-the-right (left) the observer sees a radial pattern in which the FOE is displaced to the right of gaze (right) to indicate a rightward heading.

posterior cortical centers [9, 18]. The loss of landmark orientation might force AD patients to rely more on path integration from self-movement perception. Self-movement perception uses the global pattern of visual motion in optic flow [19] that contains information about heading direction and the threedimensional structure of the visual environment [20, 21] (fig. 1). Posterior cortical areas contain neurons involved in optic flow analysis [22–24] with heading selective responses [25–27] that may be linked to the updating current position by path integration [17, 28]. Lesion studies have long shown that posterior parietal cortical areas support the visuospatial capacities of humans [29–31]. This is consistent with neuropathological [2, 32] and functional imaging [33, 34] studies in AD that link visuospatial disorientation to the posterior cortical extension of pathology later in the disease [35, 36]. Thus, optic flow analysis by posterior parietal cortical neurons might support self-movement perception for path integration. Involvement of parietal neocortex

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in the later stages of AD [37] could impair path integration. This would leave AD patients with neither hippocampal landmark orientation mechanisms nor selfmovement orientation mechanisms and result in overt spatial disorientation. We have shown that AD patients with spatial disorientation are unable to process the self-movement cues in optic flow [38]. More recently, we have used behavioral and psychophysical analyses to probe the mechanisms of visuospatial disorientation in AD. We find that AD patients suffer a selective impairment of visuospatial orientation that is not linked to their memory deficits. Instead, this disorientation reflects the impaired spatial and temporal integration of visual orientation cues. We speculate that impaired spatiotemporal integration in visual information processing reflects changes in cortical neuronal dendritic architecture that may be caused by the disproportionate loss of cortico-cortical connections in AD [32].

Visuospatial Navigation in AD

We have studied the mechanisms of visuospatial orientation using a newly developed test of open-field navigation set in the lobby and first floor of the Strong Memorial Hospital [39] (fig. 2). Subjects were escorted on a route and then asked questions about the route in a series of eight sub-tests: Route Learning was assessed by repeating the route with ten choice points where subjects were asked whether they had gone left, right or straight at that point. Self-orientation was measured at the end of the route by presenting ten photographs from the route and asking subjects to point in the direction of the depicted location. Route Drawing was assessed by asking subjects to draw the route on an outline map correcting wrong answers along the way. Photograph recognition was assessed by asking which of ten photographs were from the route, five were and five were not from other public locations in the Medical Center. Photograph location was assessed by asking subjects to link ten scene photographs to locations on a route map. Video location was assessed by asking subjects to link ten short video clips to locations on a route map. Free Recall was measured by giving subjects 1 min to name as many objects or landmarks as they could recall from the route. Landmark Recall was assessed by asking subjects to name all objects or fixtures that were helpful in finding their way on the self-directed, route-learning trip. Four subject groups were studied: young normal (YN, n ⫽ 47, mean age ⫾ SD 23.5 ⫾ 5.9), middle age (MA, n ⫽ 24, age 51.8 ⫾ 4.9), healthy older adults (OA, n ⫽ 26, age 73.0 ⫾ 7.6) and probable AD (AD, n ⫽ 14, age 73.4 ⫾ 5.9). A random subset of subjects in each group (31 YN, 13 MA, 9 OA and 5 AD) were re-tested on the entire navigational task within 48 h of their initial test.

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Fig. 2. Schematic representation of the spatial orientation test route in the Strong Hospital Lobby. a Survey map of the test environment showing the outline of the lobby (bold lines) and the route traveled in testing. Subjects started and stopped at the location of the ‘S’ on the map. Each arrow represents a segment of the route, beginning and ending at a decision point. b Examples of three scenes from the test route. The numbers on the map (a) and on each scene indicate the correspondence between location along the test route and the scene from that location: (1) information desk, (2) typical intersection, and (3) a coffee shop.

Total spatial orientation scores were significantly lower in the AD group than in all other groups (F3,100 ⫽ 63.75, p ⬍ 0.0001; post-hoc Tukey HSD, p ⬍ 0.001), and the OA group scored significantly lower than the YN and MA groups (HSD, p ⱕ 0.001) (fig. 3, center panel). Retest reliability yielded a high correlation for total test scores (r ⫽ 0.90, p ⬍ 0.0001). The Photo Location and Video Location sub-tests were most highly correlated with the total score. The Photo Location sub-test alone explained most of the variance in the total scores (R2 ⫽ 0.78, p ⬍ 0.0001). Photo Location scores were not predicted by Photo Recognition and Route Drawing scores (R2 ⫽ ⫺0.044, F2,11 ⫽ 0.728, p ⫽ 0.51). Thus, the OA and AD groups showed Photo Location and Video

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Spatial orientation and sub-test scores Photo location (r ⫽ 0.88) 10 8

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Fig. 3. Group performance on the spatial orientation test. Center: Total scores for each subject group. The AD group showed significantly poorer performance than all other groups. a–h Sub-test scores for each group are arranged clockwise in order of decreasing magnitude of the correlation (r) between that sub-test score and the total score. Asterisks indicate significant group differences (HSD, p ⬍ 0.05). Y ⫽ Young normal; M ⫽ middle age; O ⫽ older adult; A ⫽ Alzheimer’s disease.

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Fig. 4. OA subjects were classified as those who would, or would not, become lost in the Route Learning sub-test. a Spatial orientation sub-test scores in the not-lost (open bars) and OA lost (solid bars) sub-groups were most readily distinguished by the Photo Location sub-test. b Neuropsychological test scores did not distinguish between the lost and not-lost sub-groups. Asterisks indicate significant group differences (Bonferroni adjusted t-test, p ⬍ 0.007).

Location impairments that do not reflect memory deficits, but rather reveal defects in linking a scene from the route to a location along the route. We assessed navigational competence by dividing OA subjects into two groups: those who made at least one Route Learning error and would have become lost (38%, 10/26), and those who did not (62%, 16/26). The lost performed more poorly on the remaining sub-tests than the not-lost (ANOVA interaction F6,144 ⫽ 4.27, p ⫽ 0.006) with significant differences (Bonferroni adjusted t-test, p ⬍ 0.007) on all sub-tests except Free Recall and Landmark Recall on which both sub-groups did well and Video Location on which both sub-groups did poorly (fig. 4a). Photo Location showed the largest difference between the lost and not-lost OA sub-groups and the highest correlation with the lost/not-lost classification (r ⫽ ⫺0.64, p ⬍ 0.0001). There were no significant differences between the lost and non-lost OA sub-groups on any of the neuropsychological tests (ANOVA F1,23 ⫽ 3.81, p ⫽ 0.063) (fig. 4b). These findings suggest that navigational incapacity and getting lost are related to visuospatial skills, rather than verbal memory about the route or other cognitive capacities.

Impaired Spatial Integration for Optic Flow Perception

We have conducted psychophysical studies of the visual perceptual capacities of AD patients related to optic flow analysis for visuospatial orientation [40].

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Fig. 5. Elevation of radial motion thresholds in AD. a Horizontal motion stimuli contained either leftward or rightward moving dots along with randomly moving dots. b Radial outward optic flow stimuli contained an FOE 30⬚ to the left or right of center along with randomly moving dots. c Discrimination thresholds (ordinate) for horizontal motion (open bars) and radial optic flow (filled bars) for each subject group (abscissa): YN ⫽ young normals (n ⫽ 29); EN ⫽ elderly normals (n ⫽ 48); AD patients (n ⫽ 26).

Psychophysical tests were administered with subjects seated in a dark room, 4 ft away from an 8 ⫻ 6ft screen that covered the central 90 ⫻ 74⬚ of their visual field. Electro-oculographic recordings monitored that the subject’s gaze did not move out of the 10⬚ fixation window during test trials. Visual motion and visual pattern stimuli were combined with an adaptive algorithm [41] that controlled visual stimulus coherence level to determine psychophysical coherence thresholds for each subject. The psychophysical tests included: Left/right horizontal motion stimuli contained leftward or rightward moving dots superimposed on various numbers of randomly moving dots (fig. 5a). Left/right FOE outward radial motion stimuli contained outward radial motion with a focus of expansion (FOE) 30⬚ to the left or right of center with superimposed randomly moving dots (fig. 5b). Left/right FOE in-out radial motion stimuli contained the same left- or rightsided radial motion FOEs described for left/right radial optic flow but included stimuli with motion either inward to or outward from the left- and right-sided FOEs (fig. 7a). We determined the horizontal and outward radial visual motion coherence thresholds in young normal (YN, n ⫽ 29), elderly normal (EN, n ⫽ 48), and probable Alzheimer’s disease (AD, n ⫽ 26) subjects. AD patients showed elevated left/right FOE outward radial motion thresholds and normal left/right horizontal motion thresholds (group-by-stimulus interaction F 2,99 ⫽ 13.97,

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Fig. 6. AD subjects with elevated radial motion thresholds. Horizontal and radial motion thresholds (ordinate) are shown for subjects in each group (abscissa). One-third of the AD subjects (36%, 9/25; bold lines) had selectively elevated radial motion thresholds (⬎50% coherence). All other subjects showed both horizontal and radial motion thresholds ⬍50%.

p ⬍ 0.0001) (fig. 5c). However, not all AD subjects showed this effect; there was a broad range of those thresholds in AD with an apparent gap in the distribution near 50% coherence. Non-parametric cluster analysis confirmed that some AD patients (36%, 9/25) had elevated radial thresholds when compared to other AD subjects (p ⬍ 0.0137) or all other subjects (p ⬍ 0.0001) (fig. 6). We conclude that about one-third of the AD patients show a selective impairment of left/right FOE outward radial motion discrimination. Left/right FOE outward radial motion can be discriminated by either the global pattern of radial motion or by local motion at a selected point in the stimulus. That is, the left/right FOE outward radial motion stimuli might be discriminated based only on the direction of motion at the center of the stimuli. We tested whether our subjects could use global motion by removing the utility of local motion. To do so, we presented left/right FOE in-out radial motion stimuli that contained either inward or outward radial motion with either a left- or right-sided FOE (fig. 7a). In this case, any direction of local motion might be from a left- or right-sided FOE and cannot aid FOE discrimination. Left/right FOE in-out radial motion thresholds (without local motion cues) were much higher than left/right FOE outward radial motion thresholds (with local motion cues) in AD and somewhat higher in EN (fig. 7b). This is reflected in a significant group-by-stimulus interaction effect (F2,93 ⫽ 12.81, p ⬍ 0.001)

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Fig. 7. Global pattern perceptual impairments in EN and AD. a Global pattern radial motion stimuli were created to remove the utility of local motion cues by intermixing stimuli having radial motion outward from (upper) or inward to (lower) left- or right-sided FOEs. b Left/right discrimination thresholds (ordinate) for radial outward motion (open bars) and for global pattern radial motion (filled bars) for each group (abscissa) showed elevated global pattern thresholds in the EN and AD groups.

comparing thresholds to left/right FOE outward radial motion and left/right FOE in-out radial motion across the three subject groups. Individual subject thresholds showed a reliance on local motion cues in almost all AD subjects (85%, 17/20), and some EN subjects (32%, 12/38), seen as thresholds above 50% when local motion cues were removed (fig. 8). Thus, almost all AD patients, and some EN subjects, show impaired use of global pattern cues for optic flow perception. All subjects with elevated thresholds for discriminating left/right FOE outward radial motion also had elevated thresholds for discriminating left/right FOE in-out radial motion, but the converse was not true. This implies that impaired global pattern radial motion perception is necessary, but not sufficient, for the development of impaired left/right FOE outward radial motion perception.

Impaired Temporal Integration for Visual Processing

Optic flow analysis requires the temporal integration of the changing pattern of visual motion. Temporal constraints on visual processing can be measured using rapid serial visual presentation (RSVP) [42] that reveals the attentional blink, a failure to perceive the second of two task-defined targets separated by intervening distracters [43]. The attentional blink is exaggerated in patients

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Fig. 8. Left/right FOE radial motion discrimination thresholds for subjects (ordinate) in each group (abscissa) are shown for outward-only radial stimuli (left side for each subject group) and for in-and-out global pattern radial stimuli (right side for each subject group). Almost all AD patients (85%, 17/20), and some EN subjects (32%, 12/38), showed thresholds ⬎50% coherence when local motion cues were removed.

with right posterior parietal cortical lesions [44], the same regions activated by both RSVP tasks [45] and optic flow stimuli [46]. This suggests that both paradigms access related mechanisms for high-level visual processing. We have combined studies of RSVP’s attentional blink and optic flow perception to examine links between temporal constraints on perception and impaired optic flow analysis in AD [47]. We tested older normal subjects (ON) and AD patients who viewed RSVP stimuli that included two target letters separated by distracter numbers (fig. 9). We analyzed attentional blink errors in trials where the first target was identified correctly such that only the second target might be missed. In these trials, the ON group performed significantly better than the AD group (F12,78 ⫽ 12.78, p ⫽ 0.002) (ON ⫽ 80% correct, AD ⫽ 63% correct) and there was a significant effect of the number of intervening distractors (F6,120 ⫽ 12.30, p ⬍ 0.0005) (fig. 10a). The ON group showed the same pattern of attentional blinks seen in young [48] and older [44] normals, failing to report the second of two targets separated by one or two distracters (360 ms). The AD group’s attentional blink was more severe and more prolonged, lasting until the sixth intervening distracter (900 ms).

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Fig. 9. RSVP trials presented two letter targets imbedded in a series of number distracters. Trials began with centered fixation followed by 7–15 preceding distracters, then the two targets (T1 and T2) separated by 0–6 intervening distracters, and ending with a single distracter. Each item was presented for 130 ms, followed by 50 ms blank interval. The stimuli were black, Arial bold, numbers and capital letters on a uniform gray field (9 cd/m2) subtending a visual angle of ⬃0.8 ⫻ 1⬚ on a computer monitor. The numbers ‘0’ and ‘1’, and the letters ‘I,’ ‘Q’ and ‘Z’ were omitted for clarity.

AD subjects also made errors in which the first target was missed and the second target was identified correctly. There was a significant group-bydistracters interaction (F6,54 ⫽ 4.61, p ⫽ 0.01) which post-hoc analyses attributed to group differences with zero to four intervening distracters (fig. 10b). The number of intervening distracters affected the AD group (F6,54 ⫽ 5.41, p ⬍ 0.003), but not the ON group (F6,66 ⫽ 1.37, p ⫽ 0.24). The loss of the first target is not from forgetting, or from perceptual masking by the distracters. If either were the case, such errors would persist across any number of distracters rather than being limited to trials with no more than four distracters (⬍800 ms). These errors represent attentional masking: It is masking because the loss of the first target is related to the arrival of the second target, and it is attentional because only items of the attended target category have the effect, even a long series of five or six intervening distracters do not evoke such effects. We also determined horizontal motion and radial optic flow perceptual thresholds in the ON and AD groups finding significant task X group interaction effects (F1,14 ⫽ 5.24, p ⬍ 0.04). The ON and AD groups had significantly different radial optic flow motion-coherence discrimination thresholds of 17 and 44%, respectively (t16 ⫽ 2.10, p ⫽ 0.05). RSVP performance was compared to visual motion perceptual impairment in AD patients. RSVP errors were significantly correlated with elevated radial optic-flow thresholds (attentional blink r ⫽ ⫺0.70, p ⫽ 0.04; attentional masking r ⫽ ⫺0.71, p ⫽ 0.03)

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Fig. 10. a Attentional blinks (AB) were trials in which T1 was reported correctly and T2 was not. ON subjects showed ABs with two or three intervening distracters. AD patients showed a greater number of ABs with up to five intervening distracters. b Attentional maskings (AM) were trials in which T2 was reported correctly and T1 was not. ON subjects did not show AMs. AD patients showed AMs with up to four intervening distracters. Graphs show errors trials as a percentage of trials in which both targets were identified correctly (ordinate) as a function of the number of intervening distracters (abscissa).

(fig. 11). There was no correlation between RSVP performance and horizontal motion thresholds. This suggests a link between temporal constraints on the processing of a rapidly presented series of targets embedded in distracters, and the discrimination of self-movement headings simulated by optic flow. The task-related constraints on the temporal dynamics of visual perception revealed in the RSVP study may limit the processing of the serial images in optic flow. Based on these findings, we have developed a two-stage concurrent inhibition model of visual processing (fig. 12). We view the attentional blink as the combined suppression of input to perceptual processors by: (a) lateral inhibition between neighboring perceptual elements activated by the intervening distracters, and (b) feedback inhibition from memory mechanisms occupied by the first target. We view attentional masking as reflecting unreliable feedback inhibition from an impaired memory mechanism that sometimes allows the second target to enter its perceptual processor and over-write the first target. This is consistent with memory impairments in AD [49] that delay consolidation to prolong the attentional blink and weaken the feedback inhibition to allow attentional masking. Thus, our findings support a two-stage model in which visual input drives a competitive network [50] of category-specific, sample-and-hold mechanisms that mutually inhibit each other’s input streams. These drive second-stage short-term memory mechanisms that provide feedback inhibition of visual input to the active

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Fig. 11. The radial optic flow thresholds of AD patients (abscissa) were significantly correlated with AB and AM errors (ordinate) in the RSVP task. AB, r ⫽ ⫺0.80; AM, r ⫽ ⫺0.66. This links selective optic flow perceptual impairment with the temporal dynamics of visual processing in AD.

categorical processor until memory has registered the current content of that processor. Task-dependent biasing in the feedback network might also serve as an attentional filter. First-stage categorical processors might reside in the functionally distinct areas of extrastriate visual neocortex [51] and second-stage memory mechanisms might be associated with hippocampal areas affected by the early stages of Alzheimer’s pathology [7] typical of our AD patients. Impaired Spatiotemporal Integration in AD

Our behavioral studies demonstrate that visuospatial disorientation in AD is related to a selective incapacity to link a visual percept with a location in a cognitive map. Memory mechanisms play a lesser role with little relationship between scene recognition, verbal, or figural memory and our subjects’ becoming lost along the test route. Our earlier studies demonstrated links between optic flow perceptual thresholds and incapacity in both ambulatory [38] and vehicular [40] orientation that is consistent with frequent episodes of their becoming lost in a variety of circumstances [52, 53]. Our psychophysical studies show that AD patients, and some otherwise normal older adults, rely on local motion cues in optic flow. This suggests an inability to spatially integrate visual motion information from the large area of optic flow stimuli. We have also found a link between impaired optic flow

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Two-stage concurrent inhibition model Older normal subjects

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Fig. 12. A schematic diagram of the two-stage concurrent inhibition model. This model hypothesizes that visual input drives an array of perceptual integrate-and-hold modules (rectangles). Each module is tuned to respond to a specific category of visual input. Active modules inhibit input to neighboring modules and present their content to the working memory store (oval). The working memory store provides feedback to the active categorical processor as a second source of inhibition of visual input that persists until working memory has consolidated the current content of that processor.

perception and poor RSVP performance in AD patients that suggests that these tasks both rely on the temporal dynamics of visual processing. Together, these findings lead us to hypothesize that a variety of cognitive deficits in AD may reflect a failure in the spatiotemporal integration of sensory information. We speculate that impaired spatiotemporal integration in AD patients and some older adults is linked to the progressive atrophy of neuronal dendritic fields. The progressive involution of neuronal dendritic fields occurs in aged rats [54–56] and human aging and AD [57, 58]. These effects are

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relatively independent of cell loss [59] or other AD pathology [60] and may represent an early manifestation of aging [61] and AD [62] pathology, and a primary mechanism of cognitive pathophysiology in aging and AD [63, 64]. In our model, impaired spatial integration is thought to result from the loss of dendritic area that progressively limits the number of available synaptic sites for convergent input from large segments of the visual field [65]. Impaired temporal integration may reflect the related loss of neuronal surface area and its effect of decreasing the overall electrotonic capacitative properties of neurons, limiting the time-course of the temporal summation of synaptic events and potentially linked to cognitive deficits in aging monkeys [66]. The cause of dendritic field atrophy might be traced to neuronal deafferentation from AD’s predilection for destroying cortico-cortical projection neurons [32]. Such deafferentation might have robust effects on the extensive reciprocal connections in the hippocampal-neocortical distributed system for navigation and spatial perception [18, 67, 68]. These reciprocal connections link parieto-temporal association neocortex and parahippocampal and perirhinal areas [69–71] that than project to entorhinal cortex [72] which has extensive reciprocal interconnections with the adjacent hippocampus [73–75]. Such hippocampo-cortical interactions [76, 77] may allow sensory integration in parieto-temporal areas to support the elaboration of cognitive maps. AD’s early manifestations in hippocampal and subicular areas may reflect the dense convergence of inputs on those neurons creating vulnerability for corticocortical pathology that might have diverse pathophysiological foundations.

Acknowledgements We gratefully acknowledge the important contributions of our colleagues Mark Mapstone, Anthony Monacelli, Hope O’Brien, Teresa Steffenella, Sheldon Tetewsky, and William Vaughn to the work presented in this chapter. We are also grateful for the support of grants from NIA, NEI, and the Alzheimer’s Association.

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Charles J. Duffy, MD, PhD University of Rochester Medical Center 601 Elmwood Avenue, Rochester, NY 14642-0673 (USA) Tel. ⫹1 585 273 1696, Fax ⫹1 585 442 8766, E-Mail [email protected]

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Cronin-Golomb A, Hof PR (eds): Vision in Alzheimer’s Disease. Interdiscipl Top Gerontol. Basel, Karger, 2004, vol 34, pp 173–198

Magnocellular Deficit Hypothesis in Alzheimer’s Disease Grover C. Gilmorea,b, Sarah R. Morrisona, Karen E. Grotha a

Department of Psychology, bMandel School of Applied Social Sciences, Case Western Reserve University, Cleveland, Ohio, USA

This chapter addresses the hypothesis that Alzheimer’s disease (AD) involves a deficit in the magnocellular pathway of the visual system. Evidence from the neuroanatomical, neurophysiological, and psychophysical literatures that assesses the magnocellular deficit hypothesis is reviewed. A meta-analysis of the studies on coherent motion perception is presented that offers an account of the conflicting results regarding a deficit in global motion perception by patients with AD. Finally, new data from a longitudinal study of spatial contrast sensitivity that supports the hypothesis of a rapidly deteriorating magnocellular system are presented. As this volume attests, there is clear evidence that AD involves a processing disorder that has its origin in the visual sensory pathways. The sensory problems add to and perhaps exacerbate the memory problems that are a hallmark of the disease. Vision deficits, such as a decline in spatial contrast sensitivity and motion perception, can be subtle and are usually not evaluated as part of a medical exam. Also, AD patients are less likely than healthy elderly individuals to report vision problems to their physicians [1]. As a consequence, the sensory deficits can be hidden and may masquerade as higher order deficits.

Magnocellular Deficit Hypothesis

The visual system has been described as being composed of two parallel pathways [2–4]. One is specialized for the processing of color and form while the other is sensitive to motion. These pathways have been described as the P- and M-pathways, respectively, after the parvo- and magnocellular in the

lateral geniculate nucleus which are part of the respective neural channels [2]. In the psychophysics literature, the pathways were named on the basis of their temporal response properties as the sustained and transient channels, respectively. As the visual system was examined in AD it was natural to investigate the involvement of these distinct processing channels. Sadun [5] may have been the first to suggest that AD may involve a deficit in the large cell, M-pathway neural system. There followed a series of studies to determine the validity of this observation in the neuropathology, neurophysiology, and psychophysical areas. This has been a controversial area of investigation for two reasons. First of all, no study has presented conclusive evidence. Each investigation has some weakness such as its sample size or methodological procedure that renders it open to criticism. That is the nature of science. Also, the observations of M-pathway deficits could not be shown to be present in each AD patient. Secondly, there has been a bias against acknowledging a specific visual deficit associated with AD because of the longstanding supposition that AD did not involve primary sensory deficits. The preponderance of studies examined in this chapter will show that a magnocellular or M-pathway deficit does occur in a sufficient number of AD patients to warrant support for the existence of a magnocellular deficit associated with some variants of AD.

Neuropathological Evidence in the Retina

Hinton et al. [6] reported that 8 of 10 AD patients had widespread axonal degeneration in the optic nerve and three-quarters of the retinas had a significant reduction in the number of ganglion cells and a diminution of the nerve fiber layer. The observation of optic neuropathy [5] and retinal ganglion cell degeneration related to AD and not to aging has been supported by several investigations [7–9], whereas other studies have not found evidence for specific optic neuropathy in all AD eyes [10, 11]. Sadun and Bassi [12] examined the retinas of 10 AD patients and reported that there were significantly fewer optic nerve axons and diminished nerve fiber layers compared with control eyes of older adults. Furthermore, they observed that the largest cells in most of the AD retinas seemed to be selectively affected by neural degeneration. They concluded that in some cases of AD, M-cell degeneration may be involved and P-cells may be relatively spared. Curcio and Drucker [10] challenged the conclusions reached by the Sadun [5, 12] and the Blanks [7–9] teams. In a study of 4 retinas from AD patients compared with age-matched control retinas, Curcio and Drucker reported no significant difference in cell counts. This study is often cited as evidence that there is not a retinal cell deficit associated with AD. However, even in this

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small-sample study there is evidence that 3 of the 4 age-matched AD retinas had fewer retinal ganglion cells. The youngest set of retinas was the one pair that did not show an AD deficit. Since all of the pairs did not show an AD deficit, in this small sample there would not be a statistically significant effect. An objective evaluation of the data would suggest that the Curcio and Drucker [10] results did agree with the data of the Sadun and Blanks investigations that there is a loss of retinal ganglion cells in at least a majority of AD patients. Davies et al. [11] examined optic nerve characteristics in a small group of AD eyes (n ⫽ 9) and did not find evidence of optic nerve degeneration. Davies et al. suggested that the reports of optic nerve and retinal ganglion cell deterioration may be confounded with age effects. The criticisms levied by Curcio and Drucker [10] and Davies et al. [11] were addressed by Blanks and her colleagues [13, 14]. The retinas were from AD patients who had been severely demented. The central regions of 9 patients and the peripheral regions of 16 patients were examined. In the central region there was a 25% loss of retinal ganglion cells while the periphery showed a 39% deficit. The loss of cells was associated with a thinning of the neural fiber layer. All sizes of neurons were affected similarly in AD. Importantly, the loss of cells was not related to age in the AD samples but it was in the normal adult samples. When one goes beyond the descriptive statistics for the groups and examines the data from individual retinas in the Blanks et al. [13, 14] studies, it is clear that there is overlap in the distribution of cell counts for the samples of retinas drawn from normal individuals and AD patients. That is, despite the fact that the mean number of cells counted for the AD group was 36% lower than the comparison group, retinas from some non-demented older adults yielded cell counts within the range of the AD patients. This empirical finding demonstrates that individual differences in the number of retinal ganglion cells can be substantial. Studies that used relatively small samples of retinas [e.g. 10, 11] may have selected individuals who were not representative of the population. The studies reported by the Sadun and Blanks groups all used larger samples. For these reasons, the conclusions of the latter investigative teams that AD patients have a reduction in the number of retinal ganglion cells and a thinning of the neural fiber layer are deemed to be more reliable. In vivo examinations of the retinal nerve fiber layer have yielded contradictory results. Hedges et al. [15] and Tsai et al. [16] did not find abnormalities in the retinas of the AD patients they examined. However, Hedges and Barbas [17] and Parisi et al. [18] did report significant reductions in the thickness of the nerve fiber layer. Given the weight of the neuropathological evidence of a thinning of the nerve fiber layer, one may conclude that the reports of null effects may be instances of type II statistical errors – that is, the failure to find an effect

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that does exist. Such errors occur when there is a lack of statistical power, which is related to the size of the samples and how representative they are of the population. The lack of power also may indicate that the nerve fiber layer thinning may not be sufficiently severe to be detected in many AD patients who are still capable of being tested. It is expected that as the numbers of investigations in this area increases, the sample sizes grow, and the techniques become more sensitive, one will see a convergence of evidence that is consistent with the neuropathological data of a reduction in the nerve fiber layer.

Neuropathological Evidence Beyond the Retina

The lateral geniculate nucleus (LGN) of AD patients has been shown to be affected. Senile plaques have been reported in both the magnocellular and the parvocellular layers of the LGN with the latter showing the higher number of plaques [19]. Further, there is evidence of impaired capacity for oxidative metabolism in the LGN as indicated by reduction of cytochrome oxidase levels [20]. AD-related pathology is quite evident in visual cortical areas. Wong-Riley et al. [20] found senile plaques in both primary and secondary visual cortices. The decrease that they reported in cytochrome oxidase levels in the visual cortices is indicative of a reduction in neural capacity. Hof and Morrison [21] demonstrated that, while there is much less neurofibrillary tangles (NFT) formation in the occipital areas than in the prefrontal and temporal association areas, there is a significant loss of cells in specific layers of areas 17 and 18. There was a significant reduction of cells in layer IVB of area 17 and in layer IIId of area 18. The cells of these layers have long corticocortical projections to the medial temporal area (MT or V5). Hof and Morrison [21] argued that AD patients may exhibit specific visual problems linked to poor transmission of visual information to area V5, which has been referred to as the movement area. Hof et al. [22] examined the tissue of AD patients who had presented with prominent visual problems. This group of AD patients had particularly high NFT in area V5 as compared to their occipital fields. This is evidence of extensive M-pathway dysfunction. A general weakness of the neuropathological studies, with the exception of that by Hof et al. [22], has been that they have been conducted on a relatively small number of patients who had not been well characterized in regard to their functional vision. Given the growing body of evidence that AD is expressed heterogeneously [23, 24; Cronin-Golomb, this volume], it is likely that the neuropathology studies reflect this heterogeneity. That is, some AD patients may experience significant visual pathway disturbance while others are relatively spared. Further pathology work is needed on patients who have been well

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characterized in regard to their functional vision capabilities in areas that are suspect in AD. A cautious interpretation of the majority of the existing evidence is that there is a loss or shrinkage of both P and M ganglion cells in the retina, a thinning of the nerve fiber layer, pathology in both the magnocellular and parvocellular layers of LGN, and in the primary and secondary cortical areas with specific evidence of poor transmission in the M-pathway including pathology in area V5.

Neurophysiological Evidence

Rizzo et al. [25] argued that the visual deficits of AD patients are due primarily to dysfunction in the visual association cortices rather than from precortical damage as hypothesized by Sadun and Bassi [12]. Their conclusion was based on an extensive clinical neuro-ophthalmological examination of AD patients and healthy elderly adults. While the patients in their study did have demonstrably lower spatial contrast sensitivity, they also had normal critical flicker fusion thresholds, pattern visual-evoked potentials (VEPs), and fullfield electroretinograms (ERG). Rizzo et al. [25, p. 98] concluded that there was ‘no convincing evidence of damage to the retinocalcarine pathway associated with AD’. Justino et al. [26] and Kergoat et al. [27] also reported that ERG measurements were normal for AD patients suggesting no significant problem in the propagation of the visual signal from the retina. However, in contrast to Rizzo et al. [25], Kergoat et al. [27] did find in a sample of 27 mild to moderately demented AD patients that the latency of the visual evoked response was delayed. Parisi et al. [18] reported that both the latency and the amplitude of the pattern ERG was reduced in the 17 AD patients they examined. Further, Parisi et al. [18] reported that there was a correlation between the thinning of the neural fiber layer and the abnormal ERG responses. As in the neuropathology literature, the contradictory findings on the propagation of the signal in the visual pathways may be related to the heterogeneity of AD and the relatively small samples used. Large samples are more likely to capture AD patients with visual symptoms who may have underlying abnormal visual processing pathways.

Psychophysical Investigations

Neural deterioration in the visual system, if it is sufficiently severe, should result in deficits in visual behavior. Careful examination of vision through

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sensitive psychophysical tests may reveal patterns of performance that are indicative of specific neural pathway dysfunction. This logic was the impetus for a study by Kurylo et al. [28], who set out to examine whether ‘broad-band’ (M-pathway) functions were more impaired than parvo-driven functions. In all of the tests, the 14 AD participants yielded lower levels of performance than the control group. However, when a strict criterion of individual deficits (performance at least 2 SDs below the mean of the normal group) was applied, the AD participants were found to be deficient on only the parvo-dominated tests. Further, the pattern of deficits did not resemble the dysfunction observed in monkeys who had lesions of the magnocellular layers of LGN. Kurylo et al., concluded that AD does not involve a specific deficit in the broad-band (M-pathway) channels. The deficits associated with AD appeared to be due to neuropathology in the visual association cortices. This conclusion is tempered, however, by the small sample of AD patients and the fact that one of the two critical tests of broad-band function, the motion perception task, did not have stimulus characteristics that would require the M-pathway. The motion signal, which was all of the dots in the display moving in one direction, could be resolved by tracking a single dot. The tracking of a dot can be done easily by the P-pathway cells in primary visual cortex. Only a task that requires the integration of motion vectors over a large area by V5 (MT) can claim to drive the broad-band pathway. The weakness of the Kurylo et al. [28] study was addressed recently in a powerful study by Jacob et al. [29]. In an examination of the temporal processing capabilities of 27 AD patients, these authors reported that the AD patients exhibited a pattern of deficits that was consistent with dysfunctions in the M-pathway. The eyes of 34–40 AD patients were evaluated on the separate tests. Specifically, there was a reduction in the steady-state pattern VEPs and abnormal transient pattern flash VEPs. Coherent Motion: A Task Used to Measure Magnocellular Functioning Studies of motion detection have appeared which strongly support the contention by Hof and Morrison [21] that AD patients have difficulty with information processed by area MT. A psychophysical task that has demonstrated a strong relationship with MT activity in macaques uses a random dot cinematogram (RDC) to elicit global motion perception [30]. This type of display [31, 32] features some number of dots that are displaced from one frame to the next, resulting in apparent motion. Some of these dots are displaced randomly, while others are displaced along the same vector (random motion vs. coherent motion). The global direction of motion is created by the percentage of dots that are displaced in the same direction, and the local motion is created

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FRAME 1 FRAME 2

Fig. 1. Illustration of the direction of displacement for individual dots. The dim dots represent the direction of ‘travel’ for the first frame, and the bright dots represent the direction of displacement for the next frame refresh. Note that the direction is not always consistent for each dot, and that each frame is independent of the prior frame. In this example, the direction of global motion is UP at a coherence of 60%, since for each frame refresh, 3 out of 5 dots are traveling in an upward direction.

by the random signals. Each single dot is displaced along a calculated trajectory based on a re-sampling for each frame refresh [33]. In other words, for a given coherence, the same number of dots is displaced in the same direction from one frame to the next. However, each dot’s direction of displacement is computed independently of its prior direction of displacement (fig. 1). This strategy has been employed to avoid ‘local’ tracking (judging coherent direction through the tracking of one element within the stimulus), and has been demonstrated to more fully activate area MT. Figure 2 illustrates what a single frame of a RDC may look like. A coherent motion threshold is defined as the lowest percentage of coherently displaced dots needed to accurately judge the global direction of motion within a given criterion of accuracy (generally 75–80%). Using an ‘ideal’ motion stimulus, a coherent threshold of as low as 5% is sufficient for correctly identifying direction [34]. This seems fairly impressive, as it means that even amidst 95% randomly moving dots (distracting), one is able to accurately judge the direction of only 5% unison movement. In figure 2, 5% would be only 10 dots. This task has demonstrated a close marriage with magnocellular activity through neurological [for review, 35] and physiological [36, 37] research. The overall literature in motion perception has been prolific [for review, 38], likely due to the significance of detecting motion in our daily lives; however, only seven studies have been published using the coherent motion paradigm with AD. Coherent Motion Studies and AD Are Not So Coherent Trick and Silverman [39] were the first to employ the coherent motion task to test motion perception changes in AD. They reported a statistically significant (and large) overall threshold elevation in AD (27.6 ⫾ 10.96% coherence)

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Fig. 2. One frame shot of a random dot cinematogram containing 200 dots at 50% contrast. Each successive frame would display a shift (or displacement) of each of the dots – some randomly, and some coherently. In appearance, the visual display resembles a snowstorm, where highly coherent stimuli have a salient appearance of direction (usually up, down, left, or right), and as the percentage of coherence is reduced, it becomes more difficult to judge the global direction of motion (more stormy). In this sample, only 10 dots need to travel in unison to create 5% coherence.

compared to a healthy control group (13.4 ⫾ 5.2% coherence). Additionally, they found a trend toward increased thresholds with increased dementia severity, and suggested that the lack of statistical significance may have been due to a small sample size (n ⫽ 11). Trick and Silverman [39] concluded from their data that the observed decrease in motion sensitivity most likely resulted from agerelated neurodegeneration in the retinocortical pathway. They further suggested that motion perception problems may contribute to spatial disorientation in AD. The second study to appear used a similar coherent motion paradigm with AD [40]. This study supported the motion deficit originally reported by Trick and Silverman [39], and found a 16.9% threshold elevation in AD (27.6 ⫾ 12.6% coherence) compared to the healthy older adult group (10.7 ⫾ 7.4% coherence). There was minimal overlap between groups, where only 2 of 15 AD patients obtained thresholds under 18%, and all but one of 15 non-demented older adults fell within this range. In this study, the same trend was found relating increasing motion thresholds with increasing dementia within the AD

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group. In addition to comparing motion thresholds between groups, the Gilmore et al., study correlated motion thresholds with contrast sensitivities obtained across a range of spatial frequencies within the AD group. One correlation stood out between AD contrast sensitivity to a sinusoidal image of 2 cpd presented at 7.5 Hz and their motion threshold (r ⫽ ⫺0.57). This correlation was statistically significant, and supported the proposed magnocellular deficit explanation, as the high temporal presentation (7.5 Hz) of the sinusoidal grating has been shown to predominantly stimulate M-cells [41]. An additional study published in the same year [42] examined motion threshold elevations in AD under two different conditions. The investigators described these conditions as unconscious detection of motion (determined by measuring the presence of optokinetic nystagmus, or OKN) and conscious motion perception (behavioral response to direction of motion). In the conscious motion perception condition, which used a coherent motion task with a measure of behavioral directional response comparable to the two above studies, a similar and significant elevation was reported in the AD group (13.6 ⫾ 13.6% coherence) compared to that of the healthy older adults (6.0 ⫾ 2.2% coherence). While the unconscious detection of motion cannot be compared to the above studies because it relied on the OKN effect, no difference in threshold was found; however, both groups needed more coherence for a correct OKN response (AD: 24.8 ⫾ 12.4% coherence; older control group: 22.1 ⫾ 12.9% coherence), and demonstrated high variability. Silverman et al. [42] concluded that their finding could be explained by the selective damage evidenced in the visual layers responsible for corticocortical projections to area MT [21, 43]. Finally, they cited Trobe and Baur’s [44] description of the separation of primary and higher levels in visual functioning clinically observed in AD (‘seeing but not recognizing’) as an explanation of the dissociation between conscious perception and unconscious detection of motion in AD. If these were the only three studies conducted using coherent motion to test AD, it would seem that (1) AD patients experience a motion perception deficit, and (2) AD patients very likely experience a magnocellular deficit. However, a fourth study appeared in which the coherent motion elevation was not found in AD in a global coherent motion task [45]. The mean threshold obtained from the AD group in the Mendola et al. study was 11.7% compared with the healthy older adults who performed at a mean threshold of 10.9%. Since the older adult threshold is consistent with the previous reports, it may be inferred that something in the Mendola et al. design improved motion performance of AD. Mendola et al. proposed that the poor AD performance observed in the earlier studies was likely due to a larger demand on attention, memory, and motor skill, and the use of discrimination, rather than detection, resulting in the elevated thresholds in AD.

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While the stimuli used in the Mendola et al. study differed from the prior two studies in many ways, we suggest that the most salient difference was that there were two displays presented, one to the left, and one to the right of center. For each trial, one of the displays exhibited random motion entirely, and the other contained coherent global downward motion. The participant was to identify which display contained downward motion. Mendola et al., proposed that they were able to eliminate the threshold elevation because in their task, patients needed only to detect the presence of coherent motion, rather than to discriminate the direction of that motion. This data supports the explanation proposed by Silverman et al. [42]. Opposing Results Based on the four studies discussed, it would be fair to conclude that the deficit in AD is in motion perception (discrimination), and not in motion detection. However, three additional studies have been conducted more recently, and have reported opposing findings. Two of these studies [46, 47] used the coherent motion discrimination (conscious perception) procedure, and reported no statistical difference in thresholds between AD and healthy older adults, directly opposing the elevations reported above. In another study, Neargarder [48] used a similar detection (two-field) task as Mendola et al. [45] and reported significant AD motion elevation, again opposing the above reports and conclusions. Rizzo et al. [47] used a one-field display and tested more participants than any of the earlier studies. This group found no statistical difference between the healthy older adults and AD performance, though they did make note of the high variability in the AD performance (24.7 ⫾ 20.4% coherence, n ⫽ 43), relative to the healthy older adults (20.6 ⫾ 8.3% coherence, n ⫽ 22). O’Brien et al. [46] examined both horizontal coherent motion and radial optic flow in older adult and AD groups. While the AD group demonstrated greatly elevated thresholds in radial optic flow, the horizontal coherent motion performance of the AD group was judged to be normal. Finally, Neargarder [48] used a twofield display similar to that used by Mendola et al. [45], and reported an AD motion deficit that was not found in the Mendola et al. study. Magno and Parvo, Collaborators in Motion Detection While early reports were very convincing and consistent in their implication of the magnocellular pathway as the motion-detecting pathway, a two-pathway model [49, 50] has emerged in recent literature, demonstrating the involvement of both the ventral and dorsal streams. The M channel is still hailed with a dominant role in motion processing. It has been postulated that a higher-order feature analysis through the detection of speed and direction results in a more accurate but temporally delayed global analysis [51] than the directionally

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‘confused’ local signals [52] carried through the ventral stream. There is a clear beneficial influence of the P cells in motion processing [49, 53]. This benefit has been supported by findings that chromatic cues can improve performance in motion signal detection sixfold in a coherent motion paradigm. Further, since we are better at detecting relative motion than absolute motion [54] the ‘what’ (P) stream must contribute substantially to the ‘where’ (M) stream in motion processing capabilities. One of the major problems with the RDC motion literature is the lack of consistency and/or reporting of the complex array of stimulus characteristics that contribute to the strength of a motion signal. While studies often report the examination of ‘single-element’ changes such as density, dot size, box size, and speed (or displacement) on motion thresholds, there is a general lack of reporting the resulting cascade of changes in other stimulus characteristics caused by the interaction with that ‘single’ stimulus change. In other words, if two separate studies report an examination of dot density, the results may not be comparable to each other because few of the other characteristics are the same in either study (e.g., an increase in box size would result in a decrease of dot density, if the number of dots was equivalent). This highly interactive matrix of stimulus parameters in RDC presentations may explain the overwhelming discrepancies in results across the literature, and potentially shed light on the ‘ideal’ combination of parameters resulting in a ‘visually fair’ global signal tailored to those who have previously demonstrated deficits. Once this combination can be understood, we may be closer to understanding whether or not there is a magnocellular deficit in AD. If the motion deficit sometimes reported in AD is due to a magnocellular dysfunction, we should find that the studies reporting deficits in coherent motion thresholds use stimuli that drive the magno channel more actively than the parvo. If, however, this coherent motion threshold change in AD is attributable to an interactive magno/parvo deficit or even a sole parvo deficit, we should find the opposite. If we can determine the stimulus parameter that results in AD motion deficits, we can then understand what kind of deficit exists, and how to reduce or eliminate that deficit. Stimulus Eccentricity and Speed Stimuli presented in the periphery of the visual field will activate more magno than parvo channels [55, 56]. Also, stimuli driven at higher speeds will selectively stimulate the cells in the magno channel [57]. While these parameters have not been examined explicitly in AD, a study was conducted by Atchley and Anderson [58] with older adults that is instructive. They used RDCs to measure motion thresholds using two different speeds (4.8 and 22⬚/s), presented at four different retinal eccentricities (0, 10, 20, and 40⬚) in young and healthy

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older adults. The lowest thresholds for detecting motion were with foveal presentation. As the stimulus was moved into the periphery, the motion thresholds elevated with eccentricity. This finding demonstrates that the foveal region which is rich with parvocellular representation and also has magnocellular coverage yields the best coherent motion performance. In the highest speed condition, which would drive the magno channels optimally, both groups obtained lower thresholds than in the lower speed condition, indicating the benefit of magno activity. However, in this high speed condition, the older adults performed more poorly than the young adults at all eccentricities except at 40⬚, where performance was equated because of a decline in young adult performance. The interaction between speed and eccentricity suggests a beneficial relationship between the magno and parvo channels. Ideal motion performance depends on both the magno and parvo channels. The small receptive fields of the parvo channels in the center of vision permit the dots of the coherent motion stimuli to be well represented, yielding a strong local motion signal. The magno channels with their fast transmission time and large receptive fields permit the integration of global motion signals over a large area. Global motion perception is best when both the parvo and the magno channels are optimally stimulated. A deficit in motion perception can be related to weak activation of either the parvo or the magno channels. Stimulus Factor That Accounts for Presence of AD Motion Perception Deficits: Weighted Stimulus Density Upon examination of the published literature, there emerges one consistent stimulus characteristic that is linked to the presence or absence of AD deficits in coherent motion. This characteristic is calculated by the interaction of three factors that are commonly reported because they have been shown independently to have an effect on motion thresholds in healthy populations: dot size, dot density, and overall stimulus size (or ‘box’ size). The combination of these factors can be used to determine the figure strength of the motion stimulus, which we term weighted stimulus density (WSD). WSD is simply the product of dot area ⫻ dot density. It is an index of the distal strength of the stimulus. We suggest that since the coherent motion effect is based on the integration of motion vectors across the entire display field, it is reasonable to use a measure of stimulus strength that takes into account both the size of the moving dot and the density of the dots. This measure captures the importance of both the parvo and the magno channel activation. Table 1 includes the dot density, dot size, and the resulting WSD calculations for each of the motion studies from the literature that examined AD coherent motion thresholds. When the signal strength indexed by WSD exceeded 0.01,

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Yes

Gilmore, 1994 [40] Neargarder, 1998 [48] Silverman, 1994 [42] Trick, 1991 [39] O’Brien, 2001 [47] Mendola, 1995 [46] Rizzo, 2000 [48] 10.9% (NR) 20.6% (8.3%)

10.7% (7.4%) 13.72% (2.66%) 6.0% (2.2%) 13.4% (5.2%) NRf 11.7% (NR) 24.7% (20.4%)

27.6% (12.6%) 26.42% (1.94%) 13.6% (8.1%) 27.6% (10.96%) NR C

9 11 48 11 43

26 12 22

29

P&C

P

P

C

14

15 12

P

15 C

stimulus locationb

9.375

1.16

0.093

0.028

0.111

1.560

1.7

dot densityc

Motion stimulus characteristics

15

AD

OA

OAa AD

Sample size

Coherent motion threshold:mean (SD)

0.0011

0.1031

0.0116

0.0523

0.5625g 0.01

0.002

0.006

0.0005

0.0007

WSDe

0.0713

0.0544

0.0003

0.0004

single dot aread

d

a

OA ⫽ Older adult group. bStimulus location: P ⫽ presented peripherally (non-centrally), C ⫽ presented centrally. cDot density ⫽ # dots/deg2. Single dot area ⫽ height ⫻ width of ‘dot.’ eWSD ⫽ weighted stimulus density, calculated by multiplying dot density ⫻ dot area. fNR ⫽ not reported. gDot size was not reported in this publication so the calculation was based on the dot size reported in the study cited for the stimulus parameters used [70].

No

No

No

Yes

Yes

Yes

AD deficit?

Publication (first author)

Table 1. Meta-analysis of the seven studies that examined coherent motion thresholds in Alzheimer’s disease (AD) and older adult (OA) participants. The critical stimulus characteristics and a derived measure, weighted stimulus density (WSD), are given along with the reported thresholds of the participants

the AD participants were not different from the older adult comparison groups in their ability to report coherent motion. It is important to note that the four studies that did report AD deficits in coherent motion perception used a broad range of dot sizes, retinal locations, and density. Indeed, the largest dot stimuli were used by Silverman et al. Yet, in combination, the variables used in these studies resulted in relatively weak motion signals over the field of display. This meta-analysis suggests that AD patients have the capability of integrating motion vectors over a large area, a function of V5 in the M stream of processing. However, V5 must receive a relatively strong stimulus in order to accurately calculate the vector of motion. This observation is supported by recent findings using fMRI to compare human brain activation during the observation of incoherent (random) vs. coherent dot motion. In one study using RDCs, random motion seemed to more highly activate area V5 in the human than did coherent motion [59]. This finding is the opposite of reports using macaque monkeys and is also discrepant with another fMRI study on humans [60]. Braddick et al., attributed the opposing findings (coherent strongly stimulates V5 and V3A, but not the primary visual cortex, and incoherent more strongly stimulates the primary visual cortex) to a difference in stimulus parameters between the two studies: ‘McKeefry et al., [59] report that V5 is more strongly activated by ‘incoherent’ than ‘coherent’ motion. However, the dot density in their display (average 0.19 dot deg⫺2) was over 100 times lower than ours, giving little opportunity for summation within any receptive field; the effect of activating multiple directions may therefore predominate even in V5. The balance of the two effects must depend on receptive field size relative to stimulus density’ [60, p. 68]. This density-dependent, random vs. coherent activation of V5 may be additionally affected by the reduction in overall stimulus strength experienced by AD patients because of their spatial contrast sensitivity deficits. While our meta-analysis does not support a direct relationship with density, it does demonstrate a relationship with WSD, suggesting a relationship between dot density and dot size. Perhaps the density governs the V5 response to coherent vs. incoherent, and the dot size influences response strength. This response strength would additionally be influenced by the proximal strength of a signal, which is influenced by age, as well as by AD. Additional studies would need to be conducted in order to examine this hypothesis further. Contrast Sensitivity Studies with AD patients of contrast sensitivity are consistent with the neuropathological evidence. To appreciate this work one must note that M and P channels respond optimally to different types of visual events [2]. The M channel neurons are optimally responsive to abruptly changing, low spatial frequency

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stimuli. The P channel neurons respond best to static, high spatial frequencies. From the extensive loss of both P (small) and M (large) ganglion layer neurons, it would be expected that spatial contrast sensitivity would be reduced for all spatial frequencies in AD patients. This effect has been reported by a number of investigators [61–66] in studies of 9–25 AD patients. The one exception in the literature is a study by Schlotterer et al. [67] which examined only 6 AD patients and presumably did not have the power to detect group differences, although the contrast sensitivity curves of the AD and control groups did differ in the expected direction. Furthermore, in a longitudinal investigation, Gilmore and Whitehouse [68] have demonstrated that the sensitivity of AD patients to flickered, low-frequency stimuli declines more rapidly over a 1-year period than does their sensitivity to higher spatial frequencies. This last result is consistent with the proposition that the M channel neurons become dysfunctional more rapidly than P channel neurons. Thus, in studies of spatial contrast sensitivity with sufficient statistical power, through large sample sizes or within-subject longitudinal designs, the evidence is clear that AD patients experience a significant loss of contrast sensitivity in both the M and the P channels with the rate of decline being possibly greater for the large cell M channel. Longitudinal Spatial Contrast Sensitivity Assessment: Two Years The Gilmore and Whitehouse [68] study is critical because it is the only study on contrast sensitivity in AD that demonstrated an interaction between temporal presentation rate and spatial frequency. In studies where it was only found that the AD group had lower contrast sensitivity than the comparison group, it can be argued that the effect was due to the cognitive deficit of the patients in understanding the task. However, an interaction effect, particularly one that shows a selective change in performance over time, cannot be attributed to a simple artifact. The report by Gilmore and Whitehouse that only the AD participants yielded a specific decline to rapidly flickered, low spatial frequencies is critical to the argument that there is a spatial contrast sensitivity deficit and that the magnocellular pathway deteriorates over time. Here we will present results from that project for participants on whom there is complete data for five testing sessions over a 2-year period. While the sample size is relatively small, the longitudinal nature of the project yields fine sensitivity for detecting patterns within participants. Participants. The study compared the performance of 11 patients with probable AD (7 women and 4 men) and 14 healthy older adult participants (6 women and 8 men) across five sessions of testing at 6-month intervals. The data from 8 of the healthy older adults and 8 of the AD patients were reported previously in Gilmore and Whitehouse [68], which evaluated performance across three sessions in 1 year. The mean age of the older adult participants

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Table 2. Mean Mini Mental State Exam (MMSE) scores and (SD) for AD patients (n ⫽ 11) and healthy older adults (n ⫽ 14) at each test session

Session

MMSE

AD patients 1 2 3 4 5

20.09 (3.2) 19.27 (3.0) 18.09 (4.4) 17.18 (5.4) 15.82 (6.4)

Healthy older adults 1 2 3 4 5

28.14 (1.9) 28.43 (1.5) 28.71 (1.5) 29.07 (1.0) 28.50 (1.4)

across sessions of testing was 68.6 years. The mean age of the AD patients across sessions of testing was 75.2 years. This difference in age between the groups was significant, t(23) ⫽ ⫺2.651, p ⫽ 0.0143. AD participants were recruited from the Alzheimer Center of University Hospitals, Cleveland, Ohio. The center enrolled candidates aged 60 years or older who matched the profile of clinically probable AD defined by the NINCDS-ADRDA work group and the Health and Human Services Task Force on Alzheimer’s Disease. Some of the healthy older adult participants were spouses of the AD patients. The remaining healthy older adults were recruited from the community through advertisements. All participants were compensated for their time and effort. Each participant was administered the Mini Mental State Exam (MMSE) at the time of testing in order to assess overall mental status. Table 2 gives the mean MMSE scores for each group of participants. During the course of testing, the AD patient’s MMSE scores ranged from 7 to 27. The MMSE scores of the healthy older adults ranged from 23 to 30. The MMSE level of the healthy older adults did not change significantly over the 2-year period represented by the test sessions. The AD patients did exhibit a significant decline in MMSE scores, F(4,40) ⫽ 2.98, MSe ⫽ 31.318, p ⬍ 0.05, indicating a progression of their dementia. Visual acuity was measured for each participant using the Good-Lite Sloan Letters Acuity Test at the test distance of 3 m. There were no significant differences in acuity between the healthy older adults and the AD patients (mean acuity across sessions for older adults ⫽ 20/38; AD patients ⫽ 20/46). All vision testing was conducted binocularly.

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Apparatus. Contrast sensitivity was measured with the Nicolet Vision Tester CS2000, dedicated hardware designed specifically to test a wide range of visual spatial frequencies under a variety of temporal conditions. The monitor had a mean luminance of 115.5 cd/m2, as measured with a Spectra Pritchard Photometer model 1980A, and subtended a visual angle of 4.6 ⫻ 5.8⬚. Viewing distance was 3 m. The Vistech VCTS6500 Contrast Sensitivity Wall Chart was also used to measure spatial contrast sensitivity. The chart was wall-mounted so that the mean background luminance fell into the prescribed range of 100–240 cd/m2. Each circular test patch subtended a visual angle of 1.4⬚ at the viewing distance of 3 m. Design and Procedure. Each participant was tested in five sessions. The sessions were scheduled at 6-month intervals. At the beginning of each session, each participant’s visual acuity was tested. Also, each participant was administered the MMSE. The procedure for administering the Vistech and the Nicolet tests has been described in Gilmore and Whitehouse [68].

Results

Vistech VCTS6500 All analyses were conducted with log contrast sensitivity values. A challenge in the reporting of the Vistech performance arises from the fact that not all participants could detect the highest spatial frequencies tested. In the healthy older group, 2 of the 14 participants could not report the orientation of the 18 cpd grating at the highest contrast level. In the AD group, 1 of the 11 patients had the same problem with 18 cpd in all sessions. In other sessions, up to 5 AD patients could not report the orientation of 18 cpd. The inability to collect data from these individuals illustrates both the age-related and Alzheimer-related loss of contrast sensitivity for higher spatial frequencies. In order to evaluate group differences in Vistech performance, only the 1.5-, 3-, 6-, and 12-cpd gratings were used in a mixed analysis of variance with session and spatial frequency as within-subject variables and group as the between-subject variable. This was the analysis procedure adopted by Gilmore and Whitehouse [68]. The analysis included 14 healthy older adults and 11 AD patients. The log contrast sensitivity scores of the participants are shown in figure 3. The curves of both groups show the characteristic sensitivity function with peak performance at 3 cpd. The AD participants yielded contrast sensitivity scores across spatial frequencies and sessions that were 0.265 log units lower than the

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Vistech

Vistech

Older adults

2.6

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Log contrast sensitivity

2.4

2.2 2.0 1.8 1.6 1.4 1.2

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a

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2.4

1

2

3

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0.5

b

Spatial frequency (cpd) Session 1

10

Session 2

Session 3

1

2

3

6

10

Spatial frequency (cpd) Session 4

Session 5

Fig. 3. Mean log spatial contrast sensitivity and standard errors obtained with the Vistech VCTS6500 for older adults (a) and AD patients (b) in five test sessions separated by 6 months.

older adult comparison group, F(1,23) ⫽ 10.731, MSe ⫽ 8.709, p ⬍ 0.01. There was a small but significant change in sensitivity across the 2-year period for both the older adults, 0.09 log units, and the AD patients, 0.188 log units, F(4,92) ⫽ 4.376, MSe ⫽ 0.291, p ⬍ 0.01. There were no interactions among the variables that approached significance. The Vistech yielded stable group differences in performance and measured only a small change over 2 years in contrast sensitivity. As a measure of static contrast sensitivity, the Vistech suggested that the neural systems primarily responsive to stable stimuli undergo relatively little change in 2 years. This observation can be validated by an examination of performance in the 0 Hz condition of the Nicolet. Nicolet CS2000 Static (0 Hz). The stability of static contrast sensitivity is apparent in the performance of the older adults, shown in figure 4a, where the five contrast sensitivity functions are tightly clustered. In 2 years the older adults experienced a change in sensitivity of only 0.045 log units, which is quite minor. The AD participants, however, dropped in performance by 0.214 log units. Figure 4b illustrates that the major change in contrast sensitivity for the AD group occurred in the second year of testing. It may be noteworthy that this drop in performance coincided with a drop in mental status (see table 2). The change

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Nicolet 2.6

Nicolet 0 Hz

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0 Hz

2.4 Log contrast sensitivity

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2.4 2.2 2.0 1.8 1.6 1.4 1.2

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1

2

8 10

4

Session 1

0.5

b

Spatial frequency (cpd) Session 2

Session 3

1

2

4

Spatial frequency (cpd) Session 4

Session 5

Fig. 4. Mean log spatial contrast sensitivity and standard errors for spatial frequencies presented at 0 Hz (static) with the Nicolet CS2000 for older adults (a) and AD patients (b) in five test sessions separated by 6 months.

over the 2-year period yielded a significant main effect because both groups showed some change, F(4,92) ⫽ 4.715, MSe ⫽ 0.435, p ⫽ 0.001. The interaction of group and testing session showed only a trend to significance, F(4,92) ⫽ 2.238, MSe ⫽ 0.206, p ⫽ 0.071, despite the fact that the change in the AD group was nearly five times that of the healthy older group. The lack of significance may be attributed to the relatively small samples and the subsequent loss of power. The analysis of the 0 Hz Nicolet condition demonstrated, as in the Vistech analyses, that the AD patients had lower substantially lower sensitivities, 0.369 log unit difference, than the healthy older adults, F(1,23) ⫽ 20.547, MSe ⫽ 20.94, p ⬍ 0.0001. The peak performance for both groups was consistent with the Vistech findings in that the groups yielded their best performance at either 2 or 4 cpd in each session, F(4,92) ⫽ 164.116, MSe ⫽ 9.056, p ⬍ 0.0001. The results from the Vistech and the Nicolet 0 Hz condition are consistent. The AD group had substantially lower contrast sensitivity than the older comparison group at all spatial frequencies. The performance of both groups showed some decrease over time with the AD patients exhibiting a trend for a larger drop in sensitivity in the second year of testing. Flicker (7.5 Hz). The flickering of the gratings elicited very different performance functions than the static conditions. It is evident in figure 5 that both groups had substantially higher sensitivities for the lower spatial frequencies when the gratings were flickered as compared with the static presentations.

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8 10

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Nicolet 2.6

Older adults

7.5 Hz

2.2 2.0 1.8 1.6 1.4

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1

2

8 10

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0.5

Session 2

Session 3

2

4

Spatial frequency (cpd)

b Session 4

Session 5

Fig. 5. Mean log spatial contrast sensitivity and standard errors for spatial frequencies presented at 7.5 Hz counterphase (flicker) with the Nicolet CS2000 for older adults (a) and AD patients (b) in five test sessions separated by 6 months.

Indeed the peak sensitivity shifted to 1 cpd for the older adults at each session, whereas the AD group had their highest sensitivities at either 1 or 2 cpd across sessions, F(4,92) ⫽ 2.324, MSe ⫽ 0.172, p ⫽ 0.0157. The heightened sensitivity to low spatial frequencies indicates that the magnocellular channels were being driven strongly by the flickered stimuli. Overall, the AD group yielded lower sensitivities by 0.374 log units than the healthy older adults, F(1,23) ⫽ 20.892, MSe ⫽ 21.958, p ⫽ 0.0001. These values were very similar to the group difference in the static Nicolet condition. Figure 3a shows that the older participants, as in the static conditions, changed very little over time. The older adults experienced a small drop of only 0.069 log units over the 2-year period. Unlike the static conditions, the flickered displays elicited a changing pattern of performance from the AD group over time with the largest changes occurring in the lower spatial frequency range. As early as the second testing session, the AD subjects yielded a drop in sensitivity of 0.382 log units to the 0.5-cpd stimulus. This substantial decline spread to 1 and 2 cpd over the testing sessions. That is, the AD patients experienced a substantial loss of sensitivity to the lower spatial frequencies in the flickered condition. Over the 2-year period there were changes of 0.444, 0.319, 0.342, 0.209, and 0.135 log units to 0.5, 1, 2, 4, and 8 cpd, respectively. The older adults showed only a drop in overall sensitivity. These observations were supported by the interaction of session, participant group, and spatial frequency, F(16,368) ⫽ 2.063,

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MSe ⫽ 0.034, p ⫽ 0.0094. The main effects and secondary interactions of the variables were all significant.

Discussion

The major findings of Gilmore and Whitehouse [68] were replicated and extended in this evaluation of spatial contrast sensitivity over 2 years. In the static presentation conditions, Vistech and Nicolet 0 Hz, small but significant declines in contrast sensitivity were reported. It is likely that these changes reflected the aging of the sensory system. Indeed, since the AD group was nearly 7 years older than the healthy elderly participants, it may be suggested that a portion of the difference in sensitivity between the two groups may be linked to the age difference. It is well known that spatial contrast sensitivity declines with age [69]. However, an age change has not been demonstrated to occur in as little as 2 years. The longitudinal nature of the current dataset lends a power to the analysis that permits the identification of the inexorable decline at a small scale. The most striking finding is the dramatic change in performance that occurred only for the AD participants in the Nicolet 7.5 Hz condition. As we argued in Gilmore and Whitehouse [68], this pattern of results strongly suggests that the M-pathway cells, which are most sensitive to rapidly changing, low spatial frequencies, are deteriorating very rapidly in the AD patients. In as little as 6 months the large decline in performance is evident. Because this pattern of decline is unique to the AD patients and occurs in only one temporal condition and a subset of spatial frequencies, it cannot be attributed to global variables such as age or difficulty in understanding the task. Instead, it is compelling behavioral evidence of a rapidly declining M system. The deterioration of magnocellular processing is quite important in understanding the behavior of AD patients. The reduced sensitivity of the patients may adversely affect routine perceptual activities that depend on low spatial frequencies, such as face and object recognition, reading, and visually guided postural behavior. Since the M system responds to low contrast, moving objects, one would expect the AD patients with a deficit to experience more problems in driving and in walking through dimly lit rooms. Further work is needed to draw explicit links between these subtle visual deficits and behavioral and cognitive performance.

Summary and Conclusion

The question as to whether or not there is a magnocellular deficit associated with AD has led to a healthy debate for nearly 15 years. The hypothesis

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has been investigated with a wide variety of neuroanatomical and behavioral measures. The weight of the evidence that has emerged supports the hypothesis that a magnocellular deficit does occur in a substantial number of patients who are diagnosed with AD. These patients also experience dysfunctions in their parvo processing streams [e.g. 25, 47, 61]. The literature review suggests that the deficits may be expressed more strongly in some patients, indicating that there may be subcategories of AD with vivid visual problems. Identification of these subgroups should be a goal of future investigations. In combination, the loss of both parvo and magno functions must have profound effects on the ability of the patients to interact with their environment. Further work on the implications of the vision deficits for daily living is needed [see Dunne, this volume]. A lesson in experimental logic has emerged that is important to remember. It is never appropriate to accept the null hypothesis. In the absence of statistically significant evidence of a magnocellular deficit, some investigators were willing to accept the null hypothesis of no difference as truth. Instead it is wise to always consider that there is a chance of type II error, the failure to reject the null hypothesis when it is false. This leads one to consider the power of the test and to investigate alternative methods to test the null hypothesis. As suggested here, there was a bias to accept the null hypothesis because of the longstanding assumption that AD did not involve a primary visual deficit. Investigators need to be vigilant to the influence of such bias in their interpretation of results. It was through the persistence and tenacity of investigators such as Blanks and her colleagues, who increased power by testing larger samples and provided independent replications, that a preponderance of evidence eventually was produced that AD patients do experience a loss of both P and M cells in the retina. Also, the meta-analysis of the global motion perception ability of AD patients demonstrated that a stimulus characteristic may determine the detectability of a deficit. This illustrates that in the face of conflicting results, we should seek the opportunity to refine our measurement ability and create more sensitive tests. The visual deficits of AD patients typically are undiagnosed. This means that the patient, the caregiver, and health professionals are liable to misinterpret the disabilities linked to the hidden vision deficits as cognitive problems. Interventions can be designed to compensate for vision problems, such as increasing signal strength through the use of higher contrast or larger stimuli. But to develop effective interventions we need to detect the vision deficit and understand its true nature. This review has illustrated that a substantial number of AD patients may experience problems linked to both parvocellular and

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magnocellular processing. Sensitive tests that can be used clinically are needed to assess the functions of each of these processing systems.

Acknowledgements We would like to thank James Levy, Heidi Wenk, and Lisa Townsend for their leadership in data collection and analyses. Beth Patterson, Henry Galperin, and Asim Sheriff were very helpful in assembling materials for the review. The longitudinal data were collected with the support of the National Institute on Aging (2P01AG04391). We wish to thank the research participants and their family members for their interest and dedication to the project. The preparation of the chapter was supported by the National Institute on Aging (5R01 AG15361).

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Grover C. Gilmore, PhD Mandel School of Applied Social Sciences Case Western Reserve University 10900 Euclid Ave, Cleveland, OH 44106-7164 (USA) Tel. ⫹1 216 368 2256, Fax ⫹1 216 368 2850, E-Mail [email protected]

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Perceptual Organization in Alzheimer’s Disease Daniel D. Kurylo Psychology Department, Brooklyn College CUNY, Brooklyn, N.Y., USA

Perceptual Organization in Alzheimer’s Disease

Perceptual organization is a fundamental component of visual processing in which individual elements of a visual scene are resolved into a series of unified forms. Perceptual organization includes the synthesis of separate stimulus elements into a series of integrated relationships (perceptual grouping), and the segregation of discrete objects from background components (figure/ground separation). These processes accommodate complex visual stimuli composed of a broad range of stimulus configurations. Perceptual organization serves to organize the visual scene in preparation for object identification. For object recognition to occur accurately, constituent components must be associated without erroneous inclusion of unrelated elements. Consider a person wearing a patterned green shirt standing among the leaves of a tree. The scene must be organized so that the shirt is grouped with the face, which greatly contrast in luminance and color, and not with the leaves, which contain similar elements as the shirt. A multitude of cues, including continuity of borders, spatial proximity, and coherence of motion, interact to bind elements of the individual into a unified object. To accomplish this function, the visual system extracts relevant information from the scene, and processes these features as cues for grouping. To accommodate novel stimuli with speed and accuracy, perceptual organization likely follows established neural algorithms that mediate grouping strategies. These algorithms operate by applying a set of criteria to stimulus parameters that specify grouping arrangements best suited for form identification. In this regard, figure construction is based upon the physical metrics of the stimulus [1, 2]. With increased stimulus ambiguity, knowledge-driven processes may play an increased role in perceptual organization [3]. Under these conditions,

algorithms for grouping assignment may be modified by top-down processes, and thereby related to the integrity of higher-order cognitive capacities. Perceptual organization occurs at an intermediate level of visual processing. Initially, stimuli are detected and transduced into the nervous system where constituent elements are encoded. Separate elements are then segregated into congregate objects by organizational processes. At this level of processing, elements are differentiated and grouped, independent of image degradation and resistant to camouflage by extraneous elements [4]. Following figure/ground separation and grouping, visual components undergo high-order processing that includes memory and recognition [5]. Because perceptual organization operates as a component of a serial process, impairment at the sensory level will degrade its performance. Similarly, disruption at the level of perceptual organization will interfere with the subsequent processing of object recognition [1], or exacerbate deficits in other cognitive processes, such as memory. Perceptual organization is based upon regularities that exist among stimulus features. Organization may thereby be established from a variety of spatial and temporal cues. Spatial relationships include elements that are in close proximity, or elements that form smooth curves (good continuation). Commonalities across stimulus features, such as color, luminance, and shape, also serve as strong organizational cues. Similarly, perceptual organization may be based upon dynamic features, such as coherence in motion direction and synchrony of luminance change. With complex stimuli, perceptual organization is based upon the interaction of multiple stimulus features, which provide sources for various perceptual strategies of grouping assignment.

Deficits in Alzheimer’s Disease and Aging

Among visual deficits found in Alzheimer’s disease (AD) is impaired object recognition, including disrupted facial recognition [6] and impaired recognition of familiar objects [7]. It has been suggested that impaired object recognition in AD may stem from abnormal organization of the visual scene. Specifically, there appears to be improper use of discrete visual elements, either in segregating and identifying coherent objects [6, 8], or in simultaneously processing multiple elements to interpret the image [9]. Consistent with this idea are reports that AD patients are impaired on the identification of fragmented pictures [10], as well as the ability to integrate visual elements into global images [11]. It was further shown that perceptual organization ability in AD contributes to the recognition of novel faces [12]. Mendez et al. [13] found deficits in AD patients on tests of figure-ground discrimination (Luria hidden figures, groups of overlapping figures, traced patterns) as well as visual synthesis

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(Street figures, Gollin figures, Hooper test). Decreased efficacy in integrating visual information would produce confusion among components of individual forms, leading to the misinterpretation of objects. In order to evaluate the integrity of perceptual organizational capacities, measurements need to directly index the operation of grouping processes, and not resultant secondary effects on other cognitive capacities, such as attention, reaction time, or high-order visual function. In addition, to assess visual capacities in AD while reducing the effects general dementia, testing procedures should be designed to minimize demands on memory, language, and other high-order cognitive processes. Furthermore, the use of a forced-choice procedure reduces the possibility of response bias, which may impact perceptual measures in an elderly or clinical population. In this context, perceptual organization in AD was evaluated by measuring the ability to identify degraded forms embedded in visual noise [14]. To identify these stimuli, missing sections must be perceptually filled in (visual synthesis) while avoiding inappropriate inclusion of background elements. The analysis of organizational processes is maximized by displays in which the elements comprising the object and the background are identical, so that stimulus configurations are defined by element position, referred to as type P configurations [15]. In this way, discriminating the object and background by means of local features is eliminated, leaving the global arrangement of the elements as the sole cue for visual organization [1]. The ability to identify these stimuli decreases as the density of the background increases [16]. In contrast, performance progressively improves as the density of the figure increases, thereby increasing figure integrity [17]. Furthermore, the relative densities of the figure and noise, rather than the absolute distance among elements, determines the discriminability of the figures [18]. The systematic relationship between perceptual ability and stimulus parameters of these displays therefore makes them well suited to index perceptual organization. Perceptual capacities were compared between patients diagnosed with probable AD and demographically matched elderly control participants. Younger control subjects were also tested in order to provide information on normal age-related effects on these capacities. For each test, stimuli were presented briefly on a computer monitor, and participants indicated whether the display contained a circle or a square (fig. 1). Across trials, the level of visual noise progressively increased until participants could no longer discriminate the forms, defining the perceptual threshold. Perceptual measurements were made at four levels of form degradation (5, 25, 50, and 70% density of stimulus elements), each presented at three durations (16.7, 50.0, and 150.0 ms). Reaction time was not a factor, and participants were instructed to maximize the accuracy, and not the speed, of their response.

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Fig. 1. Examples of stimuli at near threshold signal-to-noise ratios: 5% pixel density of figure embedded in 0.5% pixel density background; 25% figure density in 12% background noise; 50% figure density in 30% background noise; 70% figure density in 50% background noise.

At the shortest stimulus duration, performance declined as a function of age. This effect was greatest for the most degraded form. However, AD patients had significantly elevated thresholds, relative to age-matched control participants, at all stimulus durations. Furthermore, thresholds followed a logarithmic function across form degradation, in that small amounts of noise disrupted discrimination of the most degraded forms, whereas participants tolerated comparatively high levels of noise with more intact forms. These differences across form degradation likely reflect different perceptual strategies used for discrimination. For the most degraded forms, in which relatively large distances separated individual elements, discrimination depended upon accurate visual synthesis of the forms. For more intact forms, visual synthesis likely played a lesser role, and discrimination was based upon texture differences. Nevertheless, the degree of impairment in AD did not differ across levels of degradation, indicating that in addition to disrupted visual synthesis, impaired texture discrimination also contributes to impaired perceptual organization. To better understand the characteristics of impairment in AD, an analysis was made of the characteristics of perceptual organization based upon spatial relationships. To accomplish this, separate tests were administered that examine different components of spatial relationships [19]. AD patients, elderly control

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c Fig. 2. Examples of stimulus types used in each of the five perceptual organization tests. For (a) Proximity test and (b) Alignment test, participants discriminated between horizontally or vertically grouped dots. For (c) Glass Patterns test, participants indicated whether the central tendency was towards the left or right. For (d) Large Shapes test and (e) Small Shapes test, participants indicated whether dots formed a square or a diamond.

participants, and young control participants received five tests of perceptual organization (Proximity, Alignment, Glass Patterns, Large Shapes, and Small Shapes) (fig. 2), as well as a standardized test of facial recognition (Benton Facial Recognition). For each trial, a stimulus appeared briefly on a monitor, and participants indicated which of two possible patterns were formed by the stimulus. Tests of Proximity and Alignment (Good Continuation) examined two basic Gestalt principles of perceptual grouping. For Glass Patterns, a systematic displacement of otherwise randomly distributed points elicits global organization of the pattern. For the tests of Large and Small Shapes, stimuli consisted of an array of elements that formed the fragmented shape of either a square or a diamond. Because greater spatial separation of elements requires integration from more distal locations, these two tests were used to determine whether greater impairment in perceptual organization occurs with increased spatial scale. Each series began with a strong cue for perceptual organization. Across trials, the cue was progressively diminished until participants selected the alternate grouping pattern. Psychophysical thresholds were determined by means of a descending method of limits. Each measurement was accompanied by a control

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condition that monitored participants’ ability to discriminate figures constructed of solid lines. Performance on the control conditions provided an assessment of their ability to understand the requirements of the tasks, to perceive and discriminate each pair of stimuli, and to respond appropriately. Impaired performance on experimental conditions was therefore attributed to perceptual deficits, and not other cognitive factors. Examining the effects of normal aging, scores for the elderly control group were significantly lower than those for the young control group for the tests of Alignment, Large Shapes, and Small Shapes, whereas young and elderly control participants did not differ significantly on the tests of Proximity and Glass Patterns (fig. 3). Discriminant analysis indicated that the relative importance for each test in discriminating subject group ranked, from greatest to least, from the test of Alignment, to Large Shapes, to Small Shapes, to Glass Patterns, to Proximity. AD patients showed a different pattern of impairment. With the exception of the Alignment test, in which performance greatly declined with age, AD patients were impaired on all tests relative to elderly control participants. In addition, contrary to the hypothesis, the degree of separation among stimulus elements did not exacerbate impairment. Discriminant analysis ranked the relative importance of each test, from greatest to least, from the test of Proximity, to Large Shapes, to Glass Patterns, to Small Shapes, to Alignment.

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For the AD group, whose members demonstrated mild to moderate dementia (as evaluated with the information, memory, and orientation section of the Blessed Dementia Scale, scores ranged from 4 to 21, with maximum possible score of 37), no test of perceptual organization, nor the Benton Facial Recognition test, correlated significantly with level of dementia. The level of functional decline therefore differed across test. Furthermore, the pattern of decline found for normal aging differed from that found for AD. These results demonstrate variance in the mechanisms by which spatial relationships are used to establish perceptual organization. Performance on the Benton Facial Recognition test did not differ significantly between young and elderly control participants, nor did any test of perceptual organization correlate with performance on the facial recognition test for the elderly control group. AD participants, however, performed at a level significantly below elderly control participants on Benton Facial Recognition. Subgroup analysis indicated that those AD participants who were impaired on facial recognition were also impaired on all tests of perceptual organization. Although the relationship between perceptual organization and object recognition in AD requires further investigation, these results demonstrate a link between these two levels of visual processing. In addition to spatial relationships, perceptual organization is based upon other regularities among stimulus characteristics. To a large degree, the processing of stimulus features throughout visual cortical areas is modular [20]. In this regard, separate strategies for organization are derived from distinct processing schemes, intrinsic to the feature upon which organization is based. The effectiveness of a stimulus feature to establish perceptual organization is therefore related to the integrity of its associated neural module. With the operation of parallel mechanisms, more intact systems can compensate for those that are degraded. It is therefore important to understand the relative effectiveness of stimulus features in establishing perceptual organization. To determine whether age-related changes in perceptual organization differ across stimulus features, our laboratory compared performance of young and elderly control participants on perceptual grouping based upon similarity of line orientation, color, flicker, and motion direction. Each of these features represents a fundamental component of visual processing [21]. In addition, processing of orientation and color, which represent static features, may be distinguished from that of flicker and motion, which represent dynamic features for which information is integrated over time. This distinction is reflected by organization in precortical channels [22], and to some degree in ventral and dorsal cortical streams [23, 24]. Participants received tests of perceptual organization that were based upon element similarity. In all cases, stimuli were composed of two types of elements

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Fig. 4. Example of stimuli in which the grouping cue is applied to the vertical orientation. Actual stimuli consisted of arrays of 20 ⫻ 20 elements that spanned 19.3⬚. Grouping is based upon (a) orientation, (b) color, (c) flicker and (d) motion. Color differences in b are depicted in shades of gray, whereas actual stimuli contained green and red elements. Flickering elements in c are depicted by radiating lines. Direction of motion in d are depicted by arrows, which were not present in actual stimuli.

that made up a 20 ⫻ 20 grid. Each column (vertical condition) or row (horizontal condition) contained elements of the same element type, alternating between types across columns or rows (fig. 4). For the Orientation condition, elements consisted of paired line segments oriented at 45⬚, forming either v or ⬎. For the Color condition, elements consisted of isoluminant red or green squares. For the Flicker condition, stimuli consisted of squares that either flickered or remained on. For the Motion condition, stimuli consisted of squares that moved along orthogonal directions. Psychophysical measurements determined the level of similarity necessary to establish organization, as well as the time required to complete processing. Participants viewed stimuli that were briefly presented on a computer monitor. One of two conditions (vertical or horizontal) was selected randomly by computer on each trial. Participants indicated whether the patterns appeared to be organized as a series of vertical or horizontal lines. Across trials, the level of similarity was progressively reduced until stimuli became ambiguous, thereby establishing the grouping threshold.

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Fig. 5. a Grouping thresholds (mean and standard error of the mean), in terms of percent similarity, across stimulus features. b Masking thresholds (mean and standard error of the mean) across stimulus features.

In a separate set of measurements, a backward pattern mask was used to determine the duration of processing necessary to establish perceptual organization. The test stimulus was followed by a pattern mask, which served to disrupt processing of the test stimulus. Mask onset times were progressively reduced across trials until the organization of the stimulus could no longer be determined. Under these conditions other stimulus characteristics, such as color or direction of motion, were identifiable, but perceptual organization fails to be achieved [25]. Masks were therefore not intended to eliminate detection of stimulus features, but rather to interfere with processes associated with perceptual organization. Furthermore, these measurements do not reflect reaction time, but instead correspond to perceptual processes. Relative to young participants, elderly participants had significantly elevated grouping thresholds on tests of line orientation and flicker, but not for color or motion (fig. 5). In this regard, elderly participants required a higher level of stimulus organization in order to accurately perceive grouping for these two stimulus features. In addition, elderly participants had significantly slower processing times for line orientation and flicker, but not for color or motion. Again, these two features were less effective for cueing grouping patterns. It is unlikely that age-related decreases in sensitivity accounted for diminished organizational capacities measured here. Limits of perceptual organization were associated with the pattern of similarity among elements and not with discrimination within a stimulus feature domain. In this regard, all participants were able to reliably organize stimuli at high levels of similarity, and thereby did not show evidence of impaired discrimination of element pairs. In addition,

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distinctions in perceptual organization abilities across stimulus features do not correspond to visual system organization, in which processing of features used here are distinguished at precortical and cortical levels. Functional degradation and preservation for elderly participants occurred within both systems, and significant correlations within systems did not occur. It therefore does not seem likely that deficits were related to processing component features, but instead were specific to subsequent grouping mechanisms. In addition, differences in perceptual organization abilities across stimulus features suggests that visual displays may be manipulated to optimize object recognition in elderly or clinical populations.

Neural Correlates

Perceptual organization represents a process in which the visual system constructs properties not directly contained within the stimulus. Elements that are not physically connected are integrated across spatial areas to form cohesive patterns. This is accomplished along a sequence of events: Component features are initially processed, regularities among elements are identified, and finally grouped components are integrated. Disruption of any of these processes by degenerative conditions will reduce the effectiveness of perceptual organization. Although the neural mediation of perceptual organization is not known, certain characteristics may be suggested. At early levels of visual processing, stimulus elements are represented by increased neural activity at specific cortical sites. The spatial location of elements is encoded retinotopically, such that adjacent positions in space are represented at adjacent positions on the cortex. In addition, stimulus features, such as color or motion direction, are represented at each location by a set of neurons optimally responsive to a specific stimulus. Separate stimulus elements are therefore represented by cortical point images (sites of activation) that are separated by areas of less activity. In the process of perceptual organization, neural mechanisms determine which sites of activation are associated, likely by means of neural algorithms that best identify components of unified forms. In the case of grouping by proximity, neural algorithms may be based upon relative separation among elements [26]. For grouping by similarity, algorithms are likely based upon activation of multiple units that code for common stimulus features. Once grouping assignment is determined, neural representations become integrated and treated as a unit. Integration may be mediated by several types of neural connections: horizontal (lateral) connections within and across cortical areas, reciprocal cortico-thalamic connections, or feedback connections

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from higher-order visual areas. Examination of neuropathological changes in clinically and neuropathologically confirmed AD patients has indicated that long corticocortical projections are particularly vulnerable [27, 28]. The regional and laminar plaque distribution found in these studies indicated that neuropathological changes in AD most greatly affect cross-cortical connections. The authors noted that the specific loss of this subset of neurons would reduce effectiveness of distributed processing within the cortex, which is likely reflected in reduced abilities on complex cognitive functions. Because cortical integration is a fundamental component of perceptual organization, this pattern of neural loss is likely to significantly degrade its operation. Perceptual organization of complex or ambiguous stimuli may be facilitated by top-down processing [3, 29]. Under these conditions, high-order visual areas, which contain larger receptive fields and perform more global analysis, are better suited to identify regularities among elements. Feedback from these areas can then impact primary processing, for example by modulating classical receptive field properties [30–32]. Relatively greater pathological changes at higher-order cortical areas in AD [33] may compromise this feedback influence, and thereby interfere with perceptual organization of complex visual scenes. Perceptual organization was found to be most impaired in AD when stimuli were presented for brief durations. Furthermore, perceptual organization based upon flicker and element orientation required longer processing time with aging. These results may reflect perceptual changes resulting from neural degeneration. For the visual system to compensate for distributed neural loss, surviving neurons may require additional processing time. In this model a trade-off exists between the number of units active in a system and the amount of time required to process information. In this context, natural viewing conditions that contain transient stimuli and changing conditions would significantly disrupt perceptual organizational processes, thereby degrading visual perception. Variability in perceptual organization in AD is similar to that found for other visual capacities, in which cases range from individuals who are free from any observable dysfunction, to those who manifest severe impairment [34]. Variability in impairment suggests differential patterns of cortical pathology [35, 36], specifically, whether neuropathological changes have invaded visual areas [37]. Consistent with this idea are reports in which patients who demonstrated clinical symptoms of either visuospatial function or object recognition were later identified as having significant neuropathology in posterior parietal or inferotemporal cortex, respectively [38, 39]. Similarly, visuospatial and object recognition deficits were reported to selectively relate to parietal and tempo-parietal metabolism in AD [40]. The relationship between specific characteristics of impairment and associated cortical processing areas may allow better characterization of the pattern of neuropathological changes for individual patients.

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Experimental analysis has indicated that perceptual organization is disrupted in a subgroup of AD patients, depending on stimulus characteristics and perceptual strategies employed. These analyses of perceptual organization enable a better understanding of the constituent components of visual impairment in AD, as well as its possible contribution to other cognitive impairments, such as highorder vision and memory. In addition, by identifying stimulus features and perceptual strategies most effective in establishing perceptual organization, characteristics of the visual environment may be designed to optimize object recognition, thereby improving the level of functioning and the care of patients. Acknowledgements This work was supported (in part) by a grant from The City University of New York PSC-CUNY Research Award Program, and NIH grant R15 AG13758.

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From Segmentation to Imagination: Testing the Integrity of the Ventral Visual Processing Pathway in Alzheimer’s Disease Lynette J. Tippett Department of Psychology, University of Auckland, Auckland, New Zealand

Increasing evidence indicates impairment in some aspects of visual processing in mild-to-moderate Alzheimer’s disease (AD) [1–9]. This evidence is based on AD performance on a wide variety of tasks, from those targeting early levels of visual perception (e.g., color vision, stereoacuity, contrast sensitivity) to tasks involving high-level vision [2, 3, 7] and object recognition [10–12]. Early studies reporting higher-level visual impairments typically measured AD performance on complex tasks involving multiple cognitive processes or components, one of which was visual. More recently the focus has moved towards identifying which specific components of visual processing are impaired in AD, which requires the use of tasks with a high degree of cognitive specificity. For example, a number of elegant studies have addressed the deficits underlying visuospatial disorientation in AD. Kavcic and Duffy [13] demonstrated temporal constraints on visual attention that might impair optic flow analysis (the visual motion seen during observer self-movement) and contribute to the spatial disorientation in AD [see Duffy et al., this volume]. Rizzo and his colleagues have investigated motion processing in AD and shown that although motion direction discrimination is relatively spared, the perception of structure from motion is impaired, which also has implications for navigation and orientation during motion, such as during walking and driving [e.g., 14]. Visual processing is a very broad term that includes not only the products of primary visual cortex and subsequent early levels of processing (such as color vision and stereoacuity), but also the visual processes associated with both the dorsal or parietal visual stream and the ventral or inferotemporal visual

pathways [15]. Visual processes predominantly associated with the ventral processing pathway are the focus of this chapter. I will provide an overview of one framework of intermediate and high-level visual perceptual and visual imagery processing, and examine whether AD, in its mild-to-moderate form, typically affects specific components of processing. As well as reviewing several studies conducted in the spirit of this approach, I will provide a summary of the performance of a group of mild-to-moderate AD patients on a set of tasks known to tap selectively specific stages of visual perceptual and visual imagery processing [16]. Studies that systematically isolate visual perceptual processing in AD patients have mostly focused on early levels of visual perception, such as color vision, stereoacuity, contrast sensitivity and susceptibility to backward masking [e.g., 1, 2, 4, 5, 9, 17]. Although certain impairments have been documented at these early levels, they do not necessarily imply that higher levels of perception will be impaired. People can be color blind, have poor acuity, or even lack stereovision, but have no trouble with higher levels of perception such as object recognition. Studies investigating intermediate and high-level visual processing in AD, however, have frequently not used tasks that isolate the stages of visual processing that can be interrupted by neurological disease and injury. For example, difficulty with confrontation naming is an early and characteristic sign of AD, and poor performance (particularly visual errors) has been used to infer a deficit in object recognition [e.g., 18, 19]. Of course, this task requires semantic and lexical processing as well as visual object recognition, and AD patients are more commonly thought to fail for this reason [e.g., 10, 11, 20–22]. AD patients are also impaired on several nonverbal tasks involving high-level vision, but here too the underlying cause of the impairment is ambiguous. For example, in some studies poor performance on the Picture Arrangement subtest of the Wechsler Adult Intelligence Scale-Revised (WAIS-R) has been noted, and the possibility that object recognition deficits are at least partially to blame is raised [e.g., 2, 3, 7]. Although the task does involve recognizing a number of line drawings, it also places significant demands on executive functions, including sequencing and abstraction. Visuospatial tasks such as Block Design (a subtest of the WAIS-R), and copying the Rey-Osterrieth Complex Figure are also sensitive to AD [e.g., 8, 23–25]. Such tasks are free of semantic content, yet they often involve a constructional element, and are also known to be sensitive to executive functions which again may themselves be compromised in AD [e.g., 26]. Of course the same criticisms cannot be leveled at the study of rare visual variants of AD [e.g., 27], in which visual processing has been studied in a detailed and specific way. We are concerned with visual processing in the typical or modal course of early AD.

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Stages of Visual Processing: Object and Face Perception, and Visual Imagery

Before noting the findings of the relatively small literature that has utilized an approach similar to the one I am advocating, a brief review of the different stages of visual processing will be presented. This will summarize what is known about the critical brain regions associated with each stage of processing and the nature of impairments corresponding to deficits of each stage. Study findings, including our own, will then be discussed in the light of this framework. Visual stimuli are normally encoded at multiple levels within the visual system, and neurological damage may interrupt this process at any stage. For another discussion of visuoperceptual organization in AD, see Kurylo [this volume]. Image Segmentation After the initial registration of local features of the visual field including color, contour, depth, motion and so forth, the image must be segmented. In other words, locations in the visual field that are likely to belong to a single object must be grouped together, and segmented away from locations likely to belong to other objects. Image segmentation has been dissociated from the perception of local visual features in the syndrome of apperceptive agnosia. While apperceptive agnosics are able to perceive local features of the visual field such as brightness, color, motion, and line orientation, they cannot recognize even the most basic of shapes, nor letters, objects, or faces [e.g., 28–30]. The neural substrates of image segmentation are difficult to deduce from apperceptive agnosia as it is almost always the result of carbon monoxide poisoning or anoxia, which result in diffuse structural brain damage. Single cell recordings in monkeys, however, indicate that neurons crucial to the segmentation process appear as early as V2 and certainly in V4. For example, there are neurons in V2 that respond to illusory contours [e.g., 31] and cells in V4 with large, silent surrounds adjacent to the excitatory or classical receptive fields that respond maximally only when the stimulus stands out from its background on the basis of a difference in features such as form, wavelength, and spatial frequency [32]. Shape Constancy A segmented image is a kind of shape representation in that it describes the shapes of regions in the picture plane, but it lacks the various constancies that enable objects to be recognized over changes in size, location or orientation. More abstract shape representations do possess these constancies. Shape constancy appears to be at least partially dissociable from other visual abilities, as demonstrated by both the relatively mild disorder of ‘perceptual categorization deficit’ in which the visual impairment is confined to recognizing unusual

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views [33], and by the more pervasive problem with high-level shape representation found in associative visual agnosia. Individuals with the latter impairment have difficulty recognizing visually presented objects seen from any perspective, although their difficulties are amplified with unusual views (see Farah [34], chapter 4, for a review). Perceptual categorization deficit is not highly localized, although it is more common after right hemisphere lesions [33]. Associative agnosia, on the other hand, is strongly linked to lesions in inferotemporal cortex. Inferotemporal lesions also cause a loss of object constancy in experimental animals [35]. Converging evidence for this localization comes from single-cell studies of monkeys [36] and functional imaging studies in humans [37]. There is evidence that the necessary neural representations for object and face constancy are distinct (and thus that they should be tested separately). Some patients with associative visual agnosia have particular difficulty recognizing faces [e.g., 38, 39], while others have object agnosia but not face agnosia [e.g., 40, 41]. Recent neuroimaging studies provide additional evidence that the representations of shape for common objects and faces are distinct. In general, face-specific regions appear to be located either bilaterally or in the right fusiform gyrus whereas object-specific regions may be located in the left fusiform gyrus [e.g., 40, 42–44]. Visual Object Memory Object recognition requires more than just visual representations that are invariant over changes in perspective; stored knowledge of the shapes of previously seen stimuli must also be present. The neural substrate of this ability, as inferred from studies of patients, appears to be the same as for the high-level shape representations assessed by the shape constancy tasks, although some investigators suggest the possibility of distinct substrates [45]. Lexical Access from Visual Representations To name a visually-presented object, the stored high-level shape representation must activate the appropriate semantic representation of the object, enabling the activation of the lexical and phonological representation associated with the object. If there is a primary impairment in semantic knowledge, naming and recognition of objects is affected because the impoverished semantic representation is inadequate to activate the correct name, or precise identity, of the object. This is the most widely held view of the confrontation naming deficit frequently seen in AD [e.g., 46]. Although the neural organization and representation of semantic knowledge is still widely debated [e.g., 47], there is significant evidence (including neuroimaging studies) supporting the view that representations are widely distributed across the cortex in systems that respond to the particular kinds of information relevant to particular semantic concepts [e.g., 48–50].

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Visual Imagery Semantic memory knowledge may be used to reinstantiate earlier visual representations in a process known as mental image generation, which is used exclusively or disproportionately during the generation of images relative to normal stimulus-driven perception [51]. Measurement of image generation involves tasks that have appropriate control conditions to assess additionally the contribution of perceptual processes and also semantic knowledge when relevant. The selective impairment of visual image generation in a number of neurological patients with grossly intact visual perception and semantic knowledge indicates that this process is dissociable from other visual abilities [see 51 for a review]. Lesion studies and imaging studies that have used validated imagery tasks indicate that the most likely neural substrate underlying image generation is the posterior left hemisphere, particularly the left temporo-occipital region [e.g., 52–57]. However, lesions in this area do not invariably result in obvious image-generation impairments. Indeed, selective loss of image-generation ability is very rare, which, as Farah [51] suggests, most likely indicates that the left hemisphere is specialized for image generation but that the degree of specialization varies within the population with most individuals having some capability for generation in the right hemisphere also. What might be predicted about the performance of AD patients on a series of visual tasks sensitive to the different components of visual processing, given the regions of brain that appear to be crucial? In the earliest stages of AD when the primary deficits are of new learning and memory, the primary sites of pathology typically involve medial temporal regions. More specifically, neurofibrillary tangles (NFTs) are found in layers III and V of the entorhinal cortex first, followed sometime later by the hippocampus and adjoining subiculum [e.g., 58, 59]. As the disease advances, the density of senile plaques which may be present in layer III throughout the neocortex increases, and NFTs may also become present in multimodal association areas followed by unimodal association areas, and to a lesser extent in primary cortical areas [60]. Within this general pattern, the precise progress of the neuropathology and neuropsychological dysfunction is difficult to predict in an individual [see Cronin-Golomb, this volume]. The seemingly random accentuation of this generalized process may result in variable neuroanatomical and neurophysiological changes from person to person [61]. In addition, the locations of the characteristic plaques and tangles of AD may not be the only regions functioning abnormally. Degraded inputs from damaged areas, and the widespread changes in neurochemical functioning (e.g., the cholinergic system [62]) contribute to more widespread cortical dysfunction. Given this complex pattern of AD progression, predictions are limited. In the very earliest stages of AD, I would not predict changes in the visual

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perceptual and visual imagery processes outlined above since these, for the most part, involve inferior regions of the temporal and occipital cortices rather than the paralimbic cortex in the temporal lobes. However, by the time many individuals are clinically diagnosed with probable AD, the pathology is likely to involve considerably more cortex and generalizations about the affected regions become harder to make. If the pathology progresses outwards from medial temporal regions, it would be reasonable to predict impairments in higher levels of visual processing and visual imagery earlier in the course of the disease than impairments at lower-level stages of visual processing (such as image segmentation).

Evidence for Visual Object and Face Processing Deficits in AD

A number of studies have focused upon differentiating performance of AD patients on tasks sensitive to the dorsal visual-processing stream and the ventral visual-processing stream [e.g., 15]. For example, Kurylo et al. [7] selected four clinical neuropsychological tasks associated primarily with visual processing for object recognition (Mooney Closure Faces, Face Recognition Test, WAIS-R Picture Arrangement, Discrimination of Complex Pictures) and four associated primarily with visual processing for spatial perception (Mental Rotation task, Money-Road-Map Test, Stick Test, Discrimination of Spatial Position test), and administered them to a sample of mild-to-moderately impaired AD patients. They found impaired performance on all tasks, significantly more pronounced for the visual object perception tasks, which they argued suggests differential degradation of object, relative to spatial, vision. Consistent with this conclusion, Cronin-Golomb et al. [3] found that low-level visual dysfunction predicted cognitive deficits on tasks of object recognition, but not spatial localization. However, a finer-grained characterization of impairment within the visual object perception system was not possible in either study, given the tasks used or on the basis of the results. Higher-level visual and spatial perception in patients with mild AD was also assessed by Binetti et al. [63, 64] using the Visual Object and Space Perception (VOSP) battery [65]. The tasks in this battery are intended to tap different stages of object and space perception in a relatively selective way (although the authors of the VOSP did not make specific claims about the sensitivity of individual tasks to the integrity of specific neural regions, or specific visual disorders). The selection of this battery indicates the intent to identify which stages of visual processing are impaired in AD. In contrast to Kurylo et al. [7], these researchers found a slight impairment in spatial functioning when

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scores from all four spatial tasks were combined, but no impairment over the four object perception tasks. It is possible that this null result simply reflects the very early stage of the disease in their patients. It is also possible that a different and larger set of tasks, that distinguished different stages of visual perception with more specificity, would increase the sensitivity of assessments of object perceptual processes. Caine and Hodges [66] reported two studies that sought to investigate the heterogeneity of cognitive deficits in early or mild AD. They were particularly interested in knowing whether deficits in visuospatial processing could occur prior to semantic deficits, and if so, what proportion of patients might show such a pattern. Caine and Hodges also noted the poor task selection in earlier studies on this question, with most testing visual processing with tests that tax several cognitive skills, such as planning and praxis, in addition to visuoperceptual and visuospatial abilities. In two studies they identified a small number of individuals with visual processing problems. In study 1, 5 of 26 patients had marked visual impairment (assessed by the Judgement of Line Orientation Test, an unusual views object-matching test and Object Decision test). Three of these individuals had, in addition to episodic memory deficits, relatively pure visual problems without semantic problems. In their second study, they used three visuospatial tasks from the VOSP [65], and found 3 of 21 AD patients had visual deficits. The tasks used by Caine and Hodges have a relatively high degree of cognitive specificity, but the components of visual processing investigated within each study are relatively narrow in scope. Giannakopoulos et al. [67] reported a study focused upon the functional and pathological integrity of the ventral processing stream in AD. They administered several tests involving visual shape and object recognition to patients with relatively advanced AD, and correlated their performance with the types and distributions of neuropathological changes found at autopsy. For present purposes, their results demonstrate that visual perception may be severely compromised at later stages of the disease. However, we cannot infer whether these deficits began to emerge at an earlier stage, before the more global effects of the disease were manifest. In addition, the selection of tasks was guided by Lissauer’s distinction between apperceptive and associative agnosia, and combined a number of levels and types of visual processing, in some cases involving semantic knowledge as well. Thus a fine-grained analysis of the stages of visual processing compromised is not possible. In summary, identification of the underlying nature of intermediate and high-level visual perceptual processing difficulties in typical AD has been limited, but recently the design of studies and selection of tasks used by researchers has increasingly reflected the application of current knowledge about the architecture of visual processing from cognitive neuroscience.

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Recently, with colleagues Kirsty Blackwood and Martha Farah, I reported a study that sought to utilize this body of knowledge directly by administering to AD patients and control participants a set of tasks that have been shown to tap different stages of intermediate and higher levels of visual perception and visual imagery with reasonable specificity [16]. This set consisted of seven visual perception tasks and four mental imagery tasks all drawn from the cognitive neuropsychology literature. Each of these tasks has been shown to be sensitive to perceptual impairments in neurological patients, and do not place significant demands on constructional ability, executive functions, or (with one intentional exception) lexical-semantic processing. The unique aspect of this study is that it assessed performance on each of the levels of visual object and face processing and mental imagery in each participant. We were accordingly able to analyze performance at both the group and individual level, as well as look at the impact of performance impairments on tasks assessing the intermediate stages of visual processing on later stages of processing. This was also the first systematic investigation of visual imagery abilities in AD. A total of 16 AD patients (with a diagnosis of probable AD according to NINCDS-ADRDA criteria [68]) and 19 healthy elderly volunteers completed the study. Standard exclusion criteria were applied to both groups, but in addition all were required to have a Snellen acuity better than 20/40, either normally or corrected. Notably 5 AD volunteers were excluded from the study for failing this acuity requirement and 13 healthy volunteers were excluded because they were found to have basic vision problems (7 had cataracts, 3 had useful vision in only one eye, and 3 had best corrected acuity of ⬍20/40). In addition, control participants were required to have an MMSE score of 24/30 or higher. A summary of the demographic characteristics of these two groups is provided in table 1. They did not differ on any of these variables except the expected difference on the MMSE [69]. There was no difference between the two groups with regard to visual acuity. The novel feature of this study was the selection of tasks, each of which was completed by all participants. We included seven visual perceptual tasks that assessed various stages of visual processing as outlined above, up to and including the access of semantic and lexical information from vision, and four visual imagery tasks with their appropriate control conditions. To illustrate the underlying rationale I will provide a brief description of the tasks we used and their relation to the different stages of visual processing. First we identified two tasks in the neuropsychological literature that tax image segmentation in particular: The Shape Detection task from the VOSP battery [65] and the Shape Perception task described by Efron [29]. The Shape Detection task, comprising 20 trials, involves discrimination of the presence, or absence, of a region within a noisy image that corresponds to a complete shape,

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Table 1. Demographic characteristics of AD and control group participants Characteristic

Control participants mean (SD)

AD participants mean (SD)

Gender, M:F

9:10

9:7

Age, years Range

74.0 (5.4) 63–83

73.1 (7.4) 59–84

Years of education Range

12.4 (2.2) 9–18

11.8 (2.9) 8–20

MMSE score Range

28.6 (1.3) 26–30

18.63 (4.2) 10–25

namely an X. The simple Shape Perception task was based on Efron’s shape discrimination task, developed originally for examining the shape perception of an apperceptive agnosic patient. We required participants to perform samedifferent judgements of pairs of shapes, either two identical squares, or a square paired with one of 10 rectangles of different aspect ratios but equivalent area and luminosity. The ratio of the sides of the 10 rectangles were: 8.05:1; 6.56:1; 2.78:1; 1.96:1; 1.68:1; 1.22:1; 1.08:1; 1.06:1; 1.04:1; 1.02:1. Participants were told that half of the pairs would be the same and half different. We used an adaptation of a staircase procedure to determine the threshold at which a participant could reliably perceive a difference between shapes. Beginning at the easiest level of discrimination, ‘same’ and ‘different’ pairs of shapes were presented randomly, and following two sequential correct responses on ‘different’ trials the task proceeded to the next level of discrimination difficulty. If an error was made on a ‘different’ trial, the participant was returned to the previous level of difficulty until the criterion to move (in either direction) was met. The task ended following ten ‘turn-arounds’, which are changes in direction of movement either upwards (more difficult discriminations) or downwards (easier discriminations). The level at which a participant reliably discriminated the difference between the shapes was determined by calculating the geometric mean of the ratio of the sides of the rectangles corresponding to last eight turn-around points. Responses on ‘same’ trials (i.e., both squares) were recorded so that it was possible to exclude a participant who simply adopted a strategy of saying ‘different’ to all or most stimulus pairs, which would invalidate performance on the task. To assess shape constancy for common objects two types of so-called ‘unusual views’ tests were used from the Birmingham Object Recognition Battery (BORB) [70]. In both, participants had to match two different views of

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Fig. 1. Shape constancy: common objects – example of an item on the Unusual Views task – Foreshortened match, of the Birmingham Object Recognition Battery, p. 135 [70]. The trunk of the target item, the elephant, is visible but the overall shape of the animal body has been distorted by rotation. Reproduced with the permission of Psychology Press.

an object which differ most dramatically either in the spatial properties of the two images or in the features visible in the two images. In one task, shape constancy for common objects is tested when the overall shape is distorted by rotating the object and producing extreme foreshortening of a major axis (fig. 1). In the second task, shape constancy is tested when the main identifying feature of an object (e.g. an elephant’s trunk) is obscured by a slight rotation, with relatively little overall shape distortion (fig. 2). A third test, the Benton Facial Recognition Test, was selected to assess the ability to derive a constant representation of the human face from varying views [71]. The task involves matching a target face to a face, or faces, within an array of six photographs. The array may contain identical

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Fig. 2. Shape constancy: common objects – examples of an item on the Unusual Views task – Minimal feature match, of the Birmingham Object Recognition Battery, p. 170 [70]. The trunk of the target item, the elephant, has been obscured by rotation (bottom right). Reproduced with the permission of Psychology Press.

views of faces, or in two conditions that test shape constancy, faces photographed from different angles or under different lighting conditions. Object recognition also requires stored knowledge of the shapes of previously seen stimuli. To assess the integrity of stored shape representations we used the Object Decision task of the BORB [70]. In this task participants were required to distinguish which of 64 items were drawings of real objects and animals, and which were drawings of made-up items. The made-up items consisted of stimuli made up by interchanging parts from two real objects (e.g., a donkey’s head on a chicken’s body) as can be seen in figure 3. This task requires the use of stored object shape representations, but is thought to not require

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Fig. 3. Stored shape representations: two sample stimuli from the Object Decision task of the Birmingham Object Recognition Battery, pp. 199 and 168 [70]. On the left is an example of a ‘not-real’ animal, made-up by putting a donkey’s head on a chicken’s body. On the right is an example of a ‘real’ animal, a chicken. Reproduced with the permission of Psychology Press.

semantic or lexical information, and is typically failed by associative visual agnosic patients [e.g., 45]. To assess object recognition and the ability to access the correct lexical label from visual representations (presumably via the activation of semantic representations), we required participants to name 85 black-and-white line drawings. These were derived by Funnell and Sheridan [72] from the original corpus of 260 line drawings of Snodgrass and Vanderwart [73]. We expected to find an impairment on this confrontation naming task, and thus replicate the findings of many previous studies. Visual imagery was assessed with four tasks from the cognitive neuroscience literature that have been shown to be sensitive to visual imagegeneration impairments. Each task required the recall of visual attributes of known objects in the absence of a visual stimulus, and had control conditions to assess the contribution of perceptual processes and semantic knowledge when relevant. Little is known about the fate of mental imagery in mild-tomoderate AD. As a cognitive ability at the interface of vision and semantics, involving the use of information stored in memory to generate visual representations, it might well suffer an early decline in the course of the disease. The first task assessed color imagery. Participants were required to visualize the color of each of 24 black-and-white line drawings of objects and to select this from one of three colored pencils [54]. This task did not require either naming items or accessing a semantic representation via a verbal input or label. In the perceptual control condition, participants selected which of three colored copies of each picture (using the three color choices from the imagery condition) was most accurate. This required discriminating among the pictures and recognizing which looked more realistic. We also used the taller-wider task from the work of Kosslyn et al. [74]. In the imagery condition, 22 object names were read out one at a time. The task was to think of what each object looks like, and decide whether it is taller than it is wide or wider than it is tall. Pictures of the same 22 objects were presented in the perceptual control condition, and this time participants were required to

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name the object and make the taller/wider classification using their perceptual judgment of the picture. In a letter-shape task (used by Farah et al. [75] and originally by Coltheart et al. [76]) participants had to visualize the upper case version of 22 letters read out loud one at a time, and decide whether each contained only straight lines (e.g., H, A), or whether it contained some curves (e.g., G, S). In the perceptual version of the task, 22 written uppercase letters were presented randomly on a page, and when each letter was read out, participants first pointed to the named letter and then made the straight-curved judgments based on the appearance of the item. Finally we used the animal tails task developed by Farah et al. [54] when studying case R.M. Participants were asked to generate images of each of 28 animals from memory and decide whether the animals have long or short tails. They were cued with both the names of the animals and with pictures in which the tail region was blocked out. There were two control conditions, a perceptual control in which participants made the long-short tail judgment when looking at a complete picture of each animal, and a semantic knowledge condition which required judging whether the same animals are natives of New Zealand or non-natives of New Zealand. In other words, it involves an assessment of one aspect of nonimaginal, semantic knowledge. Performance on Visual Perceptual Tasks Performance of the AD and control participants was analyzed on the seven perceptual tasks using first a multivariate analysis of variance, which was significant, then a series of univariate analyses. We found that our AD group performed at significantly lower levels than the control participants on five tasks. These were Efron Shapes, Unusual Views – Minimal Feature (MF), Object Decision, Face Recognition and Naming (table 2). This poor performance did not simply reflect level of dementia severity of the AD participants, as there were no significant correlations between performance on any of the seven tests and MMSE scores. One participant in the AD group had an MMSE score of 10/30, which might be considered to reflect severe dementia, yet she successfully completed all of the tasks administered, and did not perform consistently the worst in the group. Nevertheless, to ensure that the overall group differences did not simply reflect the inclusion of someone with this level of dementia severity, we reconducted the analyses without this individual but found no changes in the results. The relatively poorer performance of the AD group on the Efron shapes task suggests that they have an impairment of simple shape perception, although the size of the difference between the two groups indicates that the deficit is only mild. Note that performance on this task, which provided an estimated

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Table 2. Mean test performance of AD and control groups on visual perceptual tasks Test name

Image segmentation Shape detection Range Possible score 0–20 Shape perception Efron shapes (threshold) Range Possible score 1.02–8.05 Shape constancy Common objects Unusual views: Minimal feature Range Possible score 0–25 Unusual views: Foreshortened Range Possible score 0–25 Shape constancy Faces Facial recognition Range Possible score 0–58 Stored shape representations Object decision Range Possible score 0–64 Object recognition and naming Range Possible score 0–85

AD group n ⫽ 16

Control group n ⫽ 19

F statistic (df)

p value

18.9 (1.2) 16–20

19.4 (0.8) 18–20

2.81 (1,33)

0.103

1.116 (0.004) 1.04–1.19

1.069 (0.005) 1.02–1.15

7.70 (1,33)

0.009

24.1 (1.2) 22–25

24.8 (0.5) 23–25

5.81 (1,33)

0.022

23.4 (2.1) 19–25

24.2 (1.0) 22–25

2.09 (1,33)

0.158

41.9 (5.5) 31–51

48.5 (3.6) 41–54

18.15 (1,33)

0.000

52.5 (4.7) 42–60

56.9 (4.0) 47–61

5.54 (1,33)

0.025

66.1 (18.4) 5–83

81.1 (3.6) 71–85

12.18 (1,33)

0.001

threshold of the ability to detect a difference between two similar shapes, would be falsely enhanced by using the strategy of replying ‘different’ to all stimuli. However, this was not the case as the accuracy of both groups on ‘same’ trials was high and comparable: AD group mean ⫽ 83.0%, SD ⫽ 13.4; control group mean ⫽ 85.7%, SD ⫽ 12.8. The ability to segment locations likely to belong to a single object or shape is essential to support a normal level of object recognition. It is possible that a

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deficit in this intermediate level visual process might be driving the deficits evident on the higher-order perceptual tasks including object recognition and naming in the AD group. As all participants had completed all tasks assessing each stage of processing, we were able to test this possibility by reanalyzing performance on the visual perceptual tests covarying out performance on the Efron shapes task. Interestingly, AD participants then performed at significantly lower levels than the control participants on only two of the six tasks, Face Recognition and Naming, but no longer on the Unusual Views task (MF), or Object Decision. Performance on Visual Imagery Tasks To assess imagery reliably, participants’ performance on imagery versions of each task must be considered relative to their performance on perceptualcontrol conditions, and where possible relative to semantic-control conditions. In summary, compared to elderly control participants, on two of the four imagery tasks (taller-wider and animal tails) AD participants were differentially impaired on the imagery conditions relative to a perceptual control condition. For animal tails this differential imagery impairment was also found relative to a semantic control condition. There was a nonsignificant trend in the same direction on a third task (letter shape), but on the color imagery task the relative difference in performance on the imagery and perceptual conditions by AD participants was not different from that of control participants (table 3). One possible explanation for this conflicting pattern of results might lie in the relative demands on semantic processing for completing the imagery and perceptual conditions of the three tasks. On the two tasks in which the AD group was differentially impaired on the imagery condition, the imagery component required access to semantic knowledge and the perceptual task did not. For example, one would need relatively intact semantic knowledge of each verbally-presented item (e.g., a saxophone) to know whether it was taller than it was wide, but would not need any such knowledge to make the judgment when presented with a picture of that item. A similar argument could be made regarding performance on the animal tails task, although on this task AD participants were also differentially impaired on the imagery condition relative to a semantic control condition. Even so, it could be argued that the semantic condition (a yes-no question) placed relatively fewer demands on semantic knowledge than the imagery condition. In contrast, on the color imagery task, the imagery and perceptual conditions may tax semantic knowledge to similar levels. For example, one would need to know that the inside of a watermelon is pink in order to pick out that color in the imagery condition, and also to identify the correctly colored watermelon in the perceptual condition. If this account is correct, the performance of the AD group may not reflect a deficit in visual imagery at all,

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Table 3. Mean test performance of AD and control groups on visual imagery tasks Task name

AD group n ⫽ 16

Control group n ⫽ 19

Color imagery Possible scores: 0–24 Perception Range Imagery Range Group ⫻ (AD; Control)

20.8 (2.3) 15–24 21.5 (2.3) 16–24 Condition (Perception; Imagery)

23.6 (0.6) 22–24 23.7 (0.6) 22–24

Taller-wider Possible score 0–22 Perception Range Imagery Range Group ⫻ (AD; Control)

21.3 (1.1) 18–22 18.8 (2.6) 11–22 Condition (Perception; Imagery)

21.8 (0.4) 21–22 21.6 (0.6) 20–22

Letter task Possible score 0–22 Perception Range Imagery Range Group ⫻ (AD; Control)

21.8 (0.8) 19–22 20.9 (1.8) 17–22 Condition (Perception; Imagery)

22.0 (0) 22 21.8 (0.4) 21–22

26.4 (1.4) 23–28 24.9 (1.9) 21–28 22.1 (2.7) 18–26 Condition (Perception; Semantic; Imagery)

27.8 (0.4) 27–28 27.1 (1.0) 25–28 26.2 (1.3) 24–28

Animal tails Possible score 0–28 Perception Range Semantic Range Imagery Range Group ⫻ (AD; Control)

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F statistic (df)

p value

1.51 (1,33)

0.226

12.79 (1,33)

0.001

3.85 (1,33)

0.058

8.34 (2,66)

0.001

227

but rather the relative importance of semantic knowledge in the imagery and perceptual control conditions. We did not administer a pure measure of semantic processing, but the most common interpretation of poor performance on confrontation naming in AD patients is semantic impairment. When we re-analyzed performance on the imagery tasks covarying out the effects of naming on performance, we found that the AD group did not perform differentially more poorly on the imagery condition relative to the perceptual condition of any of the four tasks. That is, when the level of semantic performance is covaried out of the imagery and perceptual conditions on each of the four imagery tasks, AD participants do not show evidence of an imagery impairment. Performance of Individual Cases One benefit of assessing performance on multiple stages of visual processing in individual members of the AD group was that we were able to examine whether there were any patterns of performance consistent with those of lesion patients with documented impairments of visual object processing. Apperceptive agnosics typically perform very poorly on tasks requiring image segmentation, basic shape processing and all higher levels of visual processing, unless provided with cues to object identity by local features (i.e., color, motion, contour, depth etc). In our study, an AD patient with an apperceptive-like impairment should perform poorly on all seven visual perceptual tasks. In contrast, associative agnosics are able to segment images and have no difficulties with basic shape processing (e.g., making same-different judgments of pairs of simple shapes). Their problems lie with high-level shape representation and may also affect their ability to process object shapes over changes in size, location or orientation. In our study, an individual with an associative-like impairment should have difficulty on the object-decision task and the picture-naming task. They may also have trouble on any, or all, of the three tests of object and face constancy. We found 1 AD participant with a pattern of performance grossly consistent with that of apperceptive agnosia and 1 whose pattern of performance was grossly consistent with that of an associative agnosic. The case resembling apperceptive agnosia was a 74-year-old man with an MMSE score of 20/30. He performed more than 2 standard deviations (SDs) below the control group mean, and below the entire range of the control group, on six of the seven visual perceptual tasks and more than 1.64 SDs below the control mean on the seventh task (object decision). This pattern of performance had a probability of occurrence of 1.6 ⫻ 10⫺8, and given his relatively good MMSE score cannot be accounted for simply by his level of dementia severity. However, his level of impairment on most of these tasks can only be described as mild. For example, on Shape Detection he scored 16/20, with 18/20 the lower end of the control

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range and 10/20 chance performance; on Unusual Views-MF, he scored 22/25 with a score of 23 constituting the lower end of the control range. In fact, on all tasks bar Face Recognition and Object naming, this AD case performed well above chance levels of performance and only slightly below the normal control range. On Face Recognition, however, he scored 31/54 (lower end of control range was 41), and on the object naming task, he scored 61/85 (lower end of control range was 71). Thus, like the general pattern found in this AD group, substantial impairments were found only on the two tasks taxing processes other than visual shape and object processing, namely face processing and the naming task, which also has a lexical-semantic component. The AD participant with a pattern of performance grossly consistent with that of associative visual agnosia was a 76-year-old woman with an MMSE score of 20/30. She performed comfortably within the normal control range on the Shape Detection task (image segmentation) and basic shape-processing task (Efron shapes), but her scores fell more than 2 SDs below the control mean (and outside of the control range) on the three tests of shape constancy, the objectdecision task, and the picture-naming task. Once again, her relatively good MMSE score indicates that her pattern of performance cannot be explained purely in terms of her level of dementia. As with the apperceptive-like case, however, her level of impairment was generally mild (scores of 22/25 and 21/25 on the Unusual Views-MF and Unusual Views-FS respectively, and 42/64 on Object Decision with the lower end of control range 47/64), with somewhat poorer scores on Facial Recognition, 32/54 and Object Naming (62/85). Finally, one can ask whether any individual had a pattern of performance consistent with a selective impairment in visual image generation, namely disproportionately poor performance on the imagery condition of the four tasks relative to performance on the perceptual and semantic conditions. No AD participant showed this pattern of performance. The individual who had the most difficulty (i.e., a discrepancy between the perception and imagery conditions outside the control range on three of the tasks) was the individual whose pattern of performance was grossly consistent with that of apperceptive agnosia. This individual was showing a mild visual impairment that encompassed processes from image segmentation through to visual image generation.

Discussion

What do these findings indicate about the status of visual perception in mild-to-moderate AD? We utilized tasks with a high degree of cognitive specificity, that is, a battery of visual tasks designed to isolate, as effectively as possible, different stages in visual processing, from image segmentation to the

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generation of visual mental images. AD patients were impaired relative to normal control participants on five of the seven visual perceptual tests, and this impairment spanned all levels of visual processing. However, when we removed the effects of the relatively low-level deficit of basic shape perception from performance on the higher-level visual tasks, we found that the AD group was impaired relative to the normal control group only on face constancy and object naming. In other words, our data are consistent with the view that weakened or distorted visual inputs from basic shape processing areas (possibly V4 and even V2) to visual regions further downstream lead to further inaccuracies. However, these distortions of basic shape processing fell short of accounting for the impairments of AD patients on the face constancy task and object naming. Given the considerable clinical and neuroimaging evidence that face processing relies on neural representations that are relatively distinct from those used for object processing [e.g., 38, 40, 41, 43, 44], it is hardly surprising that deficits of very basic shape perception did not influence performance on the face task. Notably, the visual impairments described so far were small and although the differences in performance between the AD and control groups were statistically significant, their clinical significance should not be overstated. This can be appreciated by comparing the subtle impairments on four of the visual perceptual tasks to the pronounced impairment on object naming, the one task that required lexical-semantic processing in addition to vision. As the mild deficits in visual processing (shape and face processing) did not contribute significantly to performance on object naming, our data are consistent with the view that semantic impairment is the major deficit underlying poor object recognition and confrontation naming in mild-to-moderate AD. Finally, the AD participants in this study were not impaired at generating visual images once the influence of semantic impairment on task performance was removed. Although the presence of a slight impairment across several visual tasks suggests that performance may reflect the effects of a nonspecific factor resulting from reduced attentional and executive resources that patients with AD can bring to bear on those tasks, this was unlikely. There was no relation between visual task performance and overall mental status, as measured by the MMSE, and thus the data are more consistent with small, but genuine visual impairments of two types, basic shape perception and face processing. We were unable to investigate any relation between visual impairments in our AD group and neural damage or dysfunction, as we had no neuropathological or functional neuroimaging data on our cases. With respect to this issue, however, we were somewhat surprised that there was no coarse relation between dementia severity (as assessed by the MMSE) and performance on the visual tasks, as it is reasonable to assume that increasing dementia severity is accompanied by progression of AD pathology from multimodal to unimodal cortices.

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One might expect that as the disease advances, neocortical regions underlying high-level vision would become compromised, followed later in the disease course by involvement of unimodal cortex underlying more basic visual processes. However, our findings generally did not support this broad prediction (although it is possible this reflects the limitations of the MMSE as a measure of dementia severity), nor did we find greater impairments on tasks measuring higher-level visual processing (such as object decision or visual image generation) than intermediate-level visual processing (such as Efron shapes). The two participants in our AD sample who showed profiles of impairment across tasks corresponding to the two different forms of agnosia are of particular interest. Although the degree of their impairment was considerably milder than is found in visual agnosic patients, one might nevertheless predict that these two cases in particular would show neural dysfunction in relevant parts of the visual processing pathway. The associative-like case would be expected to show dysfunction in inferior temporal cortex. The apperceptive-like case would be expected to show bilateral dysfunction perhaps as early as fusiform gyrus and even lingual gyrus. Whether or not they would additionally show marked resting dysfunction of inferior temporal regions (as would be expected from the view of AD pathology progressing from multimodal to more unimodal regions) is unknown. Indeed, whether cases of typical AD who show a marked pattern of visual impairment (albeit mild) are part of a continuous spectrum with the rare visual variant cases who have primary visual problems and marked posterior cortical atrophy [see 27 for a review], has yet to be investigated [see von Gunten et al., this volume, and Mendez, this volume].

Summary and Conclusion

Our study indicated that, to the extent that AD patients are impaired at visual perception, their impairment is a subtle one, not only in comparison to other cognitive impairments in AD, but also in comparison to the perceptual impairments that have been found with these tasks in patients with focal lesions [e.g., 28, 29, 33, 45, 54, 74]. Nevertheless, when analyzed into component parts and carefully measured, some visual abilities do show deficiencies (basic shape perception and face processing). Although these abilities are only mildly affected, they are important in that they are derived from tasks with a high degree of cognitive specificity. In this sense, they add to the early literature detailing impaired contrast sensitivity with disproportionate impairment at low spatial frequencies in AD [see 4, 77], because these studies also used tasks that allowed a specific inference to an aspect of impaired perceptual function. Similarly, our findings complement recent studies that have focused upon

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a specific component of the visual system, using tasks with a high degree of cognitive specificity. The assessment of performance of AD patients at consecutive stages of visual processing in conjunction with measurement of functional integrity of cortical regions either at rest or during task performance will potentially reveal the regions and patterns of neural dysfunction underlying these deficits. Of particular interest will be elucidating the pattern of neural dysfunction in individuals with conventional memory loss plus an apperceptive-like, or associative-like, pattern of agnosic performance on tasks of visual processing. Will these cases form part of a continuous spectrum with visual variants of AD, or will the neural patterns of change reflect more closely those found in typical AD? This general approach of measuring consecutive stages of visual processing in AD will also enable investigation of whether or not there is a consistent relation between stage of AD progression and type and level of visual impairment.

Acknowledgements This research was supported by NIH grant R01-AG14082. I thank my colleagues Kirsty Blackwood and Prof. Martha Farah for their contribution to research discussed in this chapter, and also Dr. Phil Wood of the Memory Clinic at North Shore Hospital, New Zealand, for his assistance. I am grateful to all individuals who kindly volunteered to participate in the research and generously gave their time.

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L.J. Tippett Department of Psychology, University of Auckland Private Bag 92019, Auckland (New Zealand) Tel. ⫹64 9 373 7599/ext 88551, Fax ⫹64 9 373 7450, E-Mail [email protected]

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Reading and Visual Processing in Alzheimer’s Disease Guila Glosser, Murray Grossman Department of Neurology, University of Pennsylvania, Philadelphia, Pa., USA

Several different patterns of alexia, or acquired reading disorders, have been described in patients with the diagnosis of probable Alzheimer’s disease (AD). The mechanisms underlying the different reading problems are still uncertain. Our purpose is to review the reading problems that have been described in AD, to discuss possible underlying mechanisms, and to offer a new conceptualization. As a result of substantial work in the past couple of decades, significant advances have been made in understanding the normal functioning of the cognitive systems involved in reading [1, 2]. The resulting models help to place this discussion in a theoretical framework. To briefly review, most cognitive models of reading include procedures for visually analyzing and identifying written letters. This typically occurs automatically, rapidly and in parallel for all the letters in the presented word. Following perceptual analysis, processing proceeds to the lexicons, which consist of the stored representations of word spelling, sounds and meanings. The presented letter string is matched to memories of previously encountered spelling patterns in the orthographic lexicon. Orthographic processing activates information represented in the phonological lexicon, which is needed for computing the pronunciations of words and nonwords. Simultaneously, the orthographic process (and also phonological representations) activate word meanings via the semantic lexicon. Different reading disorders are assumed to result from breakdown in different components of this reading system. Studies of the mechanisms of reading impairment in brain-damaged individuals, such as patients with AD, are relevant not only to the clinical problems of patients, but also for refining cognitive models of normal reading such as these. A number of reports have suggested that oral reading is relatively preserved in AD, at least in the early stages [3–8]. This preservation of oral reading in

many patients, along with preserved spelling and single-word repetition, has been contrasted to their notable impairments on other language tasks that are more heavily dependent on semantic processing, such as reading comprehension and written and oral picture naming [4, 9–11]. In this context, three major forms of reading disturbance have been reported in patients diagnosed with probable AD. The first is ‘semantic alexia,’ which is the retained ability to access the phonological and orthographic forms of words in the face of impaired processing of word meanings. This dissociation has been described as well in patients with transcortical sensory aphasia resulting from other forms of brain injury [12]. This reading pattern is assumed to reflect the normal operations of orthographic and phonological lexicons in the absence of input from the semanticconceptual processing system. ‘Surface alexia’ is another acquired reading disorder seen in AD. This is characterized by impaired reading of exception words with irregular spelling-tosound correspondence, and relatively preserved reading of both regular words and pseudowords [13]. Regular words are defined as those with spelling-tosound correspondences that occur repeatedly in other words in the language (e.g. game), whereas exception words have a unique or unusual correspondence between some of the letters and their pronunciation (e.g. pint, pronounced in a way that does not rhyme with mint). In surface alexia, exception word reading is affected disproportionately, resulting in a larger than expected ‘regularity effect’, or difference between accuracy of oral reading of regular minus exception words. Surface alexia is also characterized by a tendency to produce ‘regularization’ errors, in which words with irregular spelling-to-sound correspondences are pronounced according to the more frequent, regular, spelling-to-sound correspondence (e.g., incorrectly pronouncing pint to rhyme with mint). Patients with surface alexia also typically show surface or lexical agraphia [14], which involves problems spelling exception words. Several investigators have described that AD patients show a somewhat larger than expected ‘regularity effect’ in reading and have suggested that AD is characterized by a mild form of surface alexia [15–18]. Surface alexia is thought to derive from problems accessing or processing information represented in the orthographic lexicon, though the mechanisms underlying this pattern of reading impairment are still being debated and are the focus of later discussion below. Letter-by-letter reading, also called alexia without agraphia, may be seen in AD as well. In this disorder, patients demonstrate great difficulties decoding visually presented words, but their recognition of orally spelled words and their oral spelling is generally preserved [19, 20]. The hallmark symptoms of this disorder are that it takes the patient an abnormally long time to read single words, and there is a monotonic increase in reading times related to the number of letters in the word. Letter-by-letter readers are more accurate pronouncing

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short words compared to frequency- and familiarity-matched long words. Letter-by-letter reading has been reported in some AD patients who present with vision-specific deficits and who have been designated as having posterior cortical atrophy [21, 22; see also Mendez, this volume]. Letter-by-letter reading has also been observed in AD patients with the more typical presentation when they reach more advanced stages of disease [15, 19]. Based on findings that many letter-by-letter readers also demonstrate subtle, and sometimes not so subtle, deficits in identifying nonverbal visual objects, it has been suggested that letter-by-letter reading results from either a breakdown in the process of visual analysis of written words or from disruption of the transfer of this visual information to the orthographic lexicon [23–25]. It is notable that, with only a couple of exceptions [26, 27], none of the other common alexic disorders have been reported in AD patients. Phonological alexia, which is thought to reflect specific impairment of lexical phonological processing, and its more extreme form (deep dyslexia), are two frequently occurring central alexia syndromes that have not been reported to occur in AD. Similarly, neither of the two common peripheral forms of alexia, such as attentional and neglect alexia, seems to occur in AD. How can we understand the selectivity of reading impairments in AD patients? One possible explanation derives from the idea that different aspects of reading are subserved by different cognitive mechanisms that are localized in different brain regions. Thus, different distributions of cortical pathology in AD might result in disruption of different component mechanisms. This argument has been used to explain how semantic processing mediated by certain regions in the left temporal lobe can become impaired in AD, while leaving intact the modules for lexical phonological and orthographic processing which are localized in other perisylvian regions of the left cerebral hemisphere. For example, correlation studies of cortical atrophy in AD associate impaired semantic memory with lateral portions of left temporal cortex, while sparing adjacent areas located in auditory portions of temporal cortex and visual areas more ventrally in the temporal lobe that are implicated in orthographic processing [28]. A recent functional neuroimaging study by our group has shown difficulty recruiting posterolateral regions of the left temporal lobe while making a semantic decision about single written words [29]. A similar argument has been offered to explain region-specific correlations between specific types of spelling errors and specific regions of PET scan activation in AD patients [30]. In a related manner, it has been proposed that as neuropathological changes evolve over the course of AD, there is an orderly progression of involvement of different cognitive components, leading to a standard evolution of reading patterns [31]. Early involvement of posterolateral temporal lobe regions compromises semantic memory but spares reading functions, resulting

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in semantic alexia [29]. As the disease progresses, involvement of structures in the ventral-lateral left temporal lobe, associated with functioning of the orthographic lexicon, results in a pattern of surface alexia [32]. Finally, as pathology spreads more posteriorly, the left inferior temporal-occipital and mesial occipital cortices associated with pure alexia become involved, resulting in letter-by-letter reading in more advanced cases of AD [2]. Taken together, this account assumes that different patterns of reading impairment occur in AD as a result of disturbances in reading-specific cognitive mechanisms, which are thought to have different anatomic localizations. This explanation can potentially account for the diversity and selectively of reading problems across patients with AD, but it has been difficult to verify this account empirically in the absence of good localization of the neuropathology in individual patients. Alternative explanations of the different alexic patterns in AD derive from the notion that the reading problems in these patients actually result from disruptions in other cognitive abilities, such as attention, semantic processing and vision. These hypotheses arose, in part, from the observation that the reading patterns of AD patients differ qualitatively from the pure forms of alexia seen in patients with focal lesions. For example, AD patients’ difficulty in reading exception words usually is less commonly accompanied by an increased number of regularization errors than is the case in typical instances of surface alexia. Certain unique aspects of the reading patterns in AD have led some investigators to explore the relationship of their reading disorders to other cognitive impairments. According to one account, the core problem in AD is a disturbance in lexical semantic processing, and this impacts on the process of translating orthography to phonology [16, 33]. It is proposed that representations in the orthographic and phonological lexicons function at the subword level. The subword elements are believed to become joined into whole word patterns by their frequent cooccurrence in familiar words. For less frequent words, however, it is assumed that the word’s meaning functions as the ‘glue’ that integrates the subword elements into whole word patterns. Semantics is necessary for the translation between orthography and phonology. With disrupted access to semantic information or degradation of semantic processes, reading becomes a process of segmental translation from orthography to phonology of subword units. Regular words remain unaffected by this, because computing the pronunciations of the individual elements within such words leads to the same outcome as pronouncing the whole word. However, this sequential process leads to errors with exception words, because the pronunciation of each of the component elements does not follow the usual spelling-to-sound correspondences. The whole word configuration is required to determine the correct pronunciation. Thus, in the

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absence of semantic support, exception words, especially low-frequency exception words, are expected to be pronounced less accurately than regular words with resulting regularization errors. Much of the evidence for this view comes from studies of patients with a fluent form of primary progressive aphasia, also known as semantic dementia, who have profound semantic impairments and who often show a marked deficit in reading low-frequency exception words [34, 35]. Item-specific correspondence between failures in comprehending the meanings of particular words and impaired reading of these low-frequency exception words has been taken as evidence that semantic processing is necessary for computing the pronunciations of words with irregular spellingto-sound correspondence. This argument has been extended to AD patients, for whom significant relationships have been shown between overall performance on word comprehension and naming tasks, which measure semantic memory, and measures of single-word oral reading, especially for exception words [16, 33]. The hypothesis that oral reading impairment in AD might derive from a semantic disturbance is quite intriguing, and it has important implications for broader theories about the modularity of mind [36]. This hypothesis is challenged, but not totally refuted, by several reports of patients with various types of neurological disorders who also have significant semantic impairments, but who demonstrate completely preserved exception word reading and writing [7, 37–43]. Another view of alexia in AD proposes that the critical deficit underlying the apparent mild surface alexia and its spelling counterpart, lexical agraphia, is in attentional control, which interferes with the ability to inhibit partially activated but inappropriate information [44, 45]. This account suggests that encoding or decoding exception words, such plaid, results in the activation of both the regular pronunciation/spelling, played, and the exceptional pronunciation/spelling, plad. Attentional resources are required to select the appropriate irregular pronunciation and inhibit the regular pronunciation. AD patients’ recognized attentional deficits [see Mapstone and Weintraub, and Vecera and Rizzo, this volume] make this selection process inefficient, thereby resulting in increased reading response times, lower accuracy and some regularization errors for words with exception spellings. The authors point out that AD patients’ reading problems are not merely the result of a failure to suppress interference from regular spelling-sound correspondence patterns when reading exception words, since at other times they show abnormally strong lexicality effects when performing a task that involves nonlexical rhyme judgments [46]. Rather, it is concluded that reading failure in AD patients derives, at least in part, from a general problem of attention and response selection. Like in the previous account, reading failure from this perspective results from a breakdown in cognitive mechanisms outside of the reading and spelling systems.

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A third account of alexia in AD patients attempts to unify different observed reading patterns and relate them to a common underlying problem of visual perception. This account was proposed to explain the observed co-occurrence of surface alexia and letter-by-letter reading in AD [15]. In a review of the literature, Behrmann et al. [23] documented the co-occurrence of letter-by-letter reading and surface alexia in a number of patients who had been tested for both of these effects. Most of these patients also presented with surface agraphia, which suggests that, in addition to problems in visual letter processing, a deficit within the orthographic lexicon was at least partially responsible for these individuals’ difficulties with irregular words [14, 19, 47, 48]. The co-occurrence of letter-by-letter reading and surface alexia in these patients was assumed to result from coincidental lesions in anatomically contiguous temporal-occipital regions subserving the orthographic lexicon and visual letter analysis. Recently, Tainturier et al. [49] described a head-injured patient with letter-by-letter reading who showed typical features of surface alexia, but whose spelling of all words was normal. In this case, there did not appear to be a deficit in the orthographic lexicon that might explain the disproportionate deficits in reading irregular words. Rather, it was suggested that irregular word reading in this patient was disrupted because of an inability to process global visual forms, such as whole words. Assuming that the visual processing problem in this patient, and others with letter-by-letter reading, involves a constriction of the visual window of attention [50], then it is possible that irregular words, which cannot be decoded as reliably using small subword units, would be more affected by limitation in the size of the attentional window than regular words. As an alternative to postulating that semantics binds together elements in the orthography of the whole word, this hypothesis proposes that visual perceptual processing is the glue that integrates whole word configurations. A similar argument can be made that a visual processing disorder underlies the apparent co-occurrence of mild surface alexia and letter-by-letter reading in patients with probable AD [15, 31, 51]. Such a deficit in visual processing could also explain the presence of a mild, but significant, deficit of nonword reading in AD [15, 16, 33, 51]. We sought to test the hypothesis that a disorder of visual processing underlies impaired reading in AD in two studies involving AD patients. The first study charted patterns of single-word reading and spelling on carefully constructed lists of words with regular, ambiguous or exception spellings in a consecutive series of AD patients unselected for reading abilities [15]. We found, as have others, that AD patients exhibited larger than normal reading regularity effects, defined as the difference between regular and exception words. Importantly, however, AD patients differed from other patients whose surface alexia is believed to reflect a problem within the central orthographic lexicon. The magnitude of

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the observed regularity effect in AD patients was modest compared to the effect seen in stroke patients with surface alexia. When reading, AD patients did not make a disproportionate number of regularization errors. Regularization errors accounted for fewer than 50% of AD patients’ reading errors, which is similar to the pattern seen in normal readers. Also, AD patients showed no difficulties reading ambiguous words that have several possible pronunciations (e.g., read), indicating that they were able to appropriately access a wide array of detailed orthographic-phonological lexical correspondences. The presence of mispronunciations of nonwords is also incompatible with the notion that AD patients are relying primarily on sublexical spelling-to-sound conversion for decoding written words. Finally, the data indicated that AD patients actually had a greater abnormality in the magnitude of the regularity effect when reading than when spelling. Among patients with surface alexia, spelling is almost always more disrupted than reading [14]. All of these facts argue against a deficit located within the orthographic lexicon. Several types of evidence point to the possibility that the reading problem in AD might be related to a disorder of visual processing. AD patients have recognized impairments in visual processing, as described throughout this volume. There are several reports of individual AD patients whose predominant and earliest symptoms involve pervasive visual disturbance and who have prominent neuropathology in posterior visual association areas [22, 52–57; see also Mendez, and von Gunten et al., this volume]. These patients often present first with complaints of visual loss, difficulties recognizing objects, deterioration in eye-hand coordination and topographic disorientation. All of these patients are said to have impaired reading, with letter-by-letter reading described for patients in whom this ability has been examined in detail. Importantly, there have been a couple of reports of patients with the visual variant of presumed AD who exhibited a specific reduction in the range of their field of visual attention (‘spotlight of attention’), resulting in simultanagnosia and alexia [50]. Unselected groups of AD patients early in the course of disease also demonstrate impairments in various aspects of visual processing, such as contrast sensitivity, oculomotor scanning, pattern masking, motion perception, object form discrimination, spatial localization and visual integration [58–67]. These impairments in low-level and intermediate visual perceptual processes have been related to AD patients’ higher-order cognitive symptoms [68] and their functional capacities [69; see Dunne, this volume]. As indicated above, longitudinal monitoring has revealed that reading deficits in a number of AD patients evolved from having relatively isolated difficulties reading exception words in the early to middle stages of the disease, to exhibiting slow and labored reading in a letter-by-letter fashion in the more advanced stages [15, 31]. Since the latter reading pattern has been associated

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with a disturbance of visual processing, we directly tested the relationship of reading to visual difficulties in a group of AD patients, none of whom were letter-by-letter readers [51]. AD patients were evaluated on a test of single-word oral reading, on tasks involving visual discrimination of letters, faces, objects and spatial locations, as well as measures of semantic language ability. When dementia severity was controlled for, significant correlations were found between single-word oral reading and difficulties discriminating words written in different fonts and objects pictured in different orientations, both functions thought to be mediated by ventral occipito-temporal visual processing areas in the left hemisphere near the regions thought to be critical for letter-by-letter reading problems [2, 2, 70]. No significant relationships emerged between reading impairment and performances on tasks tapping visual abilities subserved by other brain regions (i.e., discrimination of unfamiliar faces and spatial localization). Though many of the AD patients were impaired in face processing and spatial localization, scores on these tasks did not correlate with oral reading performance. Importantly, in contrast to the significant associations that were found between reading performance and certain visual perceptual processes, there was no relationship between AD patients’ reading and their lexical semantic abilities demonstrated on measures of naming. These results clearly establish a special relationship between visual dysfunction and reading impairment in AD and suggest that the apparent associations between surface alexia and letter-by-letter reading in AD might be mediated by a common disorder of visual processing. We hypothesize that bottom-up effects can lead to a situation whereby a disorder early in processing (letter identification) can affect analysis at a later stage (orthographic lexicon). What appears to be a central orthographic problem in some patients with surface alexia and letter-by-letter reading may, in fact, be due to a peripheral visual disturbance. This idea still remains to be tested in neurological patients. In conclusion, we have reviewed reading impairments in AD and discussed possible underlying mechanisms. We have noted that there are, in fact, several different patterns of alexia in AD, which may well have different origins. The observed breakdowns of reading in AD do not conform completely to traditional alexia syndromes. Rather, new ideas are required to explain the reading (and spelling) problems of AD patients. Several explanations have been put forth to account for the findings. These accounts involve conceptualizations of novel interactions among cognitive functions, ideas which have implications not only for understanding written language processing in AD patients, but also for modeling the operations of normal cognitive functioning. Interactions that have been considered include influences of semantics on orthographic processing, the effects of attention on the translation between orthography and phonology, and bottom-up effects of visual perceptual disturbance on orthographic

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processing. These accounts are not mutually exclusive, as it is possible that reading problems in AD are the result of more than one type of disturbance. AD patients are heterogeneous, moreover, and different patients may have different types of reading problems associated with their particular sites of pathology. Further investigation of these questions may serve to elucidate the cognitive mechanisms in processing written language. Acknowledgements Preparation of this chapter was supported in part by USPHS grants NS02140, AG15116, AG17586, and NS35867.

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Murray Grossman Department of Neurology – 3 Gates University of Pennsylvania Medical Center 3400 Spruce Street, Philadelphia, PA 19104-4283 (USA) Tel. ⫹1 215 662 3361, Fax ⫹1 215 349 8464, E-Mail [email protected]

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Visual Attention and Daily Function Cronin-Golomb A, Hof PR (eds): Vision in Alzheimer’s Disease. Interdiscipl Top Gerontol. Basel, Karger, 2004, vol 34, pp 248–270

Visual Attention and Visual Short-Term Memory in Alzheimer’s Disease Shaun P. Vecera, Matthew Rizzo Department of Psychology, University of Iowa, Iowa City, Iowa, USA

The brain receives thousands of sensory inputs from the environment at every moment and only some are relevant to current behavior. For example, the location and speed of an approaching automobile are relevant inputs for the task of safe driving, but the song on the radio is not. Because the brain does not have the capacity to process all inputs simultaneously, processes exist that select relevant inputs (the approaching car) and filter out others (the song on the radio). These processes collectively are referred to as ‘attention’. Attentional selection has been studied intensively by both the cognitive and brain sciences [see 1–3 for recent reviews]. Early research assumed that attention is a unitary process, but current evidence suggests that there are multiple forms of attentional selection [e.g., 4]. This chapter focuses on changes in the operation of visual attention produced by typical Alzheimer’s disease (AD). Ample evidence suggests a visual variant of AD [e.g., 5–9] in which visual impairments are predominant. In our experience, patients with the visual variant of AD represent approximately 5% of all observed cases of AD. Patients with the visual variant of AD present with symptoms that resemble Balint’s syndrome, such as simultanagnosia [e.g., 7], making these patients important cases for examining the operation of visual attention. Because the visual variant is less frequent, however, we restrict our discussion to patients who do not appear to have the visual variant of AD. Although there are multiple attentional processes and effects, we will attempt to argue that many of the attentional impairments evident in AD might be produced by limitations in visual short-term memory (VSTM), a visual memory system that affects the operation of visual attention. We begin with a brief overview of normal attentional processes by defining a framework within which to understand the operation of visual attention.

Defining Attention and a Framework

Before attempting to understand attentional processes in AD, we must first discuss the concept of selective attention. A fundamental issue that must be addressed is how ‘attention’ is defined. The term attention appears in everyday language, but this intuitive, folk psychological usage does not provide a solid definition of attention. The use of ‘attention’ in the psychological literature is also problematic because it is often used to refer to tasks that require attention, as opposed to the processes of attention. Our previous work [1, 10] has outlined two ways in which the term attention is used. Task-defined attention defines attention in terms of an observer responding based on some stimulus dimension. For example, consider a task in which an observer views two spatially adjacent letters, one red and one green, and is asked to report the red letter. This task requires the observer to ‘pay attention’ to the red letter, but does not elucidate the mechanisms of attention. The difficulty with such a definition for AD patients is that the deficits observed in these patients could arise from a variety of mechanisms (e.g., disordered attentional processes versus disordered perceptual processes). Any single task will involve multiple cognitive operations, and, therefore, an overall deficit on a task could arise from damage to any of these operations. A more precise mechanistic or process-oriented definition of attention is useful for understanding when attention is necessary and how attention might operate. Perhaps the most well-known process-oriented definition of attention comes from William James [11], who defined attention as involving ‘ . . . withdrawal from some things in order to deal effectively with others . . .’ [pp. 381–382]. An information-processing perspective would provide a process-oriented definition of attention as those processes that restrict processing to a subset of stimuli or events, with the overall goal of improving behavior (i.e., decreasing response time or increasing accuracy). Having defined attention in terms of the restriction of cognitive processes, we now turn to two theoretically important issues for attention: How is attention controlled, and what are the effects of directing attention to an item? Attentional control involves those parameters and processes that determine which items become attended to and which do not. Attentional control parameters determine which items attention selects. For example, abruptly appearing stimuli, such as a peripheral flicker, control the allocation of attention by capturing attention automatically [12]. There are different parameters that influence attentional control. Two general classes of control are top-down sources that arise from the current behavioral goals and bottom-up sources that arise from sensory stimuli present in a scene [2, 13, 14]. These two sources can be illustrated by considering a visual

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a

b

Fig. 1. a A visual search display in which the target differs from the distracters by a single feature (color). Search for a feature is efficient; adding additional distracters does not increase the time to search through the display. b A visual search display in which the target differs from the distracters by a conjunction of features (color and orientation). Search for a conjunction of features is inefficient; adding distracters increases the time to search through the display.

search task. In a typical visual search task, observers are instructed to search for a particular target, such as a black vertical line, that appears in a field of distracters (fig. 1). The representation of the target – that is, the target that an observer is actively looking for – can be conceptualized as a representation (a ‘template’ in some theories) that is temporarily stored in visual memory. This memory representation influences visual search in a top-down manner; observers would actively attempt to look at black and vertical items. The actual scene presented in a visual search task provides the bottom-up information that is searched through; this information indicates where objects are located and which features (e.g., color, orientation, shape, etc.) are present at each location. Visual search requires the observer to find a balance between the top-down information and the bottom-up information. An example of an effective search would be searching for a single feature, such as a black vertical line among white vertical lines (fig. 1a). When the target differs from the distracters by one feature (e.g., color), the bottom-up information is consistent with the top-down information in constraining where an observer should search. Such a search is efficient; the target ‘pops out’ at the viewer and can be identified rapidly, irrespective of the number of items in the scene. A less efficient search would involve searching for a conjunction of features, such as a black vertical lines among black horizontal lines and white vertical lines (fig. 1b). In this search, any single piece of bottom-up information is not unique to the target item, so the bottom-up constraints are weaker than in the feature search. Top-down constraints are required to resolve the competition among the input items. Conjunction searches are inefficient and operate more slowly than feature searches. When the bottom-up constraints are no longer unique, search becomes progressively slower as items are added to a display. Bottom-up and topdown control of spatial attention has also been examined using spatial cuing tasks, which direct attention to a location before a target event occurs.

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Another important issue in the study of attention is how an attended stimulus is processed differently from an unattended stimulus, that is, the effects of attention. For example, the neural representation of an attended item could be enhanced relative to the representations of unattended items [15]. Or, an attended item could integrate together the visual attributes of the attended stimulus, allowing the features of attended objects to be bound together [16]. There are several effects of attention that have been highlighted in recent theories and supported by empirical data: Attention reduces an observer’s uncertainty in making judgements about a stimulus. Under this decision-noise account or noise reduction account [17], optimal performance (e.g., accuracy) decreases as the number of stimuli increases because each stimulus contains some uncertainty (or random noise). Attention reduces the random noise associated with the attended stimulus, suggesting a decision-noise reduction effect of attention. Attention may also operate within a perceptual-level representation. Perceptual-level attention has many effects. For example, it can enhance the signal-to-noise ratio of attended items. This sensory-gain account hypothesizes that attention enhances the perception of attended items compared to unattended items. This effect of attention is to allow attended items to be of higher fidelity than unattended items [15, 18, 19], suggesting a sensory enhancement effect of attention. An additional perceptual-level process is the ability to narrow the attentional window around a stimulus, as in attending to a local feature of a larger object [20]. There are several other perceptual-level effects of attention, including solving the binding problem. Attention may be needed to bind together the features of an object [16]. Consider a display that contains a red circle and a blue square. How does the visual system bind the features ‘red’ and ‘circle’ together and avoid the incorrect combination of ‘red’ to ‘square’? One solution to this binding problem is to focus attention on a single stimulus, thereby ‘gluing’ or binding the features together. Directing spatial attention to a location reduces the number of incorrect feature combinations at that attended location [21]. Also, attention has a perceptual-level effect in object-based attention, in which objects are selected for further processing. Object-based selective attention can be separated from location-based (spatial) attention [22–24], suggesting complementary attentional processes for these forms of selection. Finally, attention appears to influence the entry of items into VSTM [25, 26]. When the appearance of multiple visual objects must be retained across a delay, VSTM processes are required to retain 3–4 of the objects. If one of the objects is preceded with a small spatial cue, this item is more easily remembered than other, uncued items. Such results occur even when the spatial precue occurs after the display of objects has disappeared, suggesting a VSTM

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level effect of attention. The post-cuing effects suggest that attention is capable of selecting items that have already entered VSTM. The fact that attention is used in several situations to produce different effects suggests that it is unlikely to be a single process. Instead, attention is likely to be influenced (controlled) by several sources of information (i.e., topdown and bottom-up sources) and is likely to arise from several different anatomical areas that work in concert. Such a view of attention predicts that attentional impairments in any neurological syndrome will probably not be unitary, that is, across-the-board attentional impairments. The attentional impairments in AD patients should be no different: The attentional impairments in this population might be produced by only a subset of attentional processes, such as attentional processes that operate at the level of VSTM instead of at the level of perception.

Visual Short-Term Memory and Visual Attention

VSTM is an important visual process for most everyday tasks in which visual information (e.g., an object or the location of an object) must be maintained across a substantial delay. Such substantial delays are produced by eye movements, which produce a suppression of the input to visual areas, effectively ‘turning off’ the visual input. Several theoretically-motivated studies have demonstrated that VSTM can hold or maintain a small number of items across the delay produced by an eye movement [27, 28]. Such studies highlight the limited capacity of VSTM, which is capable of representing a relatively small number of items [between 3 and 4 items for VSTM, see 29–33]. Because VSTM has such a limited storage capacity, attentional processes must regulate the information that enters this visual memory. This transfer of perceptual information into a VSTM appears to be a slow, effortful process [e.g., 34–36], suggesting a role for focal attention. Further, once items are stored, or consolidated, into VSTM, those items may themselves become the objects of attention. That is, attention not only influences the entry of items into VSTM but can also select items stored in VSTM, again demonstrating the multiple effects of attention. Studies of VSTM have documented these two interactions between attention and VSTM. The original iconic working memory studies of Averbach and Coriel [37] demonstrated that attention influences the entry of items into memory processes (although these studies may have involved verbal short-term, or working, memory, not VSTM). Averbach and Coriel presented younger adult observers with an array of 10 letters, which exceeded the capacity of short-term memory. A small cue that appeared adjacent to one of the letters indicated which

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Memory array

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Fig. 2. Change detection task for assessing VSTM. A memory array of colored squares appears, followed by a retention interval. The test array is either identical to the memory array or changed by 1 item (the lower right item here). In neurologically normal observers, accuracy is high for up to 3 items, but declines rapidly as additional items are added to the memory array, producing a VSTM capacity of between 3 and 4 objects.

letter they were to report. In one condition, the cue appeared before the letters, as in the spatial precuing tasks discussed above. In this condition, observers were highly accurate at reporting the precued letter, suggesting that the precue allowed this letter to enter a short-term memory process which maintained this letter across a delay. Others have reported similar findings [e.g., 38, 39]. More recently, the role of attention in entering items into VSTM has been demonstrated using a change-detection paradigm developed by Luck and Vogel [29, 33]. The general paradigm is depicted in figure 2. Participants first view a memory array containing highly-perceptible color squares; the memory array is present for a short period of time (typically ⬍200 ms). After a delay, a test array appears, and participants indicate if the test array is the same as the memory array or different from the memory array by one item. Accuracy is high when there are only 3–4 items in the memory array, but as the number of items in the memory array increases past 4 items, change detection accuracy decreases (fig. 2). To study how attention enters information into VSTM, a small precue appeared before the presentation of the memory array [25]. Accuracy for the precued item was high, even when the memory array contained more than 4 items, suggesting that the precued item was preferentially entered into VSTM by spatial attention. We have generalized this finding to demonstrate that object-based attention also allows items to be preferentially entered into VSTM [26]. Finally, once items are consolidated into VSTM, attention can select from these VSTM representations. The studies just reviewed [25, 26, 37] also contained postcue conditions, in which an attentional cue appeared a few hundred milliseconds after the memory array had disappeared. Participants remain highly accurate in reporting the cued object relative to uncued objects, and they remain as accurate in the postcue condition as in the precue condition [40].

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In light of such findings, it is obvious that perceptual-level attention (e.g., spatial attention) and VSTM are intimately related. Which process, perceptuallevel attention or VSTM, is impaired in AD patients? To our knowledge, there have been no studies directly addressing VSTM in AD patients. Based on our review of the literature on attentional difficulties in AD patients, we hypothesize that VSTM-level attention, not perceptual-level attention, may be the primary source of attentional deficits in AD patients. This hypothesis was generated from three points: (1) apparent deficits in perceptual-level attention, reviewed below, could be produced by VSTM deficiencies; (2) AD patients have documented difficulties in performing multiple tasks simultaneously (dual-task performance), which also could be produced by VSTM deficiencies, and (3) in early stages of AD, the primary sensory cortices responsible for elementary perceptual processes remain relatively free of the plaques and tangles associated with AD [41–44]. Having discussed attention and VSTM generally, we now turn to the specific attentional impairments observed in AD. Based on the existence of multiple attentional mechanisms, we subdivide the studies that have been conducted into those that have investigated perceptual-level attention (e.g., spatial attention) and those that investigate high-level attention (e.g., executive selection of a behavioral task). This division is relevant for the VSTM deficit hypothesis, which predicts that attentional disturbances in AD patients are produced primarily by faulty VSTM processes. Although it appears that both perceptuallevel attention and high-level executive attention show impairments in AD patients, we will discuss alternative explanations of these results that would allow such impairments to arise from VSTM deficits.

Attentional Impairments in AD: A Review and a VSTM Deficit Hypothesis

Perceptual-Level Attention in AD Recent reviews have pointed to the progressive impairment of several attentional processes in AD [see 45–47], consistent with the view of multiple attentional systems. We review the studies of attention in AD because these studies, although important, have only investigated a narrow realm of attentional functioning. This literature review will motivate our proposed studies and the VSTM deficit hypotheses. We focus on studies of AD patients that rely on attentional tasks that were developed to test information-processing models of cognition. Although neuropsychological batteries contain subtests that measure attentional function, such tests do not permit an investigation of the time course of specific attentional parameters, making such tasks difficult to relate to cognitive theories.

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Fig. 3. The order of events in Posner’s spatial cuing task. Observers are asked to detect the appearance of a target that has been validly or invalidly precued. a Peripheral precue that automatically summons spatial attention to the cued region. This panel depicts a validly cued target, but invalidly cued targets also appear during the course of an experiment. b Central, symbolic precue that can be used to voluntarily shift spatial attention to the cued region. This panel depicts an invalidly cued target, but validly cued targets also appear during the course of an experiment.

Spatial Attention. Most of the research on attention and AD has investigated impairments in spatial attention. These studies typically have used either spatial cuing tasks or visual search tasks to assess spatial attention. AD patients exhibit a number of difficulties in using spatial attention to orient to a location in advance of an upcoming event. The studies that support this conclusion typically use Posner’s spatial cuing paradigm (fig. 3). In this task, each trial begins with a cue intended to orient an observer’s attention to one of several locations. The cue can be a peripheral flicker (fig. 3a) at the peripheral location where a target may appear, or a centrally-presented symbol such as an arrow (fig. 3b) that points to the location where a target may appear. After a delay, a target is presented and the subject either (1) makes a keypress response as soon as the target appears (detection task) or (2) discriminates among several targets (discrimination task). On ‘valid’ trials the cue correctly predicts the target’s location; on ‘invalid’ trials the cue is misleading. Some experiments also include neutral trials, in which no information is provided about the target’s upcoming location. Neurologically normal control participants typically respond fastest to valid trials, slowest to invalid trials, and at some intermediate level to neutral trials. In one of the earliest studies on attention in AD, Parasuraman et al. [48] presented AD patients with a symbolic central precue – a small arrow that pointed to the left or right. Such endogenous precues require participants to voluntarily shift attention to the cued location [e.g., 12]. Following this precue, a target letter appeared, and the patients performed a discrimination task. Parasuraman et al., included a neutral precue condition to assess the benefits

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and costs of spatial precuing. Interestingly, the AD patients categorized validly cued targets faster than neutrally-cued targets, and this benefit was similar in magnitude to the control participants. However, AD patients exhibited a larger attentional cost than the control participants. AD patients were slower than control participants in categorizing an invalidly cued target than categorizing a neutrally-cued target. Similar results were obtained when peripheral precues (exogenous precues) preceded the letter that was to be categorized. What is the implication of increased attentional costs in AD? A group of parietal-damaged patients with focal brain damage studied by Posner et al. [49] showed a larger attentional cost in the ipsilesional visual field compared to the contralesional visual field. This pattern of results – an increase in attentional costs – has been used to argue for an impairment in disengaging spatial attention from its current focus. Using this reasoning, Parasuraman and colleagues [48, 50] have argued that AD patients have an impairment in disengaging spatial attention from a currently attended location. AD patients also exhibit impairments in searching through a cluttered visual scene. Such visual search tasks have been a mainstay in cognitive studies of attention. Visual search is the act of looking for a visual target among distractors (e.g., finding a friend’s face in a crowded room). In a typical visual search task, observers are asked to search for a particular target amid a field of distractors [16], such as searching for a black, vertical bar in figure 1. The number of distractors – the set size – is varied across trials, and reaction time is measured as a function of the set size. An ‘efficient’ (or ‘parallel’) visual search is characterized by search functions with shallow slopes; such results are typically found in feature searches where the target differs from the distractors on a single visual feature, such as a color difference (fig. 1a). An ‘inefficient’ (or ‘serial’) visual search is characterized by search functions with steep slopes; such results are typically found in conjunction searches (fig. 1b) where the target can only be discriminated from distractors by considering the combination of two features, such as color and orientation. Using a standard visual search task, Foster et al. [51] asked AD patients and control participants to search for a target (a filled circle) among different numbers of distractors. The distractors were chosen so that the search was either an efficient feature search or an inefficient conjunction search. In the feature search, a shaded circle appeared among filled circles; in the conjunction search a shaded circle appeared among shaded squared and unfilled circles. Foster and colleagues found that although AD patients responded more slowly than control participants in the feature search, neither group showed dramatic increases in response time, suggesting an efficient visual search. However, when AD patients performed a conjunction search task, they differed from normal control participants in two respects. First, AD patients responded

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Invalid threshold Valid Invalid

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Fig. 4. Mechanisms for producing an attentional effect. a Perceptual enhancement. b Response threshold change. See text for additional details.

more slowly than control participants. Second, and more theoretically interesting, the slope of the search function was steeper for the AD patients than for the normal control participants. That is, as set size increased, AD patients responded disproportionately more slowly than age-matched control participants. The differences among the slopes indicate an impairment in spatial attention in the AD group. As with the results from spatial cuing tasks, these visual search data could be explained as an impairment in disengaging spatial attention from the currently attended item. As the number of items to search increases, this ‘disengage’ difficulty disproportionately slows response times compared to normal participants. The results from both spatial cuing tasks and visual search tasks suggest that perceptual-level attentional processes are impaired in AD patients. However, although these results seem problematic for the VSTM deficit hypothesis, AD patients’ spatial attention impairments could be caused by problems in VSTM. The spatial precuing results are problematic because of the procedures used in this task. Most spatial precuing tasks have measured target detection reaction times (RTs); participants make a single keypress as soon as they detect the onset of the target. The attentional effect in a detection task could be due to either of two processes: perceptual enhancement or response bias [52, 53]. The attentional effect observed in these tasks could be due to cued targets being perceptually enhanced by attentional resources. A processing account of perceptual enhancement is depicted in figure 4a, which depicts information accumulation functions for valid and invalid trials. The perceptual enhancement produced by resource allocation depicted in figure 4a has the effect of separating the information accumulation functions for valid and invalid trials. Note that the response thresholds (i.e., the amount of information, or sensory

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evidence, that is required for a subject to respond) is the same for both valid and invalid trials. However, the same attentional effect can be produced if participants have different response thresholds for valid and invalid trials (fig. 4b). Under this account, less evidence is required to respond in valid trials than in invalid trials, which allows valid RTs to be faster than invalid RTs. Some researchers have attempted to overcome this ambiguity in interpreting data from spatial cuing tasks by requiring participants to perform a discrimination task rather than a detection task. Discrimination tasks, in which two or more targets are discriminated from one another, allow one to observe a speed-accuracy tradeoff, which would result from a response threshold difference (fig. 4b). Unfortunately, participants’ accuracy on these discrimination tasks is typically very high, including the accuracy of AD patients [e.g., 50], and such near-ceiling performance can mask speed-accuracy tradeoffs [54]. All of the spatial precuing tasks used with AD patients are subject to these difficulties. Thus, there is simply not enough evidence to argue that AD patients’ performance on spatial precuing tasks arises from a faulty perceptuallevel attention process. The visual search results from AD patients also do not refute the VSTM deficit hypothesis. First, searching for an elementary visual feature appears to be intact in these patients, suggesting that registering and perceiving such features is relatively intact. Elementary features appear able to guide spatial attention in a bottom-up manner in both AD patients and control participants. Second, although searching for the conjunction of two features appears disproportionately difficult for AD patients, such results could be produced by VSTM processes. Some theories of attention and visual search suggest that visual search is guided by VSTM processes [e.g., 55, 56]. Such theories propose that VSTM holds a target template – a mental representation of the target that is being searched for. Impaired VSTM would likely degrade the representation of the target template, making it difficult for VSTM to guide spatial attention in a top-down manner [also see 57, 58 for non-perceptual explanations of visual search results]. Attention to Global and Local Information. In addition to directing selective attention to a location or to a single object, attention can be directed to different levels of a hierarchically-organized object [20, 59, 60]. In Navon’s [20] classic ‘global/local’ task, participants view hierarchical stimuli in which a large letter is created from smaller letters (e.g., a large letter T is created from small Ls). Participants can perform one of two tasks; they are either instructed to report the identity of either the global letter or the local letter, or they are instructed to search for a target letter or digit. Neurologically normal participants exhibit a global superiority effect: The global letter is reported faster than the local letters, and the global letter interferes with identifying the local letters.

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A study by Filoteo et al. [61] used Navon’s global/local task to examine the shift of attention from one hierarchical level to another. AD patients showed a larger increase in RTs than normal participants in a divided attention condition in which a target digit could appear at either the global or local level. Further, the AD patients showed greater difficulty than the normal group in switching attention from one hierarchical level to the other; this switching impairment was similar for global-to-local shifts and local-to-global shifts. Filoteo et al., suggested that their results indicate that AD patients have an impairment in disengaging attention from the currently attended hierarchical level. However, it is also possible that the impairment is in shifting the attentional focus from one level to another (i.e., moving from local to global or vice versa), rather than the process of first disengaging from the currently attended level before shifting to the other level. Subsequent results with the global/local task have shown that AD patients may exhibit a local, not global, superiority effect. Coslett et al. [62] had two early-onset AD patients perform a global/local task in which the patients were instructed to report the identity of the letter at one of the two levels. The AD patients showed faster responses to local letters than to global letters, a result opposite of that seen in control participants. Coslett and colleagues suggested that at least some AD patients (possibly those with early-onset AD) have an abnormally constricted attentional spotlight or zoom-lens. On the surface, these results also appear to contradict the VSTM deficit hypothesis because in the global/local task, attention is directed to stimuli at one of the two perceptual levels presented in hierarchical stimuli. However, as with visual search tasks, global/local effects could be directed by target representations (i.e., templates) in VSTM in a top-down manner. Such target representations could permit participants to either set and maintain the appropriate window of attentional selection or to search for the target letter. Again, a VSTM deficit could appear as an impairment in directing attention to levels of hierarchical stimuli, as in Filoteo et al.’s [61] study. Coslett et al.’s [62] findings could be explained if a damaged VSTM process sampled only a subset of the local elements to accommodate a reduced VSTM capacity. The global shape would be represented poorly, if at all, under this explanation, producing a local superiority effect. Object-Based Attention. To our knowledge, only one study of AD patients has investigated object-based attention. Studies of object-based attention suggest that attention is not only directed to spatial locations but also to entire objects defined by perceptual grouping processes [23, 24, 63, 64]. Buck et al. [65] investigated impairments in both space-based and object-based attention with a widely-used object attention task [22]. In this task, participants view displays that contain two objects (two rectangles). Attention is cued to the end of one rectangle, and participants perform a detection task to a target that appears. The critical finding is that participants are faster to detect targets appearing in

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the cued object than those appearing in the uncued object, even though the targets are the same distance from the precued location. Buck et al. [65] found that a subset of AD patients showed impairments in object-based attention. Research with patients with focal damage to the parietal lobes reported that lesions of the left parietal lobe were associated with a difficulty in switching attention between objects, not locations [22]. Buck et al., found that some AD patients showed larger costs shifting attention between locations and some patients showed larger costs shifting attention between objects. The patients who showed increased object shifting costs exhibited blood flow hypoperfusion in the left parietal lobe; the patients who showed increased spatial shifting costs exhibited hypoperfusion in the right parietal lobe. These results suggest anatomically distinct processes for different forms of perceptual-level attentional selection. As with the other results we have discussed, these findings also can be explained by a VSTM deficit. First, the object-based attention task used by Buck et al., is a variant of the spatial precuing task used to study spatial attention, and, therefore, results from this task could be produced by either perceptual enhancement or response bias. Second, recent studies have linked object-based attention to VSTM processes. Barnes et al. [66] demonstrated that object-based attention effects can be reduced if VSTM is occupied by a memory load (several complex objects). Participants performed one of three working memory tasks while performing an object-based attention task. Participants performed (1) an object working memory task in which they attempted to remember the identity of a shape across a delay, (2) a spatial working memory task in which they attempted to remember a location across a delay, or (3) a verbal working memory task in which they attempted to remember a set of letters across a delay. Only the object working memory task impaired performance on the object-based attention task. If VSTM was impaired in AD patients, then patients might present with difficulties in either object-based or location-based attention, and the attentional difficulties might not be with perceptual attention processes themselves. To summarize the findings on perceptual-level attention, AD patients appear to have problems with several types of this form of attention. We have proposed that each set of findings could be accounted for by an impairment in VSTM and not an impairment in perceptual attention per se. Unfortunately, we are not aware of any direct studies of VSTM in AD patients. Perhaps the most direct support for the VSTM deficit hypothesis comes from high-level attention impairments in AD patients, to which we now turn. High-Level Attention in AD As we discussed earlier, attention also operates at the level of VSTM, allowing some items in working memory to be selectively attended over others.

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Although there have been no direct studies of VSTM and attention in AD patients, there are results from these patients that investigate other high-level attentional processes. The results from such studies have been explained in terms of an executive attentional process – an attentional selection mechanism that allows entire tasks or goals to be selected over others. Of course, because these studies have not investigated VSTM attentional selection directly, studies of executive-level attention in AD patients will only support the VSTM deficit hypothesis of AD to the extent that VSTM processes and executive-level processes share overlapping mechanisms. Although the concept of a central executive is becoming increasingly popular in cognitive theories and accounts of AD, the concept remains difficult to define and poorly understood [47]. Most of the major reviews of attention and AD have included a section that discusses the role of central executive deficits in understanding the attentional impairments in AD [45–47]. Despite the contributions of central executive impairments to attentional problems in AD, evidence for these high-level attentional or executive impairments has been difficult to come by. Most studies have relied on neuropsychological tasks, such as the Wisconsin Card Sorting Task, which are impaired in patients with frontal-lobe damage. There are few ‘executive attention’ tasks in the cognitive literature, and fewer still that have been used to test AD patients. One task that is considered an ‘executive attention’ task on some theoretical accounts [e.g., 67] is the Stroop task, in which color names (e.g., ‘red’) are printed in different ink colors (red ink – consistent with the word ‘red’ – or green ink – inconsistent with the word ‘red’). This task requires executive attention because the task demands participants to inhibit an automatic behavior (word naming) to perform a less automatic behavior (color naming). Stroop performance has been investigated in AD, although the results are often interpreted as relating to linguistic processing, not attentional processing, in these patients [46]. AD patients show a larger Stroop effect than control participants: AD patients are disproportionately slower when naming the ink color when the word name conflicts with the ink (the word ‘red’ written in green ink) than naming the ink color when the word name does not conflict with the ink [the word ‘red’ written in red ink, see 68–70]. We should acknowledge that perceptual problems can affect Stroop performance, too, as occurs when colors are confused because of hue discrimination problems in AD [71]. Executive attentional processes have also been studied in AD using dualtask performance. Baddeley’s [72] theoretical framework for working memory points to situations in which a ‘central executive’ mechanism is needed to control the performance of tasks being carried out by working memory ‘slave’ systems. The slave systems include a phonological loop (i.e., a verbal working memory system) and a visuospatial sketch pad (i.e., a spatial working memory

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system). VSTM processes that store objects temporarily might constitute a third slave system under this view. Based on results from AD patients, Baddeley et al., have suggested that the executive processes that control these slave systems are impaired in AD patients. Note, however, that the slave systems are relatively intact under this view. Baddeley et al. [73–75] have demonstrated that AD patients show greater dual-task decrements than control participants. Executive control is required to coordinate the ongoing performance of two (or more) tasks. When a task was performed alone, AD patients did not differ from age-matched control participants; however, when two tasks were performed concurrently, AD patients made more errors on both tasks. These findings suggest an impairment in the central executive’s ability to coordinate multiple, ongoing tasks. Frontal-lobe pathology could be the source of these impairments in AD because patients with lesions to some frontal-lobe areas also exhibit profound impairments in dual-task performance [76]. (Interestingly, we have observed a situation in which concurrent performance of two tasks does not impair both tasks in AD patients. Specifically, when AD patients drive an automobile and perform a demanding auditory task – the PASAT – only performance on the demanding task is impaired. Control of the automobile does not suffer relative to a driving-only condition [77], possibly because driving is an over-learned, procedural skill.) Unfortunately, the vague conceptualization of the central executive makes it difficult to determine if AD patients’ poor performance in dual-task situations is the result of impaired executive processes or impaired slave systems (including VSTM). Poor dual-task performance could result from poor representations in slave systems because there would be two poor representations in a dual-task condition but only a single poor representation in a single-task situation.

Unanswered Issues and Preliminary Results

As we have reviewed above, the basic science of attention has pointed to multiple effects of attention, including perceptual effects and VSTM effects. The literature on attentional impairments in AD patients to date has investigated only a narrow range of these effects, and most studies have focused on task-defined attention, not process-defined attention, making it difficult to understand the processes impaired in AD patients. Despite the progress that has been made in understanding the attentional impairments in AD, there are several unanswered questions on this issue. For example, the tasks that suggest perceptual-level impairments in AD patients could be explained without appealing to perceptuallevel attention (see fig. 4 and related text). Also, executive-level difficulties, such as performance on the Stroop task or dual-task performance, could result

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from damaged working memory slave systems, such as VSTM, and not from an impaired ‘central executive’. Our recent research has focused on overcoming such issues in an attempt to understand the role of VSTM in the attentional deficits observed in AD patients. We assume that there are multiple attentional parameters that govern the level at which attention operates (e.g., at the perceptual level or VSTM), an assumption grounded in both theoretical approaches and empirical results [1, 2]. This approach is important for understanding AD because attentional impairments might be present in one attention system but not another in AD patients. Only by using theoretically-motivated tasks will the source of the attentional impairments be elucidated. Of course, given the heterogeneity of symptoms in AD patients, it is possible that different patients (or subtypes of patients) have different attentional impairments. Again, however, theoreticallymotivated tasks would be crucial for understanding different attentional impairments in AD. Preserved Perceptual-Level Attention in AD In a set of ongoing studies, we have investigated the ability of AD patients to orient spatial attention to an informative peripheral cue (see fig. 3 for an example). As reviewed earlier, many of the spatial cuing results from AD patients are problematic for one of two reasons. First, when detection responses are used in a spatial cuing task, larger attentional effects in AD patients relative to control participants could result from a shift in a response threshold and not from changes in attentional enhancement or attentional disengagement. Second, when discrimination responses are used in a spatial cuing task (e.g., report if a target was a T or an L), high levels of accuracy could mask speedaccuracy tradeoffs. We have attempted to overcome these problems by testing AD patients, older control participants, and younger control participants in a cued discrimination task in which targets are presented briefly and masked. The advantage of this procedure is that we can measure observers’ accuracy in reporting the target, thereby minimizing response threshold shifts and speed-accuracy tradeoffs. Similar tasks have been used in the visual attention literature to provide more rigorous measures of spatial attention [17]. The order of events in our studies is depicted in figure 5a. Our participants are first cued to one of four peripheral locations. On 75% of the trials, a target (a 2 or 5) appears at the cued location (a valid trial), and on 25% of trials, a target appears at one of the other locations (an invalid trial). The target is presented for 50 ms, which prevents eye movements to the target and prevents identification accuracy from being near ceiling. Following the target, a pattern mask (a pound sign) is presented for 200 ms; the mask further reduces accuracy, again

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Fig. 5. a Spatial cuing task in which observers discriminate two target digits (2 vs. 5) following a peripheral cue. Targets are masked to reduce accuracy below ceiling (100%) performance, permitting accuracy to be measured. b Accuracy results from the spatial cuing task for younger control participants, older control participants, and AD patients. Although younger control participants show a smaller attentional effect (valid vs. invalid accuracy) than older control participants and AD patients, the older control participants and AD patients show similar-sized attentional effects. These results suggest that perceptual-level attention is relatively intact in AD patients, when the patients are compared to older control participants.

to ensure that performance is away from ceiling. We measure the accuracy of target detection to valid and invalid trials. Previous research using response time measures has reported greater differences between valid and invalid trials in AD patients than in age-matched control participants [e.g., 78]. That is, AD patients show a larger attentional effect (i.e., invalid RTs minus valid RTs) than control participants. Our preliminary results from 5 patients with mild AD, 20 age-matched, older control participants, and 20 younger control participants reveal a different pattern of results than published RT studies (see fig. 5b). When accuracy is measured, AD patients no longer show a larger attentional effect than age-matched control participants (p ⬎ 0.40), although we do observe a general aging effect: The younger control participants show a smaller attentional effect than the AD patients and older groups combined (p ⬍ 0.006). We have replicated these findings with new samples of participants and a modified task that includes a neutral cue condition (in which no peripheral cue precedes the target). The results from our spatial cuing studies strongly suggest that previously reported differences between AD patients and control participants could be caused by non-attentional differences between these groups. Specifically, AD patients and control participants might have similarly-functioning

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perceptual-level attentional processes, but other processes might differ between these groups. Specifically, AD patients could have higher response criteria than normal control participants, leading to a larger attentional effect in AD patients than in control participants when detection tasks are used. In discrimination tasks in which reaction time is measured, AD patients and control participants could be at different points on a speed-accuracy tradeoff function, which could produce different-sized attentional effects in the two groups. Of course, different response thresholds and different positions on a speedaccuracy function do not fully explain all of the differences reported between AD patients and control participants. Based on our foregoing literature review, we hypothesize that remaining attentional impairments in AD patients (i.e., impairments that cannot be attributed to threshold or speed-accuracy differences) are due to differences in VSTM capacity between AD patients and control participants. We now turn to preliminary evidence to support this hypothesis. Reduced VSTM Capacity in AD We have recently investigated the VSTM capacity of AD patients and older and younger control participants in a change detection task in which abovethreshold objects (color patches) must be maintained in visual memory across a retention interval. The order of events in this task is depicted in figure 2. A memory array of 1, 2, 3, or 4 colored squares first appears for 200 ms. This memory array is followed by a retention interval of 900 ms. A test array then appears until the observer responds. On half of the trials, the test array is identical to the memory array (no-change trial), and on the other half of trials, one item has changed color (change trials). A small response probe box surrounds the object that might have changed, and observers are asked to report if the boxed object is the same or different as it was in the memory array. The accuracy of change detection performance for 8 patients with mild AD and 12 age-matched, older control participants is presented in figure 6. As shown in the graph, both AD patients and control participants perform more poorly as the number of items in the memory array (the set size) increases, and this effect is statistically significant (p ⬍ 0.0001). More theoretically important is the finding that AD patients show a greater decline in performance as set size increases than do control participants. That is, group (AD patients vs. control participants) and set size interact (p ⬍ 0.0001), suggesting that AD patients are disproportionately impaired by adding items to the memory array. There are several potential alternative explanations for these results that do not appeal to a VSTM deficit in AD patients. One possibility is that the AD patients did not understand the nature of the change detection task, which produced poorer performance in this group and contributed to the group-by-set size interaction. Arguing against this alternative explanation is the fact that both

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Fig. 6. Results from a VSTM change detection task (see fig. 2) for older control participants and AD patients. AD patients show a greater decline in accuracy than control participants as additional items are added to the memory set. AD patients have a reduced VSTM capacity compared to older control participants.

the AD patients and control participants performed extraordinarily well at a set size of 1 (97.5% correct for AD patients and 99.5% correct for control participants). If the AD patients had misunderstood the task, one should not expect such high accuracy at a set size of 1. A second possibility is that the current results could have been produced by impairments in verbal short-term memory, not VSTM. Typically, VSTM tasks are coupled with an articulatory suppression task in which the observer repeats a set of words while performing the change detection task. Repeating words prevents phonological rehearsal in verbal short-term memory and thus prevents observers from assigning a verbal name to the visual object (i.e., remembering the items as ‘red, blue, and green’ instead of remembering the visual appearances of the objects). Our change detection procedure did not use an articulatory suppression task because we were unsure if AD patients could simultaneously perform both the change detection task and an articulatory suppression task. However, we are currently collecting data from AD patients performing both tasks, and these results will allow us to determine if the memory decline we have observed is due to verbal memory or visual memory. Finally, what do the present results imply about the capacity of VSTM in AD patients and older control participants? Normal younger observers can typically remember about 3–4 simple objects (colored squares) in this change detection task [29, 33]. We have computed the VSTM capacity for both AD patients and control participants using standard estimates of capacity in change detection tasks [79]. Older control participants appear to store approximately 2.4 objects in this task, whereas mild AD patients only store approximately 1.3 items. Thus, AD patients store approximately 1 object fewer than do the older control participants.

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Conclusions

We have reviewed the literature that reports various impairments in visual attention that occur in AD patients. A critical evaluation of this literature reveals that some of the reported deficits in perceptual-level attention (e.g., spatial attention) could be the result of non-attentional impairments in these patients. One important conclusion from our review is that care must be taken in choosing attentional paradigms for use with AD patients and older control participants. An inspection of the attentional literature reveals that detection tasks and speeded discrimination tasks are potentially problematic for inferring the specific processes of spatial attention that might differ between AD patients and control participants. When we use discrimination tasks that require accurate, but not speeded, responses, we find that the differences between AD patients and older control participants disappear (although both groups differ from younger control participants). Based on our review of attentional impairments in AD, we hypothesize that VSTM might be impaired in these patients. Because VSTM is intimately involved in many attention tasks (e.g., visual search), a VSTM deficit may explain many of the attentional impairments that AD patients exhibit. Our initial tests of visual memory in AD patients suggest that these patients have a reduced visual memory capacity compared to control participants. Experiments in progress will allow us to characterize the operation of visual memory in AD patients and the potential role of VSTM deficits on attentional allocation.

Acknowledgements This chapter was prepared with support from grants from the National Science Foundation (BCS 99-10727), the National Institute of Mental Health (MH60636), and the National Institute of Neurological Disease and Stroke (P01 NS19632).

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Shaun P. Vecera, PhD Department of Psychology, E11 Seashore Hall, University of Iowa Iowa City, IA 52242-1407 (USA) Tel. ⫹1 319 335 0839, Fax ⫹1 319 335 0191, E-Mail [email protected]

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Visual Attention, Genetics and Alzheimer’s Disease Raja Parasuraman, Pamela Greenwood Catholic University of America, Washington, D.C., USA

Alzheimer’s disease (AD) is generally considered to be an amnestic disorder. Yet more than two decades ago it was suggested that older adults with AD had impairments in attention in addition to their memory problems [2, 62, 73]. The early work in this area established that individuals in the beginning stages of AD exhibit deficiencies in specific components of visual attention [for a review, see 72]. Later studies confirmed this finding and further elaborated on the nature of the attention deficit in AD [for reviews, see 68, 77]. Many studies have shown that attention and memory are the first cognitive domains that are affected in the early, mild stages of AD [36, 75, 78]. As a consequence, more recently, investigators have attempted to see whether these aspects of cognition are also dysfunctional in the ‘pre-clinical’ stage, before AD can be clinically diagnosed according to consensus criteria [e.g., 60]. The search for pre-clinical markers of AD has been motivated by the need for early detection, so that efforts to prevent, slow the progression of, or treat AD can have a better chance for success before irreversible brain changes occur. Such efforts have been helped by progress in identifying genetic risk factors in the development of AD. Although many genes may potentially play a role in AD, the most important genetic risk factor for the common form of late-onset AD is the ␧4 allele of the apolipoprotein E (ApoE) gene [16]. Concurrently with these developments, the full specification of the sequence of the human genome [99] has led to renewed interest in understanding genetic contributions to individual differences in perception and cognition, both in normal and in clinical populations [79]. The budding field of the molecular genetics of cognition is growing steadily, and is already yielding significant results not only for the ApoE gene but for a number of other neurotransmitter and neurotrophic genes [23, 34, 43; see 40 for a review]. As these research trends continue, they

will undoubtedly provide opportunities for convergence. This will in turn advance understanding of the development of cognitive dysfunction in AD and its variation between individuals, particularly in the pre-morbid and pre-clinical stages. In this chapter we provide an overview of these developments. We examine changes in visual attention in individuals diagnosed with probable AD, in middle-aged, healthy adults at genetic risk for AD, and in healthy adults with normal variation in certain neurotransmission and neurotrophic genes. Studies of visual attention, memory, and their interrelationship in asymptomatic, healthy adults, and in those in the early or pre-clinical stages of AD are important for many reasons. First, they may help improve early diagnosis of AD. In addition, attentional tasks may provide more sensitive targets than memory tests for evaluating the efficacy of drug therapy [50]. The same may be true regarding the evaluation of other methods to slow, arrest, or reverse cognitive decline in older adults. Furthermore, the results of molecular genetic studies of cognition may permit identification of those individuals who are at greatest risk and therefore may benefit most from these therapies [71]. Finally, attention is also of pragmatic relevance in the daily lives of persons with clinical or pre-clinical AD. Deficits in attention in adults with AD have been linked to such everyday visual tasks as proficiency in reading [52; see also Glosser and Grossman, this volume], and accident risk in driving [74, 86; see also Mapstone and Weintraub, this volume], as well as to falls and other injuries [9, 64].

Visual Spatial Attention in AD

Selective attention is a fundamental aspect of visual processing in the brain. Visual selection may be based on several different environmental attributes, such as location, color, intensity, or spatial frequency, etc., or on groupings of stimulus features that form an object – so-called object-based selection [76; see Kurylo, this volume]. Despite the multiplicity of possible selection mechanisms, there is strong evidence for the primacy of location-based, or spatial selection [13]. Overt spatial selection involves movement of the head and eyes so that the relevant information source falls in the region of the fovea – the high-resolution area of the retina. This method for shifting attention is typically supplemented by covert attention shifts, in which stimuli can be selected without foveation, so that eye position and the focus of attention can be dissociated. Both overt and covert shifts of attention have been studied extensively in normal and in AD populations. Spatial Attention Shifts in AD Covert shifts of spatial attention can be studied by requiring participants to maintain eye fixation at some location (usually the center of a display) while a

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cue directs attention to another location. This highly influential paradigm was developed by Posner [81]. In a typical form of the task, a target stimulus is either presented at the cued location (‘valid’ cue) or at some other location (‘invalid’ cue). Occasionally a ‘neutral’ cue that points to both the correct and an incorrect location may be presented. Furthermore, because any cue can have an alerting effect in addition to providing location information, investigators have also attempted to distinguish between the two effects by using a no-cue condition [20, 28]. If the location cues are presented with equal probability, participants cannot use expectancy to guide the movement of their attentional focus. Any such shift of attention is said to occur reflexively or exogenously. When the valid cue occurs more often than invalid or neutral cues, however, attentional shifts can be voluntary or endogenous. Reaction time (RT) to detect the target is typically faster following a valid cue compared to the neutral cue that provides no location information, for both exogenous and endogenous cueing, although the time course and persistence of this effect differs for the two types of cueing. In contrast, RT is significantly slowed when the cue is invalid and directs attention to some location other than the target, presumably because of the need to disengage or shift attention away from the incorrect to the correct location [81]. The benefit of a valid cue and the cost of an invalid cue for RT are also found in target discrimination tasks in which bias-free measures of accuracy can be obtained [42], suggesting that location cues boost sensory processing of the target. Using a visual contrast sensitivity paradigm, Carasco et al. [10] also showed that location cues lead to exogenous attention shifts that boost the signal-to-noise ratio of stimuli at the attended location. Event-related brain potential (ERP) studies have confirmed this view. The ERP components that are modulated by spatial attention typically have a short latency of about 70 ms following the attended target onset, indicating that selection occurs at an early, sensory stage of processing in the visual system, probably involving the extrastriate cortex [51, 53, 54]. Extrastriate modulation of visual processing with covert spatial attention is thought to be itself under the control of a distributed network involving the posterior parietal cortex [14, 82]. Neuroimaging studies using positron emission tomography have shown that glucose metabolism in temporal-parietal cortex is markedly reduced in the early stages of AD [45]. Thus, spatial attention shifting should be impaired in early AD. This was demonstrated in a study by Parasuraman et al. [70]. They tested mildly demented AD participants and agematched controls on a modified version of the Posner [81] paradigm in which a spatially-cued letter-discrimination task was used. Location cues were presented either centrally or peripherally, to elicit covert shifts of attention that were largely either endogenous or exogenous, respectively. The AD group, like

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the controls, was faster to respond to a target when the cue was valid (RT benefit) compared to when it was neutral or invalid, indicating that the ability to focus attention on the target was not substantially compromised by AD. In contrast, for either peripheral or central cues, the AD group had longer RTs to targets when the location cue was invalid (RT cost). This pattern of results indicated the early stages of AD are associated with an attention-shifting or disengagement deficit. The deficit in attention shifting is consistent with the effects of AD on the metabolic integrity of the parietal lobe. Using positron emission tomography, Parasuraman et al. [70] found that the attention-shifting deficit in individuals with AD was correlated with the degree of hypometabolism of the right posterior parietal cortex. A similar finding using single photon emission computed tomography was reported by Buck et al. [6]. Several other studies have also examined spatial attention shifting in AD and found broadly similar results [6, 19, 48, 58, 63, 95; also see 27], although some negative findings have also been reported [7, 8, 58]. Oken et al. [63] tested AD and control participants on a simple visual discrimination (circle vs. square) presented to the left or right visual field and preceded by location cues. The AD group had disproportionately longer invalid cue RTs compared to controls, pointing to a disengagement deficit in AD. Parasuraman et al. [70] reported that this deficit occurred whether attention was driven exogenously (peripheral cues) or endogenously (central cues). In their original study, cue type and cue-target probability were not manipulated independently, so that exogenous and endogenous attention were confounded when peripheral cues were used. It is possible that when non-predictive (e.g., 50% validity) peripheral cues are used, adults with AD may not show an attention deficit. Two subsequent studies using different cue-target probabilities and both central and peripheral cues have shown that the attention shifting deficit is restricted to central cues [19]. However, Tales et al. [95] used non-predictive (50% validity) peripheral cues and did find a spatial attention deficit in an AD group. Whether such a deficit is limited to endogenous visual attention shifts, or applies to exogenous shifts as well, is currently unresolved. Some studies have also found no spatial attention deficit in AD, e.g. Caffrara et al. [7]. The negative findings may reflect factors such as task sensitivity, small sample sizes, and dementia severity. These investigators used a simple detection task (replicating the original Posner [81] paradigm) instead of the letter discrimination task used by Parasuraman et al. [70], who also found that AD participants did not differ from age-matched controls in a simple letter detection task. Simple detection of a target in an otherwise empty field imposes minimal attentional demand because there are no objects competing for selection. Accordingly, spatial attention effects in normal individuals are

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typically large only when a discrimination or search task is used and distractors are present [76]. Thus, use of a detection task is likely to be associated with a smaller effect size when comparing AD and control participants, pointing to the importance of sample size and task sensitivity as probable contributory factors. This is supported by a closer examination of the study by Caffrara et al. [7]. Despite their AD group showing mean RT costs (80 ms) that were twice as high as that of controls (38 ms), the difference was not significant for the small sample of patients they tested (n ⫽ 7). Visual discrimination tasks may therefore provide for more sensitive assessment of attentional shifting in AD than do detection tasks. The deficit in covert attention shifting in individuals with AD is also reflected in deficits in overt shifts of attention, as reflected in eye movements [18, 90]. Scinto et al. [90] found that individuals with AD were less accurate and slowed in shifting their gaze between a central fixation point and a series of target dots presented in succession at peripheral locations. The most common error was perseverative fixation of the center point of one of the peripheral targets. Scinto et al. suggested that perseveration of gaze may be associated with slowed disengagement of covert attention in AD. This is consistent with the view that attention shifts precede and guide eye movements [47], so that any delay in shifting attention will also be reflected in eye movement patterns. Adults with AD are also impaired in making anti-saccades, i.e. eye movements in a direction opposite to that of a peripheral stimulus with sudden onset, or in inhibiting covert attention to a cued location where targets seldom occur [58]. Dynamic Scale of Spatial Attention in AD In many of the covert attention studies discussed previously, participants were asked to attend to a single target at a cued location in an otherwise empty visual field, following the paradigm developed by Posner [81]. This task has the advantage that it is simple and can be performed by a wide range of individuals with neurological and psychiatric disorders, including those with dementia. It has also been adapted for use in animal studies [87, 100], which have provided valuable information on the neural systems mediating shifts of spatial attention. One drawback of the simple covert attention task, however, is that it does not capture a major feature of selective visual processing: in the real visual world, distractors are almost always present along with the target. The visual search task provides a better analog to such everyday visual tasks. Identification of a target in a field containing many distractors depends on target salience and the relation between the features defining the target as opposed to the distractors. If the target is specified by a single, unique feature, say color (e.g., a red object among blue or green distractors), it tends to ‘pop out’ and is identified rapidly. In contrast, search time is slowed for targets that

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are defined by a conjunction of features, say color and shape (e.g., a red square among other red objects or other squares), or when the difference between target and distractor is low [22, 98]. In these, more difficult conditions, search is thought to be mediated by successive covert shifts of attention [97]. Individuals with AD can perform both forms of search but typically are slowed relative to controls for the more difficult conjunction search [35, 41]. Searching for a target in a cluttered display also requires spatial attention to be distributed broadly or narrowly over the visual field. Consider searching for a hair on a dinner plate versus trying to locate the face of a friend at a crowded bar. Such changes in the spatial scale of attention can serve to supplement other mechanisms of visual selection, including covert and overt spatial attention [39]. A relatively small scale may be optimal when searching for a small object. For larger objects or composite objects made up of smaller parts, however, a wider attentional focus may be more efficient [12, 24, 25]. Spatial cues that vary in size can be used to manipulate the size of the attentional focus. In general, the smaller or more precise the spatial cue, the more efficiently the target should be identified. Studies have shown that participants can voluntarily adjust the effective area of the attentional focus from large to small or vice versa but, just like a ‘zoom lens,’ resolving power must be traded off against the size of the attended area [24, 25]. The dynamic scaling of spatial attention can be examined by trial-to-trial variations in the size (or precision) of location cues. Parasuraman et al. [68] developed a visual-search task using location cues that varied in size and hence in precision of target localization [see also 41]. Participants were required to identify a target presented in an array of letters; both feature and conjunction search conditions were used. The search array was preceded by a cue that varied in size across trials over several values from small to large. The time to identify the target showed a progressive increase with such trial-to-trial increases in cue size. The monotonic increase in RT with cue size suggests a mechanism of dynamic adjustment of the spatial scale of attention [39]. The slope of the RT-cue size function indicates the efficiency and dynamic range of the attention scaling mechanism. The greater the slope, the greater the range of the visual space or items over which a participant can effectively change their attentional focus. Conversely, a shallower slope indicates reduced ability to scale attention effectively. Given that individuals with AD have prominent parietal lobe hypometabolism and show an attentional shifting deficit [6, 70], they should also be impaired in performing cued visual search tasks in which repeated shifts of spatial attention are required. We tested this hypothesis using a task in which targets requiring feature (color only) or conjunction search (color plus shape) were preceded by cues of three different sizes [69]. As figure 1 shows, the effect of

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cue size on target RT was significantly reduced in the AD group compared to a control group of healthy older adults. These findings indicate that individuals in the mild stages of AD exhibit an overall benefit of cueing in the cued-visual search task but that the benefit is markedly reduced. This in turn suggests an impairment in AD in the ability to adjust the spatial scale of attention during visual search. Figure 1 shows that the AD group benefited from the smallest (most precise) cue but not for cues of larger size. AD therefore appears to constrict the spatial scale of attention to a very narrow range in the visual field [see also 17].

Visual Spatial Attention in Healthy Aging and in Degenerative Disorders Other Than AD

The studies of visual attention in mild AD indicate that different aspects of spatial attention are impaired in the early stages of AD. The shifting of covert attention, as well as overt eye movements, are both slowed as a result of dysfunction of the posterior parietal cortex. The scaling of spatial location is also affected over its dynamic range from small to large, with AD participants apparently unable to scale beyond a fairly narrow focus of attention. Given these changes, it is natural to ask to what extent, if any, they are specific to AD, or whether similar deficits occur in other neurodegenerative disorders or in

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Fig. 2. Effects of adult aging (in several age decades from the 20s to the 70s) on the reaction time costs and benefits of location cueing in a covert orienting task with central or peripheral cues (n ⫽ 15 for age decade group). Data for an AD group (n ⫽ 15) are also shown.

normal aging. While the issue has not been comprehensively explored, some generalizations can be made. First, these spatial attentional changes may not be specific to AD but may be a consequence of any disorder that affects the integrity of the posterior parietal lobe, such as neglect as a consequence of stroke. Patients suffering brain injury affecting this cortical region may also exhibit spatial attention deficits of the type seen in AD. Presumably, however, attentional performance in these other neurological conditions will either remain stable or show some recovery (as in neglect patients), whereas the limited longitudinal data on AD show that the attentional impairment becomes steadily worse over time [1, 66]. Moreover, disorders such as Parkinson’s and Huntington’s disease, which typically do not involve direct pathology of this brain area, lead to different patterns of deficits in visual spatial attention [30, 31]. Finally, the changes seen in AD – both in attention shifting and in attention scaling – differ qualitatively from visual spatial attention changes associated with healthy aging [41, 42], at least up to about 75 years of age [38]. Figure 2 shows the overall RT costs and benefits for both peripheral and central cues for several groups of healthy adults from the 20s to the 70s. There was no significant effect of adult aging for the peripheral cues, pointing to the stability of exogenous attention shifting. There was a small but significant increase in RT costs and benefits for central cues, suggesting that endogenous attention is modestly age sensitive. The results for an AD group [70] study are also plotted in figure 2, and clearly show that the effects of AD on exogenous

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and endogenous attention are qualitatively and quantitatively different from those associated with healthy aging.

Visual Attention in Nondemented Adults at Genetic Risk for AD: The ApoE Gene

The evidence is fairly clear that AD is associated with specific deficits in visual selective attention that are distinct from those accompanying normal aging and from degenerative disorders not involving pathology in the posterior parietal lobe. Understanding how these deficits develop in the early stages of the disease can be considerably facilitated by attentional studies in individuals who are at genetic risk for developing AD. Functional changes in such at-risk individuals, if found, might be indicative of the development of AD and could clarify the issue of the sequence in which cognitive deficits develop in AD. Several genetic risk factors for early-onset AD have been identified. The ␧4 allele of the ApoE gene is the major risk factor for the more common, lateonset AD [16, 94]. Consequently, a useful strategy to examine the precursors of AD is to investigate changes in cognition in nondemented individuals with the ApoE-␧4 genotype. Individuals with one ␧4 allele have a greater risk of developing AD than those with none, and those with two ␧4 alleles have further elevated risk. However, it is important to note that inheritance of the ␧4 allele is neither necessary nor sufficient for the development of AD: the ApoE-␧4 gene confers increased risk, no more. Despite this caveat, studies of individuals with and without the ApoE-␧4 gene can be very informative with respect to the early development of AD. A number of such studies have been reported in recent years. Reed et al. [84] found lower scores on standard neuropsychological tests in ApoE-␧4 carriers than in those without an ␧4 allele. Bondi et al. [5] also reported that verbal memory scores were lower in nondemented adults with ApoE-␧4. Other studies have found altered neuropsychological test performance in nondemented carriers of the ApoE-␧4 gene compared with non-␧4 carriers [3, 4, 11, 29, 46, 49], but negative results have also been reported [85, 91, 92]. (For a recent review, see Parasuraman et al. [71].) In our studies, we have focused on investigating specific informationprocessing components underlying attention and memory that may be linked to allelic variation in the ApoE gene. Greenwood et al. [43] examined 97 middleaged adults (mean age ⫽ 58 years) genotyped for ApoE on tests of attention shifting and attention scaling. These participants were nondemented and showed no deficits on an extensive battery of standard neuropsychological tests. The tasks used were the covert attention shifting [70, 81] and dynamic

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attention scaling tasks [41, 68] used in our previous studies of AD. For the covert attention task, RT to invalid cues was slowed in the ␧4 group compared to non-␧4 carriers, whereas RT benefits of valid cues did not vary significantly with ApoE genotype. Thus, ApoE-␧4 carriers exhibited an attentional disengagement deficit. In the attention scaling task, RT increased with cue size for all participants, but the cue size effect varied with ApoE genotype. A measure of the cue size effect was calculated as the slope of the regression of RT on cue size. The slope of this RT/cue size function was lower in the ␧4 group than in non-␧4 carriers. Thus the spatial scaling of attention was reduced in individuals with the ␧4 allele compared to those without the ApoE-␧4 allele. This pattern of findings is strikingly similar to that found previously with the same tasks in individuals with AD. Specifically, nondemented adults with the ApoE-␧4 gene show the same, selective pattern of attentional performance as do clinically-diagnosed Individuals with AD: (1) a deficit in covert attentional shifting [70]; (2) a reduction in the ability to scale spatial attention dynamically [68, 69], and (3) no change in arousal and vigilance decrement [72]. It is noteworthy that these findings were obtained in a relatively young group of adults who showed no deficits on standard neuropsychological tests. Moreover, the attentional deficits occurred without nonspecific changes in vigilance and with preserved whole-task performance on standard neuropsychological tests of cognitive function. The attentional changes are qualitatively (but not quantitatively) the same as those reported previously in individuals in the early, mild stages of AD. These results have implications for an understanding of the genetic basis of normal attentional functioning and its variation with aging and AD. Because ApoE-␧4 is a risk factor for AD, the findings are also relevant to the analysis of the pre-clinical stage of AD. What are the mechanisms by which the ApoE gene influences attention in healthy, otherwise asymptomatic individuals of middle age? A number of possibilities suggest themselves [see 71]. One is that the pattern of attention deficits in nondemented ApoE-␧4 carriers is indicative of preclinical changes that could develop into AD. Validation of this possibility will require long-term longitudinal studies, given the relative young age (50s) of the healthy but at-risk adults examined by Greenwood et al. [43]. On the other hand, the results could reflect a direct effect of ApoE genotype on cognition, or a so-called ‘cognitive phenotype’ [82]. Such a view would suggest that the influence of ApoE-␧4 on cognition should be observed in individuals younger than the middle-aged adults tested by Greenwood et al. [43], say adults in their 30s and 40s, or indeed, even younger adults. To our knowledge, no such study has yet been conducted, although Flory et al. [33] found deficits in standard neuropsychological tests in ApoE-␧4 carriers in the 24–60 age range (mean age ⫽ 46).

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Distinguishing between these possibilities would be facilitated by studies on the effects of normal variation in different genes on attentional function. Such studies will especially be needed to investigate the ‘cognitive phenotype’ hypothesis of ApoE. As a neurotrophic gene, the protein products of ApoE have fairly broad effects on neuron health, so that many different aspects of cognition, not just visual attention, are likely to be linked to the ApoE gene. Additional research on genes that control neurotransmitter systems that are dysfunctional in AD may provide further insights. We turn now to a brief examination of this work.

Molecular Genetics and Cognition

A Framework for Associating Polymorphic Genes to Components of Cognition Our approach to the genetics of normal cognition involves combining two methods: (1) the allelic association approach of behavioral genetics [80], in which normal variation in single genes is associated not with disease, as is normally the case, but with individual differences in the efficiency of executing an elementary cognitive operation, and (2) the methods of modern cognitive neuroscience, in which cognitive operations are linked to the activation of regional brain networks – as studied using neuroimaging, electrophysiological, and pharmacological techniques. This combined approach allows for theory-based, empirical analysis of the role of particular polymorphic genes in cognition [67]. Application of this approach requires the identification of ‘candidate’ genes that are involved in a specific cognitive function. No component of cognition is likely to be modified by only one or even a few genes. Furthermore, partitioning individual differences in a particular cognitive function requires analysis of the interaction of environmental factors with these genes [79]. Nevertheless, certain genes appear to contribute to individual differences in components of cognition in relatively specific ways, and in some cases with surprisingly strong effect sizes [67]. Of the millions of DNA sequences in the human genome, a significant minority of base pairs (bp) occur as different forms or alleles in unrelated individuals. Such variations, termed single nucleotide polymorphisms (SNPs), occur at a rate of about 1 every 1,000 bp in unrelated individuals. There are thought to be an estimated 1.8 million SNPs, but only about 5–10% of these are associated with disorders [80]. Polymorphisms can influence brain function through effects with a range of specificities – from effects limited to one receptor type, to effects on neurotransmitter systems, to whole-brain effects, as on neuron health [see 40, 67].

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The overall goal of our approach is to determine the role of specific genes in producing a cognitive phenotype. But even given that SNPs with functional significance for behavior represent only a small part of the human genome, there are still so many of them that forging valid links to cognition and minimizing false positive results may prove difficult. We address this problem by using the allelic association approach within a theoretical framework in which the ‘intermediate steps’ between genotype and phenotype are specified (to a degree). Such a framework capitalizes on the breakthroughs in understanding the neural bases of cognition that have been made possible by modern cognitive neuroscience. Although no such broad framework for all of cognition currently exists, enough is known to begin to undertake such an approach for particular cognitive domains. Acetycholine Receptor Genes: Their Relation to Attention and AD Attention provides an excellent model cognitive domain for investigating the utility of the approach outlined above for the molecular genetics of cognition. One reason is that the neural mechanisms of attentional function are increasingly well understood at both a systems level (cortical network) and more microscopic levels [55, 65]. For example, Posner [83] has proposed an influential ‘attentional network’ theory in which three separate attentional functionsorienting, alerting, and executive function – are linked to the activation of separate but overlapping cortical and subcortical networks. Posner and colleagues [28, 34] have also argued that performance on psychological tests of these attentional functions can serve as phenotypes against which candidate genes can be tested. We have also argued that specific information processing tests of particular attentional functions can serve as ‘behavioral assays’ of the integrity of cortical networks, thereby allowing for an assessment of the effects of neurodegeneration on these networks [66, 71]. At the same time as networks of attentional functions have been identified, their neurochemical innervation are being increasingly well specified [20, 26, 55]. A growing body of evidence from lesion and electrophysiological studies in animals and neuroimaging and pharmacological studies in humans points to the important role of the acetylcholine (ACh) neurotransmitter system in posterior brain networks in spatial attention [21]. It therefore follows that if progress is also made in delineating the genes and their protein products that influence cholinergic innervation of the cortex, then links can be established between cholinergic genes and different aspects of cognitive function. The cholinergic system has also long been an important area of investigation in AD research, given the early studies showing that depletion of ACh may play a critical role in the development of dementia [21]. Lesions of the basal forebrain have demonstrated the importance of cholinergic system integrity for

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visual spatial attention [26, 100]. Voytko et al. [100] found that monkeys with basal forebrain lesions showed greater increases in invalid cue RTs in a covert orienting task, compared to a control group. Both control and lesioned monkeys were equally fast in responding to the target when cues were valid. However, the lesion group showed disproportionately longer RTs for invalid cues. Voytko et al. [100] noted that this pattern of deficit in the basal forebrain lesioned monkeys – intact performance on valid location cues and delayed responding with invalid cues – was identical to that previously found in individuals with AD [70]. Furthermore, the same monkeys showed no deficits on a delayed nonmatch to sample task learned preoperatively, suggesting that integrity of the cholinergic basal forebrain is more important for visual spatial attention than for working memory. ACh receptors in the brain are either muscarinic or nicotinic, and each of these classes of receptors are themselves made up of different subgroups of receptor types. The nicotinic subtypes are thought to be the more important for understanding the cognitive impairment of AD [101]. A number of nicotinic ACh receptor (nAChR) subunits are controlled by polymorphic genes that may contribute to individual differences in neuromodulation of attentional processes. At present, there is evidence for the modulation of attention by two subunit genes, CHRNA4 and CHRNA7 (see below). The ␣4 nAChR subunit is a component of the most widely distributed nicotinic receptor in cortex, the ␣4/␤2 nAChR [32]. The ␣4 subunit may play an important role in cognitive decline associated with aging and AD. There is almost total loss of ␣4 nAChR subunits in the hippocampus in aged mice [88]. Human postmortem studies have also found an age-related decrease in both ␣4 and ␤2 subunit expression in frontal cortex [96]. AD is associated with a selective loss of ␣4 nAChRs in cortex, compared to ␣3 and ␣7 nAChRs [56; see also 101]. The importance of these subunits to brain function and their vulnerability to age and AD suggests that variations in the genes controlling them could be a source of individual differences in cognition. It is also possible that any such effects occur most strongly in older adults. Some recent preliminary findings from our laboratory have found an association between the CHRNA4 gene and visual spatial attention [37]. This gene is expressed strongly in parietal cortex, which is known to mediate visual spatial attention [14, 15]. We genotyped a group of healthy adults aged 18–68 years for the CHRNA4 gene, specifically for a T to C exchange polymorphism (T1545C) [93]. The visual spatial attention task was the same as the one used in our previous studies of adults with clinically diagnosed AD or at risk due to inheritance of the ApoE-␧4 gene. An arrow cue indicated which of two locations to the left or right of fixation would contain a letter target. Following a cue-target delay of 200–2,000 ms, the target letter appeared.

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Participants were required to make a speeded decision as to whether the target was a consonant or vowel. Cues were valid, invalid, or neutral. We found that both the RT benefits of valid cues and RT costs of invalid cues on this letter discrimination task were modulated by CHRNA4 genotype. With an increased ‘gene dose’ of the C allele (from 0 to 1 to 2 C alleles) RT benefits increased progressively, whereas RT costs decreased. More importantly, individual differences in this task were not significantly related to normal variation in another gene, dopamine ␤-hydroxylase (DBH), which has been linked to pre-frontal cortex mediated functions of executive control [40], rather than visual spatial attention. The sample size in our preliminary study is not yet large enough to partition these results by age, but nevertheless, these systematic results provide the first evidence of a specific role for the CHRNA4 polymorphism in visual spatial attention. The relative specificity of the single gene effects obtained in recent studies with the CHRNA4 [37] and DBH genes [23, 37] may be consistent with the role of these genes as neurotransmitter receptor modulators. In contrast, allelic variation in the ApoE gene seems to be related to changes in both visual spatial attention and working memory, and in studies in other laboratories, to other aspects of cognition [see Parasuraman et al., 71, for a review]. These broader effects are consistent with a putative role of ApoE and other neuroprotective genes in processes of neuronal repair and health.

Conclusions

Visual attention is impaired at an early stage in the development of AD. Clinically diagnosed individuals with AD show specific impairments in the shifting of covert attention and in using overt eye movements to guide attention. These have been linked to dysfunction of the posterior parietal cortex and associated cortico-cortical networks [61, 72]. The dynamic scaling of spatial location is also impaired in early-stage AD. While AD participants can focus attention on a small region of visual space in order to find a target, they are apparently unable to scale beyond this fairly narrow focus of attention. The deficits in AD in both the shifting of attention and the dynamic scaling of attention are qualitatively and quantitatively different from the visual attentional changes that accompany normal aging. Given the establishment of these attention deficits, several studies of attention in pre-clinical stages of AD have been conducted in healthy adults with the ApoE gene. The results to date indicate that middle-aged adults with either one or two copies of the risk-associated ApoE-␧4 allele show deficits in spatial attention shifting and in attention scaling that are qualitatively similar (but smaller) to those with clinically diagnosed AD. Whether these attentional

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changes are due to incipient dementia or reflect a ‘cognitive phenotype’ of a particular genotype is currently a focus of much investigation [see 71, for a review]. The ApoE gene has fairly broad effects on neuron health, and may therefore have fairly widespread effects in the brain and on many different aspects of cognitive function. Further developments in the molecular genetics of cognition [67] may provide additional clues as to the specificity and generality of the results of genetic risk studies in AD.

Acknowledgment Preparation of this chapter was supported by NIA Grant R01 AG19653.

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Rogers SW, Gahring LC, Collins AC, Marks M: Age-related changes in neuronal nicotinic acetylcholine receptor subunit ␣4 expression are modified by long-term nicotine administration. J Neurosci 1998;18:4825–4832. Saunders AM, Strittmatter WJ, Schmechel D, George-Hyslop PH, Peikac-Vance M, Joo S, Rosi B, Gusella J, Crapper-MacLaghlan D, Alberts M, Hulette C, Crain B, Goldgaber D, Roses AD: Association of apolipoprotein E allele ␧4 with late-onset familial and sporadic Alzheimer’s disease. Neurology 1993;43:1467–1472. Scinto LF, Daffner KR, Castro L, Mesulam M: Impairment of spatially directed attention in patients with probable Alzheimer’s disease as measured by eye movements. Arch Neurol 1994;51: 682–688. Small BJ, Basun H, Backman L: Three-year changes in cognitive performance as a function of apolipoprotein E genotype: Evidence from very old adults without dementia. Psychol Aging 2000; 13:80–87. Smith GE, Bohac DL, Waring SC, Kokmen E, Tangalos EG, Ivnik RJ, Petersen RC: Apolipoprotein E genotype influences cognitive ‘phenotype’ in patients with Alzheimer’s disease but not in healthy controls. Neurology 1998;50:355–362. Steinlein OK, Magnusson A, Stoodt J, Bertrand S, Weiland S, Berkovic SF, Nakken KO, Propping P, Bertrand D: An insertion mutation of the CHRNA4 gene in a family with autosomal dominant nocturnal frontal lobe epilepsy. Hum Mol Genet 1997;6:943–947. Strittmatter WJ, Saunders AM, Schmechel D, Perikac-Vance M, Enghild J, Salvesen G, Roses AD: Apolipoprotein E: High avidity binding to ß-amyloid and increased frequency of type 4 allele in late-onset familial Alzheimer disease. Proc Natl Acad Sci USA 1993;90:1977–1981. Tales A, Muir JL, Bayer A, Snowden RJ: Spatial shifts in visual attention in normal aging and dementia of the Alzheimer type. Neuropsychologia 2002;40:2000–2012. Tohgi H, Utsugisawa K, Yoshimura M, Nagane Y, Mihara M: Age-related changes in nicotinic acetylcholine receptor subunits ␣4 and ␤2 messenger RNA expression in postmortem human frontal cortex and hippocampus. Neurosci Letts 1998;245:139–142. Treisman A: The binding problem. Curr Opin Neurobiol 1996;6:171–178. Treisman A, Gelade G: A feature integration theory of attention. Cognit Psychol 1980;12:97–136. Venter C, Adams MD, Myers EW, et al: The sequence of the human genome. Science 2001;291: 1304–1351. Voytko ML, Olton DS, Richardson RT, Gorman LK, Tobin JR, Price DL: Basal forebrain lesions in monkeys disrupt attention but not learning and memory. J Neurosci 1994;14:167–186. Woodruff-Pak DS, Gould TJ: Neuronal nicotinic acetylcholine receptors: Involvement in Alzheimer’s disease and schizophrenia. Behav Cogn Neurosci Rev 2002;1:5–20.

Raja Parasuraman, PhD Cognitive Science Laboratory, The Catholic University of America Washington DC 20064 (USA) Tel. ⫹1 202 319 5755, Fax ⫹1 202 319 4456, E-Mail [email protected]

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Closing the Window of Spatial Attention: Effects on Navigational Cue Use In Alzheimer’s Disease Mark Mapstonea, Sandra Weintraubb a

Department of Neurology, University of Rochester Medical Center, Rochester, N.Y., and bCognitive Neurology and Alzheimer’s Disease Center, Northwestern University Medical School, Chicago, Ill., USA

Attention has been vigorously investigated since the late 19th century. While there is still much debate over the nature of attention and its components, it is commonly accepted that attention is not a unitary process and that several subtypes exist. These have been characterized as sustained attention (or vigilance), divided attention, and selective attention [1]. The topic of this chapter, spatial selective attention, pertains to the focal channeling of attention to a spatial location or a particular stimulus in space. This process occurs in all sensory modalities, and consistent with early notions of limited processing capacity of the brain, serves to reduce the overwhelming stream of stimuli that are encountered on a continual basis [2]. Several metaphors have been used to describe the spatial distribution of attention in extrapersonal space, including that of the zoom lens of a camera or a spotlight [3–6]. These metaphors capture a fundamental property of spatial selective attention: the size of the attentional window can be modified according to task demands and this flexibility is associated with inversely proportional changes in resolution. In this chapter, we will use the term ‘window of spatial attention’ to refer to the spatial area within which attention can be deployed (either overtly or covertly). This conceptualization does not impose restrictions on the nature of attention within this window; attention can be spread broadly or may be directed focally to one point in space. The central theme of this chapter is a proposal for a mechanism whereby the window of spatial attention is progressively restricted by the encroaching neuropathology of Alzheimer’s disease (AD). This restriction potentially has

far-reaching behavioral implications and we shall consider its effect on the use of visual cues for spatial navigation.

Attentional Deficits in Alzheimer’s Disease

AD is well known as a disorder of memory. However, several behavioral subtypes characterized by primary non-mnemonic deficits have been proposed [7–10] and the behavioral heterogeneity of AD is becoming more thoroughly understood. Numerous recent studies have demonstrated that attentional deficits are prominent in AD [11–18]. It has been suggested, in fact, that attentional deficits may be one of the earliest manifestations of AD [19]. Although the prominence of these deficits is becoming clear, it is important to note that different aspects of attention are not affected equally in AD [13]. The specificity of early attentional dysfunction in AD supports the hypothesis of independent neuroanatomical networks for attention subtypes. In addition, this specificity also reinforces the notion of regional selectivity of AD neuropathology. Sustained attention and immediate attention span appear to be relatively spared in mild-to-moderate AD patients [19–21, but see 22 for evidence to the contrary]. However, AD patients have been shown to have difficulty on attentional tasks requiring shifts of attention from one spatial location to another either without eye movements (covert shifts) [11, 14, 23, 24] or with shifts in gaze (overt shifts) [12, 17]. In addition, several studies have shown deficits on tasks of divided attention or dual-task paradigms [25, 26].

Neuroanatomy of Spatial Attention

The evidence for attentional deficits in AD may not be surprising in light of recent formulations of neural networks for spatial attention and the regional specificity of AD-related neuropathology. Over a century’s worth of experimental evidence from animal lesion studies, studies of patients with focal brain lesions, and from functional imaging and electrophysiological studies has revealed much about the neural substrates involved in spatial selective attention. Many studies have demonstrated a prominent role of posterior parietal cortex in the allocation of attention to spatial locations [27–30]. This region of cortex (Brodmann’s areas 39 and 40) is the most common site of lesions that produce the paradigmatic disorder of spatial attention, hemispatial neglect [31, 32]. The prefrontal cortex also appears crucial to attentional processing [33, 34] especially with regard to the exploration of space either with the eyes or hands [35]. The frontal eye fields (area 8 in monkeys) appear to be critical in visual search tasks and may even play a role

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in coordinating eye movements to scan space for visual targets [3]. Finally, the anterior cingulate gyrus (area 24) is implicated in aspects of directed spatial attention [36–38]. This region may be important in selecting and recruiting brain sites necessary for particular task demands [37] or may be involved in motivational or emotive aspects of attention [35, 39, 40]. Several investigators have proposed neuroanatomically distributed attention networks that include these cortical sites [27, 39, 41]. These distributed networks have received recent empirical support from functional imaging and lesion studies [35, 39, 41–46].

AD Pathology and the Attentional Network

AD neuropathology has been shown to affect each epicenter of the spatial attention network [47, 48]. Neurofibrillary tangles (NFTs) have been found in greater concentrations in heteromodal association areas than in primary sensory areas in AD [49]. The highest densities of NFTs are found in the temporal lobes, with the parietal and frontal lobes containing the second and third highest densities of NFTs [50]. Senile plaques, on the other hand, are more evenly distributed among these cortical regions. However, there are some regional predilections with frontal and anterior cingulate cortex having higher densities of senile plaques than temporal neocortex [51]. AD-related neuropathological change in the parietal cortex has been shown to be associated with deficits in visually guided saccades in the service of attention [52]. In addition, several imaging studies have demonstrated hypometabolism of parietal lobe structures associated with attentional tasks in AD patients [53, 54]. In a recent study using human post-mortem tissue, Gabriel et al. [55] found strong evidence of cholinergic disruption in the frontal eye fields of AD patients. In an earlier study, Ferrer et al. [56] demonstrated specific neuronal changes in layers II, III, and VIb of the frontal eye field in AD patients. Finally, AD has also been shown to affect cortico-cortical connections that link these attentionally-relevant epicenters [57, 58]. AD is known to affect those striate and extrastriate regions implicated in controlling the spatial window of attention. Several studies have demonstrated significant focal atrophy and associated neuropathological changes in posterior parietal extrastriate visual association areas [59–63]. In addition, temporoparietal cortex is severely affected [58, 61]. Cortico-cortical projection neurons are most affected so that a functional disconnection is created in the visual pathways [50, 64]. This localization is confirmed in PET studies that show a 35–40% decrease in occipito-parietal basal metabolism in the visual variant of AD [see Mendez, this volume] as compared to other clinical profiles in AD patients [65], with preservation of activity in other areas [66].

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The progression of AD pathology is known to ‘move’ from temporal regions to parietal and frontal regions and eventually to involve specific subcortical structures, while relatively sparing primary sensory cortex [48]. The spatial window of attention appears to rely at least in part on the zone of cortex bordering extrastriate and striate regions [67]. In addition, the window of attention may be subject to feed-backward processes from higher-level heteromodal visual association cortex to extrastriate areas [68]. The spread of pathology from parietal regions through retinotopic-like extrastriate heteromodal association cortex toward primary visual regions may be the functional mechanism by which the window of spatial attention is reduced in AD. Serial assessments of AD patients on measures of the window of spatial attention correlated with PET imaging of regional hypometabolism in extrastriate visual association areas may give clues to this proposed mechanism.

Window of Spatial Attention Is Reduced in AD

Relatively few studies have directly assessed the size and nature of the window of spatial attention in AD. However several recent studies have provided indirect evidence that the size is reduced in AD. Parasuraman and colleagues [69–71] have focused on changes in the window of spatial attention based on a cued visual search task. In these studies, subjects are required to search a moderately complex visual display for a single target in an array of distractors. The target is distinguishable from the distractors based on a single feature (e.g., color) or a conjunction of features (color and shape). Before each search trial, subjects are given a spatial cue as to where the target will appear. Cues are not always correct. In addition, they identify the spatial location of the target with varying specificity, from the hemifield of the target to the exact location of the target. This experimental paradigm was designed to quantify the size of the window of attention. In one such study [69], it was demonstrated that as the valid cues became more specific, AD patients and control participants both benefited by finding the target faster. However, the AD patients received significantly less benefit than did control participants. In a separate study [71], the authors replicated their previous findings in AD [69] and also found that old-old normal control subjects (age ⬎75) received less benefit than young-old normal control subjects (age ⬍75). The authors suggested that this finding could have been due to the presence of pre-clinical AD patients in the old-old group. Thus, a reduction in the window of spatial attention may be an early marker of AD-related pathological changes. In another study of visual search in AD, Foster et al. [72] provided indirect evidence for a reduction in the window of spatial attention. In this study, feature and conjunction search tasks were employed to dissociate the effects of

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attentional processes and cognitive slowing on conjunction search deficits in AD. In order to dissociate these processes, they utilized different array sizes of 0, 3, 6, or 12 distractors in addition to one target. The targets and distractors differed on either one (feature search) or multiple (conjunction search) physical properties. AD patients did not differ from older normal control subjects on the simple feature search task, but were disproportionately slower in finding the target with increasing array size in the conjunction search task. More importantly, with regard to the window of spatial attention, the AD patients had no difficulty with the feature search task when targets appeared in or near the center of the array, but had greater difficulty when targets were located in the periphery of the large arrays. In contrast, target location (central/peripheral) did not affect their ability to perform conjunction search. It was concluded that the extent of the window of spatial attention is reduced due to AD and that this is particularly evident when patients are required to perform global (parallel) search. In a separate line of research, Ball and colleagues [73–76] have investigated the component attentional processes that contribute to the window of spatial attention. Ball [77] has described the window of spatial attention as the area of the visual field in which information can be most readily processed without eye or head movements, also known as the ‘useful field of view’ (UFOV), and has developed a rigorous approach to quantifying the size of this window based on three attentional measures: visual processing speed, the ability to divide attention, and freedom from distractibility [78, 79]. In determining an individual’s UFOV, a composite score is derived from performance on subtests that capture each of the three component measures. This composite score represents the functional UFOV and is expressed as a percentage reduction of an operationally defined optimal 35⬚ radius attentional field. In one study by Duchek et al. [76], it was found that AD patients with a Clinical Dementia Rating (CDR) score of 1 (‘mild dementia’) had a 75% reduction in the window of spatial attention as measured by the UFOV task. This was significantly greater than both the 34% reduction observed in the CDR 0.5 group (‘questionable dementia’) and the 29% reduction in the CDR 0 group (‘no dementia’). In addition, the authors reported the result of a simple visual search task in which they manipulated array size of 2, 4, and 6 items. Consistent with the result of Foster et al. [72], they found an effect of increasing array size on search times that was disproportionately longer in the CDR 1 group compared to the CDR 0.5 and CDR 0 groups. While this may be interpreted as evidence of a reduction of the spatial window of attention, the authors did not analyze search times as a function of target location as Foster et al. [72] had. In another recent study, Rizzo et al. [22] examined the effect of visual attention impairments on other cognitive functions in AD and control subjects. In the sample of 42 AD subjects, the authors report a 62% reduction in the

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optimal 35⬚ radius attentional field (UFOV) compared to a 32% reduction in a group of age-matched control subjects [80]. A reduction in the UFOV was related to poorer performance on a composite measure of cognitive function, especially on tasks requiring visual processing. The authors suggest that reduction of the UFOV in AD may be related to functional disruption of the cholinergic system. This study provides direct evidence of a reduction in the window of spatial attention in AD and also highlights the dependence of other cognitive capacities, such as memory, on the size of the attentional window. Finally, in a case description, Coslett et al. [81] suggested that a narrowing of the zone of focal attention occurs at least in a small subset of presumed AD patients with progressive visuospatial dysfunction. These patients also showed decreased tracer uptake in the posterior lateral regions of the parietal lobes when imaged with single photon emission-computed tomography, suggesting that these regions may be important for attentional processing.

Neural Correlates of the Window of Spatial Attention

The neural correlates of the spatial window of attention are under active research in humans and several imaging studies have been completed in recent years [68, 82, 83]. These studies generally point to both striate and extrastriate visual cortical areas (areas 17–19) as important for regulating the spatial window of attention. In a recent study, Brefczynski and DeYoe [67] demonstrated cortical activation in striate and extrastriate visual cortical areas related to shifts of attention in response to spatial cues that increased in eccentricity from a central fixation point. Cortical activation in this attention condition was almost identical to cortical activation patterns produced by presentation of actual stimuli in these target locations. As attention shifted from the perifoveal regions to the periphery of the visual field, the cortical activation patterns moved anteriorly from striate cortex to extrastriate regions. The authors likened this shift of activation to that seen in retinotopic mapping of visual field eccentricity effects [84]. Presumably, this shift in cortical activation represents a widening of the window of spatial attention. In a separate study, Somers et al. [82] used functional MRI to study attentional modulation of striate visual cortex. Strong effects of attention were found in both striate and extrastriate cortical regions. In addition, the authors reported activation patterns that revealed that attentional modulation is spatially specific, with enhancement at retinotopically attended-to spatial locations and inhibition at non-attended-to locations. This result suggests that the window of spatial attention operates in early visual cortical areas as well as in heteromodal extrastriate regions.

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Cholinergic Impact on Spatial Attention Deficits in AD

The cholinergic system plays an important role in such attentional functions as vigilance [85, 86] and attentional shifts [87, 88]. In rats, cholinomimetics partially reverse attentional deficits cause by decreased cortical acetylcholine [89–91]. Similar attentional effects are seen in monkeys with combined basal nucleus, hippocampal, and inferior temporal lesions that serve to decrease brain cholinergic function [92]. However, there has been relatively little work examining the relationship between the cholinergic system and the spatial window of attention in humans. One of the few studies that have addressed this issue [70] suggests that the muscarinic antagonist scopolamine broadens the size of the window of spatial attention in AD patients, but not in cognitively intact control participants. In contrast, physostigmine, a cholinesterase inhibitor, did not affect the spatial extent of attention in this study. Taken in conjunction with broadbased attentional effects of cholinergic compounds described above, this study suggests that the cholinergic system may play an important role in the modulation of the size of window of spatial attention. The well-known cholinergic depletion associated with AD may constitute one biological mechanism for the disturbance of spatial attention in this disease. Decreased cortical choline acetyltransferase (ChAT) is related to cognitive impairment in AD [93–97]. In addition, cortical cholinergic denervation with the loss of basal nucleus cholinergic neurons is related to cognitive decline in AD [98]. However, the cholinergic hypothesis of AD is somewhat limited as cholinergic denervation may occur late in AD and may not account for early or even pre-symptomatic cortical cholinergic changes [99]. In addition, brainstem monoaminergic systems are also affected in AD [100] and the cholinergic system may be affected in normal aging [101]. Nevertheless, there is an established role for cholinesterase inhibitors in AD treatment [102] and possibly slowing AD pathophysiology [103] suggesting that cholinergic dysfunction is a primary manifestation of AD.

The Window of Spatial Attention and Navigational Cue Use

AD is associated with navigation impairment that can cause significant functional impairment [104, 105]. Self-movement cues provide important information about direction of travel [106, 107] and are critical to successful navigation. Self-movement through an environment provides both optic flow information and object cues that are used to determine heading [108–110]. The perception of these cues is discussed in greater detail in the chapter by Duffy et al. in this volume. Functional imaging studies [111] have demonstrated that

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a

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Fig. 1 This figure represents stylized optic flow and the impact of a reduction in the window of spatial attention on information available for navigational processing. a A full window of attention permitting integration of optic flow information across a wide spatial extent. b A reduced window of spatial attention such as that which might be found in AD. This smaller window limits the amount of available optic flow information. A smaller window of spatial attention may lead to a reliance on object information for navigation which is a memory dependent strategy.

dorsal cortical visual areas process optic flow stimuli [112–115] while ventral visual areas process object stimuli [28, 116, 117]. In most natural environments, optic flow and object cues coexist. This rich visual motion information enhances self-movement perception [118] and provides for the use of both, or one or the other information source depending on task demands or resources. In the presence of numerous objects in the environment, landmark cues are selectively processed over optic flow [119], especially if the landmarks directly mark the direction of travel [120]. In the absence of landmarks, optic flow can be used for navigation [121]. However, successful navigation by optic flow analysis requires a large field of view in order to process the spatially distributed optic flow information [122, 123]. In addition, navigation using optic flow only may require substantially more attentional processing power in order to integrate widely distributed information for a long interval. Any restriction on elementary attentional processing power or on the size of the attentional window would have an adverse impact on the quality of information extracted from optic flow (fig. 1). An individual with a reduced window of attention might rely less on optic flow information and more on object information. However, this strategy might be problematic for AD patients as object-based strategies for navigation require a modicum of memory capacity in order to link landmarks spatially and temporally for path integration. A recent study [16] provides evidence that AD patients may rely more on objects in a navigation-like task when both objects and optic flow information is available. In this study, mild AD patients and older and younger control subjects were presented with first-person simulations of a car moving down

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a busy street. Eye movements were monitored and fixation patterns were assessed while subjects viewed the simulations. The proportion of eye fixations that fell on a central region of interest (ROI) encompassing the ‘road’ surface was the dependent variable of interest. The majority of the fixations made by the young control subjects remained within the central ROI, or on the surface of the road. On the other hand, older control and AD subjects made proportionately fewer fixations within the ‘on-the-road’ ROI than young subjects and moved their eyes more often to the periphery. The AD and older control subjects did not differ from one another with regard to the number of fixations made within the ‘on-the-road’ ROI. The data are consistent with the notion of a restricted window of spatial attention in both the older control subjects and in the AD subjects in that these subject groups were required to make excursive eye movements to the periphery of the scene in order to attend to peripheral stimuli. In addition, the data suggest that older subjects rely more on object than global motion cues because they made more eye movements to view objects in the periphery.

Summary and Conclusion

The behavioral studies reviewed above provide strong evidence for a reduction in the window of spatial attention in AD. The reduction of the scope of attention is related to the stage of illness and may represent one of the earliest changes associated with AD. Not only is the size of this window reduced in AD, but there also appears to be a reduction in speed of processing and the ability to divide attention within the window. The functional imaging studies reviewed in this chapter suggest that spatial attention relies on visual striate and extrastriate cortical areas at the occipito-parieto-temporal junction. These regions of cortex are activated when attention is directed to spatial locations and these activation patterns appear to have a retinotopic-like distribution, extending outward from striate to extrastriate cortex. In addition, these visual association areas may rely on feedback projections from additional heteromodal cortex in parietal, temporal and frontal regions. AD neuropathology generally progresses from temporal and parietal regions and eventually encroaches on primary visual cortex. The advancement of these neuropathological changes, particularly those affecting cortico-cortical connections, from heteromodal parietal and temporal regions to extrastriate to striate cortex may represent the mechanism by which the window of spatial attention is slowly closed in AD. Successful navigation in the environment is dependent on multiple visual cues including those from optic flow and from discrete objects. Meaningful use of optic flow cues requires attention to large areas of space and the ability to

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rapidly process widely distributed spatial information. A reduction in the window of spatial attention may force AD patients to rely more on objects in the environment for their cues. Path integration using object cues may be more demanding due to the heavy load placed on mnemonic processes that are impaired in most early AD patients. A reduction in the window of spatial attention may force AD patients to rely on less efficient path integration strategies and may result in spatial disorientation. Finally, the cholinergic system is implicated in AD neuropathology and also is known to affect the size of the window of spatial attention. Pharmacologic manipulation of this system may provide a means to alter the size of the window of spatial attention in AD patients thus improving use of spatially distributed visual information contained in optic flow.

Acknowledgements Dr. Mark Mapstone’s contribution was supported by a Research Career Development Award from the National Institute on Aging (AG020647). Dr. Sandra Weintraub’s contribution was supported in part by an Alzheimer’s Disease Core Center grant (AG13854) from the National Institute on Aging to Northwestern University.

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Mark Mapstone, PhD Department of Neurology, University of Rochester Medical Center 601 Elmwood Avenue, Box 673, Rochester, NY 14642 (USA) Tel. ⫹1 585 273 4859, Fax ⫹1 585 473 4678, E-Mail [email protected]

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Improved Performance on Activities of Daily Living in Alzheimer’s Disease: Practical Applications of Vision Research Tracy Dunne Alzheimer’s Design and Living Solutions, Norwell, Mass., USA

The prevalence of visual deficits in Alzheimer’s disease (AD) is a widespread, yet frequently untreated problem. One reason that impairments are sometimes overlooked is that visual abilities are typically measured in terms of visual acuity, which is usually normal in individuals with AD. Conversely, it has been demonstrated that up to 60% of people with AD show a decline in one or more visual capacities including depth perception, motion perception, contrast sensitivity and color discrimination [1, 2], which is not the result of normal aging processes. The inability to perceive depth cues accurately has been repeatedly demonstrated in individuals with AD [3–7]. This visual decline is seen even in the early stages of AD independently of other visuospatial capacities [6] and is largely attributed to deficits in local stereopsis [3]. Impaired depth perception can contribute to difficulty performing everyday tasks [3, 6] including walking, driving, and stair climbing. Impaired visual flow processing, or a deficit in motion perception, is also commonly observed in AD [8–12]. Research has shown that individuals with AD take significantly more time to identify stationary objects that are defined by a motion cue [8, 10] or moving objects such as those encountered when driving through a busy intersection. In fact, a study by Rizzo et al. [9] found that 33% of AD patients had ‘car crashes’ when participating in a test with simulated driving conditions as compared to none (0%) of non-demented elderly participants. The diminished capacity of AD patients to accurately process visual motion cues was a significant factor in predicting the crashes. A deficit in motion

perception can greatly impact the ability to navigate through the environment, whether inside the home or out in the community. It is likewise well established that there is a primary deficit of contrast sensitivity in AD [1, 2, 7, 13–15]. Contrast sensitivity can be defined as the smallest difference in intensity that a person can resolve between an object and its immediate surroundings. It is dependent on the ability to accurately discriminate visual information at various contrast levels across a range of spatial frequencies. While most elderly people are impaired at high spatial frequencies, those with AD are impaired at low spatial frequencies as well [1, 2, 7, 13–16]. High spatial frequencies convey featural visual information about details such as angles and lines. Low spatial frequencies convey configural visual information about gross form and smooth, flat, planar surfaces. Perceiving and processing spatial frequency information accurately is necessary in order to function in and navigate through one’s environment. This deficit appears to be the result of neuropathology in the visual cortex, rather than primary pathology in the retina or optic nerve [17]. The presence of senile plaques and the density of neurofibrillary tangles, neuropathological hallmarks of AD, are low in primary visual areas and increase in the higherorder associative cortex, mainly in the parietal and temporal lobes [18–21]. In patients with AD, the pathology of the extra-striate visual cortex, inferotemporal cortex, and posterior parietal cortex [18–23] results in the changes in contrast sensitivity that are observed throughout the frequency range. The behavioral and neuropathological evidence of impairment with respect to this basic visual process suggests that individuals with AD may have problems performing everyday tasks independent of memory and cognition deficits. A decline in contrast sensitivity has a direct negative impact on how an individual with AD perceives his or her environment and adversely affects the ability to perform many activities of daily living (ADLs) including bathing, dressing, toileting and eating. It cannot be corrected by traditional means such as eyeglasses, medication or surgery, but can be helped with environmental modifications. Up to 57% of the variance in ADL performance in normal elderly individuals is attributed to visual acuity and contrast sensitivity [24]. In fact, one study investigating the impact of contrast sensitivity on ADL performance in the elderly indicated that a twofold reduction in contrast sensitivity resulted in a three- to five-fold increase of difficulty with ADL performance [25]. Enhancing contrast has been shown to help improve and even normalize performance on everyday tasks such as reading, object recognition, face recognition and eating for individuals with AD. A study by Gilmore et al. [26] reported that enhancement of contrast improved AD patients’ performance on a picturenaming task relative to when contrast was not enhanced. Similarly, another study demonstrated that under normal contrast conditions, the letter-reading

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speed of AD patients was slower than that of healthy age-matched control participants. However, under very high contrast conditions, the performance of the two groups was equivalent [27]. Enhanced contrast, in effect, normalized AD patients’ performance. Cronin-Golomb et al. [28] demonstrated that by manipulating spatial frequency information, individuals with AD could improve their face discrimination abilities. Faces are composed of largely low-spatial frequency information including eyebrows, under eye, nose, lips and chin. Frequency information can be manipulated by changing the size of an object or the individual’s distance away from the object. By making faces smaller and, as a result, moving low-frequency information into higher frequency ranges, the experimenters were able to improve AD participants’ face discrimination abilities. Contrast sensitivity function affects ADLs and other aspects of everyday functioning in the environment. We will explore environmental changes that can be made to help improve performance on the most common ADLs: bathing, dressing, toileting and eating. We will also examine the use of practical applications of contrast manipulation in the home environment to improve navigation and reduce safety hazards in frequently used rooms including the bedroom, bathroom, kitchen, and living room and in potentially dangerous areas such as stairways, hallways and doors. The goal of most applied research regarding contrast sensitivity deficits is to utilize the empirical findings in a practical manner. Manipulating contrast levels in the environment can help compensate for AD-related visual problems and improve individuals’ ability to function in the environment. Improving functional capacities allows individuals to maintain or improve their current level of performance on ADLs and increases their level of safety in the environment. In this chapter, we will go step by step, and room by room, taking an in-depth look at some practical methods that can be used to change contrast, thereby maintaining or improving functional abilities in individuals with AD.

Color and Light

The level of contrast in the environment can be enhanced through the use of color and light. The three components that make up color are hue, lightness and saturation. Hue is the basic color identity (i.e., red, yellow, blue, green). Lightness is that part of brightness gleaned from the surface qualities of an object [29] and refers to the amount of light reflected from a surface in relation to other nearby surfaces. Saturation is the color intensity as it relates to white, gray, or black (e.g., pink, red, crimson).

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Perceived color is not determined simply by the amount of light that reaches the eye. Rather, it is determined by a combination of the reflectivity of a given surface and the amount of illumination on that surface [30]. In other words, a color can vary in its appearance depending on its contiguous surroundings. There are three principles of color that must be taken into account when designing a color scheme for any physical environment. First, when an object is placed on a background that is the same color as the object itself, the color is perceived to have a decrease in saturation and brightness, but little change in hue. Second, when an object is placed on a background that is a color complementary to the object itself, the color is perceived to have an increase in saturation and brightness, with little change in hue. Third, when an object is placed on a background that is neither the same nor a complementary color, the color is perceived to have a change of hue with little change in saturation or brightness [30]. Brightness or luminance is determined in the primary visual pathway by the amount of light that reaches the eye [29]. Similar to color, it is influenced by the reflectivity of the surface of an object and the intensity of illumination cast upon the object. Brightness can be perceived differently according to its surrounding area and, as such, alter the level of contrast between one object and another or within the environment.

Color Guidelines

Color discrimination is the ability to discern different colors along redgreen and blue-yellow color axes. It is mediated or regulated by the parvocellular pathway in occipital-temporal region of the brain which extends from the retina to the visual association cortex [17]. Color has been shown to act as a salient cue for immediate recognition in individuals with AD [31, 32]. While some research indicates that color discrimination abilities remain well preserved throughout the lifespan for both cognitively intact elders and those with AD [33], impairments in AD patients have more commonly been reported. Specifically, a deficit in the blue-green color range has been repeatedly demonstrated [1, 2, 7, 17, 34, 35]. Changing the color scheme in the home environment, consequently altering the level of contrast, can markedly improve an individual’s ability to navigate within a given room or area. Therefore, when designing or redesigning a color scheme it is important to minimize the use of multiple shades of blue and green. Likewise, when using colors in the blue-green color range, maximal benefits will be gained by pairing them with sharply contrasting colors such as red, yellow or orange.

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Light Guidelines

Light sensitivity decreases with age. In fact, after age 20, light intensity must be doubled every 13 years in order to perceive a stimulus accurately. Due to age-related changes in the visual system, a 60-year-old receives only 40% of available light as compared to a 20-year-old person [36]. The average 86-year-old nursing home resident may require up to five times higher light levels as a healthy young person [37] to perceive the same information accurately. There are three different types of light commonly used in households and long-term care facilities: incandescent, fluorescent and halogen. Each one has advantages and disadvantages. Incandescent light, most often used in ordinary household light bulbs, does not produce much glare at low wattages, but may require higher wattage to produce the same brightness as other bulbs resulting in increased glare. Fluorescent lights emit ample illumination but frequently give off excessive glare. Glare is not conducive to ADL performance, can decrease the ability find the way through the environment and can lead to perceptual errors, which may increase levels of anxiety and agitation in AD patients. Additionally, older fluorescent bulbs often produce a strobe effect, which can exacerbate motion perception deficits in AD [38]. Halogen light bulbs are the best alternative in terms of light sources. They offer very bright but diffuse light and produce minimal glare. The only caveat associated with halogen bulbs is the need to touch them with something other than a bare hand. The oils on the hand react with the bulbs and cause them to explode, necessitating the use of a cloth or paper towel for installation.

Color and Light Application

Before we discuss specific functional and environmental contrast interventions, there are several global principles relating to color and light that should be kept in mind when designing a living area for those with AD. They should be applied in both the home and long-term care settings. Exceptions to some of these general principles are provided in later sections. Color – Use sharply contrasted colors between background and foreground – Do not use similar colors next to each other – Use solid colors rather than floral, striped or multi-colored patterns – Try to contrast light colors with dark colors in decorating scheme

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Light – Make sure that light is distributed evenly throughout room – Minimize glare – Let in as much natural light as possible – Use task lighting when appropriate – Use lights that turn on automatically at night – Place light behind, not beside, reading or television chair to enhance vision

Contrast and Activities of Daily Living

Bathing Bathing someone with AD can be a stressful experience for a caregiver and a frightening, hazardous one for the individual. Research has shown that during personal care, the most aggressive behaviors occur around bathing [39]. In a study examining what variables predict acute hospitalization for AD patients, in which ADL performance on bathing, toileting, and eating was investigated, only dependency on a caregiver for bathing was significantly correlated with acute hospitalization [40]. The difficulties associated with bathing someone with AD are not the result of old age and physical deterioration alone. In fact, when comparing AD patients to age-matched cognitively intact elders, AD patients are shown to have excessive functional disability that is not related to age or any physical limitation [41]. While certain interventions such as increased communication and music [42] have resulted in a decrease in agitated and aggressive behaviors, visual cues and alterations of the level of contrast in the bathing area can also help to minimize disruptive bathing behaviors. – Place a non-skid bath mat outside the tub that is the same color as the floor. Using a mat with low-contrast to the floor will help to minimize any depth perception impairments and enable the AD patient to transition into and out of the bathtub with greater ease. – Place a non-skid bath mat inside the tub that is a contrasting color to the tub. AD patients are very cautious about putting their feet down when getting into the tub because of problems with depth perception. They are unsure of how far down the floor of the tub is. By using a high contrast bath mat in the bath, a depth cue is provided. – If a shower door is part of the tub configuration, use one that is made of clear tempered (shatterproof) glass rather than one that is frosted or opaque. This will help to lessen anxiety by allowing the individual to see the whole room and caregiver if one is present. It will also make the bathroom seem more open when inside the shower or tub.

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– Shower and tub grab rails are a must for safety in any bathing area. They are commonly available in white and metallic finishes. Many people choose white to blend in with typically light-colored bathroom tiles. When designing a bathing area for an individual with AD, use long grab rails that are in contrast to walls. Chrome or brushed nickel-finished rails will provide a high contrast to most tub backgrounds. Conversely, if the background is dark, a white grab rail will provide suitably high contrast. – As a safeguard and to serve as a visual reminder, use different colored knobs for hot and cold faucets, such as red for hot water and blue for cold water. This will provide an excellent color cue for the individual given that the traditional spatial arrangement of hot faucets on the left and cold faucets on the right is not always employed. – For an individual with a decline in contrast sensitivity, soap dispensers and paper towel dispensers in the bathing area in long-term care facilities can fade into the wall background. Providing large, colorful picture labels on dispensers can help the AD patient identify the items more readily. – In long-term care facilities, the bathing area is often designed very differently than a tub or shower in the home. Consider hanging towels that contrast with the wall and a clear shower curtain to provide cues to the patient as to the use of the space. Dressing Difficulty dressing presents itself even in the early stages of AD [43]. Patients frequently wear the same clothes day after day and often wear layers of clothing. Additionally, they very often dress inappropriately, such as wearing a sweater on a hot summer day or a thin nightgown and bare feet to go outside during the winter. While some of the obstacles associated with dressing are due to a decline in motor skills combined with cognitive deficits, by enhancing contrast between items of clothing and in the dressing area, individuals can improve their ability to dress appropriately and independently. – When selecting clothes for an individual with AD, make sure there is contrast between tops and bottoms. For example, pair a lilac blouse with black skirt or a burgundy shirt with khaki pants. In addition to buttons and zippers causing confusion, similarly colored clothing items can make it difficult for patients to dress and undress as needed. – If clothing is a printed fabric, use small designs only and avoid pairing patterned items together. The busy-ness of large floral or plaid clothing can be distracting for an individual with contrast sensitivity dysfunction as well as any additional color perception or motion perception deficits. Like similarlycolored clothing, patterned clothing can make it difficult for patients to get into and out of clothes easily.

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– In order to help the individual remain self-reliant at dressing, lay out clothes on a surface that is sharply contrasted with clothing. Placing clothes where they are easy to identify and making them easily seen may lessen dependence on a caregiver. – To help counteract memory deficits, aid in organization, and ensure regular changes of clothing, lay clothes out in the same place every day. – In both home and long-term care settings, place large picture labels of shirts, pants, socks, underwear, etc. on closet doors and bureau drawers. These visual cues will help the AD patient differentiate what is in each space and help to maintain their current level of functional independence. Toileting In addition to bathing, toileting can be tremendously problematic for individuals with AD. Because this basic activity is repeated many times in the course of 24 h, it often proves to be a grueling, arduous process for caregivers [44]. In fact, toileting is correlated highly with caregiver stress [45] and incontinence is a significant predictor of institutionalization [46]. The difficulty with toileting stems from many factors: motor deficits, delayed reaction, and awkward clothing in the early stages of the disease; followed by incontinence, inability to communicate needs, and lack of awareness as to what to use as a toilet in later stages. By enhancing contrast in and around the toilet area, individuals with AD, at least in the early to moderate stages of the disease, may be able to improve their toileting behaviors, simultaneously prolonging their current level of functioning and reducing the workload of caregivers. – Highlight the toilet bowl by using colored toilet water. This can be achieved simply by means of a colored cleanser that is renewed with every flush. This intervention provides both enhanced contrast and a depth perception cue, which will help to trigger related function. Colored toilet water not only emphasizes the bowl and toilet area for both men and women, it can also help to improve urinary flow aim for men, which is a significant problem in both home and long-term care settings. – If possibly drinking the toilet water is a concern or using colored toilet water is not allowed at a long-term care facility, another way to achieve the same effect is to place a contrasting toilet mat around the base of the toilet. Using a dark-colored mat around a typically white or light-colored toilet is an excellent way to accentuate the toilet and provide a cue as to where to sit or stand. – If tripping over or skidding on a toilet mat is a concern, a toilet cover that is in high contrast to the toilet can also be used effectively. – Picture labels on the toilet lid for women and inside the lid for men may be enough of a reminder for someone in the early stages of AD.

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– Safety rails placed on the toilet (usually a one-piece apparatus fitted above the rim and below the toilet seat) in a contrasting color can serve not only as additional safety equipment, but also as a visual cue to draw attention to the toilet itself. – Increasing the contrast in the toilet area using light is particularly effective at night. Place a light inside the toilet bowl or right above the toilet. There are commercially made products that use a light-emitting diode and can be fitted right under the rim. Originally designed to indicate whether the toilet seat was up or down, they can be very beneficial to someone with AD by illuminating the toilet area. Eating Eating is a basic activity of daily living known to present difficulty for AD patients. Significant weight loss affects up to 40% of individuals with AD [47] and may arise from depression [48], the need for additional caloric intake, perhaps due to excessive pacing [49], the inability to attend to more than one food at a time, or the inability to eat independently [50]. An additional explanation that has recently been investigated is a deficit in contrast sensitivity. Koss and Gilmore manipulated contrast in the eating area of an Alzheimer unit at a long-term care facility by using colored plates (black) and increasing light levels during the evening meal. Their study investigated the effects of enhanced contrast on food intake and agitation levels during the ‘sundowning’ period. Sundowning is a term commonly used to describe a cluster of behaviors that includes increased agitation, anxiety, noise and pacing thought to be caused by a disturbance in circadian rhythms [51]. This syndrome is observed in a significant segment of AD patients across all stages of the disease. They found that patients consumed significantly more at the evening meal during the intervention phase as compared to pre- and postintervention phases. One explanation for these results could be the highcontrast plate color; another could be the enhanced contrast due to increased light. The results are likely due to the combination of both contrast manipulations. A significant decrease in sundowning behaviors was also found during the intervention phase [52]. We also investigated the effects of contrast manipulations on eating behaviors. We enhanced the contrast of table settings at an Alzheimer unit at a long-term care facility by using colored plates and cups (red) at both the midday and evening meals. Results showed a significant increase in the amount of food eaten at lunch (25%) and dinner (24%) relative to the preintervention phase. Similar results were found regarding liquid intake. There was a significant increase at lunch (61%) and dinner (105%). Food and liquid

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intakes reverted back to pre-intervention levels during the post-intervention phase. A follow-up study was conducted 1 year later to determine whether contrast or color was the determining variable. Low-contrast (pastel) shades of red and blue were compared to high-contrast shades of red and blue. Blue was used because several studies have shown AD patients to be impaired at recognizing blue-hued colors [1, 2, 7, 17, 34, 35]. Similar to the original study, a significant increase in food intake was found using high-contrast blue (average across lunch and dinner, 25%). Conversely, there was no significant increase of ingestion for either of the low-contrast colors (0% for lowcontrast red, –5% for low-contrast blue). Liquid intake also significantly increased using high-contrast blue (average across lunch and dinner, 30%). Again, there was no significant increase using low-contrast colors (0.4% for low-contrast red and 0.3% low-contrast blue). These results suggest that enhanced contrast regardless of color can improve eating behaviors in AD [53]. By providing visual cues through utensils, plates and table settings, AD patients who eat independently or with minimal assistance may be able to eat more and maintain their body mass for a longer period of time as the disease progresses. – To increase the amount of food consumed at a meal, use plates, placemats and tablecloths that are highly contrasted with one another. For example, use a red plate on a small patterned or plain pastel placemat with a black tablecloth. The plate will then be highlighted, drawing visual attention to the food. – Likewise, do not use the same color for plates and placemats or tablecloths. Additionally, make the table setting in high-contrast to the table itself. For example, do not use light-colored plates on a light wood or white table. A pale yellow plate on a white table will not provide sufficient contrast. – Adequate liquid intake is always an issue with the elderly including those with AD. To increase the amount of fluid consumed throughout the day, use glasses and mugs that contrast with liquids. Pouring coffee into a white mug instead of a black mug will immediately provide visual contrast and a depth cue. When milk is poured into a white cup, the AD patient may perceive the cup as empty, but when milk is poured into a darker cup, the cup will be seen as full. – Use plates that are in high contrast to food, particularly in long-term care settings where mashed potatoes and pureed meat such as chicken or turkey are standard fare and are usually served on institutional white plates. By serving a meal of turkey and mashed potatoes on a dark plate such as red, blue or black, it will be easier for the patient to determine where the food ends and the plate begins, so that he or she may be able to better use utensils and eat independently.

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– Serve foods that are in contrast to one another. Without ample contrast, individuals may have difficulty distinguishing similar-looking foods from one another, such as a chicken breast from a baked potato or tapioca from mashed potatoes.

Contrast in the Home Environment

Bedroom In the bedroom of someone with AD, the main concerns are falling out of bed and mistakenly using items in the room as a toilet. It is also sometimes difficult to get an individual into the bedroom for the night. By enhancing the contrast in the bedroom, it may be easier for AD patients to distinguish objects within the room and easier for caregivers to give night-time care. – To draw an individual into the bedroom, paint or paper the far wall in a contrasting color to other walls and the entryway to the room. For example, in a beige room, paint the far wall cranberry or forest green. Painting the opposing wall in high contrast to the entryway provides a depth cue that will encourage the individual to enter the room. – Another way to encourage the individual to enter the room is to use continuous flooring from the hallway to the bedroom. Different colors, textures (rug to hardwood) or patterns (light tile to darker colored tile) in flooring materials can stop a person with depth perception deficits. It is common to see AD patients take giant steps as if lifting their legs over a fence when they encounter a change in flooring. To ease the transition from one space to another, use the same material throughout the space. – To emphasize the bed, use a bedspread that contrasts with floor and walls. A brightly colored spread against lighter floor and walls will not only help to direct the individual toward the bed, but will also help to prevent bumping into and tripping over the corners of the bed. – If the person is able to dress and undress independently and uses a chair to put socks and shoes on, use chairs, dressers, and nightstands that are highly contrasted with walls. – Use alarm clocks, telephones and radios with different colored buttons to encourage independent use. – To serve as a guide and increase contrast in the room during night-time hours, use a nightlight with an automatic timer. Additional light will reduce the risk of injuries and falls while going to the bathroom. It will also decrease disorientation if the person wanders out of bed at night. – Items such as laundry hampers, dresser drawers and even potted plants are all mistakenly used as toilets in the daytime as well as night-time hours.

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If possible, store hampers in the laundry room or another area that the AD patient is not likely to go in. It is also a good idea to remove plants and wastebaskets from the room. In long-term care facilities, the bedroom is often the main living quarters of someone with AD. Given the special circumstances of roommates and bed safety equipment that are not usually encountered in the home, this next section is devoted to bedrooms in long-term care residences. – There are several ways to individualize the living space in shared bedrooms by enhancing contrast in key areas. Rooms in long-term care are typically decorated with one color throughout the room and the color is only varied between rooms. Instead of using one color per room, use one color for each resident in the room on bedspreads, nightstands and closet doors. Since color recognition has been shown to be successful with individuals with AD, colorcoding their individual furniture pieces will make them more likely to discriminate between their space and their roommate’s space. – Putting name labels and picture labels on closet doors can help improve recognition of each section of the closet and clothing items within. – Safety is an issue with all AD patients, especially with residents in longterm care. Metallic bed rails can give off glare in the daylight hours and may not be seen during night-time hours. Keep bed rails down when the patient is not in bed in order to reduce the risk of injury. – If a resident is prone to falling out of bed at night, place a low-contrast soft foam pad on the floor next to the bed in a color that is the same as the floor instead of using bed rails. By substituting a floor mat for bed rails, if a patient falls there is no chance they will get tangled up in bed rails; a relatively common occurrence that can cause serious injury. Likewise, if they fall onto a mat rather than a hard floor, there is a reduced risk of injury. – As with home bedrooms, avoid placing items that can be used as a toilet, such as wastebaskets and clothes hampers, in the bedroom. – There are several ways to help residents distinguish their bedroom from the tens of other bedrooms in the facility by enhancing contrast. Place picture frames in different shapes, sizes and colors (color has also been shown to help AD patients recognize form) filled with the resident’s photos in the entryway before their bedroom. Because those with AD tend to forget in backwards order over the lifespan and frequently do not recognize themselves in the present day, use pictures that reflect their whole life from childhood on if possible. Use frames that are in high contrast to the wall to draw attention to them. – Color blocking bedrooms on each wing of a facility, such as yellow walls in one wing and pink walls in an adjacent wing, can also help a resident recognize the area in which his or her bedroom is located.

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Kitchen The kitchen is the heart of any home, including the home of someone with AD. However, the kitchen can also be a room with many hazards, particularly for individuals in the early to moderate stages of the disease who are ambulatory and like to make their own meals or coffee. By making a few simple modifications, the kitchen can remain a safe, user-friendly environment for individuals with AD where they can continue to function with a degree of independence. – To encourage independent functioning in the kitchen, use light switches and electrical outlets that are in sharp contrast to walls. If switches or outlets pose a safety hazard, use plate covers that blend in with the wall to discourage use. This can be done by painting or wallpapering the covers the same as the wall. – If the individual is still able and enjoys making his or her own morning toast and coffee, use appliances with large and multicolored buttons and automatic shut-off switches. Automatic shut-off switches help to compensate for forgetfulness and can ease the mind of a caregiver. Large buttons are easier for someone with motor problems to use and the colored buttons are in high contrast to the appliances themselves, providing visual assistance. If using appliances independently poses a safety hazard, select ones that are in low contrast to the countertops to discourage use. – To help those with AD perform kitchen tasks more easily, use task lighting whenever possible and install track lighting under cabinets. Under-cabinet lighting will provide additional illumination without giving off excessive glare. – Use high-contrast doorknobs and handles on cabinets and drawers. For example, white porcelain knobs sharply contrast with dark wood, whereas black or metallic knobs contrast with light wood or white. These can be combined with picture labels of different foods on cabinet and drawer fronts to readily identify where foods are stored. – To make finding foods that are used on a daily basis such as bread and cereal trouble-free, use open shelving or glass cabinet doors. This will also help to eliminate confusion about item location and will help to distinguish one cabinet from another. – If hazardous materials such as chemical cleansers are stored in the kitchen, use locks on cabinets. It is also wise to place a lock on drawers containing sharp knives and scissors. Bathroom The bathroom is the one room in the house that all individuals frequent many times in the course of a day. Enhancing contrast can make it easier for an individual with AD to function more independently in the bathroom. There are also several contrast effects that can make it easier for a patient to go in and out of the bathroom while reducing hesitation or confusion.

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– Place lights on either side of the medicine cabinet to increase available lighting while minimizing glare. Lights placed directly over a medicine cabinet will reflect directly onto the mirrored surface and produce excessive glare that may make functioning in the bathroom more difficult. – To increase overall contrast in the bathroom, use toilet, toilet paper holder, sink and soap dish fixtures that are in high contrast to the walls surrounding them. Instead of white fixtures in a white, beige or pastel bathroom, use white fixtures in a darker colored room such as a deep shade of green, blue or purple. – Use toilet paper that is a different color than the toilet paper holder. – Use soap and a soap dish that are different colors from each other. – To minimize confusion regarding the location of the bathroom, use picture labels on the bathroom door. – In the early stages of AD, people often wander and get confused going from room to room. Paint the door and doorways of the bathroom a sharply contrasting color from the walls adjacent to it to emphasize its location and make it noticeably different from other doorways along the same hallway. – In the later stages of AD, a change in color often confuses people. Paint the door and doorway the same color as hallway walls while painting the far wall a contrasting color from the entryway to draw the individual into the bathroom. – To ease the transition from the hallway or bedroom into the bathroom, remove thresholds from hallway to bathroom and use continuous flooring from hallway or bedroom into the bathroom. These steps will reduce the impact of any depth perception deficits. Living Room The living room, a common area in most houses where much time is spent, can be rife with safety hazards for a person with AD. Simple contrast manipulations can make the room much easier to navigate and reduce hidden dangers. – Use furniture that is highly contrasted with walls to reduce the risk of falls and other injuries. Use dark-colored furniture with light walls and lightcolored furniture with dark walls. If the individual has a favorite chair, make sure that it is decorated in a different color than the other furniture in order to highlight it. – Use continuous solid-color flooring in the living room – do not use area rugs, which pose a safety hazard in terms of their likeliness to move. They also frequently have turned up corners or frayed edges that can easily cause falls. – Avoid decorations that can be mistaken for food. People with AD often make perceptual errors in tests of object recognition. Glass beads, flowers, river rocks and potpourri may all look inviting, but a dish of candy is the safest.

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– Use ample lighting and task lighting throughout the living room to accommodate activities such as reading, sewing and television watching. – Make sure that light does not reflect onto mirrors or picture frames and use curtain sheers to minimize glare that may be produced by sunlight coming through windows. AD patients, who have been known to interpret the glare as fire on the walls, can misperceive the light falling on various surfaces in the room. Stairways Climbing up and down stairs is an important activity in everyday life. It can also be one of the most dangerous activities for all elderly people including those with AD. Navigating stairs becomes more problematic with age for many reasons including mobility limitations, impaired balance, arthritic joints and vision deficits. Without sufficient visual contrast, it can be difficult to discriminate individual steps, particularly when heading down the stairs. There are many ways to enhance contrast in the stairway. The resulting visual cues can help to guide people up and down stairs and reduce the safety risks associated with stair climbing. – One of the simplest and best ways to increase the contrast in the stairway is to place a light at the top and bottom stair landings. The increased illumination will make it easier to see the edge of each step in both directions. – To facilitate going up the stairs, use different color treads (steps) and rises (vertical pieces between steps). For example, a staircase of wooden treads with white rises can improve usability while maintaining style. – To facilitate going down the stairs, paint the edge of treads a different color than the body of the tread. From the top of the staircase, individual steps can be indistinguishable to someone with AD. Painting the edges in a highcontrast color provides a depth cue, which can improve stair navigation. If cost is an issue or the enhancement is needed on a temporary basis, the same result can be attained by using colored or reflective tape. – Using two contrasting colors on treads that alternate with each step is another way to achieve the same effect as above. Both of these methods are equally effective. Choosing one is a matter of personal style, taste and preference. – To provide strong visual guides and reduce the risk of fall and injury, use banisters that are in high contrast to the walls and make sure to place banisters on both sides of stairway. – Avoid using individual mats on each tread. Mats can slip out of place over time and develop frayed edges that shoes easily get caught in. If protective carpeting is necessary for the treads, use a continuous strip of carpet that covers the center of rises as well as treads. This can be professionally installed and anchored to the staircase minimizing safety hazards. – If the individual with AD is unable to use the stairs in any way, consider installing a door at the beginning of stairway to hide the stairs from view.

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Hallways and Doorways Hallways are the main connecting artery of any house or residential facility. By enhancing contrast, travel through hallways can remain unimpaired for someone with AD. Doorways can often be challenging in terms of wandering or simply passing through. Contrast manipulations of doors and doorways can help to encourage or discourage use. – Keep hallways free of scatter rugs or area rugs. Use wall-to-wall carpeting or a solid surface floor material such as wood, linoleum, or tile. – Use a high-contrast chair rail along the hallway wall for additional guidance. – Make sure pictures and mirrors in the hallway do not reflect glare. Glare can rebound from one wall to another in a long narrow hallway and result in impaired perception and difficulty walking through the space. – Use continuous flooring to promote travel from one room to another. – If wandering is an issue, use different colored flooring at the edges of the hallway and immediately adjacent to doorways. For example, individuals with AD will perceive a white tiled floor edged with a dark marble border as a change in depth. This change in floor color will often serve to stop them from proceeding any farther. – Another way to help prevent wandering is to paint or paper the door and doorway the same as surrounding walls to camouflage and discourage use. By keeping the door, doorway and walls in low contrast to each other, the door may not be perceived separately from the walls. Consequently the individual will not attempt to go through it. – As a general guideline, in the early stages of AD it can be beneficial to highlight doors and doorways to encourage use by painting them a color that is in high contrast to the hallway. Because of the degree of depth and motion perception deficits as the disease progresses, in the later stages of AD, paint doors and doorways the same color as hallways to encourage use. By unifying door and doorway color with wall color in late stage AD, there is no visual cue to highlight the door as an entrance to a separate space. Same color doors and walls help to minimize perceptual errors in the space and ease the transition from one room into an adjoining room.

Conclusion

Enhancing contrast enables individuals with AD to prolong their current level of functioning and maintain their current level of independence. Likewise, the risk of falls and other injuries may also be greatly reduced. These benefits not only yield a better quality of life for the individual, they also reduce caregiver

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burden. Caregiver stress levels have a major impact on the decision to place someone with AD into a long-term care facility [54, 55]. Elders who transition to long-term care frequently suffer from social isolation [56], generally have a poorer quality of life [57], incur more illnesses and have a shorter lifespan [58] than those who remain in their own homes. Ultimately, the goal of adapting the home environment is to delay long-term care placement as long as possible. Research in both laboratory and real-world settings has shown that manipulating contrast levels in the environment can improve [52, 53], and in some cases normalize performance of individuals with AD on various everyday tasks [27, 28]. Low-contrast objects have been shown to interfere with locomotion along a travel path significantly more than high-contrast objects. Additionally, low light levels significantly increase, in fact almost double, the amount of time needed to find the way along the path [59]. Over time, navigating through what was once a familiar environment can become increasingly confusing to someone with AD. Modifying the home environment or long-term residence by enhancing contrast levels provides visual cues that help to minimize the depth, motion, and color deficits experienced by many patients with AD. By allowing more accurate perception, enhanced contrast simultaneously maximizes increased functionality and independence with respect to activities of daily living.

Acknowledgement This work was supported by grant T32 AG00220 from the National Institute on Aging to the Boston University Gerontology Center.

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Wijk H, Sivik L: Some aspects of colour perception among patients with Alzheimer’s disease. Scand J Caring Sci 1995;9:3–9. Wijk H, Berg S, Bergman B, Hanson AB, Sivik L, Steen B: Colour perception among the very elderly related to visual and cognitive function. Scand J Caring Sci 2002;16:91–102. Wijk H, Berg S, Sivik L, Steen B: Colour discrimination, colour naming and colour preferences among individuals with Alzheimer’s disease. Int J Geriatr Psychiatry 1999;14:1000–1005. Wijk H, Berg S, Sivik L, Steen B: Color discrimination, color naming and color preferences in 80 year-olds. Aging (Milano) 1999;11:176–185. Cornelissen FW, Bootsma A, Kooijman AC: Object perception by visually impaired people at different light levels. Vision Res 1995;35:161–168. Frankowski-Jones B: Lighting and the elderly. Key lighting elements for an aging population. Isdesignet, 1996; www.isdesignet.com/Magazine/Sep96/Lightelements. html Tetewsky SJ, Duffy CJ: Visual loss and getting lost in Alzheimer’s disease. Neurology 1999;52: 958–965. Schreiner AS, Yamamoto E, Shiotani H: Agitated behavior in elderly nursing home residents with dementia in Japan. J Gerontol B Psychol Sci Soc Sci 2000;55:180–186. Andrieu S, Reynish E, Nourhashemi F, Shakespeare A, Moulias S, Ousset PJ, Sagnier P, Richard A, Albarede JL, Vellas B: Predictive factors of acute hospitalization in 134 patients with Alzheimer’s disease: A one-year prospective study. Int J Geriatr Psychiatry 2002;17:422–426. Thomas VS: Excess functional disability among demented subjects? Findings from the Canadian Study of Health and Aging. Dement Geriatr Cogn Disord 2001;12:206–210. Clark ME, Lipe AW, Bilbrey M: Use of music to decrease aggressive behaviors in people with dementia. J Gerontol Nurs 1998;24:10–17. Derouesne C, Thibault S, Lozeron P, Baudouin-Madec V, Piquard A, Lacomblez L: Perturbations of activities of daily living in Alzheimer’s disease: A study of 172 patients with the using a questionnaire completed by caregivers. Rev Neurol (Paris) 2002;158:684–700. Hutchinson S, Leger-Krall S, Skodol Wilson H: Toileting: A biobehavioral challenge in Alzheimer’s dementia care. J Gerontol Nurs 1996;22:18–27. Hope T, Keene J, Gedling K, Cooper S, Fairburn C, Jacoby R: Behaviour changes in dementia. 1. Point of entry data of a prospective study. Int J Geriatr Psychiatry 1997;12:1062–1073. O’Donnell BF, Drachman DA, Barnes HJ, Peterson KE, Swearer JM, Lew RA: Incontinence and troublesome behaviors predict institutionalization in dementia. J Geriatr Psychiatry Neurol 1992; 5:45–52. Guyonnet S, Nourhashemi S, Andieu P: A prospective study of changes in nutritional status in Alzheimer’s patients. Arch Gerontol Geriatr Suppl 1998;6:255–262. Volicer L, Rheaume Y, Cyr D: Treatment of depression in advanced Alzheimer’s disease using sertraline. J Geriatr Psychiatry Neurol 1994;7:227–229. Wolf-Klein GP, Silverstone FA, Lansey SC, Tesi D, Ciampaglia C, O’Donnell M, Galkowski J, Jaeger A, Wallenstein S, Leleiko NS: Energy requirements in Alzheimer’s disease patients. Nutrition 1995;11:264–268. Cronin-Stubbs D, Beckett LA, Scherr PA, Field TS, Chown MJ, Pilgrim DM, Bennett DA, Evans DA: Weight loss in people with Alzheimer’s disease: A prospective population-based analysis. BMJ 1997;314:178–179. Volicer L, Harper DG, Manning BC, Goldstein R, Satlin A: Sundowning and circadian rhythms in Alzheimer’s disease. Am J Psychiatry 2001;158:704–711. Koss E, Gilmore GC: Environmental interventions and functional ability of AD patients; in Vellas B, Fitten J, Frisoni G (eds): Research and Practice in Alzheimer’s Disease. New York, Serdi/Springer, 1998, pp 185–199. Dunne TE, Neargarder SA, Cipolloni PB, Cronin-Golomb A: Visual contrast enhances food and liquid intake in advanced Alzheimer’s disease. Clin Nutr (in press). Yaffe K, Fox P, Newcomer R, Sands L, Lindquist K, Dane K, Covinsky KE: Patient and caregiver characteristics and nursing home placement in patients with dementia. JAMA 2002;287: 2090–2097. Volicer L, Hurley AC, Blasi ZV: Characteristics of dementia end-of-life care across care settings. Am J Hosp Palliat Care 2003;20:191–200.

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Tracy Dunne, PhD, Executive Director Alzheimer’s Design and Living Solutions 27 Duncan Drive, Norwell, MA 02061 (USA) Tel. ⫹1 781 659 4192, E-Mail [email protected]

Dunne

324

Author Index

Anderson, N.D. 62 Bouras, C. 30 Cronin-Golomb, A. IX, 96 Cushman, L. 155 Duffy, C.J. 155 Dunne, T. 305 Giannakopoulos, P. 30 Gilmore, G.C. 173 Glosser, G. 236 Grady, C.L. 62

Greenwood, P. 271 Grossman, M. 236 Groth, K.E. 173 Gunten, A. von 30

Parasuraman, R. 271

Hof, P.R. IX, 30 Holroyd, S. 126

Tippett, L.J. 212

Kavcic, V. 155 Kurylo, D.D. 199

Rizzo, M. 248 Rocco, F.J. 136

Valenti, D. 1 Vecera, S.P. 248 Weintraub, S. 290

Mapstone, M. 290 Mendez, M.F. 112 Morrison, S.R. 173

325

Subject Index

Acetylcholine receptor attention modulation and subunit polymorphism effects 283, 284 types 283 Activation tasks, see Functional neuroimaging Activities of daily living, visual impairment effects in Alzheimer’s disease, see contrast sensitivity Alexia, see Reading Alignment test, perceptual organization testing in Alzheimer’s disease 203, 204 Apolipoprotein E-␧4 Alzheimer’s disease risks 271, 279 spatial attention effects 279–281, 284, 285 Attention, see Visual attention Balint syndrome, Alzheimer’s disease association 32, 33, 248 Birmingham Object Recognition Battery 220, 222 Central acuity, Alzheimer’s disease group performance 100 Cholinergic system, see also Acetylcholine receptor deficits and visual hallucinations in Alzheimer’s disease 130, 131 spatial attention role 282, 283, 296 Circadian rhythm intraocular pressure 16, 17 melatonin secretion 6, 7

retinitis pigmentosum disturbances 17 suprachiasmatic nucleus role 7 Cognitive aging, theories 63 Color discrimination Alzheimer’s disease group performance 97, 100, 101 Down syndrome 141, 143, 144 interventions for activities of daily living improvement 308–310 Confocal scanning layer tomography, optic nerve fiber layer analysis 10, 11 Contrast sensitivity Alzheimer’s disease group performance 97, 101–103 definition 306 Down syndrome 146–148 face discrimination relationship 103, 104, 307 glaucoma 13, 14 interventions for activities of daily living improvement color and light applications 309, 310 color guidelines 308 enhancement guidelines bathing 310, 311 bathroom 317, 318 bedroom 315, 316 doorways 320 dressing 311, 312 eating 313–315 hallways 320 kitchen 317

326

living room 318, 319 stairways 319 toileting 312, 313 light guidelines 309 rationale 306, 307, 320, 321 magnocellular deficit hypothesis studies flicker effects 191–193 Nicolet CS2000 measurements 190, 191, 193 study design 187–189 Vistech VCTS6500 measurements 189, 190 measurement 102 neuropathology of Alzheimer’s disease deficits 306 retinitis pigmentosum 13, 14 visual pathways 13 Critical flicker fusion, Alzheimer’s disease group performance 97 Depth perception, Alzheimer’s disease impairment 305 Down syndrome Alzheimer’s disease association 136 epidemiology 136 neuropathological similarity with Alzheimer’s disease 136–138 visual deficits Alzheimer’s disease comparison 148–150 color discrimination 141, 143, 144 contrast sensitivity 146–148 neuropathology 150 overview 137–139 stereoacuity 145, 146 study design 139–141 visual acuity 141 Edinger-Westphal nucleus, function 7, 22 Efron’s shape discrimination task 220, 224 Electroretinogram, magnocellular deficit hypothesis studies 177 Environmental modification contrast enhancement, see Contrast sensitivity vision intervention in Alzheimer’s disease 21

Subject Index

Exfoliation syndrome, linkage with glaucoma and Alzheimer’s disease 15, 16 Face processing age-related differences 64–66 Alzheimer’s disease deficits 221, 222 contrast sensitivity and face discrimination relationship 103, 104, 307 measurement 221 perceptual organization testing in Alzheimer’s disease 200, 202, 205 Frequency doubling technology, glaucoma functional testing and Alzheimer’s disease implications 18–20 Functional neuroimaging, see also Magnetic resonance imaging age-related differences in brain activation episodic memory processes episodic encoding 75–79 episodic retrieval 79–83 overview 74, 75 non-memory tasks executive functions 68–71 face processing 64–66 overview 71, 72 word processing 66–68 working memory processes 72–74 Alzheimer’s disease deficits versus healthy elderly compensatory changes 88 episodic memory 83, 84 memory tasks 84–87 non-memory tasks 84 reading disorders in Alzheimer’s disease 238, 239 Geographic distribution, visuospatial deficits in Alzheimer’s disease 21, 22 Glass Patterns test, perceptual organization testing in Alzheimer’s disease 203, 204 Glaucoma Alzheimer’s disease comorbidity 14–16, 105 circadian rhythm of intraocular pressure 16, 17 contrast sensitivity 13, 14 course in Alzheimer’s disease 1, 2

327

Glaucoma (continued) estrogen protection 105 functional testing and Alzheimer’s disease implications frequency doubling technology 18–20 short-wavelength automated perimetry 18–20 neuronal axon damage 16 visual pathway deficits 1, 2 Global motion detection, Alzheimer’s disease group performance 97, 104, 105 Hallucinations, see Visual hallucinations Hemispheric laterality, Alzheimer’s disease defects in vision 9, 10 Hippocampus, damage and visuospatial disorientation in Alzheimer’s disease 155, 156, 169 Imagery, see Visual imagery K pathway, visual system 4, 5 Large Shapes test, perceptual organization testing in Alzheimer’s disease 203, 204 Lateral geniculate nucleus glaucoma effects 16 magnocellular deficit hypothesis, neuropathological evidence 176, 177 Lens, amyloid deposition in Alzheimer’s disease 9 Letter identification with backward masking, Alzheimer’s disease group performance 97, 99, 104 Light therapy, vision intervention in Alzheimer’s disease 21 Living room, contrast enhancement guidelines for Alzheimer’s disease patients 318, 319 Local speed discrimination, Alzheimer’s disease group performance 97 Magnetic resonance imaging, see also Functional neuroimaging functional studies coherent motion tasks 186 spatial window of attention 295

Subject Index

posterior cortical atrophy 116 visual hallucination patients 129 Magnocellular deficit hypothesis, Alzheimer’s disease coherent motion task studies magnocellular function measurement 178, 179 motion discrimination versus detection 182 stimulus eccentricity and speed 183, 184 thresholds in Alzheimer’s disease 179–182 weighted stimulus density 184–186 contrast sensitivity studies flicker effects 191–193 Nicolet CS2000 measurements 190, 191, 193 study design 187–189 Vistech VCTS6500 measurements 189, 190 motion detection, M and P pathways 182, 183 neuropathological evidence lateral geniculate nucleus 176, 177 retina 174–176 visual cortex 176, 177 neurophysiological evidence 177 null hypothesis caveats 194 overview 173, 174 psychophysical investigations 177, 178 Melanopsin, vision role 6 Melatonin circadian rhythm 6, 7 light wavelength dependence of secretion 7 Melatonin-1a receptor (MT1), expression in Alzheimer’s disease 8 Motion perception Alzheimer’s disease group performance 97, 104, 105, 305, 306 discrimination versus detection 182 M and P pathways 182, 183 visuospatial variants in Alzheimer’s disease 32 M pathway, see also Magnocellular deficit hypothesis, Alzheimer’s disease

328

motion detection, M and P pathways 182, 183 visual system 4, 6, 12, 13, 173, 174 Neurofibrillary tangles distribution and clinical symptoms in Alzheimer’s disease 30, 31, 36, 37 distribution in typical versus atypical Alzheimer’s disease 39–51 visual association cortices 38 Object recognition Alzheimer’s disease deficits 217–224 tests 217 Optical coherence tomography, optic nerve fiber layer analysis 10, 11 Parietal cortex, attention role 292, 293 Parietal syndromes, Alzheimer’s disease association 33, 35 Perceptual organization, Alzheimer’s disease components 199 deficits and evaluation 200–208 neural algorithms 199, 200 neural correlates 208–210 object recognition role 199 visual processing levels 200 Positron emission tomography, see also Functional neuroimaging, posterior cortical atrophy 116 Posterior cortical atrophy, see also Balint syndrome Alzheimer’s disease comparison differences 119, 120 similarities 118, 119 clinical presentation 112–115 course 115, 116 diagnostic criteria 121, 122 diagnostic imaging 116 history of study 112 neuropathology 116–118 prevention and management 121, 122 P pathway motion detection, M and P pathways 182, 183 visual system 4–6, 12, 173 Prefrontal cortex

Subject Index

activation tasks, see Functional neuroimaging aging effects 63, 64 Prosopagnosia, Alzheimer’s disease association 33 Proximity test, perceptual organization testing in Alzheimer’s disease 203, 204 Pupillary function, Alzheimer’s disease 17, 18 Raven’s Progressive Matrices, age-related differences 69, 70 Reading cognitive systems 236 disorders in Alzheimer’s disease alexia mechanisms 239–244 alexia without agraphia 237, 238, 241 overview 236 semantic alexia 237 surface alexia 237, 240, 241 visual processing defects 241–243 functional neuroimaging 238, 239 lexical semantic processing in Alzheimer’s disease 239, 240 Retina amyloid deposition in Alzheimer’s disease 8 hemispheric laterality of Alzheimer’s disease defects 9, 10 inner retina 3, 4 magnocellular deficit hypothesis, neuropathological evidence 174–176 outer retina 2, 3 overview of organization 2 photographic studies in Alzheimer’s disease 10 Retinal ganglion cell degeneration in Alzheimer’s disease 7, 8, 12, 175 macular cell loss in Alzheimer’s disease 11, 12 Retinitis pigmentosum circadian rhythm disturbances 17 contrast sensitivity 13, 14 Scanning laser polarimetry, optic nerve fiber layer analysis 10, 11

329

Segmentation, visual processing stage 214 Semantic alexia, Alzheimer’s disease association 237 Senile plaque distribution and clinical symptoms in Alzheimer’s disease 30, 31 distribution in typical versus atypical Alzheimer’s disease 39–52 visual association cortices 38 Shape constancy, visual processing stage 214, 215 Short-term memory, see Visual short-term memory Short-wavelength automated perimetry, glaucoma functional testing and Alzheimer’s disease implications 18–20 Single nucleotide polymorphisms, cognitive function studies 282 Single photon emission computed tomography, posterior cortical atrophy 116 Small Shapes test, perceptual organization testing in Alzheimer’s disease 203, 204 Spatial attention, see also Visual attention acetylcholine receptor polymorphism effects 282–284 Alzheimer’s disease impairment attention shifts 272–275 dynamic scale of spatial attention 275–277 neuropathology 292, 293 overview 255–258, 271, 291 reaction time studies 273–275 window of spatial attention reduction 293–295 apolipoprotein E-␧4 allele effects 279–281, 284, 285 cholinergic system 282, 283, 296 covert versus overt attention shifts 272 discrimination tasks 258 healthy aging studies 277–279 metaphors 290 neural correlates of spatial window of attention 295 neuroanatomy 291, 292, 298 search tasks 256–258 visual short-term memory role 257, 258

Subject Index

Stereoacuity Alzheimer’s disease group performance 97, 100, 101 Down syndrome 145, 146 Stroop task, Alzheimer’s disease effects 261 Suprachiasmatic nucleus, circadian rhythm role 7 Surface alexia, Alzheimer’s disease association 237, 240, 241 Ventral visual processing Alzheimer’s disease deficits face processing 221 individual cases 228, 229, 231 intermediate and high-level processing 213 prospects for study 231, 232 visual imagery task performance 226–228 visual object recognition 217–224 visual perceptual task performance 224–226 overview 212, 213 stages of visual processing image segmentation 214 lexical access from visual representations 215 shape constancy 214, 215 visual imagery 216, 217 visual object memory 215 Visual acuity activities of daily living impact in Alzheimer’s disease 306 Down syndrome 141 Visual attention, see also Spatial attention and Visual short-term memory Alzheimer’s disease impairments global and local information 258, 259 high-level attention 260–262 navigational cue use 296–298 object-based attention 259, 260 overview 248 concept of selective attention 249–251 decision-noise reduction effect 251 perceptual-level attention preservation in Alzheimer’s disease 263–265

330

sensory enhancement effect 251 Visual cortex, magnocellular deficit hypothesis neuropathological evidence 176, 177 Visual hallucinations, Alzheimer’s disease association epidemiology 35, 126–128 genetics 130 neurochemistry 130, 131 neurophysiologic mechanisms 131, 132 visual dysfunction correlation 128–130 Visual imagery task performance in Alzheimer’s disease 226–228 testing 223, 224 visual processing stage 216, 217 Visual object memory, visual processing stage 215 Visual object and space perception 217–219 Visual perception, task performance in Alzheimer’s disease 224–226 Visual short-term memory attention interactions 252–254 deficits in Alzheimer’s disease and attention effects global and local information 259 high-level attention 261, 262 object-based attention 260 reduced capacity studies 265, 266 spatial attention 257, 258 level effect of attention 252, 253 storage capacity 252

Subject Index

Visuospatial disorientation, Alzheimer’s disease focus of expansion studies 161–163 hippocampal damage 155, 156, 169 navigation studies 157, 158, 160 optic flow analysis 156, 157, 160–163 spatiotemporal integration impairment 167–169 temporal integration impairment in visual processing 163–167 Visuospatial variants, Alzheimer’s disease Balint syndrome 32, 33 cortical neuropathology related to visuospatial processing 37–39 hallucinations 35 heterogeneity of clinical presentation 106 motion perception and target tracing 32 neurofibrillary tangle distribution in typical versus atypical Alzheimer’s disease 39–51 neuropathology 106, 107 overview 31, 32 parietal syndromes 33, 35 prosopagnosia and visual agnosia 33 reduplicative paramnesia for places 36 Wisconsin Card Sorting Task age-related differences 70 Alzheimer’s disease effects 261 Yellow filters, vision intervention in Alzheimer’s disease 20, 21

331