discovery.ucl.ac.uk · Web view2015-07-21 · The most common cause of dementia is Alzheimer’s disease (AD), defining features of which are the presence of senile plaques, neurofibrillary

THE VISUAL BASIS OF READING IMPAIRMENT IN POSTERIOR CORTICAL ATROPHY

SUBMITTED TO UNIVERSITY COLLEGE LONDON FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

KEIR YONG2014

INSTITUTE OF NEUROLOGY, UNIVERSITY COLLEGE LONDON

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DECLARATION STATEMENT

I, Keir Yong, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis.

Keir Yong

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ABSTRACT

This thesis explores the nature of reading impairment in posterior cortical atrophy (PCA), a

degenerative syndrome most commonly caused by Alzheimer’s disease (AD) pathology. PCA is

characterised by cognitive deficits associated with posterior brain atrophy, including disruption

to various visual domains, with relatively spared episodic memory function. Acquired dyslexia

is an early and debilitating symptom of PCA; however, there is a lack of group investigations of

reading dysfunction. Through a better understanding of dyslexia, including the contribution of

visual deficits, the optimal conditions for PCA patients’ reading might be revealed.

A series of studies were conducted to characterise reading and other visual deficits in PCA

patients, typical AD (tAD) patients and healthy controls, accompanied by a consistent and

comprehensive battery of neuropsychological tests. One form of early visual processing deficit

that has been proposed to crucially limit reading in normal peripheral vision is crowding.

Behavioural and neuroimaging investigations confirmed the qualitative similarity between

crowding and deficits in identifying centrally presented flanked stimuli in PCA.

Assessments of single word recognition and passage reading were carried out through

behavioural, eye movement and neuroimaging analysis. Perceptual and spatial factors

primarily determined single word and passage reading ability in PCA, not tAD. One counter-

intuitive finding was how PCA patients demonstrated particular difficulty reading text in large

font. Results also identified two patients who demonstrate remarkably preserved reading

despite showing grave visual impairment; this discrepancy poses problems for general visual

accounts of reading deficits. The two patients were followed longitudinally, revealing how the

development of enhanced crowding effects coincided with loss of reading ability.

Insights from the thesis informed the development of two interventions which intended

to provide the optimal conditions for reading in PCA; both interventions resulted not only in

consistent gains in reading accuracy, but also in improvements in self-reported reading ease

and comprehension.

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Table of Contents

ABSTRACT...................................................................................................................- 3 -

AIMS OF THIS THESIS.................................................................................................- 11 -

1. INTRODUCTION................................................................................................- 12 -

1.1. CHAPTER INTRODUCTION...............................................................................- 12 -

1.2. DEMENTIA......................................................................................................- 13 -

1.2.1. Alzheimer’s disease (AD)............................................................................- 13 -

1.2.2. Posterior Cortical Atrophy (PCA)................................................................- 16 -

1.3. CHAPTER CONCLUSIONS.................................................................................- 21 -

2. NEUROPSYCHOLOGY OF PCA............................................................................- 23 -


2.2. VISUAL PROCESSING.......................................................................................- 23 -

2.2.1. Early visual processing................................................................................- 23 -

2.2.2. Visuospatial processing and inverse size effects.........................................- 25 -

2.2.3. Visuoperceptual processing........................................................................- 27 -

2.3. READING.........................................................................................................- 28 -

2.3.1. Contribution of areas of early visual processing.........................................- 29 -

2.3.2. Cognitive models........................................................................................- 30 -

2.3.3. Neuroanatomical models...........................................................................- 31 -

2.4. ACQUIRED DYSLEXIA.......................................................................................- 32 -

2.5. NON-VISUAL NEUROPSYCHOLOGICAL FEATURES...........................................- 35 -

2.5.1. Language....................................................................................................- 35 -

2.5.2. Praxis..........................................................................................................- 36 -


3. METHODS OVERVIEW.......................................................................................- 38 -

3.1. PARTICIPANTS................................................................................................- 38 -

3.1.1. Patients.......................................................................................................- 38 -

3.1.2. Healthy controls.........................................................................................- 38 -

3.2. CLINICAL ASSESSMENT...................................................................................- 38 -

3.3. INCLUSION CRITERIA......................................................................................- 39 -

3.3.1. PCA.............................................................................................................- 39 -

3.3.2. Typical AD...................................................................................................- 39 -

3.4. NEUROPSYCHOLOGY......................................................................................- 39 -

3.4.1. Background neuropsychology....................................................................- 39 -

3.4.2. Visual Assessment......................................................................................- 40 -

3.5. RESPONSE LATENCIES.....................................................................................- 41 -

3.6. EYETRACKING.................................................................................................- 41 -

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3.7. IMAGING........................................................................................................- 42 -

3.7.1. MRI acquisition...........................................................................................- 42 -

3.7.2. Image processing software.........................................................................- 42 -

3.7.3. Image processing........................................................................................- 42 -

4. VISUAL CROWDING EFFECTS IN PCA..................................................................- 44 -


4.2. METHODS.......................................................................................................- 45 -

4.2.1. Participants.................................................................................................- 45 -


4.2.3. Crowding assessment.................................................................................- 47 -

4.2.4. Data analysis...............................................................................................- 49 -

4.3. RESULTS..........................................................................................................- 51 -

4.3.1. Crowding Assessment 1 - flanker and spacing effects................................- 51 -

4.3.2. Crowding assessment 2 – Polarity effects...................................................- 54 -

4.3.3. Error analysis..............................................................................................- 54 -

4.3.4. Summary of behavioural data....................................................................- 55 -

4.3.5. Neuroimaging findings...............................................................................- 55 -

4.4. DISCUSSION....................................................................................................- 56 -


5. READING IN PCA...............................................................................................- 62 -


5.2. METHODS.......................................................................................................- 63 -

5.2.1. Participants.................................................................................................- 63 -


5.2.3. Reading assessment...................................................................................- 63 -

5.2.4. Data analysis...............................................................................................- 65 -

5.3. RESULTS..........................................................................................................- 66 -

5.3.1. Perceptual corpus.......................................................................................- 66 -

5.3.2. Cursive font reading...................................................................................- 71 -

5.3.3. Neuroimaging findings...............................................................................- 71 -

5.4. DISCUSSION....................................................................................................- 72 -


6. CASE STUDIES: INTACT READING IN PCA...........................................................- 77 -


6.2. METHODS.......................................................................................................- 79 -

6.2.1. Participants.................................................................................................- 79 -


6.2.3. Experimental procedures...........................................................................- 81 -

6.2.4. Data analysis...............................................................................................- 84 -

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6.3. RESULTS..........................................................................................................- 85 -

6.3.1. Visual assessment.......................................................................................- 85 -

6.3.2. Word reading..............................................................................................- 85 -

6.3.3. Single letter processing...............................................................................- 89 -

6.4. DISCUSSION....................................................................................................- 89 -


7. CASE STUDIES: LONGITUDINAL ASSESSMENT OF READING IN PCA....................- 97 -


7.2. METHODS.......................................................................................................- 98 -

7.2.1. Participants.................................................................................................- 98 -

7.2.2. Imaging.......................................................................................................- 98 -

7.2.3. Experimental procedures...........................................................................- 98 -

7.2.4. Data analysis.............................................................................................- 100 -

7.3. RESULTS........................................................................................................- 101 -

7.3.1. Reading assessment.................................................................................- 101 -

7.3.2. Visual assessment.....................................................................................- 106 -

7.3.3. Crowding assessment...............................................................................- 106 -

7.3.4. Error analysis............................................................................................- 109 -

7.4. DISCUSSION..................................................................................................- 109 -

7.5. CHAPTER CONCLUSIONS...............................................................................- 113 -

8. PASSAGE READING IN PCA..............................................................................- 114 -

8.1. CHAPTER INTRODUCTION.............................................................................- 114 -

8.2. METHODS.....................................................................................................- 115 -

8.2.1. Participants...............................................................................................- 115 -

8.2.2. Background neuropsychology..................................................................- 116 -

8.2.3. Passage reading assessment.....................................................................- 116 -

8.2.4. Data analysis.............................................................................................- 118 -

8.3. RESULTS........................................................................................................- 118 -

8.3.1. Reading accuracy......................................................................................- 118 -

8.3.2. Reading latency........................................................................................- 121 -

8.3.3. Eye movement data..................................................................................- 121 -

8.4. DISCUSSION..................................................................................................- 121 -

8.5. CHAPTER CONCLUSION................................................................................- 122 -

9. FACILITATING READING IN PCA.......................................................................- 123 -


9.2. METHODS.....................................................................................................- 124 -

9.2.1. Pilot study 1..............................................................................................- 124 -

9.2.2. Pilot study 2..............................................................................................- 126 -

9.2.3. Main investigation- Single-word and Double-word presentation.............- 127 -

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9.2.4. Data analysis.............................................................................................- 128 -

9.3. RESULTS........................................................................................................- 129 -

9.3.1. Efficacy of reading intervention................................................................- 129 -

9.3.2. Eye movement data..................................................................................- 133 -

9.4. DISCUSSION..................................................................................................- 134 -

9.5. CHAPTER CONCLUSIONS...............................................................................- 136 -

10. THESIS CONCLUSIONS.....................................................................................- 138 -


10.2. ROLE OF EXCESSIVE CROWDING IN LETTER RECOGNITION..........................- 139 -

10.3. ROLE OF PERCEPTUAL FACTORS IN WORD RECOGNITION...........................- 139 -

10.4. PERCEPTUAL FACTORS IN TEXT READING AND READING INTERVENTIONS. .- 141 -

10.5. CAUSATIVE ROLE OF VISUAL IMPAIRMENT IN READING..............................- 142 -

10.6. IMPLICATIONS FOR MODELS OF READING...................................................- 143 -

10.7. CLINICAL IMPLICATIONS...............................................................................- 144 -

10.8. PCA v tAD......................................................................................................- 145 -

10.9. CHAPTER CONCLUSION................................................................................- 146 -

PUBLICATIONS.........................................................................................................- 147 -

ACKNOWLEDGEMENTS............................................................................................- 148 -

APPENDIX 1: DUBOIS ET AL. (2007) DIAGNOSTIC CRITERIA FOR PROBABLE AD.........- 149 -

APPENDIX 2: NINCDS-ADRDA 2011 CRITERIA FOR DEMENTIA AND PROBABLE AD....- 150 -

APPENDIX 3: PROPOSED DIAGNOSTIC CRITERIA FOR PCA.........................................- 151 -

APPENDIX 4: VISUAL ASSESSMENT NEUROPSYCHOLOGICAL EXAMPLE STIMULI.......- 152 -

APPENDIX 5: READING CORPORA PERFORMANCE FOR FOL AND CLA.......................- 157 -

GLOSSARY...............................................................................................................- 159 -

REFERENCES............................................................................................................- 161 -

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Table of Figures

Figure 1.1 Grey matter and cortical thickness for PCA relative to tAD and controls...........- 19 -Figure 4.1 Crowding example stimuli...............................................................................- 47 -Figure 4.2 Accuracy and latency data for the PCA and tAD groups across spacing and letter,

shape and number flanker conditions.......................................................................- 52 -Figure 4.3 Accuracy and latency data for the PCA and tAD groups across spacing and same

and reverse polarity flanker conditions....................................................................- 55 -Figure 4.4 Statistical parametric maps of grey matter volume associated with crowding

effects in the PCA group...........................................................................................- 57 -Figure 5.1 Summary of reading and latencies for the PCA, tAD and control groups...........- 68 -Figure 5.2 Proportion of participants in each group who show an effect of each variable on

either latency or accuracy at the individual level......................................................- 69 -Figure 5.3 Statistical parametric maps of grey matter volume associated with the difference

in accuracy between large and small words in the PCA group...................................- 72 -Figure 6.1 Neuroanatomical features in FOL and CLA.......................................................- 80 -Figure 6.2 Mean reading latencies for words of different length across all corpora for FOL,

CLA and their control groups....................................................................................- 89 -Figure 6.3 Mean response latencies for flanked letter identification for FOL, CLA and their

control groups..........................................................................................................- 91 -Figure 6.4 Mean reading latencies for words of different length for FOL, CLA and previously

reported letter-by-letter readers..............................................................................- 93 -Figure 7.1 MRI sections and voxel-compression maps for FOL and CLA.............................- 99 -Figure 7.2 FOL and CLA’s accuracy and latency data across longitudinal assessments.....- 102 -Figure 7.3 FOL and CLA’s latencies for words of different length across longitudinal

assessments...........................................................................................................- 104 -Figure 7.4 Types of error made across longitudinal assessments....................................- 106 -Figure 7.5 FOL and CLA’s flanked letter identification perfomance across longitudinal

assessments...........................................................................................................- 108 -

Figure 8.1 Heatmap of PCA accuracy data from a sample passage. - 119 -Figure 8.2 Order of first forty words read by tAD and PCA patients................................- 120 -

Figure 9.1 Presentation conditions for Pilots 1 and 2 - 125 -Figure 9.2 Sequential single-word and sequential double-word presentations...............- 128 -Figure 9.3 Accuracy and latency data for the PCA, tAD and control groups.....................- 130 -Figure 9.4 PCA reading accuracy for baseline and under both reading interventions.......- 131 -Figure 9.5 PCA, tAD and control error rates under different presentation conditions......- 131 -Figure 9.6 PCA self-reported measures of reading..........................................................- 133 -

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Table of Appendix Figures

Figure A-i Visual acuity test (CORVIST) subset to scale (Snellen equivalent: 6/18- 6/9). . .- 152 -Figure A-ii Shape detection test (VOSP)..........................................................................- 152 -Figure A-iii Shape discrimination (Efron, 1968): three levels of difficulty.........................- 153 -Figure A-iv Hue discrimination (CORVIST).......................................................................- 153 -Figure A-v Object Decision (VOSP)..................................................................................- 154 -Figure A-vi Fragmented letter (VOSP).............................................................................- 154 -Figure A-vii Unusual and usual views (Warrington and James, 1988)..............................- 155 -Figure A-viii Number location (VOSP).............................................................................- 155 -Figure A-ix Dot counting (VOSP).....................................................................................- 155 -Figure A-x A Cancellation task........................................................................................- 156 -

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Table of Tables

Table 4.1 Demographic characteristics of PCA, tAD and normal control groups................- 46 -Table 4.2 Molecular pathology data for PCA and tAD patients.........................................- 46 -Table 4.3 Neuropsychological scores of patients with PCA and tAD..................................- 48 -Table 4.4 Comparisons between PCA and tAD accuracy and latency data.........................- 53 -Table 5.1 Different levels of reading variables for words from the perceptual corpus.......- 64 -Table 6.1 Neuropsychological scores of FOL/CLA relative to normative data....................- 80 -Table 6.2 Accuracy and latency data for FOL, CLA and relevant control groups on the word

reading experiments................................................................................................- 87 -Table 6.3 Performance on tests of letter processing.........................................................- 90 -Table 6.4 Performance on tests of visuoperceptual function............................................- 91 -Table 7.1 Reading and crowding assessment accuracy and latency for FOL/CLA and their

matched control groups.........................................................................................- 103 -Table 7.2 FOL and CLA’s performance on background neuropsychological measures and tests

of visual processing................................................................................................- 107 -Table 8.1 Demographic information for PCA, tAD and healthy control groups................- 115 -Table 8.2 Molecular pathology data for PCA and tAD patients........................................- 115 -Table 8.3 Neuropsychological scores of patients with PCA and tAD................................- 117 -Table 8.4 Eye movement data for PCA, tAD and controls under standard presentation. .- 121 -Table 9.1 Accuracy and comprehension performance on Pilot studies 1 and 2................- 126 -Table 9.2 Correlations between PCA performance on behavioural measures and reading

accuracy under different presentation conditions...................................................- 132 -Table 9.3 Eye movement data for PCA, tAD and controls under reading interventions.. .- 134 -

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AIMS OF THIS THESIS

The most common cause of dementia is Alzheimer’s disease (AD), defining features of

which are the presence of senile plaques, neurofibrillary tangles and severe neuronal and

synaptic loss. While in most AD patients these pathological changes tend to be accompanied

by memory complaints and medial temporal lobe atrophy, there is considerable variation in

the cognitive phenotypes of individuals with AD pathology. Posterior cortical atrophy (PCA) is a

syndrome most commonly caused by AD; it is characterised by deficits in visuospatial and

higher-order visuoperceptual processing and is associated with structural changes

predominantly in parietal, occipital and occipito-temporal regions. An early and particularly

life-limiting symptom of PCA is acquired dyslexia; however, there is a scarcity of systematic

group studies evaluating reading among PCA patients. Not only would such studies help

determine the prevalence, type and heterogeneity of reading impairment in PCA, but they

would also clarify the mechanisms through which deficits in basic, higher-order visual function

and eye movement control undermine reading. Better understanding of these mechanisms

might inform the development of aids and strategies which reduce or eliminate the

susceptibility of reading to spatial, perceptual and oculomotor impairment , allowing

individuals with PCA to maximise their reading ability over the early course of the disease.

Aims

1. To investigate the role of early visual, visuoperceptual and visuospatial function in limiting

reading ability in PCA and AD.

2. To confirm whether flanked letter identification deficits in PCA patients reflect early visual

dysfunction in the form of enhanced visual crowding.

3. To characterise the perceptual factors affecting single word recognition in PCA and AD.

4. To characterise the factors affecting passage reading ability in PCA and AD.

5. To identify whether ameliorating or curtailing visual deficits which contribute towards

reading dysfunction result in improvements in reading ability.

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1. INTRODUCTION

1.1. CHAPTER INTRODUCTION

Over a hundred years ago, Alois Alzheimer identified the amyloid plaques and

neurofibrillary tangles that have come to be associated with Alzheimer’s disease, the main

cause of dementia. Since then, dementia has emerged as one of the greatest challenges to

health and social care; there are an estimated 820,000 people in the UK with dementia, with

this number expected to double within 30 years. The cost of care in the UK is estimated at £23

billion a year; the global cost of dementia is estimated to amount to more than 1% of the

world’s gross domestic product. An internationally ageing population (Kinsella et al., 2009)

accompanied by age-related increases in the prevalence of dementia (Fratiglioni et al., 2001;

Corrada et al., 2010) and the lack of a cure or prevention for most causes of dementia

demonstrate the incredible burden that progressive dementias have on health care systems,

along with the psychological and financial strain placed on family caregivers (Etters et al.,

2007).

It is estimated that more than half of individuals with dementia live in care homes

(Macdonald & Cooper, 2007). Projections by Comas-Herrera et al. (2007), which refer to

cognitive impairment in older people but reflect mainly dementia-associated costs, suggest

that expenditure on long-term care services in England will rise from 0.60% of GDP in 2002 to

0.96% of GDP in 2031. An important factor in determining admission to care homes is the

capacity to perform activities of daily living (ADL). Diminished capacity for ADLs results in

reduced independence and increased care demands (Bullock & Hammond, 2003), while

increased needs for caregiver assistance with activities is the main predictor of a need for

skilled care, the most common reason for the institutionalisation of individuals with dementia

by caregivers (Buhr et al., 2006). Furthermore, dependency on others to perform ADLs has

been identified as the primary factor underlying quality of life measures in dementia

(Andersen et al., 2004).

In individuals with dementia, instrumental ADLs, functions which particularly reflect

independence, are predicted by higher order visuoperceptual (Glosser et al., 2002; Jefferson et

al., 2006) and visuospatial ability (Fukui & Lee, 2009; Hill et al., 1995). PCA patients show

dramatic impairment to these aspects of vision, but have relatively preserved insight into

difficulties arising from this impairment (Benson et al., 1988; Mendez et al., 2002; Tang-Wai et

al., 2004), while patients with an amnestic typical AD (tAD) presentation often have secondary

visuospatial and visuoperceptual deficits (Almkvist, 1996; Caine & Hodges, 2001; Quental et al.,

2013; Binetti et al., 1998) which become more prominent over the disease course (Grady et al., 12

1988; Paxton et al., 2007). A better understanding of visual dysfunction in PCA offers a rare and

unique perspective into later stages of tAD in which patients are unable to describe or explain

the visual problems they face. Consequently, the development of technological aids which

minimise deficits in visuoperceptual and visuospatial function might not only benefit PCA

patients in the community, day centres or care homes by allowing increased fulfilment of ADLs,

prolonging independence, reducing carer burden and improving quality of life, but also

patients who are further into the disease course of tAD.

1.2. DEMENTIA

Dementia is an acquired syndrome involving a persistent impairment of multiple cognitive

domains and activities of daily living (Cummings & Benson, 1983; Rossor, 1994; Qiu et al.,

2009). The most common cause of dementia is AD pathology; however, other non-AD forms of

dementia include dementia with Lewy Bodies (DLB), corticobasal degeneration (CBD),

frontotemporal lobar degeneration (FTLD), vascular dementia (VaD), Creutzfeld Jakob disease

(CJD) and Acquired Immunodeficiency Syndrome (AIDS) dementia. Comorbidity can exist

between dementias, posing problems for clinical diagnosis, and similar symptoms for

syndromes such as PCA can arise from different underlying pathological processes. This thesis

focuses on patients, both typical amnestic and PCA, who have probable AD based on clinical

and neuroimaging data.

1.2.1. Alzheimer’s disease (AD)

1.2.1.1. Pathology

Two structural brain changes identified in Alzheimer’s original patient still form the

neuropathological basis of AD: extracellular amyloid β-protein deposits and intracellular

neurofibrillary tangles (NFT) (Selkoe, 2000). Neurofibrillary changes develop initially in the

transentorhinal region, progressing to marked involvement of the transentorhinal and

entorhinal regions, including minor changes in the hippocampus, followed by a progressive

invasion of the isocortex (Braak & Braak, 1996). Amyloid deposits are first found in basal

portions of the frontal, temporal and occipital lobes; deposits are subsequently found

dispersed across almost all cortical regions, with only mild involvement of the hippocampal

region; finally, dense formations of deposits are found in most of the isocortex (Braak & Braak,

1991). The progressive invasion of the cerebral cortex by the hallmarks of AD pathology is

accompanied by neuronal and synaptic loss (Terry et al., 1991; Gomez-Isla et al., 1996) and

cerebral atrophy (Chan et al., 2003). There have been suggestions of a selective vulnerability of

certain cells to AD, such as cells with long, sparsely myelinated axonal projections, with

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propagation of AD pathology taking place through cortico-cortical connections (Morrison et al.,

1986a; Lewis et al., 1987; Hof et al., 1989; Braak & Tredici, 2011); such suggestions are in line

with the notion of early AD manifesting as the loss of interaction between different cortical

regions (De Lacoste & White, 1993).

1.2.1.2. Epidemiology

The strongest risk factor for AD is older age, which not only implicates age-related

biological processes in the development of AD, but also raises the possible expression of

various lifestyle and environmental effects having accumulated over the lifespan (Qiu et al.,

2009). Of the various factors associated with developing AD, vascular risk factors include

smoking (Peters et al., 2008), obesity (Kivipelto et al., 2005) and high blood pressure (Kivipelto

et al., 2001), while psychosocial protective factors include a higher educational level (Karp et

al., 2004), increased physical activity (Rovio et al., 2005) and greater occupational complexity

(Andel et al., 2005). While the majority of AD patients are elderly, some individuals develop AD

at a younger age. Young-onset AD is conventionally defined as patients with onset before 65

years of age (Rossor et al., 2010); a younger age of onset tends to be more associated with

inherited familial AD (Sampson et al., 2004).

1.2.1.3. Genetic factors

While the vast majority of AD cases are sporadic, highly-heritable, autosomal dominant

forms of AD exist. The main cause of such forms are mutations in the presenilin-(PS) 1 gene on

chromosome 21 (Janssen et al., 2003), with some cases caused by mutations in the PS-2 gene

(Mann et al., 1997) or mutations on the β-amyloid precursor protein (APP) gene (Chartier-

Harlin et al., 1991). The Apolipoprotein E (APOE) ε4 allele is a susceptibility gene for both

young- and late-onset AD (Qiu et al 2009); relative to ε3/ε3 carriers, carriers with an increased

number of APOE ε4 alleles have been associated with an increased risk of developing AD and a

decreased age of onset (Qiu et al., 2004). Previous findings suggest that cognitive decline may

be more rapid in APOE ε4 carriers (Cosentino et al., 2008) and that grey matter volume in

medial temporal regions is negatively correlated with the number of ε4 alleles carried by

participants (Filippini et al., 2009). However, one study of 328 AD patients found no associated

with APOE status and rate of cognitive or behavioural decline (Tschanz et al., 2011).

1.2.1.4. Clinical diagnostic criteria for sporadic AD

AD is characterised by a progressive intellectual deterioration, with memory disturbance

nearly always presenting as the leading symptom (Cummings & Benson, 1983), consistent with

early pathological changes in medial temporal regions. The National Institute of Neurological

14

and Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorders

Association (NINCDS-ADRDA; McKhann et al., 1984) set out criteria for the diagnostic certainty

of AD; definite AD could only be established by autopsy or biopsy, probable AD was

determined from clinical and neuropsychological examination, while possible AD reflected an

atypical presentation in the absence of other sources of dementia. More recently, diagnostic

criteria for research have been revised, emphasising a deficit in episodic memory as the

leading core diagnostic criterion for typical AD and proposing the use of one or more

biomarkers as supportive features. Biomarkers included medial temporal lobe atrophy as

identified from structural imaging, markers of pathology such as amyloid positron emission

tomography (PET) imaging or cerebrospinal fluid (CSF) samples showing low Aβ42

concentration and high t-tau concentration, reduced glucose metabolism in bilateral temporal

parietal regions shown by PET and the presence of a genetic mutation in the PS-1, PS-2 or APP

genes (Dubois et al., 2007; Dubois et al., 2010; see APPENDIX 1: DUBOIS ET AL. (2007)

DIAGNOSTIC CRITERIA FOR PROBABLE AD).

While typical AD is considered as a predominantly amnestic syndrome, current diagnostic

criteria also include secondary deficits in visuospatial ability, nonamnestic language and

executive function (McKhann et al., 2011; see APPENDIX 1: DUBOIS ET AL. (2007)

DIAGNOSTIC CRITERIA FOR PROBABLE AD), with visuospatial and visuoperceptual

deficits sometimes presenting early on in the disease course (Almkvist, 1996; Caine & Hodges,

2001; Quental et al., 2013; Binetti et al., 1998). However, atypical AD presentations in which

memory impairment is not the primary deficit are becoming increasingly well-recognised. A

frontal variant of AD has been proposed, in which patients show a higher proportion of NFTs in

the frontal lobes and perform poorly on measures of executive function and performance

intelligence quotient (IQ) relative to typical AD patients (Johnson et al., 1999). The logopenic

variant of primary progressive aphasia, involving word finding pauses in the absence of major

comprehension and syntactic impairment, has been linked with AD pathology (Mesulam et al.,

2008). While weak visuospatial ability is not considered particularly atypical in moderate AD

(Grady et al., 1988), leading defining features in some patients with underlying AD pathology

include dramatic impairments in visuospatial and visuospatial processing, apraxia, acalculia

and spelling difficulties, suggesting pathogenesis primarily within posterior regions of the brain

(Benson et al., 1988; Hof et al., 1990). The proportion of AD patients who have a non-amnestic

clinical presentation increases in young-onset patients; one study found a third of young-onset

AD patients presented with impairments in vision, praxis, language or executive function

rather than memory (Koedam et al., 2010). Numerous benefits exist from being able to better

characterise and differentiate clinical presentations of AD: improving inclusion criteria and

outcome measures for intervention trials (Dubois et al., 2010), diagnosis rates and the 15

relevance of support and clinical services to patients (Crutch et al., 2012a), and our

understanding of the disease mechanisms underlying AD.

1.2.2. Posterior Cortical Atrophy (PCA)

1.2.2.1. Nosology

Over the last three decades, there have been an increasing number of reports of patients

with progressive and relatively selective visual impairment despite having normal acuity

(Cogan, 1985; De Renzi, 1986; Benson et al., 1988; Caine et al., 2004). Benson et al. (1988) was

the first to introduce the term PCA, reporting five patients who exhibited signs of Balint’s and

Gerstmann’s syndrome and showed abnormal but relatively spared memory until later in the

course of their disease. Subsequent neuropathological evaluation found that patients with

clinical presentations consistent with PCA had hallmarks of AD in the form of NFTs and senile

plaques (Hof et al., 1990; Tang-Wai et al., 2004). The prevalence of AD pathology in PCA cases

has led to terms such as ‘the visual variant of AD’ or ‘biparietal AD’ (Levine et al., 1993; Ross et

al., 1996; Galton et al., 2000). However, some cases of PCA have been attributable to

corticobasal degeneration, dementia with Lewy bodies or prion disease (Renner et al., 2004;

Seguin et al., 2011; Mendez, 2000; Tang-Wai et al., 2003). Cases have been made for the

classification of PCA as its own distinct nosological entity based on the uniformity of its clinical

profile (Tang-Wai & Mapstone, 2006), while others have argued that PCA exists on an

continuum of phenotypic variation in AD and does not represent a discrete disease (Stopford

et al., 2008).

The core behavioural phenotype of PCA involves visuospatial and visuoperceptual

impairment, features of Balint’s (simultanagnosia, oculomotor apraxia, optic ataxia) and

Gerstmann’s syndrome (acalculia, agraphia, left-right disorientation, finger agnosia), and alexia

and apraxia (McMonagle et al., 2006; Crutch et al., 2012a). McMonagle et al. (2006) reported

that some of the most common symptoms of PCA were alexia, agraphia, simultanagnosia,

acalculia and optic ataxia. Longitudinal studies indicate that the relative preservation of

memory, language and executive function observed in early stages of PCA tend to give way to

more global cognitive impairment (Levine et al., 1993; McMonagle et al., 2006). Observations

of visual hallucinations, noted in up to 25% of PCA patients (Josephs et al., 2006), and dystonia

might implicate underlying DLB or corticobasal degeneration (Crutch et al., 2012a). Some

phonological impairment has been noted in PCA, and there is an overlap in performance

between PCA and logopenic progressive aphasic (LPA) patients on tasks of nonword repetition,

phonemic fluency and prosody processing (Crutch et al., 2013). Different subtypes of PCA have

been proposed, including distinct parietal (dorsal), occipitotemporal (ventral) and caudal forms

16

(primary visual) (Ross et al., 1996; Galton et al., 2000), based on findings from individual case

reports. However, while a group study of PCA which included detailed behavioural measures

found deficits associated with occipital and temporal lobe damage, such as achromatopsia,

hemianopia, visual agnosia, prosopagnosia, the overall pattern was one of a consistent,

disproportionate deficit in dorsal relative to ventral or primary visual processes (McMonagle et

al., 2006).

1.2.2.2. Pathology

Findings from pathological studies suggest the majority of PCA patients have underlying

AD (Hof et al., 1990; Renner et al., 2004; Tang-Wai et al., 2004; Galton et al., 2000; Seguin et

al., 2011). However, some cases of PCA have been associated with Lewy body pathology (Tang-

Wai et al., 2003; Renner et al., 2004), corticobasal degeneration (Tang-Wai et al., 2004; Seguin

et al., 2011; Renner et al., 2004), subcortical gliosis and prion disease (Renner et al., 2004).

Reporting pathological data for 21 PCA patients, Renner et al. (2004) found 13 had AD

pathology, 2 had AD-Lewy body variant, 1 had DLB with coexisting subcortical gliosis and 2 had

prion-associated disease (CJD and fatal familial insomnia). A separate investigation found 7 of 7

PCA patients had AD pathology (Alladi et al., 2007) while a CSF biomarker analysis by Seguin et

al. (2011) found that 17 of 22 PCA patients fulfilled biological criteria for typical AD; of the

remaining 5 patients, two exhibited a normal CSF profile but showed a PCA corticobasal

syndrome.

In PCA patients with AD pathology, differences have been noted in the distribution of NFTs

(Tang-Wai et al., 2004) or NFTs and senile plaques (Levine et al., 1993; Ross et al., 1996)

relative to typical AD patients. In PCA patients, differences have been localised particularly

within occipital regions which are relatively unaffected in tAD, with pathological distribution

increasing in densities from area V1 to the visual association areas (Hof et al., 1989; Hof et al.,

1990; Hof et al., 1997) and lower densities reported in frontal regions (Levine et al., 1993; Hof

et al., 1997). However, while Tang-Wai et al. (2004) found lower concentrations of senile

plaques in hippocampal regions of PCA relative to tAD patients, similar plaque densities were

found in other cortical areas; furthermore, Renner et al. (2004) did not find evidence of

differences in parietal burden of senile plaques or NFTs.

Pathological observations in PCA have been interpreted to reflect the particular

involvement of cortical pathways between the striate cortex, posterior parietal and cingulate

cortices. A disproportionate loss of Meynert cells in the striate cortex has been noted in PCA;

such cells have long projections to area MT and the superior colliculus (Hof et al., 1989; Hof et

al., 1990). High densities of NFTs found in layer III of Brodmann areas 17, 18 and 19 suggests

involvement of projections from areas 17 to 18 and feedforward projections from areas 18 and

17

19 to visual association areas. In addition, feedback projections are likely affected given high

densities of NFTs in layers V and VI of area 18 (von Gunten et al., 2006). Such findings not only

inform the understanding of AD pathogenesis; the selective vulnerability of certain cells to AD

might help explain patterns of cognitive deficits.

1.2.2.3. Imaging

Consistent with the terminology of PCA, imaging techniques have identified atrophy

particularly in posterior brain regions. Using voxel-based morphometry (VBM), cross-sectional

studies have found reductions in grey matter volume in occipital, posterior parietal and

posterior temporal regions in PCA relative to healthy controls (Lehmann et al., 2011; Whitwell

et al., 2007; Migliaccio et al., 2009; Migliaccio et al., 2012). Comparisons of PCA and tAD

patients have found lower grey matter volume in occipital and bilateral posterior parietal

regions of the PCA group (Whitwell et al., 2007; Lehmann et al., 2011); Lehmann et al. (2011)

also identified lower cortical thickness in the right superior parietal lobule of the PCA group

and in the left entorhinal cortex in the tAD group (see below). Diffusion tensor imaging studies

have identified white matter changes in posterior brain networks (Migliaccio et al., 2012), with

one case study suggesting particularly early involvement of the occipital lobe (Duning et al.,

2009). However, it is worth mentioning how not all PCA patients show clear posterior atrophy

(McMonagle et al., 2006); the inconsistency of prominent atrophy noted at autopsy by Renner

et al. (2004) led them to suggest the use of posterior cortical dysfunction rather than PCA.

Functional imaging studies of PCA patients using single photon emission computed

tomography (SPECT) and fluorodeoxyglucose- (FDG) PET point to hypometabolism in posterior

cerebral hemispheres and hypoperfusion in occipital, parietal and temporal cortices relative to

healthy controls (Nestor et al., 2003; Kas et al., 2011; Gardini et al., 2011; Rosenbloom et al.,

2010). Some of these studies have also identified bilateral hypometabolism in the frontal eye

fields compared to controls, which may relate to oculomotor apraxia in PCA (Crutch et al.,

2012a), and have observed hypometabolism in occipito-parietal regions relative to tAD

patients (Nestor et al., 2003; Kas et al., 2011). Other imaging techniques include using

Pittsburgh compound-B (PiB) PET to map amyloid-β deposition. Such techniques have either

suggested increased amyloid-β deposits in occipital and parietal regions of PCA patients

relative to tAD patients (Tenuovo et al., 2008; Lehmann et al., 2013) or that no differences

exist in amyloid plaque burden (De Souza et al., 2011; Rosenbloom et al., 2010). Interestingly,

while Rosenbloom et al. (2010) noted hypometabolism in PCA patients’ inferior

occipitotemporal regions, no significant evidence was found of differences in amyloid-β

18

19

Figure 1.1 Grey matter differences between A) controls and PCA patients, B) tAD and PCA patients, and variation in cortical thickness compared between C) controls and PCA patients and D) tAD and PCA patients identified by Lehmann et al. (2011). Colour bar scales for statistical difference represent false discovery rate (FDR) at a p<.05 level of significance.

See Lehmann et al., 2011

deposition; the lack of a strong consensus on differences in deposition might reflect similar

inconsistencies in pathological distribution observed in post-mortem studies. The considerable

overlap in grey matter volume and cortical thickness in the precuneus, posterior cingulate

cortex and left temporoparietal and medial temporal regions between PCA, LPA and early-

onset tAD patients may indicate these clinical syndromes represent different points within an

AD spectrum (Migliaccio et al., 2009; Ridgway et al., 2012).

1.2.2.4. Epidemiology

Precise estimates of the prevalence and incidence of PCA are difficult to obtain. This is

largely a consequence of variability in diagnostic criteria, misclassification or lack of awareness

of PCA. Of 154 cases in a memory clinic, 4% had PCA (Croisile, 2004), while Snowden et al.

(2007) reported that out of 523 patients who attended a specialist centre for cognitive

disorders, 5% had a visual presentation (labelled as PCA). Regarding Snowden et al.’s (2007)

study, it is possible some of the 7% of patients with a language presentation might be

considered PCA patients, given how clinical classification included reading, writing, calculation

and spelling difficulties. While investigations of PCA suggest it can be considered a young-onset

dementia with a mean age of onset in the late 50s (Mendez et al., 2002; McMonagle et al.,

2006), PCA patients with a wide range of ages have been reported (40-86 years: Tang-Wai et

al., 2004). Some studies have reported women making up a disproportionate number of PCA

patients (Tang-Wai et al., 2004; Snowden et al., 2007), while others have found no difference

in gender distribution (Mendez et al., 2002; McMonagle et al., 2006; Renner et al., 2004).

1.2.2.5. Genetic factors

To date, there have been no clear suggestions of the genetic basis of PCA. While a study of

40 PCA patients found 11 had a family history of dementia, none presented with a phenotype

akin to PCA, and the incidence of family history in PCA does not significantly differ with that in

tAD (Mendez et al., 2002; Tang-Wai et al., 2004). It is possible that APOE ε4 status may differ

between PCA and tAD patients; two studies have found around 20-30% of patients with a

visual presentation of AD were ε4-positive, relative to over 80% of amnestic AD patients

(Schott et al., 2006; Snowden et al., 2007). However, other studies have reported that around

45-55% of PCA patients were ε4-positive and suggested a lower prevalence of APOE ε4-

positive tAD patients (55-75%; Tang-Wai et al., 2004; Migliaccio et al., 2009; Rosenbloom et al.,

2011). Larger group studies with stringent inclusion criteria are required to investigate the

intriguing possibility that reduced APOE ε4 allele load might confer some form of protection to

medial temporal regions from AD pathogenesis (Filippini et al., 2009) and also identify novel

genetic risk factors for PCA. Recently, a mutation was identified on the PS-1 gene of a 67 year

20

old PCA patient with no family history of dementia (although her father died at 51) (Sitek et al.,

2013). PS-1 mutations have been related to disruptions in the amyloid-β 40/42 ratio of familial

AD patients through over production of amyloid-β 42 (Growdon & Rossor, 1998; Scheuner et

al., 1996); this ratio has been strongly implicated in AD pathogenesis (De Strooper & Annaert,

2010).

1.2.2.6. Proposed diagnostic criteria

The core features of PCA include an insidious onset and gradual progression, presentation

of visual impairment in the absence of significant ocular disease, an absence of stroke or

tumour, relatively preserved episodic memory and the presence of any of the following

symptoms: simultanagnosia, optic ataxia, oculomotor apraxia, dyspraxia, environmental

disorientation or aspects of Gerstmann’s syndrome. Supportive features include alexia,

ideomotor apraxia, young age of onset and neuroimaging evidence of parieto-occipital atrophy

or hypometabolism (Mendez et al., 2002; Tang-Wai et al., 2004; McMonagle et al., 2006; see

Appendix 3). While there is reasonable agreement regarding these criteria, slight differences

exist in definitions of PCA, which may result from variations of clinical experience at different

centres and the fact that proposed criteria have not been validated in a wider population

sample. Such differences limit comparisons between studies, and likely contribute to

inconsistencies in pathological or imaging literature. There is a need for a consensus on certain

issues, such as whether the term PCA should be applied to patients with a pure visual

presentation, with more global cognitive impairment but with visual deficits as the earliest

presenting or most prominent feature or with patients who present with apraxia, acalculia or

spelling difficulties but do not show early or prominent visual impairment (Crutch, 2013). The

lack of pathological confirmation of underlying AD in PCA patients raises questions on whether

pharmacological interventions for AD are suitable in PCA, and whether PCA patients who show

low Aβ 1-42 and high total tau levels consistent with AD diagnosis (Dubois et al., 2007) should

be included in new pharmacological trials for AD given how they may be unsuitable for

outcome measures, such as executive function tasks with a visuospatial component (Crutch et

al., 2012a). In order to address some of these concerns, the PCA Working Party, consisting of

researchers representing 23 institutions in 9 counties, was set up; the party intends to

facilitate improvement and harmonisation of the diagnostic criteria for PCA and promote

better clinical and research practice (Crutch et al., 2012b).

1.3. CHAPTER CONCLUSIONS

Dementia is a debilitating and life-limiting condition; degenerative dementias carry

significant social and economic costs to patients, their caregivers and society as a whole. The

21

most common cause of dementia is AD, which can result in different clinical phenotypes.

Typical AD involves a primary amnestic presentation accompanied by additional deficits in non-

memory domains such as visuospatial ability or language. Additional deficits can be present at

diagnosis or can emerge later in the disease course. A form of atypical AD is PCA. PCA patients

tend to experience early complex and disruptive visual deficits while exhibiting relatively

preserved episodic memory, subsequently progressing to a more global dementia state.

22

2. NEUROPSYCHOLOGY OF PCA


The posterior cortex supports a number of cognitive domains, each of which can be broken

down into subcomponent skills. Calculation, spelling, praxis and purportedly reading are

abilities that are lateralised to the left hemisphere, while spatial skills, object and face

perception are largely lateralised to the right hemisphere. Early visual and spatial processing

are not lateralised skills, instead maintaining retinotopic distribution in the occipital lobes.

Neuropsychology is of particular relevance to the diagnosis and characterisation of PCA.

Standardised neuropsychological measures provide a means to evaluate whether patients fulfil

proposed core diagnostic criteria for PCA. Through detailed neuropsychological assessment in

PCA, future group studies might clarify whether discrete ventral, dorsal and caudal forms of

PCA exist, and could offer ways to discriminate between PCA patients with underlying AD, DLB

or CBD pathology. Longitudinal analysis of neuropsychological data might offer suggestions of

the biological mechanisms underlying PCA, or could allow a greater insight into the effect of

emerging visual deficits on other areas of the visual system. From a practical perspective,

better characterisation of patients’ cognitive profiles can have a significant bearing on how

they are treated clinically (Crutch, 2013).

2.2. VISUAL PROCESSING

2.2.1. Early visual processing

While deficits in higher-order visual domains, such as spatial or object perception, tend to

be reported more frequently than disrupted early visual processing in PCA, it is likely that

deficits in early visual processes at least contribute to higher-order visual problems (Crutch et

al., 2012). Early visual dysfunction includes visual crowding and deficits in perceiving form,

colour, motion and single point localisation and is associated with the occipital lobes

(Warrington, 1986; Farah, 2000; Heider, 2000; Qiu & Heydt, 2005; Levi, 2008). Consistent with

AD pathology in areas V1, V2, V3 and V4 (Hof et al., 1997), some investigations suggest

widespread occipital lobe dysfunction in PCA, occurring as early as V1 (Metzler-Baddeley et al.,

2010) while colour after-effects in PCA have been interpreted as showing degeneration of

excitatory neurons and relative preservation of inhibitory interneurons in V1 (Chan et al., 2001;

Crutch et al., 2011).

Visual localisation relates to the inability to localise a single object and is often assessed

using single point localisation (McCarthy & Warrington, 1990); such localisation deficits may

constitute part of visuospatial impairment. Holmes (1919) gave the classic account of visual

23

disorientation, reporting patients with difficulties in the localisation, maintenance of fixation

and visual tracking of objects. The functional impact of visual disorientation is considerable,

with many patients being unable to correctly reach for objects or walk across a room unaided

(McCarthy & Warrington, 1990; Langdon & Thompson, 2000). Visual disorientation has been

associated with damage to the occipital or occipitoparietal region (Yealland, 1916; Godwin-

Austen, 1965; Warrington, 1986) and forms one of the features of Balint’s syndrome. Visual

disorientation has been identified in PCA in a range of case studies (Kaida et al., 1998; Chan et

al., 2001; Galton et al., 2000; Tenuovo et al., 2008; Crutch et al., 2011), with a more recent

study of 21 PCA patients finding 60% of them were impaired on a task of single point

localisation and 81% were impaired on dot counting (Lehmann et al., 2011). Problems in visual

localisation may be variable, with visually-disorientated PCA patients sometimes being able to

pinpoint small objects with surprising accuracy or better localise moving stimuli (Crutch, 2013;

Midorikawa et al., 2008).

Form perception is often assessed using tasks such as the Efron Squares test (Efron, 1969)

or figure-ground detection (Warrington & James, 1991) which are measures of shape

discrimination or shape detection processes; performance on these tasks has been found to

dissociate, leading to suggestions that such processes operate in parallel (Kartsounis &

Warrington, 1991; Davidoff & Warrington, 1993). Abnormalities in shape discrimination, shape

detection, orientation discrimination, contour integration, form coherence, visual field

detection and colour perception have all been noted in PCA (Mendez et al., 1990; Wakai et al.,

1994; Chan et al., 2001; Tang-Wai et al., 2004; Lehmann et al., 2011; Metzler-Baddeley et al.,

2010; Pelak et al., 2011; Whitwell et al., 2007; McMonagle et al., 2006); studies have identified

how 65-80% of PCA patients showed impaired performance on the figure-ground detection

task, while 67% showed deficits in discriminating squares and oblongs (Lehmann et al., 2011;

Shakespeare et al., 2013). Group studies have also suggested a greatly varying incidence of

hemianopia or quadrantanopia in PCA (5%-78%; McMonagle et al., 2006; Whitwell et al., 2007;

Pelak et al., 2007); while Pelak et al. (2007) identified visual field defects in 7 of 9 patients,

these may reflect confounding visual phenomena such as the inverse size effect (chapter

2.2.2), neglect or visual disorientation.

PCA participants in Lehmann et al.’s (2011) study, all of whom showed impairment on

higher order object and/or space perception tasks, also demonstrated impaired performance

on one or more early visually processing tasks. Object and space detection were correlated

with figure ground and colour discrimination performance, shape discrimination was

correlated with object but not space perception and point localisation was correlated with

space but not object perception. These results provide evidence that deficits in higher-order

24

visual domains are frequently related to different patterns of early visual processing deficits in

the PCA syndrome.

2.2.1.1. Visual crowding

One form of prominent early visual processing deficit in PCA is enhanced visual crowding.

Crowding is a form of inhibitory interaction which is present in normal peripheral vision,

involving the diminishing effect of nearby stimuli (‘flankers’) on identification of a target

stimulus (Levi, 2008). The occurrence of crowding when target stimuli and flankers are

separately presented to different eyes indicates a cortical locus (Flom et al., 1963; Tripathy &

Levi, 1994). In the healthy periphery, the core characteristic of crowding is that it is dependent

on eccentricity (distance from fixation), which determines the critical spacing between target

and flankers (Pelli et al., 2007). The critical spacing is the distance at which flankers diminish

identification of the target stimulus, and has been roughly localised as being half the

eccentricity of the target from fixation in peripheral vision (Bouma, 1970). Beyond spacing,

increasing visual similarity between target and flanker stimuli exacerbates the crowding effect.

While crowding is independent of stimulus type, font and contrast (Pelli et al., 2004; Tripathy

& Cavanagh, 2002; Pelli & Tillman, 2008), crowding effects diminish with target and flanker

stimuli of opposite polarity (Kooi et al., 1994; Chakravarthi & Cavanagh, 2007).

While crowding is most evident in normal peripheral vision, it has been observed over very

small distances in healthy individuals’ foveal vision (0.05◦ to 0.08◦ of visual angle: Flom et al.,

1963; Strasburger et al., 1991). However, PCA patients have exhibited deficits on centrally-

presented flanked letter identification tasks that are in line with crowding; such deficits

suggest crowding is operating over large distances in foveal vision (Crutch & Warrington,

2007a; Crutch & Warrington, 2009; Mendez et al., 2007). Two case reports of PCA patients

have identified beneficial effects of increased spacing on flanked letter identification,

erroneous feature integration of flanking letter fragments on target letters, interactions

between letter spacing and letter confusability and an ameliorating effect of reverse polarity

flankers (Crutch & Warrington, 2007a; Crutch & Warrington, 2009); all of which are

characteristic of crowding. While the occipital lobe has been cited most often as the locus of

crowding, a divergence of opinion occurs with more specific localisation (Levi, 2008). In PCA, it

is possible that the diffuse distribution of AD pathology in the occipital lobe may affect the

expression of crowding.

2.2.2. Visuospatial processing and inverse size effects

Prominent deficits in visuospatial ability are some of the most frequent and striking

characteristics of PCA. While visual disorientation emphasises a deficit in the ability to localise

a single object, higher-order visuospatial impairment is more associated with deficits in

25

representing the spatial relationships between multiple objects and/or integrating spatial

information across modalities (e.g. visuomotor tasks; Warrington et al., 1967; Freedman &

Dexter, 1991; Mendez, 2001; Binetti et al., 1998). Visual disorientation can be restricted to one

half of the visual field (Cole et al., 1962); in contrast, visuospatial disorders are mostly

associated with right parietal damage and are not restricted to the contralateral visual field.

Deficits in visuospatial ability can lead to poor performance on tasks such as position

discrimination (Taylor & Warrington, 1973), dot counting (Warrington & James, 1967),

matching orientation (Benton et al., 1975), cancellation or search tasks (Albert, 1973; De Renzi

et al., 1970). Impaired performance on such tasks has been noted in a range of studies of PCA

(Benson et al., 1988; Mizuno et al., 1996; Stark et al., 1997; Ross et al., 1996; Delazer et al.,

2006; Videaud et al., 2009; Tenuovo et al., 2008; Metlzer-Baddeley et al., 2010; Lehmann et al.,

2011).

Poor performance on measures of visuospatial ability may reflect visual neglect in some

PCA patients; various studies have noted neglect (Cogan, 1985; Wakai et al., 1994; Mendez &

Cherrier, 1998; Migliaccio et al., 2012) with one recent group study identifying signs of neglect

in 16 of 24 PCA patients (Andrade et al., 2010). Earlier studies had identified much lower rates

of neglect (15-20%; Tang-Wai et al., 2004; McMonagle et al., 2006); this discrepancy may be

due to these investigations employing clinical examination, rather than neuropsychological

measures. However, the line bisection and Bells test (Gauthier et al., 1989) used by Andrade et

al., (2010) are not ‘pure’ measures of neglect in PCA; they may be confounded by visuospatial

and visuomotor impairment, and the counterintuitive tendency of some PCA patients to

misperceive large stimuli.

A diminished ability to recognise large rather than small pictures, words and letters has

been noted in PCA for some time (Saffran et al., 1990; Coslett et al., 1995; Stark et al., 1997;

Crutch et al., 2011); such performance occurs in response to stimuli presented in isolation and

is not restricted to one half of space. This deficit has been referred to as the inverse size effect

(Coslett et al., 1995), and likely contributes to problems in scene perception in PCA

(Shakespeare et al., 2013) and clinical complaints of patients who report having particular

difficulty in reading large font, such as newspaper headlines (Crutch et al., 2011). This inverse

size effect may reflect a reduced effective field of vision, which has been associated with

parietal and parieto-occipital damage (Michel & Henaff, 2004; Russell et al., 2004, 2013).

Disproportionate deficits in perceiving stimuli presented in peripheral vision have been

attributed to an inability to cope with high attentional demands (Russell et al., 2013), although

they may also relate to poor eye movement control, inefficient scanning strategies

(Shakespeare et al., 2013) or visual field defects (Delaj et al., 2010; Pelak et al., 2011).

26

2.2.3. Visuoperceptual processing

Progressive and disproportionate deficits in object recognition were some of the earliest

noted features of PCA (De Renzi, 1986; Benson et al., 1988). Lissauer (1890) proposed a two

stage model of object recognition: the apperceptive stage, involving a “conscious awareness of

a sensory impression”, and the association stage, involving “associating other notions with the

content of apperception”. This model was based on observations of patient GL who could

perceive and discriminate visual stimuli, but could not recognise objects. Subsequent models

of object recognition have specified that a deficit arising at the level of perceptual

categorisation subsequent to visual sensory processing presents as an apperceptive agnosia,

while a deficit in semantic categorisation presents as an associative agnosia (Warrington,

1985).

The term apperceptive agnosia refers to patients with impaired object recognition; such

patients demonstrate intact visual sensory functions and their impairment does not relate to a

semantic deficit. Apperceptive agnosia has been linked to right parietal damage (Warrington &

James, 1967; Warrington & Taylor, 1973) and has been assessed using stimuli that degrade

visual complexity, including everyday objects shown from different angles (Warrington &

Taylor, 1973; Warrington & James, 1988), silhouette drawings of objects (Warrington & James,

1988), overlapping drawings (De Renzi et al., 1969), incomplete line drawings (Gollin, 1960) or

degraded letters (Faglioni et al., 1969; Warrington & James, 1967). Apperceptive agnosia has

been noted in various case studies of PCA (Hof & Bouras, 1991; Wakai et al., 1994; Aharon-

Peretz et al., 1999; Galton et al., 2000); one group study observed that patients with

apperceptive agnosias tended to have longer disease durations (McMonagle et al., 2006). Two

group studies have placed the incidence of visual agnosia at around 45-65% and have

attributed these to deficits in apperception (Mendez et al., 2002; McMonagle et al., 2006);

however, neither of these studies assessed performance on measures of early visual

processing such as shape detection or discrimination. All three PCA patients with

visuoperceptual impairment from Sala et al. (1996) had difficulties in form and line

discrimination, emphasising the likely contribution of early visual impairment towards higher-

order visual deficits. In a study of 21 PCA patients, Lehmann et al. (2011) found all 21 showed

impaired performance on recognition tasks of fragmented letters and objects shown from

unconventional angles; however, all patients also showed deficits on at least one measure of

early visual processing.

Associative agnosia refers to patients who can construct a sufficient perceptual

representation of an object, but the representation suffers from a loss of meaning (McCarthy

27

& Warrington, 1990). Such patients often have bilateral temporal lesions (Capitani et al., 2003),

although associative agnosia has been found in a patient with unilateral posterior temporal

damage (McCarthy & Warrington, 1986). Selective associative agnosias tend to be rare in PCA,

with group observations of visual agnosias in PCA not being attributed to semantic deficits

(Mendez et al., 2002; McMonagle et al., 2006). While some patients have been described as

having associative agnosias (NS: Aharon-Peretz et al., 1999; RM: Giovagnoli et al., 2009),

patient RM also demonstrated problems perceiving overlapping and degraded figures and

discriminating lines. Pathological distribution may relate to the incidence of associative

agnosia; in AD patients, NFT densities in occipito-temporal regions have been correlated with

poor performance on tests of conceptual knowledge, while a lack of neuroanatomic

correlation with tests involving overlapping or hidden figures has been interpreted as

apperceptive agnosia resulting from more diffuse AD pathology (Giannakopoulos et al., 1999).

Another form of visuoperceptual deficit is prosopagnosia, which has been defined as a

disproportionate impairment in recognising faces relative to other objects (Bodamer, 1947).

Prosopagnosia can result from damage to right occipito-temporal regions, although it is not a

common consequence of such damage (De Renzi et al., 1994). Problems identifying faces can

be an early (Wakai et al., 1994; Aharon-Peretz et al., 1999) and common (20-25%: Mendez et

al., 2002; Tang-Wai et al., 2004; McMonagle et al., 2006) symptom of PCA, leading to

suggestions of prosopagnosia as a supportive diagnostic feature for PCA (Tang-Wai et al.,

2004). However, disorders of face perception in PCA are rarely very selective; they have been

accompanied by visual agnosias (Wakai et al., 1994; Aharon-Peretz et al., 1999; McMonagle et

al., 2006) and deficits in early visual processing (Wakai et al., 1994; Sala et al., 1996).

2.3. READING

A multitude of studies demonstrate literate adults’ impressive ability to rapidly recognise

words of varying script, size, font and case, and even words in highly unfamiliar presentations

such as mixed case (Besner, 1989; Mayall et al., 1997). Such studies have led to the notion that

an aspect of visual word recognition is the generation of an invariant word representation

which is remarkably unsusceptible to variations in visual input. The notion of word recognition

relying on the perception of words via letters integrated into a word form has existed for some

time. Cattell’s (1886) study demonstrated a word superiority effect, in which letters were read

more rapidly within words than in random combinations of the same letters. Subsequent

investigations identified how, when controlling for participants’ better memory for words than

nonsense letter strings and their ability to guess word identities, performance for identifying

single letters was superior when letters were embedded in words versus letter strings (Reicher,

1969; Wheeler, 1970). The pseudoword superiority effect refers to how letters are identified

28

more efficiently in orthographically regular, pronounceable nonwords (or pseudowords)

relative to irregular, unpronounceable nonwords (McClelland, 1976; Paap et al., 1982). Other

evidence which has been interpreted as supporting the existence of word form and/or parallel

letter processing includes healthy readers’ consistency of reading latencies for words of 3-6

letters (Behrmann et al., 1998; Nazir et al., 1998), the perception of words despite variations in

size, case and font, and priming effects occurring across such variations (Rayner & Pollatsek,

1989). Whether words possess properties not only exceeding their basic perceptual aspects

but also differing from their phonological and semantic properties is a theme that has shaped

various neuropsychological and neuroimaging investigations.

2.3.1. Contribution of areas of early visual processing

The transition of simple feature detection to font invariant letter and word recognition is

classically considered a hierarchical process, with simple features forming progressively more

abstract visual representations. Cell responses measured in the cat striate cortex have found

that both simple and complex cells strongly respond to oriented bars; however, while simple

cells have small receptive fields with distinct inhibitory and excitatory subfields (strong phase

dependence), complex cells have large receptive fields with no phase dependence (Hubel &

Wiesel, 1962). Authors of this study proposed a model in which complex cells are fed

information from simple cells with neighbouring receptive fields, generating phase invariant

feature representations. A more recent model of object recognition proposed by Riesenhuber

and Poggio (1999) suggests that simple visual features processed by simple cells are

progressively pooled across different locations (through complex cells) and combinations of

features (through composite cells). In both models, moving up the visual hierarchy leads to

increasingly abstract and location invariant representations, which in the context of letter

recognition culminates in shape-specific letter cells (for example, cells selectively responding

the letter ‘a’ in either lower, upper-case or possibly italicized font) and ultimately more

abstract shape invariant complex cells (i.e. cells responding the letter ‘a’ regardless of font;

Grainger et al., 2008). A functional anatomical model of word recognition proposed by

McCandliss et al. (2003) attributes processing of letters to retinotopically distributed occipital

regions. The model suggests pathways responsible for generating letter representations in the

left and right hemisphere are modulated by visuospatial attention governed by parietal

regions. These pathways are resolved in the left fusiform gyrus, an area which the authors

maintain is the site of a region dedicated to the processing of abstract visual word forms, the

purported visual word form area (VWFA; see Chapter 2.3.3).

29

2.3.2. Cognitive models

2.3.2.1. Dual-route models

The dual-route model offered by Marshall and Newcombe (1973) has often been

employed as a framework within which to understand the deficits underlying deep dyslexia,

surface dyslexia and visual dyslexia. It was termed the dual-route model as it involves two

processing routes from orthography to speech; a semantic route and a route dependent on

reading via grapheme-phoneme correspondences. A dual-route approach has also been

proposed to explain orthographic processing, in which location specific letter detectors

generate two forms of location invariant sublexical representations; i) informative letter

combinations and ii) frequently co-occurring multi-letter graphemes (Grainger & Ziegler, 2011),

with both routes supporting whole word orthographic representations or route ii) feeding into

a phonological route. The more recent Dual-Route Cascaded (DRC) model (Coltheart et al.,

2001) is a computational implementation of the dual-route theory, maintaining that words are

read aloud via two routes. The first, a direct, lexical route, operates by parallel cascaded

processing, in which activation spreads from letter features to letters and the orthographic

lexicon, subsequently activating appropriate phonological output; irregular words, being

reliant on the orthographic lexicon, are read via this route. The second is an indirect, non-

lexical route, in which orthographic input is parsed into graphemes which are serially

converted into phonemes using grapheme-phoneme correspondences; nonword words can

only be read using this route as they do not exist in the orthographic lexicon. Critics of the DRC

model cite findings which show nonword reading does not always result in responses

predicted by rules of grapheme-phoneme correspondences (Andrews & Scarratt, 1998); more

generally, the model has troubling accounting for effects of orthographic neighbourhood size,

including facilitating effects of increased neighbourhood size on reading speed (Ziegler et al.,

2001), as it is insufficiently sensitive to orthographic bodies (Grainger & Dufau, 2012). In

addition, a prediction of the DRC, in which accurate reading aloud of known words may be

achieved without referring to semantic knowledge, is questioned by the high prevalence of

surface dyslexia among individuals with semantic dementia (Woollams et al., 2007) and

imageability effects on normal readers’ reading of low-frequency exception words (Cortese et

al., 1997; Strain et al., 1995).

2.3.2.2. Triangle model

30

Triangle models provide a framework for a connectionist approach to reading, in which

cognitive processes exists as competitive or cooperative interactions between units. Processing

is determined by weights on connections between units; weighted connections are a

consequence of the accumulated experience of the reading system; in this way, a

connectionist approach emphasises the adaptive nature of the reading system. Aspects of

reading which are particularly reflected in connectionist models include how children learn to

read an increasing number of words over a long period, through learning, children may read

novel items, and brain damage may result in graded and sometimes inconsistent declines in

reading performance (Plaut, 2005). The computational model proposed by Seidenberg and

McClelland (1989) is formed of three interconnected domains; orthography, semantics and

phonology. The model features two routes from orthography to speech; a direct route from

orthography to phonology, and an indirect route from orthography, through semantics, to

phonology; it suggests the direct route is specialised for representing more frequent and

consistent mappings between orthography and phonology, with correct readings of exception

words depending more on the indirect route. The Seidenberg and McClelland (1989) triangle

model emphasised parallel interactions between semantic and phonological information

rather than distinguishing lexical and sublexical procedures. While the model accounted for

various aspects of reading performance in healthy individuals, it was criticised for its poor

reading performance for orthographically regular nonwords (Besner et al., 1990). A model

proposed by Plaut et al. (1996) aimed to address this limitation by applying constraints on

orthographic and phonological representations based on grapheme-phoneme

correspondences; implementations of the phonological pathway produced nonword reading of

comparable competency to skilled readers.

2.3.3. Neuroanatomical models

A controversial and enduring debate that has arisen from neuroimaging studies of reading

is the existence of the visual word form area (VWFA; Cohen et al., 2000), an area supposedly

specialised for processing of visual word forms. The anatomical site of the purported VWFA is

often placed in the left fusiform gyrus, in the junction between inferior temporal and fusiform

gyri on the occipitotemporal sulcus (Jobard et al., 2003). This region has been proposed as the

critical lesion site for pure alexia, a dyslexia classically considered a consequence of a specific

deficit in acquiring word form representations (Binder & Mohr, 1992; Leff et al., 2001;

Pflugshaupt et al., 2009). The VWFA has been found to selectively respond to printed and

handwritten words rather than chequerboards or consonant strings (Cohen et al., 2000),

objects of matched visual complexity (Qiao et al., 2010; Szwed et al., 2011), letters in upper

and lower case (Dehaene et al., 2004) and activation in this region has been found to increase

31

proportionately with sentence reading rate (Dehaene et al., 2010). Furthermore, the VWFA

shows reduced activation to conscious words which have been subliminally primed; these

priming effects occur independently of letter case (Dehaene et al., 2001). While the region has

been found to be disproportionately active in response to real words versus orthographically

illegal letter strings (Cohen et al., 2000), differences in activation have not been found in

response to words versus pseudowords (Jobard et al., 2003); such results have been

interpreted as demonstrating a preference for processing sublexical units, such as graphemes,

within the VWFA. While proponents of the VWFA suggest that it governs efficient parallel word

recognition, words presented in unfamiliar formats, such as rotated words, words in mixed-

case or with excessive inter-letter spacing, might demand involvement of serial reading

strategies mediated by dorsal systems (Vinckier et al., 2006; Cohen et al., 2008).

Given how writing as a cultural invention has not existed long enough to promote an

innate specialisation for visual word recognition, how might the proposed specialisation of the

VWFA come about? McCandliss et al. (2003) suggested that the inferotemporal region is an

area with a particular affinity for complex object recognition and a great capacity for plasticity;

the developmental process of accumulating reading experience would provoke the progressive

specialisation of this region for processing of orthographic stimuli. However, critics of the

VWFA have dismissed its proposed specialisation for recognising word or word like stimuli on

several grounds. Price and Devlin (2003) question the neuropsychological evidence base for

the purported role of the left fusiform gyrus in processing visual word forms by referring to

both the extensive lesions of previously cited pure alexics and the co-occurrence of deficits in

non-orthographic processing. They also cite studies showing VWFA activation in response to

object recognition (Murtha et al., 1999; Etard et al., 2000), to auditory words and descriptions

of objects (Price et al., 2002; Thompson-Schill et al., 1999) and when making rhyming

judgments (Booth et al., 2002). The suggestion that VWFA activation is not specific to word

stimuli is echoed by other authors, who suggest that this region is involved in shape processing

of objects and false fonts as well as words (Ben-Shachar et al., 2007) or processing of

information of high spatial frequencies (Roberts et al., 2013). Such findings are not necessarily

inconsistent with McCandliss et al.’s (2003) point on functional specialisation of the VWFA;

from their perspective, this specialisation is most likely a consequence of a recycled area of

visual cortex which has evolved for other purposes, such as processing of complex visual

stimuli. As such, the left fusiform cortex would only be partially specialised for letter,

grapheme or word form recognition in skilled readers; only very local regions of the cortex

would selectively respond to orthographic stimuli, with such regions often eluding the spatial

resolution of imaging techniques, particular PET imaging.

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2.4. ACQUIRED DYSLEXIA

Acquired dyslexias are often divided into two broad categories: those relating to the visual

attributes of written words and those involving subsequent phonological and semantic stages

of reading (McCarthy & Warrington, 1990). Peripheral dyslexias involve impairment to

prelexical and/or word form processing, while central dyslexias involve impairments to

lexical/post-lexical processes (Riddoch, 1990). Peripheral dyslexias include neglect dyslexia,

hemianopic dyslexia and pure alexia (sometimes referred to as “word form dyslexia” or “letter-

by-letter” (LBL) reading). LBL reading involves intact letter identification and relatively

accurate, but slow, reading, whereby response latencies increase in a linear manner

proportionate to word length (Shallice and Warrington, 1980; Farah and Wallace, 1991; Binder

and Mohr, 1992; Warrington and Langdon, 1994; Hanley and Kay, 1996; Cohen et al., 2000).

Central dyslexias include surface dyslexia, phonological dyslexia and deep dyslexia.

Characteristics of surface dyslexia include the production of phonological nonwords in oral

reading due to phoneme omissions, substitutions or additions and regularisation errors when

reading irregular words. Phonological dyslexia involves poor nonword reading in the absence

of semantic errors. Deep dyslexia involves poor reading of abstract words in particular, an

inability to read nonwords and the occurrence of semantic errors and visual and morphological

effects in errors (Plaut & Shallice, 1993; Schattka et al., 2010).

Acquired dyslexia often presents as an early (Benson et al., 1988; Freedman et al., 1991;

Berthier et al., 1991) and common (80-95%; Mendez et al., 2002; McMonagle et al., 2006)

symptom of PCA, and alexia is a proposed supportive feature of a diagnosis of PCA (Tang-Wai

et al., 2004; Mendez et al., 2002). PCA patients often find spatial aspects of reading particularly

challenging; various reports exist of patients having difficulty following text along a printed line

or moving from one line to the next (Rogelet et al., 1996; Mendez, 2001; Crutch et al., 2011),

seeing lines of text in ‘false order’ (Tenuovo et al., 2008) and losing their place on a page or

even on reading cards (Crutch et al., 2011; Kirshner & Lavin, 2006). Such difficulties may arise

from posterior brain damage; Levine et al. (1985) identified a patient with parieto-occipital

lesions, who could read isolated words in paragraphs but in a disordered manner, and would

move irregularly between or within lines of text. A combination of visual disturbances likely

contribute to text reading difficulties in PCA, including a restriction in the effective field of

vision, enhanced crowding, neglect (Andrade et al., 2010), visual field defects (Pelak et al.,

2011) and perceived motion of static stimuli (Crutch et al., 2011).

Most previous studies on reading in PCA have investigated single word recognition and

have identified a range of dyslexias: neglect dyslexia (Catricala et al., 2011), attentional

dyslexia (Saffran & Coslett, 1996), pure alexia (Price & Humphreys, 1995; Freedman et al.,

1991; Berthier et al., 1991), crowding dyslexia (Crutch & Warrington, 2009) and word form

33

access dyslexia (Crutch & Warrington, 2007b). Increased perceptual complexity most likely

adversely affects word recognition in PCA, given poorer performance for reading words in

cursive font (De Renzi, 1986), stylised font (Mendez, 2001) or words with crosshatched letters

(Mendez & Cherrier, 1998). PCA patients’ poor reading for nonwords might suggest the

presence of phonological deficits, including the ability to use print-to-sound correspondences

(Mendez, 2001); however, PCA patients have also been noted to make a disproportionate

number of visual errors when reading nonwords (Mendez et al., 2007). PCA case studies tend

to give the impression that reading dysfunction manifests as a peripheral rather than central

dyslexia, although O’Dowd and de Zubicaray (2003) interpreted the quality of dysgraphia and

spelling impairment in one PCA patient as indicative of an intermediate deficit in a graphemic

buffer (Kay & Hanley, 1994). Given the complexity of visual impairment in PCA, it is important

to exercise caution when making inferences of the specificity of reading deficits. For example,

while Catricala et al. (2011) considered one patient (RR) to have neglect dyslexia, there was

some heterogeneity in error responses, with RR misreading initial and terminal parts of words

(bombabombardamento, palazzolazzo). It is possible that a reduced effective visual field

might result in errors that superficially seem a consequence of neglect dyslexia, although such

errors would likely not consistently show a bias to one visual half. This reduction might also

contribute to length effects on reading latencies given how words of increased length present

as perceptually larger. Group studies of reading in PCA would be better placed to not only

disentangle and gauge the heterogeneity of patterns of reading deficit, but also reveal the

contributions of other parts of the visual system in supporting reading ability.

The only group study of reading dysfunction in PCA to date identified frequent visual errors

in response to regular, irregular words and nonwords, an absence of regularisation errors and

disproportionate difficulty reading nonwords (Mendez et al., 2007). Along with deficits on

flanked letter identification tasks, these data led the researchers to suggest the term

“apperceptive alexia” to reflect the contribution of deficits in visuoperception and visuospatial

attention. However, while Mendez et al. (2007) stressed the role of attentional deficits as

evidenced by performance on a flanker letter task, performance on this task is more suggestive

of crowding rather than attentional effects. Results demonstrated an effect of the visual

similarity of flankers on target letter identification; unlike standard definitions of attentional

dyslexia, this flanker effect occurred regardless of flanker category (numbers [e.g.55S55],

letters [e.g. KKXKK]).

Excessive crowding is a promising candidate for a form of early visual processing deficit

that may particularly impinge on reading. Bouma and Legein (1977) found that dyslexics’ letter

identification in peripheral vision was disproportionately inhibited by the presence of flankers.

Our uncrowded vision corresponds to the visual span (Pelli et al., 2007); the visual span is the

34

extent to which we can read without moving our eyes, and been suggested to limit our reading

rate (Chung et al., 2004; Legge et al., 2001). Increased inter-letter spacing has produced

benefits to dyslexics’ reading ability (Spinelli et al., 2002; Zorzi et al., 2012), while letter

confusability, a measure of the visual similarity of letters, has been suggested to account for

word length effects in LBL readers (Arguin et al., 2002; Fiset et al., 2005). In PCA, beneficial

effects of moderate inter-letter spacing and lower summed letter confusability have been

identified on whole-word reading, leading to the proposal that observed reading deficits may

be attributed to “crowding dyslexia” (Crutch & Warrington, 2009).

2.5. NON-VISUAL NEUROPSYCHOLOGICAL FEATURES

Characteristic deficits of PCA include those in non-visual domains associated with

dominant parietal function, including calculation, spelling and praxis (Delazer et al., 2006;

Aharon-Peretz et al., 1999; Kas et al., 2011; McMonagle et al., 2006). Relative preservation of

memory and semantics has been noted in PCA (Mendez et al., 2002; McMonagle et al., 2006;

Tang-Wai et al., 2004); the majority of patients do not show early episodic memory

impairment as assessed clinically or through neuropsychological testing (Whitwell et al., 2007;

Lehmann et al., 2011) and demonstrate competent performance on semantic fluency and

synonym discrimination tasks relative to tAD patients (Mendez et al., 2002) and healthy

individuals (Lehmann et al., 2011). Some studies have suggested that executive function is

relatively preserved in PCA compared to tAD (Mendez et al., 2002), while others have found no

evidence of differences between PCA and tAD patients (Rosenbloom et al., 2011). An

important caveat is that visual impairment can confound tasks with a visual component that

intend to assess performance IQ or executive function, which likely accounts for PCA patients’

performance IQ being lower than verbal IQ (McMonagle et al., 2006).

2.5.1. Language

While verbal language has been considered relatively intact in PCA compared to visual

domains (Mendez et al., 2002; McMonagle et al., 2006), there is evidence that some language

skills are impaired in PCA. Benson et al. (1988) noted how all five of the PCA patients he

reported developed transcortical sensory aphasia, which included anomia and comprehension

disorder. Subsequent studies have observed some prevalence of early language difficulties in

PCA; out of 14 PCA patients, over a third presented with language complaints at disease onset

(Migliaccio et al., 2009), 7 of 9 presented signs of anomia in spontaneous speech (Magnin et al,

2013) while another investigation found 24 of 27 PCA patients showed signs of anomia (Tang-

Wai et al., 2004). However, classification of language complaints in Migliaccio et al.’s (2009)

study includes reading difficulties, while it is not possible to discern whether anomia was

assessed visually or verbally in Tang-Wai et al.’s (2004) investigation. Studies comparing PCA

35

with LPA patients have found overlapping deficits in phonemic fluency and word retrieval in

response to verbal descriptions (Crutch et al., 2013; Magnin et al., 2013). In the context of

poor repetition of nonwords and comprehension of longer sentences in the absence of

syntactic deficits, it is possible that such performance could reflect weaknesses in phonological

processing and short term memory, particularly given the purported role of parietal systems in

mediating the short-term phonological storage and retrieval of verbal information (Jonides et

al., 1998), and/or damage to early stages of auditory processing (Goll et al., 2010).

2.5.2. Praxis

A common symptom in PCA is limb apraxia, which previous group studies have observed in

around 95% of patients (McMonagle et al., 2006; Kas et al., 2011). Previous reports exist of

individuals presenting with progressive apraxia and symptoms suggestive of posterior cortical

dysfunction but with relatively intact visual and memory function. Green et al. (1995)

identified how one AD patient whose recognition memory was within the normal range

presented with prominent apraxia, along with difficulties performing mental arithmetic and

hypoperfusion particularly within the posterior parietal cortex. Another case involves a patient

who performed within the normal range on four tests of verbal and spatial memory and

measures of early visual and visuoperceptual processing; the primary deficit was a progressive

loss of manual praxis, with additional dyscalculia (De Renzi, 1986). These studies raise

questions as to whether the term PCA might be used for patients with progressive apraxia,

acalculia and/or spelling difficulties who do not demonstrate early and prominent visual

deficits; if so, 47 patients defined as having a language or apraxic presentation in Snowden et

al. (2007) may actually be considered to have PCA. Some investigations have been explicit in

describing patients with apraxia as the leading symptom as having PCA (Aharon-Peretz et al.,

1999; Goethals & Santens, 2001); Aharon-Peretz et al. (1999) proposed that PCA represents

two clinically related behavioural phenotypes involving either predominant visuospatial

impairment or apraxia. An apraxic presentation of PCA may reflect an overlap with corticobasal

degeneration (Goethals & Santens, 2001; McMonagle et al., 2006) which has been noted as

the underlying pathological cause of PCA in other patients (Mendez, 2000; Tang-Wai et al.,

2003; Seguin et al., 2011).


The cognitive phenotype of PCA includes various neuropsychological deficits associated

with posterior function, most prominent of which are a range of visual disturbances. Deficits in

early visual, visuoperceptual and visuospatial function have been documented, with the

leading impairment usually arising within the dorsal system. The nature of reading difficulties is

36

suggestive of a peripheral deficit, although the precise manner in which visual dysfunction

relates to acquired dyslexia in PCA is unclear. Visual deficits in PCA might predict particular

forms of reading difficulty; for example, the inverse size effect could restrict recognition of

large words, visuospatial impairment might limit word localisation within sentences or

passages and enhanced crowding might disrupt parallel letter processing or identification of

words flanked by adjacent words. More generally, visuoperceptual impairment and alexia are

likely to coincide given the role of the ventral system in object and word recognition.

37

3. METHODS OVERVIEW

3.1. PARTICIPANTS

3.1.1. Patients

The majority of patients had attended the Cognitive Disorders Clinic at the National

Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London, UK. This is a

secondary/tertiary referral centre, with attending patients often presenting with younger or

atypical presentations of dementia. All patients underwent a clinical and neuropsychological

assessment. Participants recruited from the clinic included PCA and sporadic AD patients of an

amnestic presentation. Participants who had evidence of an ischaemic stroke or brain tumour

were not recruited into the investigation. Ethical approval for the investigation was provided

by the National Research Ethics Service London-Queen Square ethics committee and informed

consent was obtained from all participants. Disease duration was defined as the time elapsed

since first onset of cognitive symptoms.

3.1.2. Healthy controls

Control participants were neurologically healthy and lacked a family history of dementia or

contraindications to Magnetic Resonance Imaging (MRI) scanning. Demographic information

was collected for controls; controls with hearing or visual problems were excluded from

testing.

3.2. CLINICAL ASSESSMENT

All participants who attend the Cognitive Disorders clinic completed a full clinical

assessment involving the following:

Recording of the full clinical history from patient and an informant.

Detailed neuropsychological assessment to establish the form and severity of the

behavioural phenotype.

Blood tests to exclude other causes of cognitive problems.

Electroencephalography (EEG) to exclude seizures or identify brain activation

patterns suggestive of types of dementia.

MRI scanning to exclude other causes of cognitive problems such as tumours,

ischaemic strokes or subdural haematomas or identify ischaemic damage and/or

patterns of brain atrophy.

Some patients also underwent the following:

38

Blood tests to screen for known genetic mutations may be carried out on

symptomatic individuals with a family history and/or age of onset suggestive of an

autosomal dominant inheritance, or individuals whose family history indicates that

they are at risk of inheriting a known mutation.

Lumbar punctures in order to collect and analyse CSF samples, giving a measure of

levels of tau and Aβ 1-42 proteins.

3.3. INCLUSION CRITERIA

Informed consent was obtained using procedures approved by the National Hospital for

Neurology and Neurosurgery.

3.3.1. PCA

PCA patients all fulfilled clinical criteria for a diagnosis of posterior cortical atrophy

(summarised in 1.2.2.6). Core features of diagnostic criteria included visuospatial and

visuoperceptual impairments, features of Balint’s (simultanagnosia, oculomotor apraxia, optic

ataxia) and Gerstmann’s syndrome (acalculia, agraphia, finger agnosia, left-right disorientation)

with relatively preserved memory (Tang-Wai et al., 2004; Mendez et al., 2002; McMonagle et

al., 2006). While PCA patients fulfilled these criteria at some point in their clinical history, by

time of experimental testing some patients had progressed to more global cognitive

impairment. PCA patients fulfilled research criteria for probable Alzheimer’s disease (Dubois et

al., 2007, 2010).

3.3.2. Typical AD

Typical AD patients had a clinical history of an amnestic AD presentation and fulfilled

NINCDS-ADRDA criteria, including recent revisions, summarised in chapter 1.2.1.4 (McKhann et

al., 1984; McKhann et al., 2011; Dubois et al., 2007). All typical AD patients performed below

5th %ile on verbal and/or visual recognition memory tests (Warrington, 1984; Warrington,

1996).

3.4. NEUROPSYCHOLOGY

3.4.1. Background neuropsychology

i) Short Recognition Memory Test for faces/words (Warrington, 1996): task involved

1) learning of faces through visual presentation and words through joint auditory

and visual presentation and 2) subsequent identification of faces/words from 1)

paired with distractor faces/words.

39

ii) Concrete Synonyms (Warrington et al., 1998): for each target word participants

were requested to identify which of two semantically-related words were closest

in meaning to the target. All words were of high concreteness.

iii) Graded Naming Test: all participants were requested to name objects of

decreasing frequency from their verbal description.

iv) Cognitive estimates (Shallice & Evans, 1978): this task involved participants

estimating answers to questions which can be effectively guessed using general

knowledge.

v) Calculation (Graded Difficulty Arithmetic: Jackson & Warrington, 1986)

vi) Spelling (Graded Difficulty Spelling Test: Baxter & Warrington, 1994)

vii) Gesture production test (Crutch, unpublished): this task involved pantomiming the

use of objects (joint auditory and visual presentation) and replicating

experimenter’s gestures.

viii) Digit Span (forwards/backwards)

ix) Single Word recognition from the Cortical Visual Screening Test (CORVIST; James

et al., 2001)

3.4.2. Visual Assessment

See Appendix 4 for example stimuli of measures of visual processing.

3.4.2.1. Early visual

i) Visual acuity test (CORVIST): task required discrimination of squares, circles and

triangles at decreasing stimulus sizes corresponding to Snellen form acuity levels

ranging from visual acuity of 6/9 to 6/36.

ii) Shape detection test from the Visual Object and Shape Perception battery (VOSP;

Warrington and James, 1991): Figure-ground discrimination task involving random

black pattern stimuli (N=20), half with a degraded ‘X’ superimposed. Patients were

requested to state whether an “X” was present.

iii) Shape discrimination: The stimuli (N = 60) for this boundary detection task,

adapted from Efron (1968), were a square (50 x 50 mm) or an oblong matched for

total flux. There were three levels of difficulty: oblong edge ratio 1:1.63 (Level I),

1:1.37 (Level II), and 1:1.20 (Level III). The task was to discriminate whether each

shape presented was a square or an oblong.

iv) Hue discrimination (CORVIST): The stimuli (N=4) comprised nine colour patches,

eight of the same hue but varying luminance and one target colour patch of a

different hue.

40

3.4.2.2. Visuoperceptual

v) Object Decision (VOSP): Stimuli (N=20) each comprise of four silhouette images,

one of a real object (target) plus three non-object distractors.

vi) Fragmented Letters (VOSP): Participants were asked to identify visually degraded

letters (N=20).

vii) Unusual and usual views (Warrington and James, 1988): Participants were asked to

identify photographs of real objects (N=20) pictured from an ‘unusual’, non-

canonical perspective. Items not identified from the non-canonical perspective are

subsequently re-presented photographed from a more ‘usual’, canonical

perspective.

3.4.2.3. Visuospatial

viii) Number location (VOSP): Stimuli (N=10) consist of two squares, the upper square

filled with Arabic numerals in different positions, and the lower square with a

single black dot. Participants are requested to identify the Arabic numeral whose

spatial position corresponds to that of the target dot.

ix) Dot counting (VOSP): Stimuli (N=10) are arrays of 5-9 black dots on white

background. Participants were asked to count the dots as quickly as possible

without touching stimuli.

x) A Cancellation (Willison and Warrington, 1992): Participants were requested to

mark as quickly as possible with a pencil the location of 19 targets (letter As)

presented among distractors (letters B-E) in a grid on an A4 sheet.

3.5. RESPONSE LATENCIES

Reading/letter naming responses were recorded using an Olympus DS-40 digital voice

recorder; reading latencies were manually determined from the onset of each

word/letter/passage using the digital audio editor Audacity (http://audacity.sourceforge.net).

Latency data for erroneous responses and responses where participants had become overtly

distracted from the task were removed from the analysis.

3.6. EYETRACKING

Eye movements were recorded using the head-mounted Eyelink II system, an infrared

video-based eye tracker. The Eyelink II recorded gaze location at 250Hz. Fixations and saccades

used in the present analysis were parsed by the Eyelink system, using standard velocity and

acceleration thresholds (30o/s and 8000o/s2). Blinks were removed using Eyelink’s automated

blink detection. Five point calibration was carried out at the start of the experiment, and a

41

single point centrally-presented drift-correct was carried out prior to each passage. Fixations

and saccades that spatially preceded the first word in passage experiments were discounted

from analysis.

3.7. IMAGING

3.7.1. MRI acquisition

T1-weighted volumetric magnetic resonance images (MRIs) were acquired on a Siemens

Trio TIM 3T scanner (Siemens Medical Systems) for 20 PCA patients. Images were acquired

using a 3D magnetization prepared rapid gradient echo (MP-RAGE) sequence producing 208

contiguous 1.1 mm thick sagittal slices with 28-cm field of view and a 256 × 256 acquisition

matrix, giving approximately isotropic 1.1×1.1×1.1 mm voxels; a 32-channel head coil was

used.

3.7.2. Image processing software

3.7.2.1. MIDAS

The MIDAS (Medical information Display and Analysis System) software (Freeborough et

al., 1996; Freeborough et al., 1997) allows simultaneous display of 3D imaging data. MIDAS

allows semiautomatic MRI segmentation into brain and nonbrain regions (Freeborough et al.,

1997). The MIDAS allows the overlaying of images, such as voxel-compression maps or region

of interest masks, on top of MRI scans.

3.7.2.2. Matlab

Matlab is a high-level language and interactive environment for numerical computation,

visualisation, and programming. Matlab 2012© is developed by MathWorks (Sherborn,

Massachusetts) and was used to implement of software packages such as SPM8 (see 3.7.3.1).

3.7.3. Image processing

3.7.3.1. Voxel-based morphometry

For the voxel-based morphometry (VBM) analysis, magnetic resonance brain images were

preprocessed using SPM8 software (Statistical Parametric Mapping, Version 8;

http://www.fil.ion.ucl.ac.uk/spm) running on MatLab 2012©. Images were converted to NIFTI

format (http://nifti.nimh.nih.gov) and rigidly orientated to standard space based on the

international consortium for brain mapping template using the “New segment” function in

SPM8. Rigidly-orientated scans were segmented into native space grey matter, white matter

42

and cerebrospinal fluid. The DARTEL toolbox (Ashburner, 2007) was used to perform spatial

normalization, modulating the grey matter and white matter volumes according to the

deformation fields and smoothing at 6mm full-width half-maximum. Total intracranial volume,

age, gender and Mini Mental State Examination (MMSE) score were included as covariates.

Total intracranial volume was calculated by adding the volumes derived from the native-space

cerebrospinal fluid, grey and white matter segments. An explicit mask was applied in all

analyses to include voxels for which the intensity was >.1 in at least 80% of the images; this has

been shown to reduce anatomical bias in participants with greater cortical atrophy (Ridgway et

al., 2009). A voxel-wise statistical threshold of p< 0.05, family-wise error (FWE) corrected for

multiple comparisons was applied in all analyses. A more liberal threshold (p<0.001

uncorrected) was applied in figures for better visualization of additional areas where grey

matter differences may be present.

3.7.3.2. Fluid registration

Fluid-based non-rigid image registration (Freeborough & Fox, 1998) was used to identify

local volumetric changes in grey matter, white matter and cerebrospinal fluid between paired

images from different time points. It uses a viscous fluid model to calculate the warping or

deformation needed to achieve correspondence of both images at the voxel level (Scahill et al.,

2002). The Jacobian determinants of the deformation fields represent the location and extent

of warping, and can be displayed as voxel-compression maps which show longitudinal

expansion and contraction of local brain regions. The MIDAS was used to overlay voxel-

compression maps on rigidly aligned MRI scans for visualisation.

43

4. VISUAL CROWDING EFFECTS IN PCA


Chapter 2.2.1.1 outlined a specific early visual processing deficit, crowding, which limits

identification of target stimuli in the presence of adjacent flanker stimuli. Crowding effects are

dependent on critical spacing, the distance at which flankers reduce identification of target

stimuli, and are independent from the category of target/flankers, font of letter flankers and

contrast (Pelli et al., 2004; Tripathy & Cavanagh, 2002; Pelli & Tillman, 2008). Crowding effects

are greater with target and flankers of increased visual similarity, and are reduced with target

and flankers of opposite polarity (Kooi et al., 1994; Bernard & Chung, 2011).

Although some theories of crowding suggest that it may be a consequence of poor

resolution of attention (Intriligator & Cavanagh, 2001), crowding tends to be considered a

preattentive process related to the interaction between simple visual features. Three main

types of theories have been proposed: the first, a classic lateral masking perspective,

associates crowding with low level masking, a consequence of competition between a finite

amount of feature detectors (Townsend et al., 1971; Wolford & Chambers, 1984). This masking

is said to occur at the level of the retina, lateral geniculate nucleus, or the primary visual cortex

(V1) (Chakravarthi & Cavanagh, 2007). The second theory suggests that crowding arises as a

problem of excessive feature integration; when cells responsible for pooling information over a

large area encounter flankers, flanker stimuli information is assimilated with the target

stimulus (Levi et al., 2002; Pelli et al., 2004; Greenwood et al., 2010). The third, source

confusion theory, proposes that features from flankers are mistaken to be target features

(Wolford, 1975; Krumhansl & Thomas, 1977); this theory has been associated with accounts of

crowding which emphasise spatial attention (Strasburger et al., 2005) or spatial uncertainty as

a consequence of larger receptive fields (Dayan & Solomon, 2010). Some researchers have

discriminated between the first two theories by suggesting that crowding limits identification

of target stimuli, while lateral masking limits both identification and detection (Parkes et al.,

2001; Pelli et al., 2004), while others have proposed how both may represent the same effect

(Pernet et al., 2006). Levi’s (2008) review of crowding suggests that there is a growing

consensus that crowding involves a two-stage process, encompassing both detection of simple

features, possibly in V1, and integration of features downstream from V1. While the current

investigation refers to visual crowding, some of the findings and conclusions may be equally

applicable to lateral masking.

44

Previous studies on crowding place the occipital cortex as the anatomical locus (Levi, 2008;

Bi et al., 2009; Fang & He, 2008; Anderson et al., 2012). More precise localisation of crowding

varies between studies, ranging from V1 (Blake et al., 2006), V2 (Chung et al., 2007) and V4 (Liu

et al., 2009), while a two-stage model of crowding including both feature detection and

integration might respectively involve V1 (Levi, 2008) and the extrastriate cortex (Robertson,

2003). Crowded stimuli have been shown to provoke increased fMRI activation from areas V1-

V4 (Bi et al., 2009; Anderson et al., 2012), suggesting that crowding may be a multistage

process.

In healthy individuals, crowding only tends to inhibit perception in parafoveal vision; in

PCA patients, prominent flanked letter identification deficits in line with crowding are evident

in central vision (Crutch & Warrington, 2007a; Crutch & Warrington, 2009; Mendez et al.,

2007). However, there have been no group investigations that have set out to specifically test

crowding effects in PCA. While a range of visual deficits have been noted in PCA, we are

currently unable to evaluate whether these deficits may in fact be contributed towards by

enhanced crowding, and if so, what the incidence of such crowding is within PCA and tAD. This

study aims to confirm whether patterns of flanked letter identification deficits in PCA are in

line with crowding, and if so, gauge its prevalence in our PCA cohort and compare it to

performance on the same tasks within a tAD group. The administration of a set of reading

tasks to the same participants, reported in chapter 5, intends to clarify the nature between

enhanced crowding and acquired dyslexia in PCA. Given the attenuating effects of increased

spacing (Bouma, 1970; Townsend et al., 1971) and reverse polarity on crowding in normal

peripheral vision (Kooi et al., 1994; Chakravarthi & Cavanagh, 2007), we hope that the

manipulation of these variables will facilitate letter identification in such a way that might

suggest means of supporting diminished reading ability in PCA.

4.2. METHODS

4.2.1. Participants

The study participants were 26 PCA patients, 17 typical AD patients and 14 healthy

controls. The PCA patients all fulfilled clinical criteria for a diagnosis of posterior cortical

atrophy (see chapter 1.2.2.6; Tang-Wai et al., 2004; Mendez et al., 2002; McMonagle et al.,

2006) . Both PCA and tAD patients fulfilled research criteria for probable Alzheimer’s disease

(see chapter 1.2.1.4; Dubois et al., 2007, 2010). Molecular pathology (18F amyloid imaging or

CSF) was available for 7/26 PCA and 11/17 tAD patients (see Table 4.2); results for all tAD

patients and 6/7 PCA patients were consistent or borderline consistent with AD pathology

(positive amyloid scan on standard visual rating or CSF Aβ1-42 ≤450 and/or tau/Aβ ratio >1) The

healthy controls were matched to the PCA and tAD groups on age and years of education, with 45

the PCA and tAD participants additionally matched disease duration and Mini-Mental State

Examination (MMSE; see Table 4.1).

Table 4.1 Demographic characteristics of PCA, tAD and normal control groups

PCA

Typical Alzheimer's

diseaseControl

Number of participants 26 17 14Gender (male/female) 10/16 12/5 5/9Age (years) 61.4 ± 7.7 65.0 ± 5.1 62.7 ± 5.0Education level (years) 14.6 ± 2.3 14.9 ± 2.4 16.1 ± 2.4Disease duration (years) 4.7 ± 3.1 5.0 ± 1.7 -MMSE1 (/30) 17.7 ± 5.0 17.5 ± 4.9 -β-Amyloid PET/ CSF consistent with AD 6/7 11/11

Table 4.2 Molecular pathology data for PCA and tAD patients; interpretation symbols indicate where results do not support AD pathology (-), are borderline consistent with AD pathology (+) and are >85% specific for AD pathology (++).

Diagnosis Amyloid F18

imaging CSF total tau CSF Aβ 1-42 CSF Tau:Aβ ratio CSF interpretation

PCA - 310 488 0.64 -

PCA - 931 625 1.49 +

PCA positive 1072 126 8.51 ++

PCA - 151 147 1.03 +

PCA positive - - - ++

PCA positive 1082 365 2.96 ++

PCA positive - - - ++

tAD - 289 280 1.03 +

tAD - 757 285 2.66 ++

tAD - 940 348 2.70 ++

tAD - 952 195 4.88 ++

tAD - 977 322 3.03 ++

tAD - 625 277 2.26 ++

tAD - >1200 313 >3.83 ++

tAD - 913 191 4.78 ++

tAD - >1200 217 >5.52 ++

tAD - 1099 195 5.64 ++

tAD - 850 362 2.35 ++


PCA and tAD participants were administered a battery of background neuropsychological

tests (chapter 3.4.1) and tests examining early visual, visuoperceptual and visuospatial

46

processing (chapter 3.4.2). Mean scores for the PCA and tAD groups and an estimate of their

performance relative to normative data sets appropriate for the mean age of each group are

shown in .

On tasks without a core visual component, the performance of the PCA group was mostly

equivalent to (Concrete Synonyms, Naming, Digit Span forwards) or better than (Short

Recognition Memory Test: words) that of the tAD group. PCA patients had lower scores than

tAD patients on tests sensitive to parietal dysfunction (Calculation, Digit Span backwards,

Cognitive estimates). Visual assessment identified how PCA patients showed greater

impairment than the tAD group on all tests of basic visual function (except colour

discrimination and single letter naming), visuoperceptal function (except unusual [non-

canonical] object perception) and visuospatial processing.

4.2.3. Crowding assessment

All participants were requested to name target stimuli (upper-case letters excluding I, J, O,

Q, W and X) under the following conditions (example stimuli are shown in Figure 4.1):

Task 1 - Unflanked letter identification (N=20): The target stimuli were alphabetic

items presented in isolation. Letters were resented in random order for 6000ms in

a fixation box (3.2° in width, 2.9° in height) at the centre of the screen.

Task 2 - Letter flankers (N=24): Target letters were flanked on each side by a letter,

forming a 3-letter non-word combination.

Task 3 - Shape flankers (N=24): Target letters were flanked on each side by a

triangle presented at different orientations. Triangles were of equal height and line

thickness to target letters.

Task 4 - Number flankers (N=24): Target letters were flanked on each side by an

Arabic numeral, chosen from a range between 2 and 9.

Task 5 - Same-polarity flankers (N=24): Target letters were flanked on each side by

black letters; this condition was the same as Task 2 except that items were

presented on a grey background to match Task 6 (see below).

Task 6 - Reverse-polarity flankers (N=24): Target black letters were flanked on each

side by white letters, all presented on a grey background.

47Figure 4.1 Example stimuli used in the letter, shape, and number experiments and same/reverse

polarity experiment under condensed and spaced spatial conditions.

Test

Max Scor

e

Raw Score

PCA(mean age: 61.0)

AD(mean age: 65.0)

mean min|max mean min|max Difference Norms/commentBackground Neuropsychology

Short Recognition Memory Test1 for words*

(joint auditory/visual presentation)

25 19.5 ± 3.7 14|25 14.7 ± 1.5 13|19 p<.0001 PCA: 5th-10th%ile, AD: ~<5th

%ile (Cut off: 19)

Short Recognition Memory Test for faces*

25 17.8 ± 4.0 10|24 16.8 ± 3.0 12|22 p>.3 Both ~<5th %ile (Cut off: 18)

Concrete Synonyms test2 25 20.0 ± 3.7 11|24 20.9 ± 2.5 17|25 p>.4 Both 10th-25th %ileNaming (verbal description) 20 11.4 ± 6.6 2|20 13.7 ± 6.4 4|25 p>.2 Both ~<5th%ile (Cut off: 15)

Cognitive estimates3 (error score) 30 14.6 ± 7.5 1|27 10.6 ± 5.0 2|20 p=.074 Both ~<1st%ile (Cut off: 9)

Calculation (GDA4)* 24 1.6 ± 2.9 4.9 ± 5.3 p<.05 PCA: ~<5th%ile, AD:5th-25th%ileSpelling (GDST5- Set B, first 20 items)* 20 8.9 ± 6.5 0|19 10.8 ± 5.6 0|19 p>.3 Both 10th-25th%ile

Gesture production test6 15 12.7 ± 3.4 2|15 14.1 ± 1.4 10|15 p>.1 -Digit span (forwards) 12 6.0 ± 2.6 1|11 6.1 ± 1.4 3|8 p>.8 Both 25th-50th%ileMax forwards 8 5.6 ± 1.8 1|8 5.5 ± 0.8 4|7 p>.9 -Digit span (backwards) 12 2.6 ± 1.7 0|7 3.6 ± 1.9 1|9 p=.078 Both 5th-10th%ile Max backwards 7 2.3 ± 1.3 0|4 3.3 ± 1.1 2|6 p<.05 -CORVIST7 reading test 16 13.8 ± 3.0 6|16 15.7 ± 0.8 13|16 p<.05 -

Visual AssessmentEarly visual processingVisual acuity (CORVIST): Snellen 6/9 (median

6/9) 6/9|6/36 (median 6/9) 6/9|6/12 - Normal acuity

Figure-ground discrimination (VOSP8) 20 16.3 ± 3.0 10|20 18.6 ± 1.3 16|20 p<.01 PCA: ~<5th%ile, AD: 5th-10th

%ile

Shape discrimination9 20 12.6 ± 3.9 6|20 17.2 ± 3.2 9|20 p<.0005 Healthy controls do not make any errors

Hue discrimination (CORVIST) 4 2.6 ± 1.1 0|4 3.0 ± 1.3 0|4 p>.3 -

Visuoperceptual processing

Object Decision (VOSP)* 20 10.0 ± 4.1 3|16 15.9 ± 2.4 3|20 p<.0001 PCA: ~<5th%ile, AD: 10th-25th

%ileFragmented letters (VOSP) 20 2.9 ± 3.9 0|17 13.5 ± 6.6 1|20 p<.0001 Both ~<5th%ile (Cut off: 16)Unusual and usual views10: Unusual 20 6.6 ± 6.8 0|19 9.9 ± 5.1 2|16 p>.1 Both ~<1st%ile (Cut off: 12)

Unusual and usual views10: Usual 20 8.4 ± 5.5 0|20 16.5 ± 4.0 4|20 p<.0001 Both ~<1st%ile (Cut off: 18)

Visuospatial processingNumber location (VOSP)* 10 1.8 ± 2.5 0|8 5.7 ± 3.8 0|10 p<.005 Both ~<5th%ile (Cut off: 6)Dot counting (VOSP) 10 3.4 ± 3.2 0|10 8.1 ± 3.1 0|10 p<.0001 Both ~<5th%ileA Cancellation11: Completion time 90s 79.5s ±

17.4 48s|111s 36.3s ± 15.7 17s|69s p<.0001 Both ~<5th%ile (Cut off: 32s)

A Cancellation11: Number of letters missed 19 6.6 ± 5.1 0|18 0.53 ± 1.1 0|4 p<.0005 -

Table 4.3 Neuropsychological scores of patients with PCA and tAD relative to normative data *Behavioural screening tests supportive of PCA diagnosis. 1 Warrington (1996). 2 Warrington, McKenna and Orpwood (1998). 3 Shallice and Evans (1978). 4 Graded Difficulty Arithmetic test (GDA; Jackson & Warrington, 1986). 5 Graded Difficulty Spelling Test (GDST; Baxter & Warrington, 1994). 6 Crutch (unpublished). 7 Cortical Visual Screening Test (CORVIST; James, Plant & Warrington, 2001). 8 Visual Object and Space Perception Battery (VOSP; Warrington & James, 1991). 9 Efron (1968): oblong edge ratio 1:1.20. 10

Warrington and James (1988). 11 Willison and Warrington (1992).

48

In each flanking condition, target letter identification was probed under two spatial

conditions, condensed and spaced. The edge-to-edge distance between the target letter and

flankers was 0.875mm in the condensed condition and 8.75mm in the spaced condition, with

the height of stimuli (10.5mm) corresponding to a visual angle of 1.20° at a viewing distance of

50cm. On each trial, if participants named the flanker, they were given one prompt to name

the letter in the middle; this prompt was intended to limit errors resulting from visual

disorientation. The same combination of flankers was used for each target letter under both

spatial conditions within each flanker condition. Alphabetic items excluded the letters I, J, O, Q,

W and X, and occurred with equal frequency within each task. The stimuli were presented in

blocks of 6 items, with blocks being administered in an ABBA design. All flanked stimuli were

presented in the centre of the screen within a fixation box (6.4° in width, 2.9° in height) which

was intended to limit visual disorientation. All 26 PCA patients completed tasks 1, 2 and 4; 24

completed task 3 and 22 completed tasks 5-6.

4.2.4. Data analysis

4.2.4.1. Background Neuropsychology

Differences between the PCA and tAD groups were calculated using a t-test.

4.2.4.2. Behavioural Covariates

Tests of early visual, visuoperceptual and visuospatial processing (see Visual Assessment)

were transformed and averaged to form composite scores for each visual domain. Raw scores

were transformed into a standardised range (0-100) in which 0 and 100 corresponded to the

minimum and maximum score achieved by any patient (irrespective of PCA and tAD group

membership) respectively. The following raw scores were also transformed into a standardised

range (0-100) for the PCA v tAD regression analysis: unflanked letter identification, digit span

(backwards), A cancellation time. MMSE and disease duration were also used as behavioural

covariates.

Crowding indices for VBM analysis: Scores on shape and number flankers (Tasks 3-4) were

used as crowding indices, based on the rationale that errors with non-letter flankers were less

likely to reflect attentional or executive deficits. Indices were based on raw score differences:

Spacing (shape): difference in accuracy between spacing conditions (spaced –

condensed) in task 3 (shape flankers).

Spacing (number): difference in accuracy between spacing conditions (spaced –

condensed) in task 4 (number flankers).

49

Spacing (shapes/numbers): difference in accuracy between spacing conditions

(spaced – condensed) in tasks 3 & 4 combined.

Polarity: difference in accuracy between tasks 5 & 6 (reverse polarity – same

polarity).

Polarity (condensed): difference in accuracy between tasks 5 & 6 (condensed

condition only).

4.2.4.3. Naming Latencies

See chapter 3.5 for details of latency recording and determination. Latency data greater

than 2 standard deviations from the mean of each participant were removed. Prior to latency

regression analysis, latency data were transformed using an inverse transformation due to

non-normal distribution of residuals. Naming latency data were only analysed for participants

who made no errors or too few errors to permit meaningful error analysis using logistic

regression or chi squared tests (Crowding assessment 1: PCA: N=12, MMSE=18.8, disease

duration=3.9 yrs; Crowding assessment 2: N=9, MMSE=20.0, disease duration=3.0yrs; all tAD

and control participants).

4.2.4.4. Statistical Analysis

A one-way repeated measures ANOVA was used to examine overall differences between

letter, shape and number flankers. Analysis of accuracy and latency data was conducted using

logistic and linear models respectively; both models included spacing and flanker type as

predictor variables, with the linear model also including accuracy rate as a covariate. Between

patient group (PCA v tAD) regression analyses used the same logistic and linear models but

also included diagnosis and one of the behavioural covariates listed above. Models were used

to test for interactions between spacing and flanker type. Differences between PCA and tAD

groups were calculated using a Wilcoxon rank-sum test and differences within groups were

calculated using a Wilcoxon signed-rank test.

4.2.4.5. Neuroimaging Analysis

MRI acquisition and preprocessing were carried out as described in chapters 3.7.1 and

3.7.3.1 respectively. Associations between regional grey matter volume and crowding indices

(see chapter 4.2.4.2) were assessed using voxel-wise linear regression models. In addition to

the explicit mask outlined in chapter 3.7.3.1, a region of interest (ROI) mask covering the

occipital lobe was applied given prior anatomical hypotheses about the locus of crowding. The

ROI mask was created using the Hammers atlas, which was warped to the groupwise average

image created from the bias-corrected T1-weighed images of all 20 PCA participants using the

50

DARTEL toolbox (Ashburner & Friston, 1997). Associations between grey matter volume and

behavioural performance were assessed over the whole brain and within the occipital ROI.

4.3. RESULTS

The mean and standard deviation percentage accuracy and naming latency results for each

group on Tasks 1-6, plus group comparisons, are shown in Table 4.4.

4.3.1. Crowding Assessment 1 - flanker and spacing effects

Task 1 - Unflanked letter identification: There was no significant difference in unflanked

letter identification accuracy between the PCA and tAD or control groups. One PCA patient

made one error, while the tAD and control groups did not make any errors. There was a trend

towards the PCA group having longer naming latencies than the tAD group. Both PCA and tAD

groups were slower than the control group.

Tasks 2-4 - Letter, shape and number flankers: A summary of the PCA and AD accuracy and

naming latency data is shown in Figure 4.2. The PCA patients were consistently worse than

both tAD patients and controls in terms of both naming accuracy and latency on Tasks 2-4. The

accuracy of tAD patients was not significantly different from that of controls on any Task, but

they were slower on Task 4.

4.3.1.1. Effects of spacing

PCA patients (N=26) showed significantly poorer accuracy for target letter identification in

the condensed than spaced condition (z=7.81, p<.001). Analysis of latency data (N=10)

identified longer naming latencies in the condensed condition (t=3.33, p<.01). At the individual

patient level, 18/26 (69.2%) showed a spacing effect in accuracy and/or latency; all but three

of those (15/26: 57.7%) showed this spacing effect even when analysis was restricted to non-

letter flankers. tAD patients did not demonstrate an effect of spacing (p>.5) on accuracy but

latencies were significantly longer in the condensed condition (t=4.73, p<.001). Controls made

no errors, but did show longer latencies in the condensed condition (t=2.89, p<.05). Analysis of

combined accuracy data from the PCA and tAD groups revealed a significant interaction

between diagnosis and spacing (z=-2.77, p<.01), with PCA patients showing a greater spacing

effect; no such interaction was found for naming latencies (p>.1). Analysis of combined latency

data for the tAD and control groups found no evidence of an interaction between diagnosis

and spacing (p>.4).

4.3.1.2. Effects of flanker category

PCA patients showed significantly poorer accuracy for letters relative to other flanker

categories (vs shapes: z=2.68, p<.01; vs numbers: z=2.61, p<.01). However, this between-

51

category difference only held in the spaced condition (vs shapes: z=3.65, p<.001; v numbers:

z=3.55, p<.001); in the condensed condition, there was no significant difference between

letters and other flankers (v shapes/numbers: p>.1). PCA patients showed significantly slower

naming latencies for letters relative to other flanker categories (v shapes: z=3.74, p<.005; v

numbers: t=2.60, p<.05): this between-category difference was consistent across spacing

conditions. tAD patients did not show poorer accuracy for letters (v shapes/numbers: p>.1) but

did show longer latencies (v shapes: t=6.13, p<.001; v numbers: t=5.13, p<.001). Controls made

no errors, but showed longer latencies for letter flankers (v shapes: t=2.59, p<.05; v numbers:

t=3.55, p<.005). Unlike the PCA group, both tADs and controls showed longer latencies for

letter flankers in both spaced and condensed conditions.

4.3.1.3. Post-hoc Analysis of Covariates

None of the behavioural covariates (early visual, visuoperceptual and visuospatial

processing, unflanked letter identification, Digit Span (backwards), A Cancellation time, MMSE

or disease duration) could account for the spacing effect within the PCA or tAD groups. None

of the covariates could account for the overall group difference in naming accuracy between

the PCA and tAD groups. Similarly, none of the covariates could account for group differences

when considering the condensed condition alone, however, visuoperceptual (z=2.13, p<.05;

diagnosis: p>.2) and visuospatial (z=3.60, p<.001; diagnosis: p>.1) function did account for the

group difference in the spaced condition. Linear regression analysis did found that none of the

covariates could account for the group difference between the PCA and tAD groups for naming

speed overall or in either spacing condition.

52

Figure 4.2 Accuracy and naming latency data for the PCA and AD group for letter, shape and number flankers in both spatial conditions (* = p<0.05; **= p<.005, ***= p<.0005).

Table 4.4 Comparisons between PCA and tAD accuracy and latency data. Asterisks denote where the PCA or tAD group significantly differs from each other or the control group (vs controls: * = p<0.05; **= p<.005; vs tAD: ^=p<.05; ^^=p<.005).

Naming accuracy (%)Groups

Naming latency (ms)Groups

Task PCA tAD Controls PCA tAD Controls

N mean min|max mean min|

max mean min|max mean min|

max mean min|max mean min|

max1. Unflanked letter identification

20

99.8 ± 0.2

95.0|100

100 ± 0 100|100 100 ± 0 100|

100785 ± 315** 360|3390 583 ±

95*390|1050

497 ± 53 340|750

2. Letter flankers 24

75.8 ± 25.1**^

^

12.5|100

99.3 ± 1.6

95.8|100 100 ± 0 100|

100

2726 ± 3196**^

^

470|23560

634 ± 128

260|2610

570 ± 113 210|900

3. Shape flankers 24

83.5 ± 18.6**^

^

33.3|100

99.7 ± 1.0

95.8|100 100 ± 0 100|

1001631 ±

867**^^400|

11870546 ±

86210|1160

538 ± 95 280|830

4. Number flankers 24

83.6 ± 23.5**^

^

33.3|100

99.7 ± 1.0

95.8|100 100 ± 0 100|

100

2072 ± 1842**^

^

290|23760

548 ± 92*

220|1610

469 ± 119 230|860

5. Same polarity letter flankers

24

78.8 ± 22.5**^

^

29.2|100

98.5 ± 2.9

91.7|100 100 ± 0 100|

100

1836 ± 1167**^

^340|9320 609 ±

128280|2100

518 ± 124

230|1130

6. Reverse polarity letter flankers

24

86.5 ± 15.6**^

^

54.2|100

99.3 ± 2.2

91.7|100 100 ± 0 100|

100

2133 ± 1635**^

^

310|11550

591 ± 91*

280|1610

506 ± 131 240|890

Summary data

Total (Tasks 2-4) 72

81.3 ± 19.7**^

^

20.8|100

99.6 ± 1.1

95.8|100 100 ± 0 100|

100

2054 ± 1694**^

^

290|23760

573 ± 94

210|2610

524 ± 100 210|900

Total condensed (Tasks 2-4)

36

72.0 ± 26.7**^

11.1|100

99.7 ± 0.9

97.2|100

100 ± 0 100|100

2510 ± 2481**^

420|23760

591 ± 98

210|2610

537 ± 100

250|900

53

^ ^

Total spaced (Tasks 2-4) 36

90.0 ± 16.0**^

^

30.6|100

99.5 ± 1.5

94.4|100 100 ± 0 100|

100

1659 ± 1073**^

^

290|20820

555 ± 95

220|1610

515 ± 101 210|880

Total (Tasks 5-6) 48

82.7 ± 18.3**^

^

50.0|100

98.9 ± 2.2*

91.7|100 100 ± 0 100|

1002004 ±

140**^^310|

11550597 ± 104

280|2100

512 ± 126

230|1130

54

4.3.2. Crowding assessment 2 – Polarity effects

Tasks 5 & 6 - Same- and reverse-polarity flankers: A summary of the PCA and AD accuracy

and naming latency data is shown in Figure 4.3Error: Reference source not found. The PCA

patients were consistently worse than both tAD patients and controls in terms of both naming

accuracy and latency on Tasks 5-6. Differences in accuracy and latency between the tAD and

control groups did not reach formal levels of significance, except for tAD patients being slower

on Task 6.

4.3.2.1. Effects of polarity

PCA patients (N=22) showed significantly poorer accuracy for target letter identification

with same- rather than reverse-polarity flankers (z=-3.07, p<.005). This polarity effect only

occurred for condensed flankers (condensed: z=4.82, p<.001; spaced: p>.8). Analysis of latency

data (N=10) found a significant effect of spacing (t=2.66, p<.05) but not polarity (p>.9) on

naming speed. In the tAD group, there was no significant effect of polarity on naming speed

(p>.8) or accuracy (p>.1); however, there was an interaction between spacing and polarity (t=-

2.63, p<.05), with condensed flankers of same polarity having longer naming latencies. While

none of the controls made any errors, there was a trend towards longer naming latencies with

same polarity flankers (t=-2.18, p=.050), though there was no interaction between spacing and

polarity (p>.1).

4.3.3. Error analysis

Of all error responses in the PCA group (overall error rate: 17.7%), 22.9% were from the

target being unidentified, which could result from participants being unable to either detect or

identify the target, and 32.2% were from flanker identification (e.g. ZNHZ). However, in

44.9% of errors, the response was neither the target nor a flanker: the majority of these errors

were suggestive of perceptual integration of flanker and target stimuli (YMTV, 3T6C). This

was despite accurate unflanked letter identification (overall error rate: 0.2%), with only one

PCA patient making one error (CG).

55

4.3.4.

4.3.4.

4.3.4.

4.3.4.

4.3.4.

4.3.4.

4.3.4.

4.3.4.

Summary of behavioural data

In the PCA group, there was a consistent effect of spacing, regardless of flanker type, on

accuracy and an inconsistent effect of flanker type of accuracy; there were also effects of

spacing and flanker type on naming speed. At the individual level of patients, effects of spacing

on naming speed or accuracy were more prevalent than effects of flanker type. However, the

spacing effect on accuracy is ameliorated by reverse polarity flankers. These findings suggest

that enhanced crowding is the primary factor in determining letter identification. None of the

behavioural covariates account for the difference in naming speed or accuracy between the

PCA and tAD groups in the condensed condition. The pattern of performance was similar in

both the tAD and healthy control groups.

4.3.5. Neuroimaging findings

T-values for neuroanatomical associations of performance on tasks 3-4 in the PCA group

are shown in Figure 4.4. No significant associations between indices of crowding (crowding

(shapes), crowding (numbers), crowding (shapes/numbers) and grey matter volume were

found when correcting for multiple comparisons over whole brain volume. When restricting

analysis to the pre-specified occipital region, a significant negative correlation was found

between crowding (shapes/numbers) and grey matter volume in the right collateral sulcus,

between the fusiform and lingual gyri after correcting for multiple comparisons (p<.05); a more

pronounced crowding effect for letters surrounded by shapes and numbers was associated

with reduced grey matter volume in this region. In tasks 5-6, there were no significant

associations between the discrepancy in accuracy between flankers of opposite polarity

56

Figure 4.3 Accuracy and naming latency data for the PCA and AD group for same and reverse polarity flankers in both spatial conditions (* = p<0.05; **= p<.005, ***= p<.0005).

(polaritydiff, polaritydiff (condensed)) and grey matter over whole brain volume or within the

pre-specified occipital region.

4.4. DISCUSSION

These findings support previous reports of crowding in PCA, in that performance on

centrally-presented flanker letter identification tasks reflect a phenomenon qualitatively

similar to that of typical crowding in healthy individuals but to a much greater degree. In the

PCA group, spacing, not flanker type, primarily determined accuracy in tasks of flanked letter

identification. Results also indicate a high prevalence of enhanced crowding in PCA, with 58%

of the PCA group exhibiting a spacing effect on speed or accuracy in a non-letter flanker

condition. A particularly interesting phenomenon observed in the PCA group is how reverse

polarity flankers ameliorated the crowding effect, consistent with observations of crowding in

normal peripheral vision.

Despite similar accuracy in naming letters presented in isolation (Task 1), the PCA group

were considerably less accurate than the tAD and the healthy control group in all tasks

involving flanked letter identification. The tAD group were not significantly less accurate than

controls on any of the tasks. The PCA group showed a trend towards being slower on

unflanked letter identification than the tAD group but were considerably slower on flanked

letter identification tasks. The tAD group were slower than the control group on unflanked

letter identification, but not flanked letter identification, tasks. Tasks 2-4 demonstrated a

consistent effect of spacing on accuracy across flanker type and an inconsistent effect of

flanker type on accuracy across spacing in the PCA group. Tasks 5 and 6 demonstrated how the

spacing effect occurred with same but not reverse polarity flankers. There was a similar

pattern of performance in both naming speed and accuracy in the tAD and healthy control

groups in both experiments.

The current findings demonstrate that the PCA group has disproportionate deficits in

speed and accuracy on tasks of flanked letter identification relative to control groups;

however, it is necessary to rule out other factors in being able to account for this poor

performance, particularly (i) attentional dyslexia and (ii) poor visuospatial processing.

Attentional dyslexia involves deficits in the recognition of multiple, concurrently presented

stimuli of the same category, for example letters presented with other letters (Humphreys &

Mayall, 2001; Warrington et al., 1993); these deficits, however, are not as pronounced when

multiple stimuli are of different categories. In tasks 2-4, spacing, not flanker type, consistently

57

58

Figure 4.4 Statistical parametric maps of grey matter volume associated with a measure of crowding (crowding (shapes/numbers)). The statistical parametric maps are displayed on axial (A), coronal (B) and sagittal (C) sections of the mean normalized bias-corrected images in MNI space: the right hemisphere is shown on the right on coronal and axial sections. When restricting analysis to a pre-specified region of interest (see region below in blue), there was an association between a greater degree of crowding and lower grey matter volume in the collateral sulcus (FWE corrected: p<.05; peak location: x=30 y=-58 z=-8): uncorrected t-values for this association are displayed below in a colourmap.

determined naming accuracy. While there was an interaction between spacing and flanker

type, this may be a consequence of perceptual similarity, and hence crowding, as opposed to

the category-specific deficit previously linked with attentional dyslexia.

Another possibility is that poor visuospatial processing may account for the current

findings, especially given the prominence of deficits in spatial localisation in PCA. In tasks 2-4,

performance on tasks involving unflanked letter identification, early visual, visuoperceptual or

visuospatial processing, digit span backwards or the A cancellation task did not account for the

overall difference in accuracy between the PCA and tAD groups. However, measures of

visuospatial and visuoperceptual ability did account for the group difference in the spaced, but

not condensed, condition. This suggests that poor accuracy in the spaced condition may be

contributed towards by visuospatial and visuoperceptual impairment, while poor accuracy in

the condensed condition is a consequence of enhanced crowding. The types of errors made by

the PCA group have implications for accounts of crowding. A classic lateral masking perspective

would predict errors such as being unable to detect the presence of target stimuli (Polat &

Sagi, 1993); deficits in the feature detection process within two-stage models of crowding may

also lead to such errors (Levi, 2008; Pelli et al., 2004), although it is plausible that, in either

case, a lack of target detection might result in errors involving misidentifying flanker stimuli.

Source confusion theories would predict errors to arise from misreporting flanker instead of

target stimuli (Wolford, 1975). While not by a large margin, the greatest proportion of error

responses did not involve identification of target or flanker stimuli; the majority of these errors

are consistent with an averaging of flanker and target information, in line with feature

integration and compulsory pooling theories.

With enhanced crowding being the most likely candidate in being able to account for poor

letter identification in the PCA group, what might be the locus for this phenomenon in the

visual system? Previous studies have suggested the neural correlates of crowding as being

somewhere within the occipital lobe (see chapter 2.2.1.1). Our imaging data suggests that,

within the occipital region, scores indicative of prominent crowding effects were associated

with lower grey matter volume within the right collateral sulcus, between the fusiform and

lingual gyri. Without retinotopic mapping, it is difficult to be confident of the exact

correspondence between anatomical location and visual area. In previous studies, similar

regions have been classed as area V4 (Gallant et al., 2000; Sereno et al., 1995; DeYoe et al.,

1996; Hadjikhani et al., 1998), V3 (Yeatman et al., 2013) and V3a (Grill-Spector & Malach,

2004). Interestingly, V4 fulfils a variety of criteria that make it a promising locus for crowding.

Receptive field size and anisotropy in V4 are similar in orientation and size to the

radial/tangential anisotropy of crowding (Pinon et al., 1998; Toet & Levi, 1992), while studies

have suggested that V4 is an area in which information from different stimulus types,

59

orientation and spatial frequencies converge (Ferrera et al., 1992; Ferrera et al., 1994;

Logothetis & Charles, 1990; David et al., 2006) and estimates of V4 receptive field size overlap

with the extent of crowding in peripheral vision (Smith et al., 2001; Chung et al., 2007). Bias

competition, in which patterns within receptive fields compete to determine the firing rate of

individual neurons, has been localised in areas V4 and higher (Chelazzi et al., 2001; Desimone

& Duncan, 1995; Reynolds et al., 1999); this may underlie crowding as a possible consequence

of competitive feature-integration processing (Nandy & Tjan, 2007). Anderson et al. (2012) cite

how there is a significant increase in population-based receptive field size from V1 to V4

(Amano et al., 2009; Smith et al, 2001), and suggest that crowding effects might accumulate

from pooling of target and flanker stimuli over receptive fields of increasing size. While V4

lesions in macaques have been found to impair boundary discrimination (De Weerd et al.,

1996) and induce a spacing effect on orientation thresholds surrounded by distractors (De

Weerd et al., 1999), one study has found that V4 lesions in macaques did not alter the

amplitude of the crowding effect (Merigan, 2000).

The clear effect of polarity observed in these results (tasks 5 and 6) might suggest which

regions govern some aspects of crowding. Reverse polarity has been shown to segregate

information via ON and OFF pathways at the level of bipolar cells in the outer retina, which

continues to stay relatively distinct until reaching the early visual cortex (Schiller, 1992). This

segregation of information between target stimuli and flankers of reverse polarity presumably

is what alleviates the crowding effect, although there is evidence of interaction between the

two pathways (Harris & Parker, 1995; Wassle & Boycott, 1991). Regarding the neural correlates

of where information from ON and OFF pathways is integrated, Zhou et al. (2000) found that

48% of V1 and 20% V2 and V4 neurons in macaques encoded local contrast polarity, while the

majority of neurons in V2 and V4 encoded direction-specific contrast polarity edges.

Motoyoshi and Kingdom (2007) proposed a two stream model of 2nd-order processing, with

the first stream composed of complex V1 cells sensitive to orientation and the second

composed of lateral geniculate nucleus or V1 blob cells sensitive to polarity. However, similar

temporal limits of the polarity advantage and attention (Chakravarthi & Cavanagh, 2007)

contest the notion of this effect being an exclusively low-level process. Our imaging data do

not provide a means to discriminate between low- and high- theories of the polarity effect, as

measures of this effect were not significantly associated with grey matter volume.

Integration and bottom-up pooling models of crowding (Parkes et al., 2001; Wilkinson et

al., 1997; Greenwood et al., 2009) propose that, while isolated contours are processed by

simple cells, a high concentration of flanking contours in a small region, or “integration field”

(Pelli et al., 2004), leads to a greater response of complex cells which then suppress simple cell

activity within their receptive field area. Histopathological reports of PCA (see chapter 1.2.2.2)

60

have observed a selective vulnerability of certain neurons to AD, particularly cells with long

axonal projections (Morrison et al.,1986a; Lewis et al., 1987) such as V1 Meynert cells in PCA

(Hof et al., 1990), and extensive disruption of feedforward and feedback projections by NFT

and SP in Brodmann areas 17 to 19 (von Gunten et al., 2006), while some have suggested that

AD pathogenesis might spread through cortico-cortical connections (see chapter 1.2.1.1). In

the context of previous pathological findings in PCA and the current data, it is possible that, in

these patients, simple cell activity in areas such as the primary visual cortex may be less

disrupted by AD pathology than complex cells in more downstream visual areas, such as V4.

Cells in V4 which suppress simple cell activity through connections with areas earlier in the

visual system may be particularly susceptible to AD pathology; this would result in a

diminished ability to suppress signals of high contour concentrations within an integration

field. The polarity advantage suggests that visual information in opposite polarity is being

either successfully segregated and resolved within early parts of the visual system or

discriminated through attentional mechanisms. While the current crowding and imaging data

do not favour low- or high-level explanations of the polarity effect, the high distribution of AD

pathology in the posterior parietal relative to primary visual cortex noted by Hof et al. (1997)

and relatively intact visual acuity compared to gravely impaired visuospatial ability

demonstrated by the PCA group on neuropsychological measures, tentatively suggests that the

polarity advantage would more likely be conferred by a relatively preserved early visual system

than parietal-mediated attentional systems (Intriligator & Cavanagh, 2001). However, the

primary purpose of this study was to explore crowding from a particular perspective;

neurodegenerative patients who exhibit prominent flanked letter identification deficits. Given

how PCA patients have distributed brain atrophy particularly in the occipital lobe and parietal

cortex (Lehmann et al., 2011); caution should be exercised in drawing inferences about the

precise neural correlates of crowding in healthy individuals from the current data.


These findings demonstrate the grave inhibitory effect in spatial vision that is of qualitative

similarity to crowding in PCA, as evidenced by consistent effects of spacing across different

categories of flankers and the ameliorating effect of reverse polarity flankers. Performance on

tasks of early visual, visuoperceptual or visuospatial processing cannot account for the

difference in overall letter naming accuracy between the PCA and tAD groups. In addition, we

found an association between measures of crowding and the right collateral sulcus, an

anatomical region that may correspond to area V4. Future investigations include assessing the

evolution of crowding in longitudinal studies; such studies might reveal how emerging

61

enhanced crowding effects impact other aspects of visual processing, including the reading

system.

62

5. READING IN PCA


Reading difficulties are a characteristic and debilitating symptom of PCA (see chapter 2.3).

Various forms of dyslexia have been identified in case studies of PCA; however, group studies

are required to gauge the extent and heterogeneity of reading dysfunction in PCA, and in

particular to clarify the role of other aspects of visual function in influencing reading ability.

Authors of the only group study of reading in PCA (Mendez et al., 2007) stressed that further

studies analysing not only reading accuracy, but reading latency, were necessary to precisely

assess differences in word processing.

The primary focus of the current investigation is upon the effect of perceptual variables on

single word reading ability in PCA. Two perceptual attributes of words - inter-letter spacing and

font size – merit particular consideration given previous evidence of their potential impact on

reading in some individuals with PCA. First, the manipulation of inter-letter spacing in letter

identification paradigms is well known to modulate the size of the so-called ‘crowding’ effect.

Crowding is implicated in reading dysfunction by previous observations that increased inter-

letter spacing facilitates reading ability in dyslexics (Spinelli et al., 2002; Zorzi et al., 2012) and

letter confusability predicts performance in LBL readers (Arguin et al., 2002; Fiset et al., 2005).

If enhanced crowding is a component of dyslexia in PCA, this would raise the possibility that

the conditions in which crowding effects are diminished in flanked letter identification tasks

(increased spacing, reverse polarity flankers (Kooi et al., 1994) might be applied in order to

facilitate whole word reading.

The second perceptual attribute of particular interest in the current investigation is font

size. Many PCA patients describe greater difficulty perceiving large than small objects (perhaps

most strikingly by a patient who was unable to read the headlines of his newspaper but could

read those of another passenger reading the same paper further down the train carriage on

which he was travelling; see Crutch, 2013). Such ‘inverse size effects’ have been formally

documented in a small number of patients with progressive visual disturbance who exhibited

more impaired identification for large relative to small pictures, words and letters presented in

isolation (Saffran et al., 1990; Coslett et al., 1995; Stark et al., 1997). This common clinical

complaint in PCA has been attributed to a reduction in the effective visual field (Russell et al.,

2004; Crutch et al., 2011). However the magnitude, prevalence and specificity of this effect in

PCA remain unknown.

63

The aim of this chapter was to improve the characterisation of single word reading in PCA

by manipulating the perceptual properties of words in a manner predicted to influence reading

accuracy and speed. The perceptual properties examined included inter-letter spacing, font

size, length, case, font type and confusability, and the performance of PCA patients was

compared directly with that of tAD patients and healthy controls. It was hypothesised that

perceptual properties would be a primary determinant of reading ability in the PCA but not

tAD or healthy control groups. A secondary aim was to consider the role of basic visual,

visuoperceptual and visuospatial processing in PCA and tAD patients in order to improve our

understanding of the causal and associative relationships between these different aspects of

visual function and reading ability in PCA.

5.2. METHODS

5.2.1. Participants

The study participants were the same patients and controls from chapter 4. The healthy

controls were matched to the PCA and tAD groups on mean age and years of education, with

the PCA and tAD participants additionally matched for mean disease duration and Mini-Mental

State Examination score (MMSE; see Table 4.1).


PCA and tAD participants were administered a battery of background neuropsychological

tests (chapter 3.4.1) and tests examining early visual, visuoperceptual and visuospatial

processing (chapter 3.4.2). Mean scores for the PCA and tAD groups and an estimate of their

performance relative to normative data sets appropriate for the mean age of each group are

shown in . For a summary of visual assessment and performance on background

neuropsychological tests, see chapter 4.2.2.

5.2.3. Reading assessment

All words in the main and subsidiary reading experiments were presented for an unlimited

duration on a Dell Latitude E5420 laptop at a viewing distance of 50cm. Words were presented

at the centre of the screen within a rectangular fixation box (22.5° in width, 4.3° in height); the

fixation box remained on the screen throughout the experiment (including the inter-stimulus

interval) to help maintain participant fixation within an area proximate to the word stimuli.

5.2.3.1. Perceptual corpus

All participants read aloud a total of 192 single words which involved simultaneous

manipulations of five different perceptual properties:

64

Inter-letter spacing (2 levels: no spaces and 2 blank s p a c e s)

Font Size (2 levels: small and large): words were presented with a visual angle of

letter height subtending 0.5° for small words vs 2° for large words

Case (2 levels: UPPER CASE and lower case)

Length (3 levels: 3-, 5- and 7-letter words)

Mean letter confusability (2 levels: high and low): upper case ratings for each

letter were averaged from the confusability matrices of Van der Heijden et al.,

(1984), Gilmore et al., (1979), Townsend, (1971), and Fisher et al., (1969). Lower

case ratings were averaged from the confusability matrices of Geyer, (1977), and

Boles and Clifford, (1989).

The stimulus pool of 192 words was constructed from 24 8-word sets matched for mean

frequency (CELEX: Baayen et al., 1995), age of acquisition (AoA: Gilhooly & Logie, 1980) and

concreteness (Coltheart, 1981) (see Table 5.1). The structure of the reading sets was such that

the effect of each individual perceptual property upon reading performance could be directly

compared as all other properties and variables were matched. For example, the font size effect

could be readily examined as the small (N=96) and large (N=96) font words were matched for

all background variables and contained an equal number of spaced and unspaced (N=48 each),

upper and lower case (N=48 each), 3-, 5- and 7-letter words (N=32 each) and high and low

confusability words (N=48 each).

All words were presented in fixed random order, divided into two blocks with a break of

approximately 20mins between blocks. All 192 words were presented in Arial Unicode MS.

Table 5.1 Different levels of reading variables for words from the perceptual corpus (N=192) matched for AoA, Concreteness and Frequency.

Variable Level N AoA Concrete FreqConfusability High 96 373 486 36

Low 96 358 498 36Spacing Spaced 96 364 493 35

Unspaced 96 367 491 37Size Large 96 365 491 37

Small 96 366 493 35Case Upper 96 364 498 42

Lower 96 367 486 30Length 3 64 319 528 44

5 64 357 499 327 64 419 456 31

5.2.3.2. Cursive font reading

65

A subset (N=12) of items were selected from the perceptual corpus fulfilling an equal

number of levels of reading variables; these were re-presented in a cursive font (Wrexham

Script) to 22 PCA patients, who were requested to read them aloud. The words were drawn

from the no letter spacing condition and were presented in random order.


5.2.4.1. Background neuropsychology

Differences between the PCA and tAD groups were calculated using a t-test.

5.2.4.2. Behavioural covariates

Composite scores: All raw scores from the Visual Assessment were transformed into a

standardised range (0-100) in which 0 and 100 corresponded to the minimum and maximum

score achieved by any patient (irrespective of PCA and tAD group membership). Transformed

scores in each visual assessment test were averaged within three visual processing domains in

order to give composite scores for the following covariates of interest:

a) Early visual processing (Early: chapter 3.4.2.1): Shape discrimination, Figure-

ground discrimination and Crowding (mean difference in accuracy for number and

shape flankers between spacing conditions [chapter 4]).

b) Visuoperceptual processing (chapter 3.4.2.2): Object decision, Fragmented letters

and Usual and Unusual views.

c) Visuospatial processing (chapter 3.4.2.3): Number location and Dot counting

The raw scores for the following nuisance variables were also transformed into a

standardised range for the PCA v tAD regression analysis: Single letter accuracy, Digit Span

(backwards), A Cancellation time (Willison & Warrington, 1992).

5.2.4.3. Reading latencies

See chapter 3.5 for details of latency recording and determination. Latency data greater

than 2 standard deviations from the mean of each participant were removed. Prior to latency

regression analysis, latency data were transformed using a log transformation due to non-

normal distribution of residuals. In order to examine reading latency data we divided

participants into 2 groups based on accuracy of reading words presented in a normal manner

(small, unspaced words). As latency analysis was restricted to correct responses, reading

latency data were difficult to interpret where there was a high error rate, resulting in a large

proportion of missing data. For this reason, we divided participants into 2 groups based on

accuracy of reading words under normal condition (small, unspaced words). 66

Group 1 (PCA: N=10, mean MMSE=20.7, mean disease duration=3.0yrs; tAD: N=16,

mean MMSE=17.7, mean disease duration=5.1yrs) made no errors on these items,

or did not make enough reading errors to produce significant effects at the

individual level using logistic regression or chi squared tests. The low proportion of

errors allowed for analysis of latency data in this group.

Group 2 (PCA N=16, mean MMSE=16, mean disease duration= 5.8yrs; tAD N=1,

MMSE=14, disease duration= 3.3yrs) made enough errors to allow for meaningful

error analysis. The high proportion of error prevented analysis of latency data in

this group.

5.2.4.4. Statistical analysis

Analyses of accuracy and latency data were conducted using logistic and linear mixed

models respectively; both models used random subject effects and fixed effects of size,

spacing, case, length, confusability, AoA, concreteness, frequency, orthographic

neighbourhood size and word order, with the linear model of latency data also including

accuracy rate as a fixed effect. Analysis of accuracy and latency data was carried out first on

each of the PCA, tAD and control groups. Subsequently, group comparisons between PCA and

tAD performance were conducted using similar logistic and linear mixed models but including

only reading variables that were significant at the PCA and tAD group level, diagnosis and each

of following behavioural covariates: Early visual processing, Early visual processing [excluding

crowding], visuoperceptual processing, visuospatial processing, MMSE, Disease duration, digit

span backwards, A cancellation, single letter naming. Differences in cursive font reading

between PCA and tAD groups were calculated using a Wilcoxon rank-sum test and differences

within groups were calculated using a Wilcoxon signed-rank test.

5.2.4.5. Neuroimaging analysis

MRI acquisition and preprocessing were carried out as described in chapters 3.7.1 and

3.7.3.1 respectively. Associations between regional grey matter volume and reading

performance were assessed using voxel-wise linear regression models. Differences in accuracy

between levels of reading variables which were significant at the group level were used in VBM

regression models (see chapter 3.7.3.1 for covariates).

5.3. RESULTS

5.3.1. Perceptual corpus

5.3.1.1. Overall summary

67

The mean percentage error rates and reading latencies are shown in Figure 5.1. The PCA

group was, on average, significantly less accurate and slower than both the AD group (t=3.5,

p<.005 and t=-2.8, p<.01, respectively) and the control group (t=3.5, p<.005 and t=-3.2, p<.005,

respectively). The AD group showed a trend towards being less accurate than the control

group and was significantly slower (t=-2.0, p=.051 and t=3.2, p<.005, respectively).

5.3.1.1.1. Response accuracy in each group

PCA: PCA patients (N=26; overall accuracy=76.8%, SD=47.1) were less accurate for words

with increased inter-letter spacing (z=-10.2, p<.001), large font size (z=-7.9, p<.001), increased

length (z=-2.8, p<.01), higher AoA (z=-6.9, p<.001) and lower frequency (z=4.5, p<.001). There

were also trends towards lower accuracy for words with greater orthographic neighbourhood

size (z=-1.8, p=.077) and higher concreteness (z=-1.8, p=.084). There were no significant effects

of case (p>.9), letter confusability (p>.3) or word order (p>.8) on accuracy.

tAD: tAD patients (N=17; overall accuracy=98.0%, SD=6.6) were less accurate for words

with higher AoA (z=-4.5, p<.001), lower frequency (z=2.6, p<.01) and for words which were

read later in the assessment (z=-2.8, p<.01).

Controls: There was no effect of any of the variables on reading accuracy at either the

group level (N=13; overall accuracy=99.8%, SD=.04) or individual level.

5.3.1.1.2. Response latency in each group

PCA: PCA patients (N=10; overall mean RT=1.17s, SD=.56) were slower to read words with

increased inter-letter spacing (z=11.8, p<.001), large font size (z=5.8, p<.001), and higher AoA

(z=4.4, p<.001). Overall reading accuracy was also a significant predictor of reading speed (z=-

3.9, p<.001).

tAD: tAD patients (N=16; overall RT=.73s, SD=.16) were slower to read words with

increased inter-letter spacing (z=4.8, p<.001) and higher AoA (z=4.4, p<.001) that were read

earlier in the assessment (z=-2.9, p<.005). There was a trend towards words of lower

frequency being read more slowly (z=-1.8, p=.073). Overall reading accuracy was also a

significant predictor of reading speed (z=-3.9, p<.001).

Controls: The control group (N=14; overall mean RT=.59s, SD=.08) were slower to read

words with higher AoA (z=5.1, p<.001), increased inter-letter spacing (z=3.3, p<.005), lower

letter confusability (z=-2.6, p<.01), decreased font size (z=-2.0, p<.05) that were read earlier in

the assessment (z=-8.2, p<.001). There was also a trend towards smaller words being read

more slowly than larger words (z=-1.9, p=.055). Overall reading accuracy was also a significant

predictor of reading speed (z=-2.4, p<.05).

68

Figure 5.1 Summary of reading accuracy and latencies for the PCA, tAD and Control groups. Asterisks denote a significant effect of each reading variable on reading speed or accuracy (* = p<0.05; **= p<.005). Error bars show standard error for each group mean.

69

5.3.1.2. Between-group comparisons

The proportion of participants in each group whose reading accuracy or speed was

predicted by one or more variables at the individual level is shown in Figure 5.2. Increased font

size reduced reading accuracy or speed in 46% of the PCA group, but increased reading speed

in 18% of the tAD and 7% of the control group.

Figure 5.2 Proportion of participants in each group who show an effect of each variable on either latency or accuracy at the individual level.

5.3.1.2.1. Between-group accuracy

As described above, differences in accuracy between the PCA and tAD groups were

modelled using mixed-effects logistic regression including as covariates reading variables that

were statistically significant at the group level for either PCA or tAD groups. These variables

were spacing, size, order, AoA, frequency and length. There were significant interactions

between diagnosis and spacing (accuracy: z=2.5, p<.05; latency: z=-8.6, p<.001) and diagnosis

and size (accuracy: z=2.8, p<.01; latency: z=2.8, p<.01), with increased spacing and size leading

to lower accuracy in the PCA group; none of these interactions could be accounted for by any

of the behavioural correlates.

There was no evidence of a group difference in overall reading accuracy after adjusting for

participants’ composite scores of the following covariates of interest: visuoperceptual,

visuospatial or early visual function, or the A cancellation task; these scores were better

predictors of reading accuracy than diagnosis whether included individually or simultaneously

in a regression model. The following nuisances variables, including markers of disease severity

(MMSE scores, disease duration), nonvisual indicators of executive function (digit span

70

backwards) or single letter recognition performance could not account for group differences in

accuracy. This suggests that the between-group differences in overall accuracy were driven

particularly by poor early visual, visuoperceptual and visuospatial abilities.

Given the possible role of crowding in limiting reading ability (Crutch & Warrington, 2009;

Yong et al., 2013), we conducted a post hoc analysis evaluating the extent to which crowding

measures accounted for the group difference relative to other measures of early visual

processing. A composite (labelled Early visual processing [excluding crowding]) was calculated

with the omission of the crowding task score; unlike the composite score for Early visual

processing which included measures of crowding, this composite did not account for the

between-group difference. In addition, a likelihood ratio test demonstrated that including the

crowding measure resulted in an improved fit of the model to the data relative to other

measures of early visual processing (Figure-ground discrimination, Shape discrimination), while

including measures of early visual processing other than crowding did not improve how well

the model fitted the data.

5.3.1.2.2. Between-group latency

Differences in latency were modelled using a mixed-effects linear regression analysis of

latency data for the PCA and tAD groups including as covariates reading variables that were

significant at the group level for either PCA or tAD groups (spacing, size, order, AoA). There

was no evidence of a group difference in overall reading speed after adjusting for participants’

composite scores on tests of visuoperceptual function. None of the nuisance variables (disease

duration, composite scores, MMSE, digit span backwards, A cancellation, single letter

processing tasks) could account for group differences in overall reading latency.

5.3.1.3. Individual differences in accuracy and latency

There was a great degree of variability in reading accuracy within the PCA group (range:

19.8% to 99.5%). 23/26 (88.5%) of the PCA patients performed below the 5 th%ile of the control

group’s accuracy and latency data. Of the three patients whose reading ability was within the

normal range of the control group, one patient’s performance could be attributed to his

relatively mild visual symptoms and short disease duration. However, the other two PCA

patients’ performance (FOL & CLA) was achieved despite showing grave deficits in almost all

measures of visual processing, including some additional single letter processing tasks,

suggesting these types of visual processing do not critically determine reading ability (see

chapter 6 for a detailed analysis of FOL and CLA’s reading ability and visual impairment).

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5.3.1.4. Error analysis

An analysis of PCA error types revealed 68.9% visual errors, 19.3% miscellaneous errors,

9.6% phonological errors and 2.1% derivational errors. In 23/26 participants the most common

errors were visual errors; the other three participants only made one error each, with one

making a phonological error and the other two making derivational errors. Within the 23

participants making visual errors, the highest proportions of any other single error type were

observed in the following patients: Participant 8: 57 miscellaneous vs 71 visual errors;

Participant 5: 15 phonological vs 30 visual errors; Participant 4: 3 derivational vs 18 visual

errors.

Of the visual errors, 52.2% of letters read incorrectly were substitution errors, 23.6% were

deletion errors and 24.2% were addition errors. 17.2% of visual errors were neglect errors (Ellis

et al., 1987). Participant 15 made the most errors in the left (n=7) relative to the right (n=1)

side of words, while Participant 24 made the most errors in the right (n=12) relative to the left

(n=3) side of words.

5.3.2. Cursive font reading

The PCA group (N=22) made, on average, more errors reading words in cursive than non-

cursive font (cursive: Mean=68.6%, SD=32.4; non-cursive: Mean=89.3%, SD=15.8: z=-3.71,

p<.0005). The tAD group scored too near ceiling to reveal any such differences (cursive:

Mean=96.1%, SD=7.3; baseline: Mean=97.1%, SD=5.0: p>.8). The PCA group was significantly

worse than the tAD group reading cursive font (z=3.29, p<.005).

5.3.3. Neuroimaging findings

Neuroanatomical associations of reading performance in the PCA group are shown in

Figure 5.3. In order to identify grey matter associations with reading ability, accuracy

discrepancy scores between levels of reading variables which significantly predicted overall

reading accuracy in PCA (Large vs Small, Spaced vs Unspaced, High vs Low AoA, High vs Low

Frequency) were used as behavioural indices. In the PCA group, a greater inverse size effect

(lower accuracy for reading large rather than small font size words)was associated with lower

grey matter volume in t the right superior parietal lobule after correcting for multiple

comparisons over whole brain volume (p<.05). There was no evidence of statistically

significant associations between grey matter volume and the other three variables tested

(spacing, age of onset, frequency) in this group.

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Figure 5.3 Statistical parametric maps of grey matter volume associated with the difference in accuracy between large and small words in the PCA group. The statistical parametric maps are displayed on coronal (A), sagittal (B) and axial (C) sections of the mean normalized bias-corrected images in MNI space; the right hemisphere is shown on the right on coronal and axial sections. Whole-brain analysis found that, within the PCA group, a greater discrepancy in accuracy between large and small words was associated with reduced grey matter volume in the right superior parietal lobule: t-values are displayed below (p<.001 uncorrected) with the FWE corrected (p<.05) peak circled in blue (peak location: x=18, y=-75, z=44). The colour bar shows the t value.

5.4. DISCUSSION

This chapter aimed to better characterise single word reading in PCA and understand the

relationship between reading and other visual processes by examining reading of words in

which inter-letter spacing, font size, length, font type, case and confusability were varied

systematically. On average, the PCA group was considerably less accurate and slower than the

tAD or healthy control group, with the tAD group demonstrating slower but not significantly

less accurate performance than controls. PCA reading accuracy was predicted by the

perceptual variables of letter spacing, size and length plus the lexical variables of age of

acquisition and frequency. Similarly, PCA reading speed was predicted by letter spacing, size

and age of acquisition. The perceptual complexities of cursive font also had an adverse effect

on PCA reading performance whilst overall case and confusability effects were not detected. In

contrast, no perceptual variables were predictive of reading accuracy in the tAD or control

groups (with high or ceiling level performance in most individuals). Letter spacing, age of

acquisition and word order were the only variables which predicted reading speed in both tAD

and control groups.

A further prominent difference between the PCA and tAD groups was the direction of the

size effect. Increasing font size significantly reduced accuracy and/or slowed reading for half

the PCA participants (50%), whilst larger text improved reading speed overall in the healthy

control group and for the minority of tAD participants who showed a size effect (18%). VBM

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whole-brain analysis within the PCA group found that this size effect (less accurate reading of

large than small font size print) was associated with lower grey matter volume in the right

superior parietal lobule.

The impact of perceptual variables on reading performance and preponderance of visual

errors (69%) are unsurprising given that visual impairment is the defining feature of the PCA

syndrome. Of greater neuropsychological interest is the determination of which aspects of

visual processing are associated with this pattern of reading dysfunction, and the interaction

between these processes and text manipulations employed in the current investigation. We

attempted to evaluate which behavioural covariates (including those derived from the detailed

visual assessment) might contribute towards reading dysfunction by accounting for the

discrepancy in performance between the PCA and tAD groups. PCA patients’ inferior reading

accuracy relative to tAD patients could not be accounted for by generic markers of disease

severity (MMSE, disease duration) but was significantly associated with performance on all

three visual covariates (early visual, visuoperceptual and visuospatial processing). However the

early visual processing covariate only predicted accuracy when this composite score included a

measure of visual crowding. By contrast, only poor visuoperceptual ability could account for

PCA patients’ increase in reading latencies relative to tAD patients. The specific effects of letter

spacing and size also could not be accounted for by any of the behavioural covariates,

suggesting it is the combination of visual deficits at multiple levels of the visual system which

give rise to the observed and distinctive pattern of reading seen in PCA.

Before considering the overall classification of reading impairment in PCA, we discuss

possible explanations for the considerable impact firstly of letter spacing and secondly of font

size upon patients’ reading of the current set of perceptually manipulated words. First, letter

spacing was included as one of the perceptual text manipulations in the current investigation

because previous case studies had shown its influence upon both single letter and word

identification (Crutch and Warrington, 2009). This study revealed optimal letter spacing is

partially task dependent. With flanked letter identification, performance was significantly

improved by inserting 2 spaces between letters (mean centre-to-centre spacing = 1.52˚) as

compared with normal presentation text (0 spaces; mean centre-to-centre spacing = 0.86˚).

With word-reading a U-shaped function was obtained; performance improved when inter-

letter spacing was increased from 0.78˚ to 1.21˚, an effect attributed to a reduction in

crowding, but declined again when spacing increased to 2.27˚, because increasing spacing past

a given point damages whole-word form and parallel letter processing. In the current

investigation, values of 0.86˚ (unspaced) and 1.52˚ (spaced) were selected to maximise

individual letter identification ability. However the results, which show significantly worse PCA

reading performance in the spaced condition, suggest that any benefits in reduced crowding of

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individual letter identities was outweighed by inevitable increases in the visual angle

subtended by the outmost letters within perceptually longer words. Nonetheless, PCA patients

showed significantly greater spacing effects than the tAD or control groups, raising questions

about the mechanism underpinning the ability to read spatially distributed words.

It has been proposed that failure to achieve parallel letter processing due to presentation

of text in unfamiliar formats invokes involvement of dorsally-mediated reading strategies such

as serial letter scanning (Hall et al., 2001; Braet & Humphreys, 2007). Reading words with

increased inter-letter spacing has been associated with the engagement of parietal lobes in

healthy individuals (Cohen et al., 2008), and double spacing has been found to disrupt reading

in a patient with occipitoparietal lesions (Vinckier et al., 2006). It is possible that reading of

spaced words in PCA patients demands support from dorsally-mediated reading strategies and

involves greater visuospatial demands; if so, the vulnerability of dorsal systems in PCA (e.g.

McMonagle et al., 2006; Lehmann et al., 2011) would help to account for these patients’

particularly poor reading ability for spaced words. The failure of dorsal-parietal systems in

reading unfamiliar text may also contribute towards disproportionately poor reading of cursive

font in PCA (chapter 5.3.2; De Renzi, 1986), especially as difficult-to-read handwriting has been

shown to activate parietal networks in healthy individuals (Qiao et al., 2010), and may account

for previous reports of poorer reading of words of greater perceptual complexity (Mendez &

Cherrier, 1998; Mendez, 2001). Another possibility is that impaired reading of words with

increased inter-letter spacing (or in cursive font) might result from a ventral deficit, possibly a

disrupted word-form system, which could accommodate word processing under familiar but

not unfamiliar presentation.

Turning secondly to the impact of font size, the PCA group’s better reading performance

with small rather than large words was not only counter-intuitive but also in direct contrast to

size effects seen overall in the control group and in a small number of tAD patients. This size

effect may be attributable to what has been termed a (spatial) restriction in the effective visual

field, which occurs in right-brain-damaged individuals when the processing demands of more

centrally-presented stimuli/tasks exhaust available attentional capacity (Russell et al., 2004,

2013). In the current task, though matched for overall form, large font words extend further

into the periphery than small print words. (This is also the case for spaced as compared with

unspaced words as varied in the inter-letter spacing condition). As noted above, grey matter

volume analysis in the PCA group found an association between the discrepancy in accuracy

between large and small words and grey matter volume in the right superior parietal lobule.

This localisation is in keeping with previous studies of peripheral spatial attention. Parieto-

occipital damage has been associated with reduced perception and localisation within the

visual periphery (Michel & Henaff, 2004; Rossetti et al., 2005; Pisella et al., 2009), and greater

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activation in the superior parietal lobule has been found for stimuli in peripheral vision which

were actively attended during an orientation discrimination task (Vandenberghe et al., 1996)

or when participants shifted attention towards peripheral vision relative to maintaining

attention at fixation (Corbetta et al., 1993).

A potentially complementary explanation of the size effect in PCA is that reading larger

words increases the demand for multiple saccades, spatial shifts in attention and/or

visuospatial ability. fMRI studies have identified saccade-related activation in the superior

parietal lobule (Sereno et al., 2001; Medendorp et al., 2005; Merriam et al., 2003), while the

superior parietal cortex has been associated with shifting rather than sustained attention

(Molenberghs et al., 2007; Kelley et al., 2008; Vandenberghe et al., 2001a), and has been

suggested as a possible anatomical locus for visuospatial attention along with the

supramarginal gyrus and intraparietal areas (Pierrot-Deseilligny et al., 2004). As previous

studies have identified reaching, perceptual and localisation deficits in the peripheral vision of

superior parietal lobule lesion patients maintaining central fixation (Wolpert et al., 1998;

Pisella et al., 2009; Rosetti et al., 2005), it is unlikely that deficits in integrating information

across multiple saccades can completely account for the inverse size effect.

Beyond the impact on single word recognition in PCA, the inverse size effect documented

in these patients also has implications for reading at and above the sentence level. Any

restriction in the effective visual field would limit the perceptual span and parafoveal preview

benefit (Hyona et al., 2004; Rayner, 1998; McDonald, 2006) and might inhibit the ability to

move between consecutive lines of text, as has been previously observed in PCA (Ross et al.,

1996) and in a patient with Balint’s syndrome (Michel & Henaff, 2004). An interesting

comparison group is patients with retinitis pigmentosa, a condition involving a progressive

pigmentary degeneration of the retina, often resulting in restricted central area of vision, or

“tunnel vision” (Madreperla et al., 1990). Increased reading speed has been observed in

patients with retinitis pigmentosa when reading words of reduced font size (Sandberg et al.,

2006) and words presented in negative polarity, i.e. white text on a black background (Ehrlich,

1987). Reverse polarity presentation may be a particularly promising manipulation, given its

ameliorating effect on crowding in both PCA patients and healthy individuals (Crutch &

Warrington, 2007a; Crutch & Warrington, 2009; Kooi et al., 1994; Chakravarthi & Cavanagh,

2007). Presentation methods that reduce the need for visuospatial processing in reading, such

as rapid serial visual presentation or horizontally scrolling text (Leff & Behrmann, 2008) may be

also beneficial in limiting visual disorientation.

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The current findings suggest that not one but a combination of deficits are associated with

the acquired peripheral dyslexia observed in PCA. Overall, poor reading accuracy is associated

with deficits in early visual processing, particularly including enhanced visual crowding, and

poor visuoperceptual and visuospatial ability. However, these deficits are not causally related

to a universal impairment of reading (as shown by preserved reading for small, unspaced

words in some patients) but rather are (con)text specific (being particularly evident for large,

spaced or crowded lengthy words). The vulnerability of dorsal systems in PCA may account for

disproportionate difficulties reading text which eludes ventrally-mediated parallel letter

processing; that is, words written in unfamiliar formats, such as text with double spacing or

cursive font. Poor visuospatial ability and restrictions in the effective visual field as a

consequence of parietal atrophy may also explain the inverse size effect. The profile of reading

impairment in PCA does not align with any classical subtypes of peripheral dyslexia (e.g. pure

alexia, neglect dyslexia), underlining why previous investigators have coined the term

“apperceptive alexia” to capture the combination of contributory deficits (Mendez et al.,

2007). However, further to the suggestions of Mendez et al. (2007) that apperceptive alexia

might be attributable to visuoperceptual and visuospatial deficits, the current findings also

indicate the role of early visual processing deficits, particularly enhanced visual crowding, in

contributing towards poor reading.

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6. CASE STUDIES: INTACT READING IN PCA


While the previous chapter demonstrated the prevalence of reading impairment in PCA, it

also identified the heterogeneity of reading performance (see chapter 5.3.1.3). Interestingly,

two individuals with PCA (FOL & CLA) exhibited remarkably preserved whole word and letter

reading despite demonstrating profound visual deficits on a range of neuropsychological tests.

Patterns of reading ability in FOL and CLA provide a means to evaluate whether impairments in

specific domains of visual processing domains necessitate reading dysfunction.

Whether visual processing deficits play a causal role in acquired dyslexia has implications

for different accounts of LBL reading. A classic view of LBL reading is that it indicates

destruction or inaccessibility of a visual word form system (Warrington and Shallice, 1980).

Subsequent imaging studies have identified an area within the left fusiform gyrus which is

specialised for letter and word recognition and which may constitute the visual word form area

(VWFA; Cohen et al., 2000). This area has been found to selectively respond to words rather

than objects of matched visual complexity (Szwed et al., 2011); it invariantly responds to

letters in upper and lower case (Dehaene et al., 2004), printed and handwritten words (Qiao et

al., 2010) and damage to this area often results in LBL reading (Binder and Mohr, 1992; Leff et

al., 2001; Cohen et al., 2004; McCandliss et al., 2003; Pflugshaupt et al., 2009). More recently,

the attribution of LBL reading to a specific word form deficit has been challenged on two main

grounds, namely that the condition and its characteristic word length effects can be accounted

for by a general visual deficit and/or a letter identification deficit.

A general visual account of LBL reading suggests that reading, as a complex behaviour, can

be disrupted by even the most subtle low-level visual deficits (Friedman and Alexander, 1984;

Farah and Wallace, 1991; Price and Devlin, 2003), which propagate by a cascade process to the

level of lexical and semantic representations within the visual system (Behrmann et al., 1998).

A number of single case and case series studies of LBL readers have reported associated

impairments on a range of perceptual tasks involving non-orthographic stimuli. For example,

Friedman and Alexander (1984) identified a LBL patient who was impaired on tasks of letter

identification, object recognition and had an elevated threshold relative to controls in

detecting briefly presented pictures. Furthermore, Farah and Wallace’s (1991) patient TU

performed poorly on tasks involving the perception of non-orthographic stimuli under time

constraints; these results were replicated by Sekuler and Behrmann (1996). More recently,

Mycroft et al. (2009) found that seven LBL readers were similarly impaired for both linguistic

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and non-linguistic stimuli on tasks of visual search and matching, and the LBL group as a whole

performed worse than the control group on a task of visual complexity. By contrast, there are

documented cases of LBL readers with no discernible impairment in letter identification speed

or the identification of rapidly displayed letters (Warrington and Langdon, 2002; Rosazza et al.,

2007) or in a range of tasks assessing visual processing, such as complex picture analysis, visual

short term memory and picture recognition from unusual views (Warrington and Shallice,

1980). However, proponents of pre-lexical theories of LBL reading tend to dismiss such cases

as reflecting insufficiently sensitive assessment of visual processing skills or the use of non-

reading tasks which are not making demands comparable to those involved in reading

(Behrmann et al., 1998; Patterson, 2000).

Alternative accounts attribute LBL reading to an impairment of letter activation. Some

accounts suggest that the critical letter processing deficits may be restricted to the

identification of individual letters (e.g. Arguin and Bub, 1992, 1993; Reuter-Lorenz and Brunn,

1990; Behrmann and Shallice, 1995). Other accounts ascribe LBL reading to a deficit in the

mechanisms responsible for rapid, parallel processing of letters, leading to the less efficient

serial encoding of the component letters of a word (Patterson and Kay, 1982; Behrmann et al.,

2001; Cohen et al., 2003). One such possible mechanism is the inability to use the optimal

spatial frequency band for letter and word recognition, with letter confusability effects

emerging at lower spatial frequencies (Fiset et al., 2006). It should also be noted that some

authors have argued that deficits in letter processing are common to all LBL readers, while

speculating that such deficits may be due to a more basic visual impairment (Behrmann et al.,

1998).

One observation regarding the general visual account of LBL reading is that the evidence

base is largely associative in nature; that is, most studies claim that the co-occurrence of the

characteristics of LBL reading (i.e. accurate but slow reading, with prominent word length

effects) and a particular deficit (e.g. impaired perception of non-lexical stimuli) confers support

for their chosen position. In addition, proponents of the general visual impairment account

have claimed support for their position from control brain-damaged patients who show the

complementary association of no perceptual deficit and no impairment of reading (e.g. patient

OL; Mycroft et al., 2009). By contrast, in the current study it is argued that such evidence does

not prove a causal link between general visual deficits and LBL reading behaviour. This is

achieved by presenting evidence from two patients who exhibit profound visual dysfunction in

the presence of accurate and rapid word reading. Rather than demonstrating a selective

impairment to the visual word form system in the absence of general visual dysfunction, these

patients’ reading abilities are remarkably preserved despite grave and diffuse impairments to

their visual system.

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The main aim of this study was to evaluate the hypothesis that general visual dysfunction

necessarily leads to LBL reading. The general visual account predicts that basic visual

impairments should be associated with slow, inefficient reading, with prominent word length

effects characterised by considerable increases in reading latency with each additional

constituent letter. Contrary to these predications, we report two PCA patients who

demonstrate highly accurate and rapid reading with equivocal or absent word length effects

despite profound visual dysfunction. This preservation of reading skills was observed despite

significantly impaired performance on non-lexical chequerboard perception and rapid serial

visual letter presentation tasks, failure on which has previously been linked to LBL reading by

proponents of the general visual accounts. The reported distinction between intact reading

and impoverished visual function raises questions as to whether the evidence cited for general

visual accounts of LBL reading truly reflects causation, or merely the association of deficits

elicited by damage to contiguous brain regions.

6.2. METHODS

6.2.1. Participants

Study participants were selected for the current study following the observation of

visuoperceptual and visuospatial impairment but preserved performance on a screening test

for reading (CORVIST- see background neuropsychology) and when reading small, unspaced

words (see chapter 5.3.1.3).

FOL is a 58 year old right-handed retired administrator for the NHS who was referred to

the Specialist Cognitive Disorders Clinic at the National Hospital of Neurology and

Neurosurgery in 2010 with a 4-year history of progressive visual impairment. When seen at

clinic she described “looking but not being able to see”, with early symptoms of visual

dysfunction including difficulty in locating objects in front of her and problems reading clocks.

FOL fulfilled the PCA behavioural criteria (failing tests of arithmetic and spatial and object

perception) but her spelling was well preserved. Her memory ability, while not robust, was still

within normal limits. Her general neurological examination was normal. Brain MRI (see )

showed predominantly biparietal atrophy somewhat more marked on the right with relative

preservation of the hippocampi, medial temporal lobe structures and no significant vascular

burden.

CLA is an 86 year-old right-handed retired classics teacher who was first seen at the

National Hospital in January 2011 as part of a clinical assessment. Presenting symptoms

included being unable to judge depth and movement and failing to see objects in front of her.

CLA fulfilled the PCA criteria, failing tests of spatial and object perception, but spelling and

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arithmetic were well preserved and she demonstrated strong performance on a test of verbal

memory. Her general neurological examination was normal. Brain MRI (see )

Figure 6.1 Neuroanatomical features in FOL and CLA: representative brain MRI sections

for each patient show the distribution of atrophy in each case. The left hemisphere is shown on the left for all coronal and axial sections (left panels in each case). Sagittal sections (right panels in each case) are through the left (Lh) and right (Rh) cerebral hemispheres. For purposes of comparison with previous functional imaging studies of the visual word form

81

area (VWFA), these brain volumes have been transformed into MNI standard streotactic space; the white arrow indicates the mean activation peak of the VWFA (x=-44, y=-58, z=-15) constituted from 17 functional imaging studies (Jobard et al., 2003).

revealed bilateral atrophy of both posterior cerebral hemispheres, more prominent on the

right with anterior extension into bilateral peri-Sylvian cortices and the inferior and medial

right temporal lobe but relative sparing of the left inferior temporal lobe; additional mild

frontal lobe atrophy was evident bilaterally, and there was a mild to moderate degree of small

vessel ischaemic damage.

The distribution of brain atrophy in FOL and CLA was compared with the location of the

visual word form area (VWFA ) in the left posterior fusiform gyrus, determined from previous

functional imaging studies (Jobard et al., 2003). There was sparing of grey matter in the region

of the VWFA relative to more dorsal brain regions (FOL) and the contralateral temporal lobe

(CLA) .

Nine control participants completed all tasks administered to the PCA patients. The

controls were split into two groups appropriate for each patient, matched as closely as

possible for age, gender and years of education (FOL controls [N=4]: mean age 58.4yrs [range

56-60], all female, mean education: 16 yrs; CLA controls [N=5]: mean 83.5yrs [range 81-84], all

female, mean education: 14.8 yrs).


In addition to the behavioural screening tests, CLA and FOL completed a battery of

background neuropsychological tests. Their scores on each task and an estimate of their

performance relative to appropriate normative data sets are shown in Error: Reference source

not found. On the Mini Mental State Examination (MMSE), FOL performed below the normal

range. She performed well on tests of concrete synonyms, cognitive estimates and naming,

and her praxic skills were only mildly impaired to verbal command. She made no errors on a

screening test for reading and one error on a nonword reading task.

CLA performed within the normal range on the MMSE. Her concrete synonym

comprehension performance was within normal limits but she was impaired on tests of

cognitive estimates and naming. CLA had some difficulties on a test of praxic skills, specifically

in pantomiming using a toothbrush and hammer. CLA made no errors on a screening test for

reading and three errors on a nonword reading task.

6.2.3. Experimental procedures

6.2.3.1. Visual assessment

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FOL and CLA were administered measures of early visual, visuoperceptual and visuospatial

processing (see chapter 3.4.2). In addition, both FOL/CLA and their respective control groups

*Behavioural screening tests supportive of PCA diagnosis. 1 Mini-Mental State Examination (MMSE: Folstein, Folstein & McHugh, 1975). 2 Warrington (1996). 3 Warrington, McKenna and Orpwood (1998). 4 Shallice and Evans (1978). 5 Graded Difficulty Arithmetic test (GDA; Jackson & Warrington, 1986). 6 Graded Difficulty Spelling Test (GDST; Baxter & Warrington, 1994). 7 Crutch

83

Table 6.1 Neuropsychological scores of FOL/CLA relative to normative data

TestMax Score

Raw Score

FOL CLA Norms/commentBackground Neuropsychology

MMSE 1 30 24 27 FOL: ImpairedShort Recognition Memory Test2 for words*(joint auditory/visual presentation)

25 21 24 Within normal range

Concrete Synonyms test3 25 20 24 Within normal rangeNaming (verbal description) 20 19 11 CLA: <1st %ile; FOL: normal limitsCognitive estimates4 (error score) 30 1 17 CLA: <1st %ile; FOL: normal limitsCalculation (GDA5)* 24 0 8 FOL: <1st %ile; CLA: normal limitsSpelling (GDST6- Set B, first 20 items)* 20 18 19 Within normal range

Gesture production test7 15 14 9 -Digit span (forwards) 12 9 10 FOL: 25th-50th %ile; CLA >50th %ileMax forwards 8 7 7Digit span (backwards) 12 4 5 Within normal range Max backwards 7 3 4

CORVIST8 reading test 16 16 16 -

Visual AssessmentEarly visual processing

Visual acuity (CORVIST): Snellen 6/9 6/9 6/18 CLA: near-normal; FOL: normal

Figure-ground discrimination (VOSP9) 20 17 14 <5th %ile

Shape discrimination10

Easy (oblong edge ratio 1:1.63) 20 19 20Healthy participants with normal vision make

no errors on difficult versionModerate (oblong edge ratio

1:1.37) 20 19 19

Difficult (oblong edge ratio 1:1.20) 20 9 14Hue discrimination (CORVIST) 4 2 2 Impaired

Visuoperceptual processingObject Decision (VOSP)* 20 15 7 CLA: <5th%ile; FOL: 10th-25th%ileFragmented letters (VOSP) 20 5 0 <5th %ileUnusual and usual views11: Unusual 20 5 0 <1st %ile

Unusual and usual views11: Usual 20 18 10 <1st %ile

Visuospatial processingNumber location (VOSP)* 10 5 5 <1st %ileDot counting (VOSP) 10 7 10 FOL: <5th%ile; CLA: normal limitsA Cancellation12: Completion time 90s 60s 50 <5th %ileA Cancellation12: Number of letters missed 19 1 0 -

Graded nonword reading test13 25 24 22 -

(unpublished). 8 Cortical Visual Screening Test (CORVIST; James, Plant & Warrington, 2001). 9 Visual Object and Space Perception Battery (VOSP; Warrington & James, 1991). 10 Efron (1968): oblong edge ratio 1:1.20. 12 Warrington and James (1988). 11 Willison and Warrington (1992). 13 Snowling et al., (1996).completed an experiment involving 24 chequerboard patterns developed by Ichikawa (1985)

and employed in previous investigations of pure alexia (Mycroft et al., 2009). Chequerboards

were composed of either 3×3 or 4×4 grids with the height/width of individual grid squares

being kept constant (subtending 0.5° of visual angle at a viewing distance of 50cm). Each

chequerboard comprised a pattern of white and black squares, constructed so as to avoid

obvious patterns and many squares of the same colour being adjacent to one another (see

Table 6.4) Each chequerboard pattern was paired once with itself and once with another

pattern that differed by a single square. This produced a total of 48 pairs, with each pair

consisting of chequerboards being presented one above the other at the centre of the screen.

Each pair of chequerboards was preceded by a fixation point presented for 1000ms.

Participants were asked to decide whether the chequerboards in each pair were the same

or different as quickly and accurately as possible by verbal response. The pairs remained on

screen until a response was given and there was a 1000ms inter-trial interval. One block of 6

practice trials preceded 2 blocks of 24 test trials. Each block contained an equal number of 3×3

and 4×4 chequerboards.

6.2.3.2. Word reading

All participants were requested to read aloud 3 corpora yielding a total of 250 words. Each

corpus was as follows:

i. Brown and Ure words (Brown & Ure, 1969): 72 words taken from the Brown and

Ure (1969) corpus, which was composed of a subset of words at three levels of

length (4, 6 and 8 letters) matched on two levels of frequency and two levels of

concreteness.

ii. Schonell reading list (Schonell & Goodacre, 1971): 100 words of decreasing

frequency, ranging in length from 3 to 14 letters.

iii. Coltheart regular/irregular words (Coltheart et al., 1979): 39 pairs of regular and

irregular words ranging from 3 to 8 letters long, matched for word frequency

(Kucera & Francis, 1967), concreteness, part of speech and number of letters,

syllables and morphemes.

All words were presented in Arial Unicode MS for an unlimited duration within a

rectangular fixation box at the centre of the screen; letter height corresponded to a visual

angle of 1.2° from a viewing distance of 50cm.

84

6.2.3.3. Single letter processing

A series of letter processing tasks were administered, with all stimuli presented within a

central fixation box to ameliorate the effects of visual disorientation:

i. Letter naming - All participants were requested to read the letters of the alphabet,

excluding I, J, O, Q, W and X, in upper case. Letter height corresponded to a visual

angle of 1.2° from a viewing distance of 50cm.

ii. Rapid serial visual presentation (RSVP) letter/number identification - Letter strings

of six letters each were presented serially in the same central spatial position,

without an interval between successive letters, as described by previous studies in

LBL reading (Warrington and Langdon, 2002; Behrmann and Shallice, 1995). There

were three exposure durations of 150, 200 and 250 ms per letter; all participants

were tested in nine blocks of 10 strings, with three blocks at each of the three

durations arranged in a Latin square design. Before the presentation of each letter

string, a target letter was named; participants were asked to decide whether the

target letter was present in each string. The target item occurred randomly in

positions two to five in each string, with the target item being present in half of all

trials. In a subsequent experiment, a similar test was administered using Arabic

numeral strings rather than letter strings. The number of trials was halved,

resulting in nine blocks of 5 strings.

iii. Flanked letter identification - All participants were requested to name letters

under letter, shape and number flanker conditions. Flanked letter stimuli were the

same as in chapter 4 (see tasks 2-4: Figure 4.1).


6.2.4.1. Reading latency analysis

See chapter 3.5 for details of latency recording and determination. Analyses of the Brown

and Ure (1969) and Schonell (Schonell and Goodacre, 1971) corpora were conducted using

multiple linear regression, as neither FOL nor CLA made enough errors to allow the use of a

logistic regression model. The regression model was used to relate response latencies to the

effects of frequency and length. Overall regression analysis was conducted using a linear mixed

model, which was fitted to reaction times with random subject and item effects and fixed

effects of length, diagnosis, their interaction and frequency.

85

Comparisons between both patients and their matched control groups were conducted

using a modified t-test developed by Crawford and Garthwaite (2002) specifically to identify

abnormality of test scores in single case studies. Comparisons between differences in a

patient’s scores on two tasks and differences between the control groups’ performance on the

same two tasks were conducted the Revised Standardised Difference Test (RSDT) developed by

Crawford and Garthwaite (2005). All reported p values represent one-way probability.

6.3. RESULTS

6.3.1. Visual assessment

The results of patients FOL and CLA on each early visual, visuoperceptual and visuospatial

processing task are shown in Error: Reference source not found, together with the

corresponding normative data. FOL failed every single early visual, visuoperceptual and

visuospatial task administered with the following two exceptions: visual acuity and Object

decision. On the chequerboard Experiment, FOL exhibited significantly poorer performance

than controls (t=-32.7, p<.001) on 3×3 and 4×4 chequerboards (15/24 v 14/24, respectively)

and disproportionately identified chequerboards as being the same (96%) rather than different

(25%) (d prime score=1.057).

CLA was also impaired on all tests of early visual processing except for only mild weakness

on a test of visual acuity. She was also impaired on all visuoperceptual tasks and all but one

visuospatial task (Dot counting). On the chequerboard experiment, CLA exhibited significantly

poorer performance than controls (t=-27.7, p<.001) on 3×3 and 4×4 chequerboards (16/24 v

15/24, respectively) and was more likely to identify chequerboards as being the same (71%)

rather than different (58.5%) (d prime score=.759).

6.3.2. Word reading

The total (and percentage) correct responses and mean (and SD) reading latency data for

word reading performance by FOL, CLA and their relevant control samples are shown in Table

6.2.

i. Brown and Ure words - FOL made no error responses, while her control group

made one error overall. There was no significant difference between FOL’s

response latencies and those of the control group. Regression analysis found a

significant effect of length (t=2.2, p<.05), but not of frequency (t=-.89, p>.3) or

concreteness (t=-1.54, p>.1) on FOL’s response latencies. When examining control

responses at the group level, neither frequency nor length was significantly related

86

to response latencies, although length was related to response latencies in one

individual control.

Neither CLA nor her control group made any error responses. There was no

significant difference between CLA and her control group’s response latencies.

Regression analysis found no significant effects of length, frequency or

concreteness on the response latencies of CLA or her controls.

ii. Schonell reading list - FOL made three error responses; two of these were

regularisation errors (colonel, homonym), with the remaining error being a

visually-based neologism (ineradicableinerascible). The control group overall

made three errors. FOL showed a trend towards being less accurate and having

longer latencies relative to controls; however, neither of these effects reached

formal levels of significance. Regression analysis found a significant effect of length

but not of frequency on response latencies for FOL (t=4.01, p<.001) and at the

group level for her matched controls (t=4.18, p<.001).

CLA again made no error responses; the control group made a total of five

errors between 3 participants. There was no significant difference in response

accuracy between CLA and her control group. When examining response latencies,

CLA was significantly slower than controls. Regression analysis found a significant

effect of length but not of frequency on response latencies for both CLA (t=2.11,

p<.05) and, at the group level, her matched controls (t=5.4, p<.001).

iii. Coltheart regular/irregular words - FOL made only one visual error response

reading irregular words (GAUGEGAUCHE). The control group made no errors;

consequently it was not possible to use a modified t-test for error analysis. There

was no significant difference between FOL and her control group in the size of

regularity effect (Revised Standardized Difference Test: t=0.4, p>0.4).

Neither CLA nor the control group made any errors. CLA’s response latencies

were significantly longer than those of controls for both regular and irregular

words. The Revised Standardized Difference Test identified CLA as being

significantly slower for irregular than regular words relative to her control group

(t=5.1, p<0.005).

87

Table 6.2 Accuracy and latency data for FOL, CLA and relevant control groups on the word reading experiments.

1Brown and Ure

(1969). 2Coltheart et al. (1979). 4Schonell and Goodacre (1971).

88

Reading Skills

FOL Control Group difference CLA Control Group difference

1. Brown and Ure

words

Total

correct

72/72

(100%)

71.8/72 ± .4

(99.7% ±.6)

- 72/72

(100%)

72/72

(100%)

-

RT .60 ± .11 .51 ± .04 t=1.9, p=.08 .64 ± .12 .57 ± .06 t=1.2, p>.1

2. Schonell words Total

correct

97/100

(97%)

99.3/100 ± 1.0

(99.3% ± 1.0)

t=-2.1, p=.063 100/100

(100%)

99/100 ± 1.2

(99% ± 1.2)

t=2.8, p<.05

Mean rt .72 ± .22 .54 ± .07 t=2.2, p=.056 .78 ± 0.31 .60 ± .06 t=2.8, p<.05

3. Coltheart words Total

correct

77/78

(98.7%)

78/78

(100%)

- 78/78

(100%)

78/78

(100%)

-

RT (Regular) .54 ± .08 .48 ± .04 t=1.2, p>.1 .72 ± .34 .53 ± .05 t=10.5, p<.001

RT

(Irregular)

.59 ± .14 .51 ± .05 t=1.3, p>.1 .92 ± .81 .55 ± .05 t=10.5, p<.001

Overall reaction time and word length analysis - Reading latencies for words of up to 12

letters, summing across the 3 reading corpora, are shown in Error: Reference source not

found. When examining the response latencies of FOL and her control group, there was a main

effect of length (z=2.5, p<.05) but not diagnosis (p>.3). There was a significant interaction

between diagnosis and length (z=2.3, p<.05). However, there was significant variation in the

size of word length effect within the control group; this was demonstrated by fitting the same

model to the control data, plus a second model extended to allow length effects to vary by

control participant. Comparison of the two models by a likelihood ratio test identified a highly

significant difference in length effects between controls (p<.0001).

When examining reading latencies of CLA and her control group, there was a main effect of

length on reading latencies (z=3.1, p<.005), but only a trend towards a main effect of diagnosis

(z=1.9, p=.06). There was no interaction between diagnosis and length (p>.2).

89

Figure 6.2 Mean reading latencies for words of different length across all corpora for (A) Patient FOL and her matched controls, and (B) Patient CLA and her matched controls, with estimated upper and lower control confidence intervals.

6.3.3. Single letter processing

The total (and percentage) correct responses and mean (and SD) latency data for letter

processing performance by FOL, CLA and their relevant control samples are shown in

Table 6.3.

i. Letter naming - Neither FOL nor her control group made any error responses.

There was no significant difference between FOL’s reading latencies and those of

her control group. Neither CLA nor her control group made any error responses.

However, CLA was significantly slower than her control group.

Rapid letter/number identification: Letters – Overall letter identification was

significantly lower for FOL than her controls; this overall effect reflected

significantly lower performance when stimuli were presented for 150ms but not

90

200 or 250ms. CLA also made significantly more errors overall, and specifically

when stimulus duration was 150ms or 250ms but not 200ms.

ii. Numbers – Overall, FOL scored significantly lower than her control group. This

difference was significant for numbers being displayed for 150ms, but ceiling

effects in the other temporal conditions prevented analysis using a modified t-test.

There was no significant difference between CLA and her controls for stimuli at any

of the tested exposure durations.

iii. Flanked letter identification – See Figure 6.3 for FOL and CLA’s naming latencies.

Neither FOL nor her control group made any errors on the flanked letter

identification tasks. Summing across all conditions, FOL was slower than her

control group. Target-flanker spacing had a significant effect on response latency

in only one flanker condition, where target letters were named slower with spaced

than condensed number flankers (z=-2.2, p<.05). There was a trend towards there

being an interaction between flanker condition and spatial condition (t=1.9,

p=0.08). As with FOL, neither CLA nor her control group made any errors. Summing

across all conditions, CLA was slower than her control group. Target-flanker

spacing had a significant effect upon response latency in one flanker condition,

where target letters were named slower with condensed than in spaced letter

flankers (z=2.0, p<.05). There was also one main effect of flanker type, with CLA’s

responses in the letter flanker condition significantly slower than in the number

flanker condition (z=2.5, p<.05). Overall, there was a significant interaction

between the group x spacing condition, with target letters being named more

slowly with condensed rather than spaced flankers relative to controls (t=7.5,

p<.001).

91

Table 6.3 Performance on tests of letter processing. Letter Identification

Example stimuli

F

O

L

Cont

rol

Grou

p

D

i

f

f

e

r

e

n

c

e

C

L

A

Cont

rol

Grou

p

Difference

Single letter reading Tota

20/2

20/20

(100%)

-20/2

20/20

(100%)

-

Mea

.59 ±

.48 ± .06

t=1

.82 ±

.56 ± .04

t=5.4, p<.005

Temporal Masking Tot

25/

31.5/35

± .6

t=-

22/

30.6/35 ± .9

t=-8.8, p<.001

Rec

62m

16ms _62m

22ms ± 8.8

_

Rapid Identification:

letters

150

25/

28.5/30

± .60

t=-

25

27.8/30

± .46

t=-5.5, p<.005

200

28/

28.25/30 ± .78

t=.

27

28.2 ± .74 t=-1.5,p >.1

250

28/

28.25/30 ± .78

t=.

26

28.8 ± .42

t=-6.1,p<.005

Tot

82/

88/90 ± 1.4

t=-

78/

87.2/90 ± .4

t=18.8, p<.001

Rapid Identification: 150

13/

14.75

± .50

t=-

14/

14.6/15

± .89

t=-.6, p>.2

92

numbers 200

14/

15/15 -

15/

14.4/15

± .89

-

250

15/

15/15 -

12/

14.6/15

± .89

t=-2.6, p<.05

Tot

42/

44.8/45 ±.5

t=-

41/

43.6/45 ± 2.6

t=-0.9, p>.2

Flanked letter

Identification

Tot

72/

72/72

(100

-72/

72/72

(100

-

Mea

1.20

.48 ± .12

t=5

1.14

.50 ± .05

t=11.2, p<.001

Group

t=1.9

t=7.5, p<.001

93

Table 6.4 Performance on tests of visuoperceptual function.

Visuoperceptual SkillsExample stimuli

F Contro Di C Contro Di

94

OL

l Group

fference

LA

l Group

ffere

nce

Chequerboard Experiment

Total correct

29/48 (60.4%)

47.3/48 ± .5

(98.4% ± 1.0)

t=-32.7, p<.001

31/48 (64.6%)

47.6/48 ± .6

(99.2% ± 1.1)

t=-27.7, p<.001

95

Figure 6.3 Mean response latencies for target letters under different flanking conditions (letter, shape and number) and spatial conditions (crowded and spaced) for (A) Patient FOL and her matched controls, and (B) Patient CLA and her matched controls.

6.4. DISCUSSION

This chapter describes two PCA patients, FOL and CLA, who demonstrate preserved

reading ability in spite of profoundly impaired visual function. Both patients were

impaired on neuropsychological tests of early visual, visuoperceptual and visuospatial

processing. Despite these grave visual impairments, both patients were able to read

aloud words with perfect to near-perfect accuracy. Reading performance was also

rapid, with FOL’s latencies not significantly different to controls on any of the 3 tests of

reading, and CLA significantly slower on 2/3 sets but showing only a trend to slower

reading overall once frequency was taken into account. In addition, word length

effects were equivocal or absent, with FOL showing a modestly increased length effect

relative to controls (amongst whom effects of length upon reading latency were also

evident) and CLA showing no increase in word length effect. In further contrast to their

gravely impaired visual processing, at the single letter level there was only minimal

evidence of impaired processing, with patient CLA showing slow (but accurate) single

letter identification under normal viewing conditions. Considering each patient’s

performance in more detail, FOL’s results seem to indicate her reading ability is almost

entirely spared. In each reading corpus, FOL did not differ from her control group in

either accuracy or reading latency. Regression analyses conducted on all 250 reading

responses (summing across tasks A1, A2 and A3) did reveal a diagnosis (FOL vs

controls) x length (number of letters) interaction. However, the same analyses found

effects of length on reading latencies within matched controls, and length has been

shown previously to influence reading speed in normal readers (O’Regan and Jacobs,

1992; Spieler and Balota, 1997). More importantly, the absolute increase in mean

reading latency for each additional letter as estimated from the regression model was

36ms per letter, a small increase which is comparable to that of controls (control

mean: 13ms/letter; control 4: 32ms/letter) and an order of magnitude different to the

increases of 90-7000ms per additional letter reported in previous descriptions of LBL

reading (e.g. Fiset et al., 2005; McCarthy and Warrington, 1990; Mycroft et al., 2009;

see Figure 6.4). It should also be noted that the trend towards a difference between

FOL and the control group’s reading latencies for the Schonell reading test may reflect

the particularly low frequency of various words in this corpus (‘somnambulist’,

‘ineradicable’) and FOL’s marginally lower educational level.

The reading accuracy of patient CLA was also excellent, with not a single error recorded on

any of the reading corpora. For example, her faultless performance on the demanding Schonell

reading test conveys an estimated IQ of at least 118 (Nelson and McKenna, 1975). Her reading

96

latencies did not differ from controls on the Brown and Ure words (A1), but reading speed did

fall below that of controls on the Coltheart and Schonell tests (A2 and A3), with a significant

regularity effect (irregular words slower than regular words) on the Coltheart set. Despite this,

the overall difference in latencies across all 250 words failed to reach formal levels of

significance. There was also no significant difference between CLA and her controls in the

effect of increasing word length.

The main aim of the current investigation was to evaluate the claim that general visual

dysfunction can account for the acquired peripheral dyslexic syndrome known as LBL reading.

General visual function accounts propose that even minor low-level perceptual deficits

propagate to or limit activation of lexical representations, ultimately resulting in impaired

reading behaviour. One specific prediction of such accounts is that pronounced word length

effects are an inevitable consequence of deficits in general pre-lexical processing (e.g. Farah

and Wallace, 1991; Behrmann et al., 1998; Mycroft et al., 2009). The data presented in the

Figure 6.4 Mean reading latencies for words of different length compared to 5 example letter-by-letter readers reported by Mycroft et al. (2009).

current study fail to support this prediction. Apart from demonstrating accurate and,

particularly in the case of FOL, rapid word reading, word length effects were equivocal

(FOL) or absent (CLA). This was despite the inclusion of very long words (up to 14

letters) which should maximise any chance of eliciting abnormal word length effects.

This failure to detect the dramatic word length effects routinely observed in LBL

readers cannot be attributed to preserved visual function, as both patients exhibited

97

dramatic impairments on a wide variety of perceptual tasks. These included a

chequerboard task previously used to support the claim that LBL readers have a

perceptual impairment that extends beyond alphanumeric stimuli (Mycroft et al.,

2009, chapter 6.2.3.1). However, in asserting that such general visual accounts of LBL

reading are incompatible with the data presented here for FOL and CLA, we would

wish to state unambiguously that we are not denying that some forms of visual

impairment may have an inevitable cost for reading function. Rather we would argue

against (i) the pejorative and under-specified use of terms such as ‘general visual

impairment’, and (ii) the assumption that any form of visual impairment can cause

reading impairment. We have previously proposed that enhanced visual crowding (the

excessive integration of visual features, sometimes referred to as lateral masking) may

be one of several specific visual deficits which can cause a particular form of dyslexia

(Crutch & Warrington, 2007a, 2009). Indeed, we predicted that any patient

demonstrating enhanced visual crowding on flanked letter identification tasks would

also show some form of visual dyslexia. In line with this prediction, neither FOL nor

CLA (whose reading is largely preserved) showed enhanced crowding; CLA did show

slowed target letter identification particularly with condensed rather than spaced

flankers (Task B4), but unlike visual crowding, this flanking effect was only present for

flankers of the same category (letter flankers but not number or shape flankers). Given

the degenerative nature of the PCA syndrome, we would predict that FOL and CLA’s

reading skills will eventually become affected; the task going forward will be to identify

any components of visual dysfunction that play a causative role in this predicted

deterioration.

The other aim of the investigation was to evaluate the hypothesis that impaired letter

processing plays a causal role in LBL reading. Such accounts posit that whole reading requires

fast parallel letter identification, and that deficits in letter processing inevitably give rise to

reading dysfunction and word length effects (e.g. Bub et al., 1989; Howard, 1991; Behrmann

and Shallice, 1995; Hanley and Kay, 1996; Price and Devlin, 2003). While both FOL and CLA

were significantly less accurate than controls at identifying rapidly serially presented single

letters, it is likely that this performance reflects a combination of their basic visual deficits

rather than a specific problem of letter processing, particularly as FOL also demonstrated

poorer accuracy on an equivalent task looking at rapidly presented numbers. The absence of

strong evidence of a deficit in single letter processing suggests that intact parallel letter

identification may account for their preserved reading in both patients.

To adequately counter the general visual processing difficulties position it needs to be

shown that any visual processing difficulty of the patients shown on some other perceptual

98

task plausibly arises from impairment to a processing system necessary for word reading and

not some potentially unrelated visual process. Naturally this is a very difficult point to disprove

absolutely. However on these grounds one can make the extremely strong statement that

none of the component visual processes required for normal performance on any of the 10

visual tasks evaluated in this investigation (which examine different levels of the visual system

and involve different levels of task difficulty: figure-ground discrimination, shape

discrimination, hue discrimination, number location, dot counting, object decision, fragmented

letters, canonical and non-canonical view perception, grid experiment), are necessary for intact

reading because our patients failed every single task. Furthermore, the impaired processes

highlighted by these tasks also do not fall into the poorly-defined category of ‘general visual

dysfunction’ which advocates of the general visual account claim cause LBL reading. However,

at the much more relative level, the crashing visual deficits highlighted in our patients are an

order of magnitude greater than the often subtle deficits claimed for patients cited in support

of the general visual account.

Having documented grave visual impairments, it remains to be established what

mechanisms support reading in FOL and CLA. The accurate and rapid reading shown by both

patients suggests preservation of word form representations or parallel letter processing

mechanisms. This notion cannot be verified by the available structural imaging data. However,

we note both MRI scans of FOL and CLA () indicate relative preservation of the left fusiform

gyrus, commonly cited as the locus of the VWFA (Cohen et al., 2000) and an area in which

lesions often result in letter-by-letter reading (Binder and Mohr, 1992; Leff et al., 2001; Cohen

et al., 2004; McCandliss et al., 2003; Pflugshaupt et al., 2009). This area perhaps provides an

anatomical substrate for preserved reading ability in these patients, with one possibility being

that strong reading performance is supported by preservation of certain inputs to the VWFA

that bypass other impaired aspects of early visual processing. Support for this notion centres

on evidence that the VWFA has connections to the primary visual cortex (Rockland and Van

Hoesen, 1994; Tanaka, 1997; Haynes et al., 2005) whose relative integrity in FOL and CLA may

be indicated by their continued strong or adequate performance on tests of visual acuity.

However this suggestion involves the visual word form system maintaining its efficacy, even in

the presence of widespread dysfunction at lower levels of the visual system.


The performance of FOL and CLA represents an impressive demonstration of the resilience

and efficiency of the reading system in the face of profound visual dysfunction, irrespective of

whether the observed reading is attributable to preservation of the word form and/or aspects

of parallel letter processing. The reading ability of these patients suggests that many types of

99

early visual, visuoperceptual and visuospatial impairment are not necessarily causally linked to

reading dysfunction, and question general visual accounts of conditions such as letter-by-letter

reading. Notably, both the patients concerned remained intact on centrally-presented tests of

visual crowding at the time of the reading assessment; longitudinal follow-up will determine

whether the (presumed eventual) emergence of excessive crowding will finally herald the

onset of reading difficulties for all types of text.

100

7. CASE STUDIES: LONGITUDINAL ASSESSMENT OF READING IN PCA


The previous chapter identified two patients, FOL and CLA, who showed a compelling

divergence between profound visual impairment and preserved reading ability. FOL and CLA

achieved rapid reading and perfect to near-perfect reading accuracy despite showing deficits

on ten measures of early visual, visuoperceptual and visuospatial processing (figure-ground

discrimination, shape discrimination, hue discrimination, number location, dot counting, object

decision, fragmented letters, canonical and non-canonical view perception, grid experiment).

The previous findings established that impaired performance on these ten tasks does not

necessitate reading dysfunction. These results, in combination with the lack of evidence of

letter-by-letter (LBL) reading in these patients, pose problems for proponents of general visual

accounts of reading (Friedman and Alexander, 1984; Farah and Wallace, 1991; Price and

Devlin, 2003; Mycroft et al., 2009), which predict that deficits in pre-lexical visual processing

result in disruption to reading in the form of prominent word length effects. Notably, both

patients showed intact visual acuity and did not show evidence of enhanced crowding deficits

on flanked letter identification tasks by making errors or showing consistent spacing effects on

task performance. The previous chapter raised the question as to how their efficient reading

was maintained, and it was predicted that any subsequent development of prominent

crowding deficits might be associated with an eventual deterioration in reading ability.

The previous chapter suggested that intact reading in both FOL and CLA might be a

consequence of the relative preservation of the left fusiform gyrus, including its connections to

the primary visual cortex, allowing for the continued efficacy of the visual word form system.

The left fusiform gyrus is often referred to as the anatomical site of the visual word form area

(VWFA; Cohen et al., 2000; Jobard et al., 2003). This region has been found to selectively

respond to printed and handwritten words (Qiao et al., 2010; Szwed et al., 2011), letters in

upper and lower case (Dehaene et al., 2004) and activation in this region has been found to

increase proportionately with sentence reading rate (Dehaene et al., 2010).Damage to the

visual word form system has been implicated in LBL readers (Warrington & Shallice, 1980);

consistent with this view, lesions to the VWFA have been shown to result in pure alexia

(Pflugshaupt et al., 2009).

The current chapter presents findings from longitudinal assessments of reading ability in

FOL and CLA which show deterioration in reading speed and accuracy over two years. The

main aim of this study was to investigate the evolving relationship between crowding and

101

word recognition. It was hypothesised that any emergence of crowding would be associated

with deficits in reading ability. One subsidiary hypothesis was that if crowding and reading

deficits did become apparent, the impact of crowding on word recognition would manifest

through greater difficulty reading words of higher letter confusability, given how visual

similarity exacerbates the crowding effect. A second subsidiary hypothesis was that

decrements in reading accuracy and speed would be most evident for longer words, whose

number of letters exceed the visual span and/or exhibit flanker effects on parallel letter

processing, given that reductions in the uncrowded window limit the visual span and elevated

numbers of flankers increase the magnitude of crowding effects (Poder & Wagemans, 2007). In

the event, both patients began to exhibit prominent flanked letter identification deficits at

follow-up, and the relationship between these enhanced crowding effects and reading is

described below.

7.2. METHODS

7.2.1. Participants

The study participants were the same two individuals with PCA as in chapter 6 (Yong et al.,

2013), FOL and CLA.

7.2.2. Imaging

Non-linear registrations of serial imaging to both patients’ baseline scans and the resultant

voxel-compression maps (see chapter 3.7.3.2) are shown in Figure 7.1. The white arrow

indicates the mean activation peak of the visual word form area (x=-44, y=-58, z=-15)

constituted from 17 functional imaging studies (Jobard et al., 2003).

FOL: Maps suggest relative sparing of left posterior fusiform (iii) and more extensive

involvement of the right than the left occipital lobe.

CLA: While maps indicate diffuse atrophy, with extensive involvement of the occipital lobe,

they also indicate the relative preservation of the left relative to the right inferior temporal

lobe, particularly in the posterior inferotemporal region (iii).

7.2.3. Experimental procedures

Subsequent to the initial baseline assessment in chapter 6, the patients each completed

two longitudinal follow-up assessments (first follow-up [FU1] and second follow-up [FU2]),

yielding a total of three assessments. FOL was assessed 16 and 25 months after her initial visit,

while CLA was assessed 18 and 27 months after her initial visit.

102

103

Figure 7.1 (i) Coronal (ii) axial and (iii) left and (iv) right sagittal MRI sections for FOL and CLA at baseline and colour coded voxel-compression maps produced from subsequent scans (FOL: 25 months; CLA: 24 months), fluid-registered to baseline scans. A region within the boundaries of the VWFA as constituted by a functional imaging meta-analysis (Jobard et al., 2003) is indicated by the white arrows.

7.2.3.1. Reading assessment

Participants were requested to read aloud the same three corpora administered at

baseline: the Brown and Ure (1969) corpus, the Schonell reading list (Schonell and Goodacre,

1971) and the Coltheart regular/irregular words (Coltheart et al., 1979). Mean letter

confusability was based on upper case ratings averaged from the confusability matrices of Van

der Heijden et al., (1984), Gilmore et al., (1979), Townsend, (1971), and Fisher et al., (1969),

with lower case ratings averaged from the confusability matrices of Geyer, (1977), and Boles

and Clifford, (1989).

7.2.3.2. Visual assessment

Participants were administered the same measures of early visual, visuoperceptual and

visuospatial processing administered at baseline.

7.2.3.3. Crowding assessment

Participants were administered the same centrally-presented single letter naming and

flanked letter identification tasks administered at baseline.


Responses were recorded using an Olympus DS-40 digital voice recorder; response latency

determination, analysis and accuracy and latency comparisons were consistent with chapter

3.5. While neither FOL nor CLA made enough errors at baseline to allow for meaningful

analysis of accuracy data, overall reading accuracy and latency analyses were conducted using

logistic regression and linear mixed models respectively. The linear mixed model used random

word order effects and fixed effects of word length, mean letter confusability, word frequency,

case (upper or lower) and assessment (baseline, FU1 or FU2), while the logistic model included

as covariates all random and fixed effect variables from the linear mixed model, clustered by

word order. Post hoc analysis of CLA’s reading latency data was conducted using the linear

mixed model but including orthographic neighbourhood size (Nsize) as an additional covariate.

Prior to latency regression analysis, latency data were transformed using an inverse

transformation due to non-normal distribution of residuals. We used a sign test to identify

letter naming differences in accuracy between spacing conditions. Overall flanked letter

identification accuracy analysis was conducted using logistic regression, including spacing,

flanker category and assessment as covariates, clustered by letter order. As latency analysis

was restricted to correct responses, latency data were not analysed for crowding follow-up

104

assessments owing to high error rates. Data collected from FOL and CLA’S first and second

follow-up assessments was compared with control group data collected at baseline. All

reported p values represent one-way probability.

7.3. RESULTS

7.3.1. Reading assessment

Mean percentage error rates and reading latencies for overall performance (summing

across reading corpora) at baseline and follow-up assessments are shown in Figure 7.2. A more

detailed breakdown of number and percentage correct responses and mean and SD latency

data for reading performance by FOL, CLA and their relevant control samples on the individual

reading corpora are shown in Error: Reference source not found.

7.3.1.1. Overall reading accuracy

FOL: Overall analysis of FOL’s accuracy data across the three assessments found accuracy

decreased in subsequent assessments (z=-3.22, p<.005). While there was a slight decline in

FOL’s overall accuracy between baseline (98.4%) and FU1 (97.2%), this did not reach formal

levels of significance (p>.1). However, there was a significant decline between baseline and

FU2 (92.8%; z=-3.38, p<.005) and between FU1 and FU2 (z=-2.26, p<.05). Across the three

assessments, words of increased length were read less accurately (z=-2.60, p<.01); there was

no significant difference in length effects on accuracy at different assessments (all p>.1). There

were no significant effects of mean letter confusability (p>.4), frequency (p>.3) or case (p>.1)

on reading accuracy.

CLA: Overall analysis of CLA’s accuracy data across the three assessments also found a

decrease in accuracy in subsequent assessments (z=-6.02, p<.001). There was a decline in CLA’s

overall accuracy between baseline (100%) and FU1 (94.4%; z=-2.51, p<.05) and FU2 (90.4%; z=-

3.20, p<.005), with decline also evident between FU1 and FU2 (z=-1.96, p<.05). Across the

three assessments, words in upper case were read less accurately (z=-1.98, p<.05) and similarly

to FOL there was a trend towards longer words being read less accurately (z=-1.78, p=.075).

There was no significant difference in length effects on accuracy between follow-up

assessments (p>.6). There were no significant effects of mean letter confusability (p>.4) or

frequency (p>.2) on reading accuracy.

105

106

Figure 7.2 Overall reading accuracy and latency data across three longitudinal assessments. Error bars show standard deviation for control groups.

A: Reading assessment

max FOL/control raw scores CLA/control raw scores

Control Group FOL Control

Group CLA

baseline baseline follow up 1

follow up 2 baseline baseline follow up

1follow up

2

1. Brown and Ure words Total 72 71.8, SD=.4 (99.7%) 72 (100%) 71 (99%) 71 (99%) 1. Brown and Ure words 72 (100%) 72 (100%) 70 (97%) 69 (96%)

RT 0.51, SD=.04 0.60 1.05** 1.66** 0.57,

SD=.06 0.64 0.91** data missing

2. Schonell Total 100 99, SD=1 (99%) 97 (97%) 97 (97%) 92 (92%)** 2. Schonell 99, SD=1.2

(99%) 100 (100%) 94 (94%)* 88 (88%)**

RT 0.54, SD=.07 0.72 1.04** 1.58** 0.60,

SD=.06 0.78* 0.92** 6.52**

3. Coltheart words Total 78 78 (100%) 77 (99%) 75 (96%) 69 (89%) 3. Coltheart words 78 (100%) 78 (100%) 72 (92%) 69 (89%)

Regular Total 39 39(100%) 39 (100%) 37 (95%) 35 (90%) Regular 39(100%) 39 (100%) 36 (92%) 35 (90%)

RT 0.48, SD=.04 0.56 0.97** 1.37** 0.53,

SD=.05 0.91** 0.88** 6.62**

Irregular Total 39 39 (100%) 38 (97%) 38 (97%) 34 (87%) Irregular 39 (100%) 39 (100%) 36 (92%) 34 (87%)

RT 0.51, SD=.05 0.59 1.15** 1.51** 0.55,

SD=.05 1.1** 1.1** 7.87**

B: Crowding assessment

Single letter naming Total 20 20 (100%) 20 (100%) 20 (100%) 20 (100%) Single letter reading 20 (100%) 20 (100%) 20 (100%) 20 (100%)

RT 0.48 ± .06 0.59 not

recorded 1.08**

0.56 ± 0.4 0.82** 0.87** 3.81**

Flanked letter identification Total 72/48 72 (100%) 72 (100%) 37/48 (77%) 58 (81%) Flanked letter identification 72 (100%) 72 (100%) 64 (89%) 57 (79%)

Condensed Total 36/24 36 (100%) 36 (100%) 16/24 (67%) 25 (69%) Condensed 36 (100%) 36 (100%) 29 (81%) 25 (69%)

Spaced Total 36/24 36 (100%) 36 (100%) 21/24 (88%) 33 (92%) Spaced 36 (100%) 36 (100%) 35 (97%) 32 (89%)

107

Table 7.1 A) Reading assessment and B) Crowding assessment accuracy and latency for FOL/CLA and their matched control groups; highlighted figures indicate where FOL/CLA’s performance was poorer than their respective control groups (*=p<.05; **=p<.005).

7.3.1.2. Overall reading latency

Overall reading latencies for words of up to 12 letters read at baseline, FU1 and FU2 are

shown in Figure 7.3.

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Figure 7.3 Mean overall reading latencies for words of different length read by patient FOL and CLA at baseline, first and second follow-up and baseline latencies for their respective matched controls, with estimated upper and lower control confidence intervals.

FOL: Overall analysis of FOL’s latency data across the three assessments found that reading

speed was slower in subsequent assessments (t=-16.82, p<.001). There was a decline in FOL’s

reading speed between baseline (633ms) and FU1 (1048ms; z=-14.59, p<.001) and FU2

(1493ms: z=-17.27, p<.001), with further decline in reading speed between FU1 and FU2 (z=-

3.11, p<.005). Across the three assessments, increased word length led to slower reading

speed (length: z=-6.18, p<.001); length effects were most pronounced at second follow-up,

with a mean increase of 116ms per letter. There was an interaction between word length and

assessment, with words of increased length being read slower at follow-up assessments

relative to baseline (FU1: z=3.27, p<.005; FU2: z=2.18, p<.05); there was no significant

interaction between follow-up assessments (p>.3). Increased letter confusability led to slower

reading speed (letter confusability: z=-1.97, p<.05). There was an interaction between letter

confusability and assessment, with words of lower confusability being read slower at FU2

(z=2.47, p<.05) and baseline (z=1.99, p<.05) relative to FU1; there was no significant

interaction between baseline and FU2 (p>.5). There was no overall effect of frequency or case

(both p>.1).

CLA: Overall analysis of CLA’s latency data also found that reading speed was slower in

subsequent assessments (z=-36.23, p<.001). There was also a decline in CLA’s reading speed

between baseline (751ms) and FU1 (940ms; z=-14.59, p<.001) and FU2 (6854ms; z=-17.27,

p<.001), with further decline between FU1 and FU2 (z=-3.11, p<.005). Across the three

assessments, CLA was also slower reading words of increased length (z=-5.17, p<.001). There

was an interaction between word length and assessment: in contrast to FOL, words of

decreased length were read slower relative to baseline at FU2 (z=3.16, p<.005), and a trend

towards words of decreased length being read slower at FU2 relative to FU1 (z=1.94, p=.052).

There was no significant difference in length effects between FU1 and FU2 (p>.1). A post hoc

analysis of CLA’s reading latencies was conducted in order to determine whether slower

reading speed for shorter words at FU2 was a consequence of such words having more

orthographic neighbours (Weekes, 1997): however, neighbourhood size did not account for

this effect (length: z=3.15, p<.005; Nsize: p>.1). CLA was slower reading words in upper case

font (z=-3.07, p<.005). Also in contrast to FOL’s reading, there was no significant effect of mean

letter confusability or word frequency on reading speed (both p>.1).

A detailed account of FOL and CLA’s performance upon the three individual reading

corpora which were combined to give the overall performance data described above can be

found in Appendix 5.

109


See Figure 7.4 for a summary of errors made by FOL and CLA at different assessments.

Visual errors made up 50% of FOL’s error responses at baseline, 57% of error responses at FU1

and 80% at FU2. CLA did not make any errors at baseline, whilst visual errors made up 60% of

her error responses at FU1 and 65% of her error responses at FU2.

7.3.2. Visual assessment

FOL and CLA’s performance on measures of early visual, visuoperceptual and visuospatial

processing and background neuropsychology is shown in Table 7.2. Consistent with baseline

performance, both FOL and CLA demonstrated impairments in a range of visual domains.

7.3.3. Crowding assessment

The number and percentage correct responses and mean and SD latency data for letter

naming performance in unflanked and flanked conditions by FOL, CLA and their relevant

control samples are shown in Error: Reference source not found

7.3.3.1. Single letter naming

FOL/CLA: Neither FOL nor CLA made any error responses at baseline, FU1 or FU2.

7.3.3.2. Flanked letter identification

For mean percentage error rates across baseline and follow-up assessments, see Figure

7.5.

110

Figure 7.4 Number of types of error made across longitudinal assessments

Table 7.2 FOL and CLA’s performance on background neuropsychological measures and tests of visual processing (not tested: NT). Shaded numbers indicate task performance is within normal limits.

TestMax Score

FOL CLA

Baseline FU1 FU2 Baseline FU1 FU2

Background Neuropsychology

MMSE1 30 24 23 15 27 13 NT

Short RMT words2 25 21 14 16 24 21 NT

Concrete synonyms3 25 20 21 20 20 20 NT

Spelling (oral) 4 20 18 6 6 19 11 NT

Digit span (forwards) 8 9 8 7 10 2 NT

Digit span (backwards) 7 4 1 0 5 4 NT

Visual Assessment

Early visual processing

Visual acuity (CORVIST5): Snellen 6/9 6/9 6/9 6/12 6/18 6/18 NT

Figure-ground (VOSP6) 20 17 16 17 14 11 NT

Shape discrimination7 20 10 17 7 10 13 NT

Visuospatial processing

Number location (VOSP) 10 5 0 NT 5 NT NT

Dot counting (VOSP) 10 7 3 0 10 1 NT


Object decision (VOSP) 20 15 14 13 7 NT NT

Fragmented letters (VOSP) 20 8 5 1 0 NT NT

Usual views8 20 18 20 NT 5 NT NT

Unusual views 20 10 6 NT 0 NT NT

1 Mini-Mental State Examination (MMSE: Folstein, Folstein & McHugh, 1975). 2 Warrington (1996). 3 Warrington, McKenna

and Orpwood (1998). 4 Graded Difficulty Spelling Test (GDST; Baxter & Warrington, 1994). 5 Cortical Visual Screening Test

(CORVIST; James, Plant & Warrington, 2001). 6 Visual Object and Space Perception Battery (VOSP; Warrington & James, 1991).

7 Efron (1968): oblong edge ratio 1:1.20. 8 Warrington and James (1988).

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FOL: FOL did not complete letter identification tasks in the number flanker condition at

FU1. Overall analysis of FOL’s accuracy data found that flanked letter identification was less

accurate at subsequent assessments (z=-3.28, p<.005). There was an overall effect of spacing,

with letters being identified less accurately in the condensed condition (z=2.98, p<.005). There

was no significant interaction between spacing and assessment (p>.3). There was no significant

effect of flanker category on naming accuracy (all p>.1).

At FU1, there was a trend towards poorer overall naming accuracy in the condensed

relative to the spaced condition across letter and shape flanker conditions (66.7% vs 87.5%;

p=.090); there was a significant effect of spacing for letters flanked by shapes (58.3% vs

100.0%; p<.05). At FU2, overall naming accuracy was poorer in the condensed relative to the

spaced condition across all flanker conditions (69.4% vs 91.7%; p<.05).

112

Figure 7.5 Accuracy for flanked letter identification tasks in condensed and spaced conditions across three longitudinal assessments (*=p<.05).

CLA: Similar to FOL, overall analysis of CLA’s accuracy data found that flanked letter

identification was less accurate at subsequent assessments (z=-3.53, p<.001). There was also

an overall effect of spacing, with letters being identified less accurately in the condensed

condition (z=2.29, p<.05). There was no significant interaction between spacing and

assessment (p>.9). There was no significant effect of flanker category on naming accuracy

(letter vs shape flankers: p>.4; letter vs number flankers: p>.1; shape vs number flankers: p>.5).

At FU1, overall naming accuracy was poorer in the condensed relative to the spaced

condition across all flanker conditions (80.6% vs 97.2%; p<.05). At FU2, while overall naming

accuracy was poorer in the condensed relative to the spaced condition, this difference did not

reach formal levels of significance (72.2% vs 86.1%; p>.1).

7.3.4. Error analysis

FOL: Of FOL’s total error responses made over both follow-up assessments (21.2%), 43.3%

were from flanker identification (e.g. ZNHZ), 35.8% were from the target being unidentified

and 20.8% were due to neither target nor flanker being identified.

CLA: Of CLA’s total error responses made over both follow-up assessments (16.0%), 46.1%

were from flanker identification, 2.8% were from the target being unidentified and 51.1% were

due to neither target nor flanker being identified.

Errors following the target being unidentified could result from both FOL and CLA being

unable to either detect the target or provide a response. Responses in which neither target nor

flanker were identified were often suggestive of feature substitution or perceptual averaging

of target and flanker stimuli (e.g. YMTV; 6F2T).

7.4. DISCUSSION

The current paper reports a two year follow-up evaluation of the relationship between

visual processing and reading ability in two PCA patients, FOL and CLA. At the baseline

assessment, both patients demonstrated remarkably preserved reading ability despite showing

impaired performance on ten tasks of visual processing. At subsequent assessments, their

reading ability showed signs of deterioration; whilst still impressive given the gravity of their

early visual, perceptual and spatial impairments, it could not be considered normal in either

patient. The emergence of these reading deficits coincided with deterioration in their

performance on flanked letter identification tasks, pointing to the evolution of an enhanced

visual crowding deficit. By contrast, visual acuity remained relatively well-preserved even at

follow-up. In this discussion, we consider how characteristics of the emergent reading deficit,

namely effects of word length and letter confusability, may provide insight into the process

through which crowding disrupts word recognition.

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To first summarise the data, the investigation revealed a significant decline in FOL and

CLA’s reading ability. At baseline assessment, reading ability for FOL and CLA was within

normal limits, with CLA achieving 100% reading accuracy. By first follow-up, both patients’

reading speed had declined relative to both their own baseline performance and their age- and

gender-matched control groups. In addition, CLA was not only slower but also less accurate,

making errors on high-frequency words (e.g., FREETREE, GLOVECLOVE). By second follow-

up, reading accuracy for both patients was well below that of controls.

Follow-up assessments of both FOL and CLA identified impaired performance on tests

sensitive to crowding, involving the identification of centrally-presented target letters flanked

by letter, shape and number flankers. Without prompting, FOL commented explicitly on how

flankers were “interfering… pushing in”. Poor performance was not more pronounced with

letter (i.e. same category) flankers as might be expected with attentional dyslexia (Humphreys

& Mayall, 2001; Warrington et al., 1993); instead, consistent effects of spacing were observed

in different flanker conditions, with elevated error rates resulting from targets with condensed

flankers. This pattern of deficit is strongly indicative of visual crowding, and mirrors flanked

letter identification deficits observed in other PCA patients (Crutch & Warrington, 2007a;

Crutch & Warrington, 2009; Mendez et al., 2007).

With regard to the effect of word length, increases in FOL’s mean latencies per additional

letter were modest at her first two assessments (FOL baseline: 36ms/letter; FOL follow-up 1:

25ms/letter; control 4: 32ms/letter). However, increases per letter at second follow-up were

within the range of previous descriptions of LBL reading (FOL follow-up 2: 116ms/letter; cf

Behrmann et al., 1998). In contrast, following comparable increases in mean reading latency

per additional letter between CLA and controls at baseline and first follow-up (CLA baseline:

19ms/letter; CLA follow-up 1: 15ms/letter: control mean: 13ms/letter), CLA was actually slower

reading shorter words at second follow-up assessment, although her differences in mean

reading speed/letter between assessments were not as pronounced as those observed in FOL

(CLA follow-up 2: -31ms/letter). The data raise the question of why FOL but not CLA showed an

increase in length effects over subsequent assessments.

Another difference between FOL and CLA’s reading performance was the emergence of

effects of letter confusability on FOL’s reading latencies, with more visually similar letters being

read more slowly. As greater visual similarity increases the magnitude of crowding, any

influence of letter confusability might reflect the impact of crowding on parallel letter

processing. Fiset et al. (2005) proposed that higher letter confusability within words or subsets

of words reduces LBL readers’ ability to achieve successful parallel letter processing. They

suggested that such words might provoke switching to a compensatory serial letter processing

strategy, and emphasised how switching to such a strategy varies depending on patients’

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reading ability. It is possible that, when attempting to read words of higher letter confusability,

FOL was quicker to adopt a serial reading strategy than CLA, resulting in more prominent

length effects. This may be because FOL was more susceptible to letter confusability effects or

was more conscious of her difficulties when reading words of high letter confusability. CLA may

have been more reliant on processing whole-word information rather than adopting a serial

reading strategy. In line with this suggestion, larger effects of frequency and reduced effects of

word length have been identified in older adults, which have been interpreted as showing an

increased propensity towards representing words as single units based on the greater reading

experience of elderly readers (Spieler & Balota, 2000).

Variations in patterns of reading performance between the two patients may also relate to

differences in the quality of crowding effects between FOL and CLA. A greater proportion of

FOL’s errors arose from her being unable to provide a response to target stimuli, while CLA

showed a greater tendency towards making error responses that named neither the target nor

flanker. If flanked letter identification errors arise from competition between feature

detectors, as proposed in lateral masking accounts (Townsend et al., 1971; Wolford &

Chambers, 1984), crowding may limit letter detection as well as identification (Parkes et al.,

2001; Pelli et al., 2004). By contrast, feature integration accounts suggest pooling of

information over multiple features of flanker and target stimuli, inhibiting identification but

not detection (Pelli et al., 2004; Greenwood et al., 2010). FOL’s lack of responses may reflect a

low-level deficit in feature detection, whereas CLA’s error responses may stem from a higher-

level deficit of excessive integration between features of target and flanker stimuli. With

regard to the possible anatomical substrates of these deficits, proposals of a two-stage process

of crowding involve a lower-level feature detection stage, possibly in V1, and a higher-level

integration of features downstream from V1 (Levi, 2008). Whilst we cannot know the current

distribution of pathology in our patients in sufficient detail, crowding as a failure of feature

detection in FOL would predict greater pathological involvement of the striate cortex, while

crowding as a consequence of excessive feature integration in CLA would predict

disproportionate involvement of the extrastriate cortex.

A reduced visual span might account not only for FOL’s slower reading for longer words,

but also for both patients’ poorer accuracy for words of increased length. The “shrinking visual

span hypothesis” (Legge et al., 1997) proposes that words whose lengths exceed the size of the

visual span demand increases in eye movements, leading to length effects on reading latency.

It is possible that prominent crowding effects in FOL are reducing her visual span, subsequently

requiring an increase in the number of fixations needed to achieve word recognition, which

result in reduced speed when reading longer words. The tendency towards less accurate

reading of longer words in both patients may be a result of poor integration of information

115

from multiple fixations, or might also be due to increased numbers of letters exhibiting greater

inhibitory flanker effects on parallel letter identification (Poder & Wagemans, 2007).

What implications do these findings have for our interpretation of results in Chapter 6? In

the previous chapter, we proposed that efficient reading in both patients was a consequence

of preserved word form/parallel letter processing, maintained by the integrity of early aspects

of the visual system (as suggested by strong performance on tests of visual acuity), the VWFA

and interconnecting projections. In the current investigation, while both patients showed

diffuse brain atrophy, we argued for the relative preservation of the region corresponding to

the VWFA at subsequent assessment. Given the dissociation between crowding and acuity

(Song et al., 2014), it is possible that even the continued efficacy of both the early visual

system and the VWFA is undermined by enhanced crowding and any accompanying occipital

atrophy.

Alternatively, the progression of occipital atrophy may have compromised the structural

integrity of the i) early visual system, ii) inputs to the VWFA or iii) the VWFA itself. FOL’s acuity,

while relatively well-preserved, was slightly diminished over subsequent assessments; this,

along with the notion that her enhanced crowding may be a particular consequence of a deficit

in feature detection, might be a consequence of pathological involvement of the striate cortex.

In contrast, we have proposed CLA’s enhanced crowding arose from a higher-level deficit in

feature integration; such a deficit might result from extrastriate involvement, accompanied by

a deteriorating condition of inputs to the VWFA. Despite the purportedly different loci of

deficits in possibilities i) and ii), both would likely constrain visual representations which form

the basis of orthographic inputs in both patients. As previously suggested, this may have

provoked FOL to adopt serial reading strategies when reading words of high confusability (i.e.

Fiset et al., 2005). In contrast, CLA may have been better able to represent whole word form

and/or parallel letters based on her greater reading experience, and so would not have had to

resort to serial reading, but may instead have difficulty selecting appropriate orthographic

units (Patterson, 1978; Crutch & Warrington, 2007b). Another interpretation is that FOL’s

emerging length effects might reflect the increasing pathological involvement of her VWFA due

to disease progression, leading to a diminished ability to achieve word form/parallel letter

processing. However, it is necessary to acknowledge the limitations of the current

investigation in evaluating the above hypotheses given the difficulty of drawing precise

conclusions about neuroanatomical localization based on two patients with diffuse atrophy.

While the current investigation suggests enhanced crowding is particularly associated with

acquired dyslexia, the patterns of reading and visual deficits exhibited by FOL and CLA are still

inconsistent with general visual accounts which maintain a deficit in general processing of

visual material causally underlies acquired dyslexia (Mycroft et al., 2009). The present findings

116

support the notion of excessive crowding, a specific prelexical deficit, undermining reading.

The locus of this deficit most likely lies earlier in the visual system than accounts which

propose peripheral dyslexia as a consequence of selective damage to orthographic processing

(Warrington & Shallice, 1980; Patterson & Kay, 1982; Rosazza et al., 2007). In contrast to these

accounts which maintain that such selective damage results in specific reading deficits, in the

form of pure alexia, disruptive effects of excessive crowding would not be limited to

orthographic stimuli. It is worth emphasising, however, that the emergence of FOL’s length

effects does not mean that she should necessarily be considered a pure alexic. Our primary

interpretation of the current findings involves excessive crowding, a low-level deficit,

restricting reading ability; Leff et al. (2001) proposed that low-level perceptual deficits might

underlie length effects in some previously reported ‘pure alexics’, while Montant and

Behrmann (2001) suggested that pure alexia is a term that should be applied to readers with a

specific word length effect in the absence of other symptoms.


The current investigation highlights the co-occurrence of crowding effects and diminished

reading ability in FOL and CLA. Although the concurrent emergence of reading and crowding

deficits in these individuals represents evidence of association rather than causation, the data

further underline the potential role of enhanced crowding in hindering word recognition.

Questions remain about differences between both patients, including the quality of crowding

effects and the employment of serial reading strategies. However, the current findings provide

an insight into how crowding may limit reading ability, and demonstrate a novel

neurodegenerative approach towards understanding the relationship between one specific

form of basic visual deficit and the reading system.

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8. PASSAGE READING IN PCA


Various reports exist of PCA patients having difficulty following lines of text, moving

between one line to another and losing their place on a page (see chapter 2.3). Although these

difficulties have been recognised clinically and anecdotally, they have yet to be empirically

examined. The current chapter characterises the spatial and oculomotor properties of text

reading behaviour in PCA; the ultimate goal being to apply any resulting insight in the

development of a reading aid.

The poor ability of PCA patients in coping with the spatial demands of reading multiple

lines of text likely relates to a range of deficits: single point localisation (visual disorientation),

spatial analysis (spatial agnosia), visual search (spatial attentional impairments) and eye

fixation stability (Mizuno et al., 1996; Stark et al., 1997; Delazer et al., 2006; Lehmann et al.,

2011; Crutch et al., 2011).Restrictions in the effective field of vision as evidenced by poorer

recognition of larger stimuli (Stark et al., 1997; Crutch et al., 2011) may also contribute to the

reading impairment, frustrating attempts to locate subsequent lines of text. Crowding may

play a particular role in text reading where letters not only within a word (as in single word

reading) but also from preceding and subsequent words within a line and from lines of text

above and below exhibit inhibitory flanking effects and create a highly cluttered visual scene.

Furthermore, both inverse size and enhanced crowding effects would be expected to limit the

perceptual span and parafoveal preview benefit in reading (Pelli et al., 2007; Hyona et al.,

2004; Rayner, 1998; McDonald, 2006), leading to increased demands in fixations and

consequent reductions in reading speed.

The current chapter aimed to identify the spatial, lexical and oculomotor characteristics of

passage reading in PCA patients using measures of reading ability and eye movements,

compared with tAD patients and healthy controls. This investigation intended to examine the

hypothesis that spatial factors are the primary determinants of PCA passage reading ability by

demonstrating that spatial attributes (position within line, position within paragraph, position

within page) are better predictors of reading accuracy than non-spatial attributes (e.g. word

frequency, class). Revealing the core components of poor passage reading in PCA will inform

the design of subsequent reading interventions which minimise or evade weak aspects of

spatial, perceptual and/or oculomotor function.

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8.2. METHODS

8.2.1. Participants

15 PCA patients, 6 tAD patients and 6 healthy controls participated in the main

experiment. The PCA patients all fulfilled clinical criteria for a diagnosis of posterior cortical

atrophy (Tang-Wai et al., 2004; Mendez et al., 2002; McMonagle et al., 2006) and research

criteria for probable Alzheimer’s disease (McKhann et al., 2011). The tAD patients fulfilled

research criteria for a diagnosis of typical amnestic Alzheimer’s disease (McKhann et al., 2011).

Diagnoses were made based on clinical and neuroimaging data. The healthy controls were

matched to the PCA and tAD groups on age and years of education, with the PCA and tAD

participants additionally matched for disease duration and Mini-Mental State Examination

score (MMSE; see Table 8.1). Molecular pathology (18F amyloid imaging or CSF) was available

for 5/15 PCA and 4/6 tAD patients; all results were consistent with AD pathology (positive

amyloid scan on standard visual rating or CSF Aβ1-42 ≤450 and/or tau/Aβ ratio >1; see Table

8.2). Ethical approval for the study was provided by the local ethics committee and informed

consent was obtained from all participants.

Table 8.1 Demographic information for PCA, tAD and healthy control groups. PCA Alzheimer's disease ControlNumber of participants 15 6 6Gender (male:female) 6:9 2:4 2:4

Age (years) 61.0 ± 6.6 62.0 ± 7.5 61.0 ± 4.6Education level (years) 13.6 ± 2.0 13.8 ± 4.5 13.8 ± 2.7

Disease duration (years) 4.2 ± 1.7 5.7 ± 2.3 -MMSE (/30)1 19.0 ± 4.2 22.8 ± 5.3 -

β-Amyloid PET/ CSF consistent with AD 5/5 4/4 -

Table 8.2 Molecular pathology data for PCA and tAD patients; interpretation symbols indicate where results do not support AD pathology (-), are borderline consistent with AD pathology (+) and are >85% specific for AD pathology (++).

Diagnosis amyloid F18

imaging CSF total tau CSF abeta 1-42

CSF Tau:Abeta

ratio

CSF interpretation

PCA positive 1072 126 8.51 ++PCA positive 1082 365 2.96 ++PCA positive - - - ++PCA positive - - - ++PCA positive - - - ++tAD - 289 280 1.03 +tAD - 757 285 2.66 ++

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tAD - 940 348 2.70 ++tAD - 952 195 4.88 ++


PCA and AD patients were administered a battery of neuropsychological tests. Mean

scores on each task and an estimate of their performance relative to appropriate normative

data sets are shown in Table 8.3. Overall PCA performance was consistent with the cognitive

characteristics outlined by McMonagle et al. (2006), with most prominent symptoms being

higher order visual deficits with relatively less impaired memory ability. PCA patients were

borderline impaired but within normal limits on tests of recognition memory (Short

Recognition Memory Test words/faces), whilst tAD patients were impaired and showed a trend

towards worse performance than PCA patients. PCA performance on most tests without a core

visual component was equivalent to that of the tAD group (Concrete Synonyms, Naming, Digit

Span). However, PCA patients were significantly more impaired than tAD patients on tests of

numeracy and literacy (Calculation, Cognitive Estimates, Spelling) and on all tests of early

visual, visuoperceptual and visuospatial function except the CORVIST colour screening test.

8.2.3. Passage reading assessment

8.2.3.1. Stimuli

Participants read aloud 6 passages (mean word count: 107.0, SD=5.2) in a standard

presentation block-of-text format (mean number of lines: 14.8, SD=1.2), with each passage

split into 3 paragraphs. Passages were selected from the BBC news archive in order to reduce

priming from current events. Words were in black Arial Unicode MS font, with a visual angle of

letter height subtending 0.45° when viewed from a distance of 50cm, presented on a grey

background.

8.2.3.2. Procedure

Participants were given a maximum of 300 seconds to read each passage. Participants who

took more than 10 seconds to provide a response for a word were prompted to move onto the

next word. Participants were not discouraged from using their finger to maintain their place

when reading. Passages were administered through a repeated-measures design that included

presentation conditions from chapter 9. Words read correctly were marked as accurate,

regardless of word order. For details of latency measurement and eye movement recording,

see chapters 3.5 and 3.6 respectively.

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Table 8.3 Neuropsychological scores of patients with PCA and tAD

TestMax Score

Raw Score

PCA

(mean age: 61.0)AD

(mean age: 62.0) Difference Norms/comment

mean min|max mean min|max

Background Neuropsychology

Short Recognition Memory Test2 for words*

25 19.0 ± 3.8 13|24 15.1 ± 3.1 9|16 p=.082 PCA: 5th%ile, AD: ~<5th %ile (Cut off: 19)

(joint auditory/visual presentation)Short Recognition Memory Test for faces*

25 19.8 ± 3.4 14|25 17.3 ± 4.6 12|21 p>.2 PCA: 5th-10th%ile, AD: ~<5th %ile (Cut off: 18)

Calculation (GDA3)* 24 1.5 ± 3.7 0|14 4.2 ± 5.1 0|13 p<.05 PCA: ~<5th%ile, AD:5th-25th%ileSpelling (GDST4- Set B, first 20 items)*

20 9.3 ± 5.0 2|17 13.8 ± 5.3 8|19 p=.081 Both 5th-25th%ile

Digit span (max forwards) 8 5.3 ± 1.0 4|7 6.2 ± 1.2 4|7 p>.1 Both 25th-50th%ile

Digit span (max backwards) 7 2.9 ± 1.3 0|7 4.0 ± 1.3 2|8 p>.1 Both 5th-10th%ile

Visual Assessment

Early visual processingVisual acuity (CORVIST5):

Snellen6/9 (median

6/9)6/9|6/12 (median

6/9)6/9|6/12 - Both within normal limits

Figure-ground discrimination (VOSP6)

20 16.4 ± 2.9 11|20 18.8 ± 1.1 18|20 p=.057 PCA: ~<5th%ile, AD: 5th-10th%ile

Shape discrimination – Efron squares7

Difficult (oblong edge ratio 1:1.20) 20 11.9 ± 4.5 6|20 17.8 ± 3.5 12|20 p<.05 Healthy controls do not make

any errorsHue discrimination

(CORVIST)4 2.6 ± 1.1 0|4 3.4 ± 1.3 1|4 p>.1 -

Healthy controls do not make any errorsCrowding 10 7.9 ± 2.4 2|10 10 10|10 p<.001


Object Decision (VOSP)* 20 10.6 ± 4.3 4|17 17.0 ± 2.2 14|20 p<.005 PCA: ~<5th%ile, AD: 25th-50th%ile

Fragmented letters (VOSP) 20 3.7 ± 4.2 0|15 18.4 ± 1.3 16|19 p<.001 PCA: ~<5th%ile, AD: 25th-50th%ile

Unusual and usual views8: Unusual

20 4.0 ± 3.9 0|12 13.8 ± 1.3 12|15 p<.005 PCA: ~<1st%ile, AD:5th-25th%ile

Unusual and usual views8: Usual

20 13.8 ± 5.3 3|20 19.8 ± 0.5 19|20 p<.01 PCA: ~<1st%ile, AD: within normal limits

Visuospatial processingNumber location (VOSP)* 10 1.4 ± 2.3 0|6 7.7 ± 2.9 2|10 p<.001 PCA: ~<5th%ile, AD: 5th-10th%ile

Dot counting (VOSP) 10 2.6 ± 3.0 0|9 8.8 ± 1.3 7|10 p<.005 PCA: ~<5tht%ile, AD: within normal limits

A Cancellation9: Completion time

90s 83.2 ± 14.1 54s|111s 30.6 ± 12.3 20|45 p<.001Both ~<5th%ile (Cut off: 32s)

A Cancellation9: Number of letters missed

19 7.5 ± 5.0 1|16 0.6 ± 0.5 0|1 p<.005

*Behavioural screening tests supportive of PCA diagnosis. 1 Mini-Mental State Examination (MMSE: Folstein, Folstein & McHugh, 1975). 2 Warrington (1996). 3 Graded Difficulty Arithmetic test (GDA; Jackson & Warrington, 1986). 4 Graded Difficulty Spelling Test (GDST; Baxter & Warrington, 1994). 5 Cortical Visual Screening Test (CORVIST; James, Plant & Warrington, 2001).6 Visual Object and Space Perception Battery (VOSP; Warrington & James, 1991). 7 Efron (1968). 8 Warrington and James (1988). 9 Willison and Warrington (1992).

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Between-group differences in reading accuracy, reading speed and performance on

neuropsychological measures were calculated using a Wilcoxon rank-sum test.

8.2.4.1. Analysis of spatial factors on reading accuracy

Two-way crossed random-effects models were used to assess the effect of spatial aspects

of text on reading accuracy; all models used random participant and passage effects, with

random participant effects nested within the passage variable, and fixed-effects of the

following nuisance variables: number of repeats, word length, frequency and order. Regression

analysis was restricted to content words. Three separate models were used, including fixed-

effects of the following variables:

• Categorical text position: paragraph (1, 2 or 3), line (3-6) and peripheral (paragraph

perimeter vs paragraph interior).

• Continuous page coordinate position: x-coordinates and y-coordinates from the centre

of the screen.

• Continuous paragraph coordinate position: x-coordinates and y-coordinates from the

centre of each paragraph.

8.2.4.2. Eye movement data

Differences between PCA, tAD and control measures of eye tracking data were assessed

using a Wilcoxon rank-sum test. Fixation measures were the overall number of fixations and

fixation duration. Saccadic measures were the overall number of saccades, number of

left/right saccades and saccade amplitude. Disinhibition in one tAD participant meant the

Eyelink head-mounted system was poorly secured; eyetracking data from this participant was

removed from analysis.

8.3. RESULTS

8.3.1. Reading accuracy

A heatmap showing the effect of word location on reading accuracy in the PCA group for

an example passage is shown in Figure 8.1. PCA patients were less accurate (overall accuracy:

57.2%, SD=21.7) than tAD patients (overall accuracy: 97.5%, SD=2.4; z=-3.43, p<.001) and

controls (overall accuracy: 99.4%, SD=.01; z=3.51, p<.001); there was a trend towards tAD

patients reading less accurately than controls (z=1.93, p=.054). Overall accuracy would have

been lower if taking word order into account, particularly in the PCA group, as words were

marked correct regardless of when they were read in each passage (see Figure 8.2).

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Analysis of spatial factors on reading accuracy: In terms of categorical text position, PCA

patients were more accurate reading words located on paragraph perimeters (z=5.98, p<.001)

or on lines at the beginning of paragraphs (z=-2.34, p<.05). Words were read more accurately

in the first or second relative to the third paragraph (first vs third: z=2.08, p<.05; second vs

third: z=2.86, p<.005). In terms of continuous coordinate position analyses, page centred

analysis found words were read more accurately when positioned further from the centre of

the screen on the x (z=-3.07, p<.005) and y (z=-5.65, p<.001) axes. Paragraph centred analysis

found words were read more accurately when positioned further from the centre of each

paragraph on the x (z=-3.39, p<.005) and y (z=-5.11, p<.001) axes. In all spatial factors models,

longer words (p<.05) and words occurring earlier in each passage (p<.005) were read more

accurately. Words of lower frequency were read more accurately in the coordinate based

(p<.05) but not text position (p>.1) analyses. In contrast to the impact of spatial factors on PCA

reading, there were no significant effects of any of the spatial variables on tAD patients’

reading accuracy. The control group did not make enough errors to allow for accuracy analysis.

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Figure 8.1 Heatmap of PCA accuracy data from a sample passage.

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Figure 8.2 Order of first forty words read by 1) a tAD patient and in two PCA patients with MMSE scores of 2) thirteen and 3) twenty-two. Numbers refer to where words were repeated, each ‘-‘ in statements indicates a pause of 3 seconds. Under our marking scheme, words were marked as correct regardless of word order: in this way participant 3) was considered to have read thirty-eight of her first forty words correct.

8.3.2. Reading latency

PCA patients (overall latency: 174.0s, SD=73.9) took longer to read passages than tAD

patients (overall latency: 52.0s, SD=18.3; z=3.50, p<.001) and controls (overall latency: 36.2s,

SD=5.2; z=3.50, p<.001). tAD patients were slower than controls (z=2.08, p<.05).

8.3.3. Eye movement data

A summary of mean and standard deviation saccade and fixation data and group

comparison statistics is shown in

Table 8.4. The PCA group made significantly more saccades, more fixations and longer

fixation durations compared to the tAD and control groups. In addition, the PCA group made

significantly more horizontal saccades than the control group and left saccades than the tAD

group, but group differences in saccade amplitude did not reach formal levels of significance.

There were no significant differences in any eye movement measures between the tAD and

control group.

Table 8.4 Eye movement data for PCA, tAD and control groups. Asterisks denote where the PCA group significantly differs from the tAD or control group (vs controls: * = p<0.05; **= p<.005; vs tAD:^=p<.05 ).

Standard paragraph presentation (Baseline)Saccades Fixations

Overall N Left Right Amplitude Overall

N Duration

PCA(N=6)

mean 384 ± 13**^

118 ± 29**^

166 ± 19** 2.0 ± 0.5 405 ±

125**^349 ± 42**^

min|max 265|552 83|180 106|239 1.5|3.0 280|579 291|411

tAD(N=5)

mean 180 ± 72 47 ± 19 98 ± 33 2.1 ± 0.5 190 ± 70 254 ± 34min|max 116|295 26|75 69|144 1.7|2.9 120|302 209|290

control(N=6)

mean 134 ± 22 34 ± 11 80 ± 9 2.3± 0.4 141 ± 22 239 ± 33min|max 92|151 22|54 64|89 2.0|3.1 100|161 176|267

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8.4. DISCUSSION

This investigation demonstrated the debilitating effects of spatial location of text on

reading ability in PCA. All measures of word position (line number, paragraph

perimeter/interior, distance from centre of screen and distance from centre of paragraph)

were significant predictors of reading accuracy, whereas word frequency and word category

were not. Not only were PCA patients slower and less accurate than tAD and control groups,

they also made longer fixations and more saccades and fixations overall.

While almost all previous empirical investigations of acquired dyslexia in PCA have focused

on single-word recognition, the current study set out to characterise reading deficits that

operate at the passage level. These deficits may underlie comments made by PCA patients of

having difficulty following, or moving between, lines of text; comments of this nature were

made by participants in-between testing, along with complaints of text moving (described as

“a gentle sliding” by one participant) consistent with previous reports of PCA patients’

perceived motion of static stimuli (Crutch et al., 2011). PCA patients were less accurate at

reading words positioned within rather than at the edge of paragraphs, words at the end of

paragraphs and words in the second or third rather than the first paragraph of each passage;

PCA patients were less accurate at reading words positioned horizontally and vertically

towards the centre of each paragraph or the centre of the overall passage. The diminished

ability to read words positioned within blocks of text may be a consequence of visual

disorientation, spatial agnosia, spatial attentional impairment and/or enhanced crowding, with

multiple adjacent words increasing the probability of mislocalisation and/or disrupted word

recognition. The propensity for making omission errors illustrates deficits in word localisation

that greatly hinder passage reading in PCA.

Eyetracking data further emphasises the disordered and inefficient quality of passage

reading in PCA. Despite having much poorer reading accuracy than tAD or control participants,

PCA patients made more fixations and saccades and showed increases in fixation duration.

These additional fixations and saccades likely relate to the slower reading speed of PCA

patients, and could arise from a reduced ability to both localise and subsequently correctly

perceive relevant words. The absence of the parafoveal preview benefit (Rayner, 1998),

possibly due to diminished perception of peripheral vision (Crutch et al., 2011; Crutch, 2013),

might account for the increase in fixation duration in the PCA group.

8.5. CHAPTER CONCLUSION126

The current investigation demonstrates the grave difficulties faced by PCA patients in

reading passage text, most prominent of which is a drastic inability to adequately localise

words. The current chapter confirmed the hypothesis that spatial factors are the primary

determinants of PCA passage reading ability; of seven spatial variables, all were found to

predict passage reading accuracy in PCA. Spatial variables did not predict reading accuracy in

tAD or healthy control participants, who read passages at ceiling or near ceiling levels.

Compared to tAD and control participants, PCA patients were slower, less accurate and made

highly inefficient eye movement and fixation patterns.

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9. FACILITATING READING IN PCA


Following the previous chapter, the current investigation intended to create the optimal

conditions for passage reading in PCA by applying perceptual manipulations that minimise or

evade visual deficits in PCA (visual disorientation, spatial agnosia, spatial attentional deficits,

fixation instability, reduced effective field of vision, enhanced crowding). It was hypothesised

that presenting words in a single, central location would reduce the challenge of localising

words within sentences or identifying the onset of subsequent lines of text. Marking this

location with a fixation box might also serve as a permanent cue to location and hence aid

fixation stability. Reports of PCA patients being able to better localise moving stimuli (Crutch,

2013; Midorikawa et al., 2008) further suggested that rather than using rapid serial visual

presentation (RSVP) paradigm to present words to a single location, successively moving words

to fixation may act as a motion cue to assist disorientated readers. Such movement might also

elude potential temporal masking effects occurring with traditional RSVP (Broadbent &

Broadbent, 1987). It was further predicted that restricting text presentation to individual

words would remove crowding effects from adjacent words, and that the risk of the horizontal

and vertical line features of the fixation box themselves crowding the target text could be

attenuated by using opposite contrast polarity (Kooi et al., 1994; Chakravarthi & Cavanagh,

2007). Two interventions were developed on these bases; both moved words successively into

a fixation box of opposite polarity to text. In addition, one intervention presented the

subsequent word in each passage to the right of the fixation box in order to assess whether

participants’ reading would benefit from the presence of words to the right of fixation, as in

healthy individuals (Hyona et al., 2004; Rayner, 1998).

The current investigation evaluated the efficacy of an intervention designed to ameliorate

the impact of spatial, perceptual and oculomotor impairments upon their reading ability by

assessing reading performance and eye movement patterns and comparing them with tAD

patients and healthy controls. The study tested the hypothesis that reducing the spatial,

perceptual and oculomotor demands of passage reading would significantly improve reading

ability in PCA. It was predicted that sequential presentation of words to a single location

marked by a permanent fixation box and motion cues (to reduce the impact of visual

disorientation, spatial agnosia, spatial attentional deficits, fixation instability, reduced effective

field of vision, and enhanced crowding) would yield improvements in both reading

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performance (accuracy, latency and independent ratings of global comprehension) and self-

rated measures of reading experience (ease and comprehension).

9.2. METHODS

Words for both pilots and the main experiment were presented in Arial Unicode MS font

with a visual angle of letter height subtending 0.5° at a distance of 50cm. All passages had a

cut-off time of 300 seconds.

9.2.1. Pilot study 1

9.2.1.1. Pilot stimuli

10 passages from the Readright© website (Leff et al., 2012) consisting of 49 words each

under the following presentation conditions (see Figure 9.1):

i. Baseline: black text in standard presentation.

ii. Reverse polarity (RP): as 1 but with words in alternating polarity (black and white)

iii. Serial single word (SSW): black text was presented one word at a time in a white

fixation box (width: 5.7°, height: 2.3°) at the centre of the screen.

iv. Moving window (MW): black text was presented one sentence at a time through a

fixation box (same dimensions as in presentation 3). Only text in the fixation box

was visible.

v. Moving window- Reverse polarity (MW-RP): as presentation 4; however all text in

each sentence was visible, with text inside and outside the fixation box presented

in opposite polarity.

Each passage was assigned to 1 of the 5 conditions, for a total of 2 passages for each

condition. Passages in each condition were matched on the time taken to read them by

healthy controls.

9.2.1.2. Pilot procedure

4 PCA participants were asked to read aloud all passages. Order of presentation was

arranged in an ABBA design. The first 5 passages were preceded by a practice sentence under

the same presentation conditions (“This is practice text”). The rate of moving text presentation

for MV and MV-RP was varied to select a speed that participants were comfortable with.

9.2.1.3. Pilot results

See Error: Reference source not found for mean accuracy data across the different

presentation conditions. Performance was varied; in the baseline passages, one participant did

129

not make any errors, while another only read 45% of words correctly. There was a trend

overall towards serially presented single words being read more accurately than in the

baseline condition (z=-1.91, p=.057).

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Table 9.1 Accuracy and comprehension performance on Pilot studies 1 and 2.

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Figure 9.1 Presentation conditions for Pilots 1 and 2 (i: baseline, ii: reverse polarity, iii: serial single word, iv: moving window, v: moving window- reverse polarity, vi: sequential triple word presentation)

Pilot Study 1 (N=4) Pilot Study 2 (N=7)

Accuracy (%) Accuracy (%) Comprehension (%)

i. Baselinemean 78.6 ± 23.8 56.0 ± 30.5 41.7 ± 25.8

min|max 44.9|100 21.7|91.9 0|75

ii. Reverse Polarity mean 73.3 ± 26.0 - -

min|max 38.8|96.9 - -

iii. Serial Single wordmean 98.6 ± 1.7 96.8 ± 2.6 54.2 ± 24.8

min|max 95.9|100 91.2|98.8 25|75

iv. Moving Windowmean 89.0 ± 13.2 75.0 ± 16.1 70.8 ± 29.3

min|max 66.3|100 43.6|94.1 25|100

v. Moving Window-

Reverse Polarity

mean 90.4 ± 9.3 - -

min|max 74.5|100 - -

vi. Sequential Triple Wordmean - 94.4 ± 5.0 62.5 ± 13.7

min|max - 84.1|98.3 50|75

9.2.2. Pilot study 2

9.2.2.1. Pilot stimuli

Four articles (mean length= 234.8 words, SD=18.8) each composed of 4 paragraphs (mean

length= 58.6 words, SD=10.1) were selected from the BBC news archive. All articles were

presented twice: 1) entirely in the baseline (standard presentation) condition, and 2) with each

paragraph presented in each of conditions i (baseline), iii and iv from Pilot study 1 (chapter

9.2.1). In addition, a sequential triple word (vi: SQTW) condition was included, in which text

was presented three words at a time, with the central word positioned in the fixation box (see

Figure 9.1). Words were presented to the left and right of the central word by a centre-to-

centre distance of 5.7°. After each response, successive words were moved from right to left

into the fixation box at a velocity of 22.8°/s.

9.2.2.2. Pilot procedure

7 PCA participants were asked to read aloud all articles. Order of presentation was

arranged in an ABBA design, with an equal distribution of presentation conditions across

paragraphs. In order to assess comprehension, statements were generated by taking one

sentence from each paragraph; in half the statements, part of the sentence was replaced with

semantically-related information. Following reading each paragraph, corresponding

statements were read aloud and participants were asked to judge whether they had seen the

statement in the previous text.

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9.2.2.3. Pilot results

See Error: Reference source not found for mean accuracy and comprehension data across

the different presentation conditions. Mean accuracy was 46.2% overall (SD=34.5) for 1) article

entirely presented in the baseline condition. Paragraphs presented in serial single and

sequential triple words were read more accurately than in the baseline condition (SSW: z=-

2.37, p<.05; SQTW: z=-2.37, p<.05).

There was a significant increase in comprehension performance for moving sentences (z=-

2.33, p<.05) and a trend towards an increase in comprehension accuracy for sequentially

presented triple words (z=-1.72, p=.086) relative to the baseline condition.

9.2.3. Main investigation- Single-word and Double-word presentation

9.2.3.1. Stimuli

To contrast with the baseline condition of standard passage reading (as described in

chapter 8) two reading interventions were designed to provide the optimal conditions for

reading in PCA by minimising the spatial, perceptual and oculomotor demands of reading.

1. Sequential Single Word (single-word): passages presented one word at a time in a

central fixation box.

2. Sequential Double Word (double-word): identical to the single-word condition except

that each target word was accompanied by the subsequent word, displayed parafoveally to

the right of the fixation box (mean centre-to-centre distance between target/parafoveal word:

5.7°).

In both the single-word and double-word conditions, words were successively moved at a

velocity of 22.8°/s from a location 5.7° degrees to the right of the fixation box into the centre

of the fixation box where they then remained stationary until read (see Figure 9.2). Both

interventions were presented within a fixation box (height: 2.1°; width: 4.3°) to limit visual

disorientation; the box was in opposite polarity to text, to limit any crowding effects exhibited

by the box on word identification.

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9.2.3.2. Procedure

Participants read aloud the 6 passages described in chapter 8.2.3.1 under 3 conditions of

presentation: baseline (standard presentation: chapter 8), single-word and double-word. A

repeated-measures design was used for PCA and tAD groups, with each passage read in every

condition by each participant to control for lexical and syntactic differences between passages.

Passages were administered in the same order to limit variability in time differences between

sets of repeated passages. Presentation conditions were arranged in an ABBA design within

blocks, with block order arranged in an ABBA design between participants and presentation

condition always differing between trials; in this way order effects were controlled both within

and between participants. The healthy control group was only administered the first block,

with presentation order varying between controls. After each passage, PCA patients were

asked to rate “How easy was it to read the passage?”, “How well did you understand the

passage?” and “How pleasant was it to read the passage?” on a 4-point auditory-verbal scale

(Very, Quite, Not really, Not at all). For the first 3 passages, PCA and tAD patients were also

asked a global comprehension question (“Can you tell me the gist of that article?”) to ensure

participants were extracting semantic information from each passage. General aspects of test

administration, measurement of reading latencies, and eye movement recording and

measures were consistent with chapter 8.


9.2.4.1. Efficacy of interventions

Regression models were identical to those used in chapter 8.2.4.1 except for the

replacement of spatial variables with presentation condition (baseline versus single- or double-

word) as the variable of interest. Pairwise correlations were used to calculate strength of

associations between PCA accuracy data and performance on measures of visual processing

(Crowding [letters with 4 number flankers, all unspaced: i.e. 48G23], spatial analysis [VOSP Dot

Counting, VOSP Number Location] and object perception [VOSP Object Decision]) and disease

severity (MMSE score and disease duration). Measures of visual processing were transformed

into a standardised range (0-100) in which 0 and 100 corresponded to the minimum and 134

Figure 9.2 Sequential single-word and sequential double-word presentations: words appear in the fixation box (1); following participants’ responses, successive words move rapidly (2) into the fixation box (3).

maximum score achieved by any patient. Global comprehension question responses were read

by blinded raters (N=14) and assigned to the paragraph that they felt corresponded most to

each response. Differences between the number of correctly assigned comprehension

questions and self-reported ease, comprehension and pleasantness measures were assessed

using a Wilcoxon signed-ranks test. Errors were classified according to criteria outlined in

Crutch and Warrington (2009).

9.2.4.2. Eye movement data

Differences in saccade and fixation measures between baseline and intervention

conditions within the PCA group were assessed using a Wilcoxon signed-ranks test. Strength of

associations with reading accuracy was calculated using a pairwise correlation test.

9.3. RESULTS

9.3.1. Efficacy of reading intervention

9.3.1.1. Reading accuracy and latency

Percentage error rates and reading latency data for the PCA, tAD and control groups on

the baseline, single-word and double-word conditions are shown in Figure 9.3. There was an

interaction between patient groups and reading intervention; compared to tAD patients, PCA

patients performed significantly more accurately in the single- (z=3.62, p<.001) and double-

word (z=5.81, p<.001) relative to the baseline condition. There was no significant interaction

between group (PCA versus controls) and the reading intervention owing to the near-ceiling

level of performance in the control group (overall error rate: 0.3%).

PCA: Compared with baseline reading performance, the PCA group showed significant

improvements in response accuracy with both the single-word (67% improvement; z=38.17,

p<.001) and double-word interventions (64% improvement; z=34.82, p<.001). These group

level effects were mirrored at the individual patient level, with all PCA patients showing

significant improvement with both single- and double-word interventions; one participant’s

accuracy increased from 24% to 93% using the single-word intervention (see Figure 9.4). The

PCA group performed more accurately in the single- than in the double-word condition (z=-

5.61, p<.001). Lower reading accuracy was revealed for words read later in each passage (z=-

11.35, p<.001) and words of higher frequency (z=-2.3, p<.05). There were no significant effects

of repeats (p>.6) or word length (p>.2). There was no significant difference in reading latency

between the baseline condition and single- (p>.3) or double-word (p>.7) conditions.

135

tAD: Compared with baseline reading performance, the tAD group showed modest

increments in accuracy with the single-word (2.3%; z=4.60, p<.001) and double-word

interventions (1.7%; z=3.24, p<.005). At the single patient level, there were effects of the

single-word intervention in 2/6 and effects of double-word intervention in 1/6 of the tAD

participants; the greatest increase in accuracy for a tAD patient was from 93% to 99%. The tAD

group performed more accurately in the single-word than in the double-word condition

(z=2.00, p<.05). There were no effects of repeated passages (p>.1), word length (p>.3),

frequency (p>.3) or word order (p>.4) on reading accuracy. Reading speed was fastest in the

baseline relative to both the single- and double-word conditions (both z=2.20, p<.05); reading

speed was faster in the double- relative to the single-word condition (z=1.99, p<.05).

Controls: Reading accuracy rates were at ceiling in all three conditions, therefore there

were no significant effects upon accuracy of presentation condition or any of the nuisance

variables. As with the tAD patients, reading speed was fastest in the baseline relative to both

the single- and double-word conditions (both z=2.20, p<.05), and faster in the double-relative

to the single-word condition (z=2.21, p<.05).

136

Figure 9.3 Summary of reading accuracy and latencies for the PCA, tAD and control groups. Error bars show standard error for each group mean.


Error rates for PCA, tAD and control groups under the different presentation conditions are

shown in Figure 9.5. Associations between behavioural measures and reading accuracy under

the different presentation conditions are shown in Table 9.2.

PCA: By far the biggest proportion of errors made by PCA patients in the baseline condition

were omission errors, with patients missing words or whole sections of each passage. Lower

numbers of omission errors were associated with better performance on tests of spatial

analysis (VOSP Dot Counting: r=-.58, p<.05; VOSP Number Location: r=-.55, p<.05) and, to a

lesser extent, crowding (r=-.50, p=.059). Higher numbers of visual errors were associated with

better performance on tests of dot counting (r=.65, p<.01) or number location (r=.47, p=.077);

no significant associations were found between the number of visual errors and measures of

137

Figure 9.4 PCA participants’ reading accuracy for baseline (standard presentation) and under both reading interventions.

Figure 9.5 Error rates on a logarithmic scale for PCA, tAD and control groups under different presentation conditions. ‘Other’ errors include phonological, derivational and miscellaneous errors.

crowding (p>.1). In the single-word condition, no significant associations were found between

numbers of omission errors and measures of dot counting (p>.4), number location (p>.7) or

crowding (p>.7). Lower numbers of visual errors were significantly associated with better

performance on measures of crowding (r=-.52, p<.05) but not dot counting (p>.3) or number

location (p>.4). In the double-word condition, lower numbers of omission errors were

associated with better performance on dot counting (r=-.64, p<.05), and to a lesser extent,

number location (r=-.49, p=.071); there was no significant association between omission errors

and performance on crowding measures (p>.2). Numbers of visual errors were not significantly

associated with measures of crowding (p>.4) or spatial analysis (Dot Counting: p>.4; Number

Location: p>.2).

Table 9.2 Correlations between PCA performance on behavioural measures and reading accuracy under different presentation conditions (*=p<.05).

Measures of visual processing Disease severity

Crowding Dot counting

Number Location

Object Decision MMSE Disease

durationBaseline accuracy .57* .61* .60* .36 .11 -.27

Single-word accuracy .53* .36 .28 .43 .04 -.01

Double-word accuracy .34 .61* .54* .23 .41 -.25

9.3.1.3. Self-reported measures

PCA patients’ self-reported measures of reading experience in the three reading conditions

are shown in Figure 9.6. Overall, the PCA group rated reading passages in both intervention

conditions to be significantly easier (single-word: z=3.41, p<.001; double-word: 3.30, p<.005),

more pleasant (single-word: z=3.24, p<.005; double-word: z=2.58, p<.05) and more readily

understood (single-word: z=3.38, p<.001; double-word: z=3.14, p<.005) than in the baseline

condition. The PCA group also considered passages in the sequential single-word condition to

be significantly easier (z=2.18, p<.05) and more pleasant to read (z=2.15, p<.05), but not better

understood (p>.2) than in the double-word condition.

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9.3.1.4.

Global

comprehension

PCA: 90.9% of responses were correctly assigned to passages in the baseline condition;

96.2% of responses were correctly assigned in the single-word and 88.3% in the double-word

conditions. There were no significant differences between measures of global comprehension

between any of the presentation conditions.

tAD: 86.9% of responses were correctly assigned to passages in the baseline condition;

72.6% of responses were correctly assigned in the single-word and 82.1% in the double-word

conditions. There were no significant differences between measures of global comprehension

between any of the presentation conditions.

There was a trend towards PCA patients having better global comprehension than tAD

patients in the single-word condition (z=1.82, p=.068). There was no significant between-group

differences in the baseline (p>.2) or double-word conditions (p>.5).

9.3.2. Eye movement data

The mean and standard deviation saccade and fixation data for the baseline, single-word

and double-word conditions and group comparison statistics are shown in Table 9.3. As with

the baseline condition (as reported in chapter 8.3.3), PCA patients made significantly more

saccades and more fixations than either the tAD patients or the controls in both the single-

word and double-word reading interventions. However, the single-word condition was the

only reading condition in which the duration of PCA patients’ fixations did not differ

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Figure 9.6 Proportion of passages under the different presentation conditions that were considered easy, pleasant or well understood by PCA patients.

Table 9.3 Eye movement data for PCA, tAD and control groups in the reading intervention conditions. Asterisks denote where the PCA group significantly differs from the tAD or control group (vs controls: * = p<0.05; **= p<.005; vs tAD:^=p<.05).

Single-word presentationSaccades Fixations


N Duration

PCA(N=6)

mean 336 ± 52**^

111 ± 21**^

131 ± 53* 1.2 ± 0.5^ 355 ±

49**^ 467 ± 71

min|max 276|385 88|144 73|199 0.8|2.0 288|410 375|577

tAD(N=5)

mean 197 ± 56 50 ± 19 69 ± 33 0.8 ± 0.2 214 ± 55 544 ± 168

min|max 142|260 31|78 15|92 0.5|0.9 148|275 362|785

control(N=6)

mean 152 ± 50 36 ± 23 69 ± 28 0.8 ± 0.2 176 ± 52 533 ± 152

min|max 99|228 13|77 27|107 0.5|1.1 106|237 390|783

Double-word presentationSaccades Fixations


N Duration

PCA(N=6)

mean 383 ± 63**^

141 ± 42*^

168.0 ± 33.2** 2.1 ± 0.8 402 ±

68**^393 ± 65^

min|max 327|464 70|190 128|213 0.9|3.4 332|501 324|499

tAD(N=5)

mean 255 ± 68 91 ± 31 132 ± 32 2.4 ± 1.0 264 ± 65 305 ± 52

min|max 168|335 39|119 88|169 1.5|4.0 185|344 216|347

control(N=6)

mean 186 ± 46 65 ± 25 102 ± 20 2.5 ± 0.6 199 ± 47 360 ± 81

min|max 146|267 42|109 71|122 1.7|3.2 147|284 277|473

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significantly from that of either tAD patients or controls, representing an increase in fixation

duration relative to the baseline condition (z=2.20, p<.05). The single-word condition also led

to saccades of decreased amplitude relative to the baseline condition (z=-1.99, p<.05).

9.4. DISCUSSION

While chapter 8 implicated spatial, perceptual and oculomotor factors in determining

passage reading ability in PCA, the current investigation applied perceptual manipulations to

text in an effort to circumvent these factors along with associated deficits. Two reading

interventions, single-word and double-word presentation, were evaluated; both interventions

intended to lessen the spatial demands of passage reading. The interventions resulted in

dramatic improvements in the reading accuracy of PCA patients; single-word presentation

resulted in a mean increase of 67.3% in reading accuracy, with one participant’s accuracy

almost tripling from 24% to 93%. Single-word presentation led to increases in PCA patients’

fixation duration and decreases in saccadic amplitude, suggesting improved fixation stability

and reduced saccadic demands under this condition.

This investigation demonstrated how two reading interventions resulted in considerable

and consistent gains in PCA patients’ reading accuracy. These gains were accompanied by

improvements in the self-reported ease of reading, reading comprehension and pleasantness.

Evidently, the serial presentation used in both interventions eliminates frequent difficulties

experienced by PCA patients in repeating or skipping lines of text. By reducing the spatial and

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oculomotor demands of reading, this serial presentation may have reduced the susceptibility

of PCA patients’ reading ability to deficits in early or spatial vision. Neuropsychological findings

suggest poor reading in the baseline condition is primarily linked to visuospatial impairment;

findings also associate enhanced visual crowding with poor passage reading ability, although it

is probable that such crowding is not only inhibiting word localisation but also word

recognition. Associations between neuropsychological measures of visuospatial ability/visual

disorientation and reading accuracy were found in the baseline and double-word, but not

single-word condition, while associations between crowding effects and reading accuracy were

found in the baseline and single-word, but not double-word condition. In addition, omission

errors were associated with measures of visuospatial ability and visual disorientation in

baseline and double-word, but not single-word conditions. One possibility is that the

detrimental effects of deficits such as visual disorientation and enhanced crowding on reading

are mitigated under intervention presentations; this might be due to the reduced amount of

visual information that can compete or interfere with a target word. However, associations

between enhanced crowding and reading ability under single-word presentation and visual

disorientation and reading ability under double-word presentation suggest that text read

under either intervention did not wholly evade weaknesses in other forms of visual processing.

While eyetracking measures identified increases in PCA participants’ fixation duration under

the single-word condition, with the resulting duration being near equivalent to that of tAD and

control participants, elevated numbers of saccades and fixations in the PCA relative to tAD or

control groups were not restricted to the baseline condition. While the single-word

intervention did not result in a decrease in the number of eye movements, the decrease in

saccade amplitude under the single-word condition might suggest a reduction in saccadic

demands.

While the single- or double- word presentation might provide the basis of a reading

application, other directions might be available which further improve upon PCA patients’

performance. Eye movement abnormalities in PCA patients may reflect similar deficits to

those observed in dyslexic children who have unsteady eye control and report stationary text

to be moving (Stein, 2003). Such deficits have been linked to impairments of the magnocellular

system (Ray et al., 2005) leading to a reduced capacity to inhibit the parvocellular system

following saccades; Lovegrove et al. (1990) suggested this may result in a lessened ability to

erase visual images from previous eye movements. As the magnocellular pathway is inhibited

by certain light wavelengths (Roorda & Williams, 1999), coloured filters might facilitate

magnocellular function in PCA patients. Coloured filters have already been shown to improve

the reading ability of dyslexic children (Solman et al., 1991; Robinson & Foreman, 1999), fluent

adult readers (Chase et al., 2003) and have resulted in a reduction in visual disorientation in a

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PCA patient (Crutch et al., 2011). Another visual deficit that might be addressed in a reading

application is the inverse size effect, which limits single word recognition in PCA and has

plausible roles in limiting the parafoveal preview benefit and exacerbating visual

disorientation. If this effect is contributed towards by visual field defects, it is possible that

visual field expansion resulting from prism adaptation might provide further support to PCA

patients. Finally, while both interventions likely moderate crowding effects by restricting the

potential for adjacent words to act as inhibitory flankers, it is still possible that enhanced

crowding is somehow interfering with reading at the single-word level (Crutch & Warrington,

2009). By varying letter contrast polarity within words without compromising the word form, it

may be possible to further limit crowding disrupting reading ability.

There are several limitations regarding interpretations of the current data. Lacking

measures of the inverse size effect, it is not possible to investigate its role in limiting passage

reading ability. Near-ceiling accuracy and small numbers of participants in the tAD group

prevent the identification of factors which are reducing reading ability in tAD relative to

control participants. While each passage was preceded by a drift correct, the time taken to

read passages meant that eye movements were often not aligned with interest areas by the

end of each passage, which meant that word-based or interest area analyses were not

feasible.


Poor passage reading in PCA is likely a consequence of various deficits: poor visuospatial

ability and spatial attention, fixation instability, a reduced effective field of vision and to a

lesser extent, enhanced crowding. This investigation outlines two reading intervention

techniques, both of which adopt text presentation methods that are less susceptible to these

deficits. Both interventions result in clinically-meaningful increases in reading ability, and

subjective increases in reading ease, comprehension and pleasantness. Eyetracking data

suggests that, in particular, the single-word intervention promotes a greater efficiency of eye

movements. These encouraging findings provide the foundations for novel software-based

assistive technology that will support reading ability in PCA patients.

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10.THESIS CONCLUSIONS


PCA is a debilitating condition involving progressive visual impairment; such impairment

prevents PCA sufferers from carrying out a range of ADLs, reducing their quality of life and

demanding increased assistance from their carers. An instrumental ADL which PCA patients

have frequent and early difficulty with is reading (Benson et al., 1988; Freedman et al., 1991;

Berthier et al., 1991; Mendez et al., 2002; McMonagle et al., 2006). The importance of reading

is highlighted by elderly participants rating reading as one of the top three instrumental ADLs

required for maintaining community living (Fricke & Unsworth, 2001).

This thesis aimed to investigate the nature of reading impairment in PCA by assessing the

impact of perceptual factors and the contribution of various visual deficits towards reading

dysfunction (chapter 2.2). One form of early visual processing deficit which may particularly

limit visuoperceptual and reading ability is crowding (chapter 2.2.1.1); however, previous

observations of enhanced crowding effects in PCA are based on a handful of case reports. Case

reports form almost all previous investigations of acquired dyslexia in PCA (chapter 2.3); these

tend to focus on single word recognition, and have not attempted quantitative analysis of

reading above the single word level. Visual assessment of PCA patients can be challenging

given how such patients often demonstrate impairments in multiple domains; not controlling

for deficits such as a reduced effective visual field (chapter 2.2.2) or visual disorientation

(chapter 2.2.1) may confound specific measures of visual or reading ability.

This thesis conducted systematic group investigations of visual crowding, single word

recognition and passage reading in PCA. These investigations attempted to gauge the

prevalence and quality of visual and reading deficits through analysis of accuracy, latency, eye

movement and self-reported data, and to understand the brain basis of specific aspects of

visual and reading functions through selected VBM neuroimaging analyses. A consistent and

comprehensive battery of neuropsychological measures was employed to assess relationships

between different aspects of visual function and identify confounding visual deficits. Findings

were contrasted with healthy controls and AD patients with an amnestic presentation. A case

series of two patients was also studied and followed longitudinally, exploring the causative

role of visual deficits in acquired dyslexia. Having identified aspects of reading which are

particularly vulnerable to visual impairment in PCA, two reading interventions were developed,

based on the notion that navigating such impairment might facilitate reading.

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The relationships between different forms of visual processing have implications for our

understanding of how perception is limited in PCA and moderate to severe tAD. Improving this

understanding will inform the design of technological aids which will maximise weak visual

ability or emphasise the use of relatively spared aspects of vision, ideally resulting in greater

independence, quality of life and a reduction in carer burden.

10.2. ROLE OF EXCESSIVE CROWDING IN LETTER RECOGNITION

Previous case reports of PCA have revealed performance on flanked letter identification

tasks in line with prominent crowding effects (chapter 2.2.1.1). Chapter 4 identified group

effects of spacing (chapter 4.3.1) and ameliorating effects of opposite polarity flankers

(chapter 4.3.2), confirming the qualitative similarity between centrally-presented flanked letter

identification deficits in PCA and flanked letter identification deficits in normal peripheral

vision. The current neuroimaging findings support the anatomical locus of crowding as being

within the right collateral sulcus in these patients, a region which may correspond to area V4

(chapter 4.3.5). Area V4 fulfils various criteria for the locus of crowding (see chapter 4.4), while

fMRI investigation of healthy individuals has found that, of areas V1-V4, crowding effects

provoked greatest activation in area V4 (Anderson et al., 2012). In the context of proposals of

a model of crowding involving feature detection and higher-level feature integration stages

(Levi, 2008), this locus, along with behavioural data suggesting errors do not predominantly

arise from a failure to detect target stimuli, evokes crowding in the current PCA patient group

as a consequence of deficits in feature integration. These investigations suggest PCA may

provide a novel perspective into the nature of crowding; a neurodegenerative approach offers

an opportunity to track the evolution of crowding deficits and assess how such deficits may

affect other parts of the visual system. Future research will further clarify the nature of

enhanced crowding effects in PCA through pathological investigation, in particular, associating

occipital distribution of AD pathology to flanked letter identification deficits and investigating

whether the susceptibility of certain cell populations to AD relates to the expression of

crowding as a deficit in feature integration versus one of feature detection. Behavioural

investigations will examine the contribution of crowding towards higher level deficits in word,

object and scene perception in PCA. Future neuroimaging studies will aim to identify neural

correlates of the polarity effect, which will improve our understanding of crowding and

provide suggestions how flankers of opposite polarity are less susceptible to spacing effects.

10.3. ROLE OF PERCEPTUAL FACTORS IN WORD RECOGNITION

Results highlight a curious visual phenomenon in PCA, in which patients were slower and

less accurate reading words in larger font. The magnitude of this inverse size effect (chapter

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2.2.2) was associated with grey matter volume in the right superior parietal lobule (chapter

5.3.3). The inverse size effect may reflect diminished ability to perceive peripheral stimuli

and/or to execute spatial shifts of attention or multiple saccades. Chapter 5.3 did not find

overall patterns characteristic of neglect dyslexia in PCA, with less than a fifth of errors being

consistent with neglect errors (chapter 5.3.1.4). The lack of LBL reading from chapters and 6

question general visual accounts of LBL reading, which propose that deficits in prelexical visual

processing lead to length effects on reading latencies. Despite deficits in word recognition,

results also outline the surprising efficacy of single word reading given the myriad of visual

difficulties experienced in PCA.

Mendez et al. (2007) suggested the term “apperceptive alexia” to describe the

contribution of visuoperceptual and visuospatial deficits towards poor reading ability in PCA.

Chapter 5.3.1.2 identified how differences in reading ability between PCA and tAD patients

could be accounted for in particular by visuoperceptual, but also visuospatial and early visual

processing. The relationship between visuoperceptual ability and word reading is unsurprising,

given associations between ventral systems and parallel letter processing (Vinckier et al., 2006)

and between deficits in object perception and alexia (Farah, 1991; Glosser et al., 2002). From a

neuroanatomical perspective, it is possible that damage to occipito-temporal ventral pathways

might result in alexia coinciding with visual agnosia (Rumiati & Humphreys, 1997).

Chapter 5.3.1.2.1 outlined the contribution of visuospatial processing towards poor word

recognition in PCA relative to tAD patients. Poor visuospatial ability may underlie PCA patients’

poor reading ability for words with inter-letter spacing or words in cursive font, given

suggestions of words in unfamiliar presentations demanding support from dorsal systems

(chapter 5.4) which are greatly disrupted in PCA. The dysfunctional involvement of dorsally-

mediated reading strategies might account for previous observations of PCA patients having

difficulty reading words with crosshatched letters (Mendez & Cherrier, 1998), in stylised font

(Mendez, 2001) or cursive font (De Renzi, 1986). Visuospatial impairment may also directly

contribute to, or at least compound, the inverse size effect; reading larger words might place

greater demands on visuospatial ability and/or increase the number of spatial shifts in

attention.

Chapter 5.3.1.2.1 also demonstrated how, among measures of early visual processing,

prominent crowding deficits were particularly important in accounting for differences in

accuracy between PCA and tAD patients. Chapter 6 identified how two patients with preserved

reading demonstrated impairments on all measures of visual function apart from tests of

acuity and crowding, while chapter 7 documented how the evolution of crowding effects in

these patients was accompanied by the emergence of reading dysfunction. Mendez et al.’s

(2007) term “apperceptive alexia” also referred to difficulties in perceptual integration; while

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authors were referring to such difficulties relating to deficits in attention, they could also

reflect the excessive feature integration observed in studies of crowding (Levi et al., 2002; Pelli

et al., 2004). Future investigations will examine the precise mechanism through which

crowding disrupts word recognition and/or parallel letter processing; for example, does

crowding lead to a loss of letter feature detection, excessive feature integration across letters

and does the nature of crowding deficits vary between PCA patients.

10.4. PERCEPTUAL FACTORS IN PASSAGE READING AND READING

INTERVENTIONS

Dramatic visual disorientation and visuospatial impairment are characteristic symptoms of

PCA (chapter 1.2.2.6; chapter 2.2.1; chapter 2.2.2). Such deficits may not necessarily limit

individual word recognition but may still result in gross disruptions of text reading. Mendez

(2001) reported a PCA patient with poor visuospatial ability who had difficulty in localising the

beginning of lines of text, but whose single word reading was significantly preserved. Previous

descriptions of sentence and passage reading in PCA outlined how patients lose their place in a

block of text, within sentences or even on reading cards (chapter 2.3); however, there have

been no attempts to empirically investigate the nature of these reading difficulties in PCA.

This thesis quantitatively assessed passage reading in PCA, and identified how spatial

aspects of text were the crucial determinants of reading ability. Words located towards the

centre of paragraphs or passages were read less accurately, words on the edges of paragraphs

or lines of text were read more accurately (chapter 8.3.1). The overwhelming majority of

errors related to patients missing whole lines of text or individual words within lines. Eye

movement recordings revealed how PCA patients made an excessive number of saccades and

fixations, which likely reflect a combination of deficits in spatial vision and relate to reduced

reading speed in PCA (chapter 8.3.3).

Assessment of passage reading was accompanied by two reading interventions which

intended to reduce the vulnerability of reading in PCA to the following deficits: visual

disorientation, spatial agnosia, spatial attentional deficits, fixation instability, a reduced

effective field of vision and enhanced crowding (chapter 9). The mechanism of both

interventions involved limiting the spatial, perceptual and oculomotor demands of reading by

presenting passages one word at a time within a fixation box. Reading interventions resulted in

consistent gains in reading accuracy for PCA patients; the efficacy of these interventions is a

direct demonstration of how reducing these factors benefits text reading in PCA. This is not to

say that the effect of deficits in spatial vision is completely abolished under intervention

conditions; relative to controls and tAD patients, PCA patients read slowly and made excessive

eye movements. In addition, findings still associate single-word presentation reading ability

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with performance on measures of crowding and double-word presentation reading ability with

measures of visuospatial ability (chapter 9.3.1.2), suggesting the continued influence of certain

visual processing deficits on text perception under both interventions. Future investigations

will assess and discriminate the relative contributions of visual disorientation, spatial agnosia,

spatial attentional deficits, fixation instability, a reduced effective field of vision and enhanced

crowding.

10.5. CAUSATIVE ROLE OF VISUAL IMPAIRMENT IN READING

The notion of reading as a highly-specialised process is controversial (chapter 6.1). Classic

studies of pure alexia emphasised the discrepancy between word or letter recognition and

other forms of visual processing (Kinsbourne & Warrington, 1962; Warrington & Shallice, 1980;

Arguin & Bub, 1993), with more recent neuroimaging studies suggesting a region termed the

VWFA is functionally specialised for letter and word processing (Cohen et al., 2000). Given the

recency of written relative to spoken language as a cultural invention, it is unlikely that a VWFA

would have evolved specifically for reading. However, one suggestion is that accumulated

reading experience promotes the specialization of a pre-existing inferotemporal pathway for

higher-order visual processing (McCandliss et al., 2003).

A general visual account perspective on LBL reading is difficult to reconcile with findings

from two patients, FOL and CLA, who show profound visual dysfunction, yet demonstrate

remarkably preserved whole word and letter reading and an absence of the length effects

observed in previous reports of LBL readers (chapter 6). Results are interpreted as showing

preservation of orthographic processing despite impaired prelexical processing, underlining

the resilience of the reading system and indicating that deficits on ten measures of visual

processing do not necessitate poor word recognition. The notion of PCA patients bypassing

perceptual deficits in order to achieve access to word forms has been previously suggested by

Mendez et al. (2007), and receives support from two PCA patients demonstrating a word

superiority effect (Mendez & Cherrier, 1998).

While both patients in chapter 6 read both accurately and rapidly, longitudinal assessment

in chapter 7 revealed decreases in their reading ability; these decreases were accompanied by

the emergence of spacing effects on flanked letter identification tasks which are characteristic

of visual crowding (chapter 2.2.1.1). This is consistent with the inhibitory effects of crowding

on reading in normal peripheral vision (Chung et al., 2004; Legge et al., 2001; Pelli et al., 2007).

However, FOL, not CLA, showed effects of letter similarity on reading speed; such effects might

be predicted given crowding is exacerbated by flankers of increased visual similarity. Findings

strongly implicate enhanced crowding in limiting FOL and CLA’s reading ability and provide

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further evidence of the relationship between crowding and word recognition. Questions are

raised regarding variations in the use of serial reading strategies and the locus of crowding in

both patients. Subsequent pathological investigation may identify different crowding

mechanisms; from the perspective of a two-stage model of crowding, striatal involvement

would predict deficits in feature detection, and extrastriatal involvement would predict deficits

in feature integration.

10.6. IMPLICATIONS FOR MODELS OF READING

While our findings emphasise the role of perceptual factors in determining reading ability,

the disruption of word recognition arising from PCA patients’ visual impairment is likely

compounded by deficits in phonology. While this investigation only assessed phonological

ability to a very limited extent, phonological deficits have been previously reported in PCA

(Crutch et al., 2013; Magnin et al., 2013). PCA patients’ tendency to make visual errors in

chapter 5 might be exacerbated by a diminished ability to monitor their phonological output,

allowing such errors to go unchecked (Crutch & Warrington, 2007b). A connectionist approach

might predict that such phonological deficits would result in a greater reliance on an indirect

route via semantic knowledge; in chapter 5, slightly more accurate reading for words of

greater concreteness in PCA but not tAD patients lends tentative support to this perspective.

Evaluating a dual-route approach is particularly difficult in the context of PCA; the indirect

route involves serial processing and phonology, both of which are likely vulnerable to

impairment of dorsal systems and parietal atrophy in these patients. However, two attributes

of the DRC model, a lack of involvement of the semantic pathway in reading aloud of normal

words and an insensitivity to orthographic bodies (Woollams et al., 2007; Grainger & Dufau,

2012) might limit the ability of the model to account for subtle effects of both concreteness

and orthographic neighbourhood size on PCA patients’ single word recognition (chapter 5).

Future directions might include a more comprehensive investigation of concurrent visual and

phonological impairment in PCA to clarify the extent to which poor reading ability might be a

result of a multicomponent dyslexic syndrome.

From a neuroanatomical perspective, our results do not directly address the purported

role of the VWFA; PCA patients did not show evidence of LBL reading, and the evolution of

length effects on reading speed in FOL (chapter 7) was attributed to the emergence of

crowding rather than the compromised integrity of the VWFA leading to a specific deficit in

orthographic processing. However, the current data add to previous findings on the

contribution of dorsal systems towards serial processing of words presented in unfamiliar

formats (Vinckier et al,. 2006; Cohen et al., 2008). Results suggest that the involvement of

parietal-mediated spatial-attentional processes in reading unfamiliar words results in a

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particularly drastic reduction in reading ability, not only for words with an abnormally great

extent of inter-letter spacing, but also possibly for words of increased font size, given how

reading words at greater eccentricities provokes greater posterior parietal fMRI activation

(Cohen et al., 2008). Furthermore, chapters 8 and 9 emphasise the role of dorsal impairment in

undermining reading ability above the single-word level. Despite this notion of dorsal systems

supporting recognition of unfamiliar words, it is not unlikely that the early visual and

visuoperceptual difficulties experienced by PCA patients are provoking dorsal involvement for

words under conditions that normal readers would consider sufficient for parallel processing;

the extent of this involvement would be clarified through functional imaging studies of

normally-presented single word recognition in individuals with PCA.

10.7. CLINICAL IMPLICATIONS

This thesis applied findings from studies of early visual processing, word recognition and

passage reading in PCA to inform the basis of two interventions founded on a similar principle:

that minimising the susceptibility of word localisation and recognition to deficits in spatial,

perceptual and oculomotor function might result in benefits to reading ability. Being able to

maximise reading in patients with early to moderate stage PCA would help maintain

professional and recreational activities, likely resulting in greater independence for patients

and benefits in quality of life for patients and carers. PCA participants showed consistent gains

in accuracy (chapter 9.3.1.1) and strong preference for passages read under both interventions

(chapter 9.3.1.3), supporting their clinical utility. A concern was that the presentation format

of the interventions would result in each trial becoming an exercise in word, not passage,

reading, so that the global aspects of passages would elude participants. However, beyond PCA

patients’ rating of their own comprehension as being better in the intervention conditions,

measures of global comprehension as judged by independent raters did not detect any loss in

comprehension in the intervention conditions (chapter 9.3.1.4).

While both reading interventions were promising, some notable caveats must be

acknowledged in order to shape future development of a patient-friendly reading application.

Despite improvements in reading accuracy, neither intervention resulted in increases in

reading speed (chapter 9.3.1.1). As the experimenter controlled the rate of text presentation

in both interventions, it is possible that, if either intervention formed the basis of a reading

application, rates of presentation maintained by users themselves might be more efficient. As

apraxia is a common symptom of PCA (Tang-Wai et al., 2004; McMonagle et al., 2006) any

potential reading application would have to be able to determine the rate of presentation

based on auditory in addition to motor information. Some degree of carer input would likely

be required to set up a future reading application, after which subsequent activation of the

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device, text selection and optimal rate of presentation could be achieved through voice

recognition software. While both interventions likely moderate crowding effects by restricting

the potential for adjacent words to act as inhibitory flankers, it is still possible that enhanced

crowding is interfering with reading at the single-word level (Crutch & Warrington, 2009). By

varying letter contrast within words without compromising the word form, it may be possible

to limit the disruptive effects of crowding on individual words.

The current findings may inform future remedial approaches that are relevant not only to

reading difficulties, but also the wider context of impaired vision in PCA. Facilitation of

magnocellular function using coloured filters may help address perceived motion of static

stimuli and visual disorientation in PCA (Crutch et al., 2011), while visual field expansion using

prism adaptation may attenuate the inverse size effect. Such approaches might result in

benefits to safety, including reduced numbers of falls, and neuropsychiatric symptoms

stemming from visual impairment.

10.8. PCA v tAD

Background neuropsychological assessment in studies of excessive crowding, single word

recognition and passage reading revealed cognitive phenotypes in line with previous

descriptions of PCA and tAD (chapter 1.2.2.6; chapter 4.2.2; chapter 8.2.2) Relative to tAD

patients, PCA patients performed better overall on tests of recognition memory, with a greater

proportion of PCA patients performing within a normal range for these tests. PCA patients

performed worse on non-visual tests associated with parietal function, such as calculation or

cognitive estimates. On tests of visual function, PCA patients consistently demonstrated

impairments relative to healthy individuals and tAD patients. tAD patients did show weak

performance on tests of visuospatial function, although this is not uncharacteristic of AD

patients with a predominantly amnestic presentation (Caine & Hodges, 2001; Quental et al.,

2013).

Findings suggest that the pattern of performance in tAD on tasks of flanked letter

identification, single word recognition and passage reading is more in line with controls than

PCA patients. Almost without exception, group differences in reading or letter identification

accuracy between tAD and controls did not reach statistical significance. However, the

development of visuospatial deficits over the course of tAD (Grady et al., 1988; Almkvist, 1996;

Salmon & Bondi, 2009) may coincide with poor reading for reading words in unfamiliar formats

or words presented in passages, given the proposed contributions of dorsal systems towards

word and passage reading (chapter 10.3, chapter 10.4). It is possible that pathological

involvement of the occipital lobe may lead to the development of prominent crowding effects

in tAD; while some post-mortem studies of tAD patients have found little to no evidence of

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atrophy or AD related pathology in the occipital region (Brun & Gustafson, 1976; Galton et al.,

2000), other studies have found considerable deposits of neuritic plaques in the occipital lobes

(Arnold et al., 1991).

The current study matched PCA and tAD groups on two markers of disease severity: MMSE

score and disease duration. While differences in visual function were apparent between

groups, decline in visuoperceptual and visuospatial ability in later stages of tAD (Grady et al.,

1988; Paxton et al., 2007) may result in visual deficits that echo those in early stage PCA.

Improving our understanding of visual ability and developing approaches and technological

aids that support vulnerable aspects of vision in PCA may have implications for a wider AD

population.

10.9. CHAPTER CONCLUSION

Investigations in this thesis not only outline the considerable contribution of visual

dysfunction to reading difficulties in PCA, but also differentiate the contributions of specific

visual processing deficits when reading words presented normally, in unfamiliar formats, in

isolation or in passages. Results also demonstrate how various visual processing deficits do not

necessitate poor reading. Findings clarify the quality and prevalence of prominent visual

crowding in PCA and emphasise its role in limiting reading ability. Reading interventions are

developed on the premise that limiting the spatial, perceptual and oculomotor demands of

text reading supports reading ability in PCA. Better understanding of the complex nature of

visual impairment in PCA will provide the foundation for approaches towards supporting or

circumnavigating particularly weak aspects of vision; such approaches may also benefit

patients in later stages of amnestic AD.

152

PUBLICATIONS

The following section refers to investigations which have been published or submitted based on the current findings along with other publications which I contributed towards during my PhD.

Chapter 5 Yong K. X., Shakespeare T. J., Cash D., Henley S. M. D., Warren J. D., Crutch S. J. (2013)

(Con)text-specific effects of visual dysfunction on reading in posterior cortical atrophy.

Chapter 6Yong, K. X., Warren, J. D., Warrington, E. K., & Crutch, S. J. (2013). Intact reading in

patients with profound early visual dysfunction. Cortex.

Other publicationsShakespeare, T. J., Yong, K. X., Frost, C., Kim, L. G., Warrington, E. K., & Crutch, S. J. (2013).

Scene perception in posterior cortical atrophy: categorization, description and fixation patterns. Frontiers in human neuroscience, 7, 621 doi: 10.3389/fnhum.2013.00621.

Witoonpanich, P., Cash, D. M., Shakespeare, T. J., Yong, K. X., Nicholas, J. M., Omar, R., ... & Warren, J. D. (2013). Olfactory impairment in posterior cortical atrophy. Journal of Neurology, Neurosurgery & Psychiatry, 84(5), 588-590.

153

ACKNOWLEDGEMENTS

I would like to thank all the patients and their carers for all their patience and goodwill and

the considerable time and effort they volunteered towards research at the Dementia Research

Centre.

This thesis was contributed to in no small part by various members of the Dementia

Research Centre. In particular, I would like to thank my supervisors, Seb and Jason and my

colleague Tim for their generous support and expertise. I would like to thank Nick Fox,

Jonathan Bartlett, Jenny Nicholas, Manja Lehmann, Susie Henley, Dave Cash, Kelvin Leung,

Elizabeth Warrington and Alex Leff for their invaluable input regarding presentation skills,

statistical methods, imaging techniques, neuropsychology and eyetracking.

I would also like to thank people at work who have helped out with various issues that

have cropped up over the last three years: Anne Parnell, Suzie Barker, Nicole Schmitz, Laura

Downey, Hannah Golden, Felix Woodward, Kirsty Macpherson, Aisling Buckley, Laila Ahsan, Liz

Gordon, Emily Manning, Ian Malone, Casper Nielsen, Shona Clegg, Kate MacDonald, Jane

Douglas, Katy Judd, Amanda Haines, Laura Monje Garcia, Ayesha Khatun, Deepali Patel, Natalie

Ryan, Yuying Liang, Tom Yeatman, Ross Paterson, Catherine Slattery and Kishan Rajdev.

I would particularly like to thank Edith Tan, who should take most of the credit for me

completing my PhD. I would also like to thank Juanita Hoe, Martin Orrell, Elisa Aguirre, Amy

Streater, Lauren Yates, Alex Feast, Nadia Crellin, Cathy Forbes and Fiona Horton for their

support and supervision as a member of the SHIELD team.

This work was supported by an Alzheimer’s Research UK Senior Research Fellowship to

Sebastian Crutch. Jason Warren is supported by a Wellcome Trust Senior Clinical Fellowship.

This work was supported by the NIHR Queen Square Dementia Biomedical Research Unit. This

work was undertaken at University College London Hospital/University College London which

received a proportion of funding from the Department of Health’s NIHR Biomedical Research

Centres funding scheme.

154

APPENDIX 1: DUBOIS ET AL. (2007) DIAGNOSTIC CRITERIA FOR PROBABLE AD

A) Core diagnostic criteria:

i. Presence of an early and significant episodic memory impairment that includes the following features:

a. Gradual and progressive change in memory function b. Objective evidence of significantly impaired episodic memory on

testingc. Association or isolation of episodic memory impairment at AD onset or

advancement

B) Supportive features

ii. Presence of medial temporal lobe atrophy (hippocampi, entorhinal cortex, amygdala) on MRI

iii. Abnormal CSF tau/Aβ 1-42 ratio: low Aβ 1-42, increased total tau concentrations, increased phosphor-tau concentrations or a combination of the three

iv. Reduced metabolism in bilateral temporoparietal regions on PET imagingv. AD autosomal dominant mutation within immediate family

C) Exclusion criteria

i. Sudden onset, occurrence of gait disturbances, seizures and behavioural changes

ii. Focal neurological features and early extrapyramidal signsiii. Presence of medical disorders that can account for memory and related

symptoms

155

APPENDIX 2: NINCDS-ADRDA 2011 CRITERIA FOR DEMENTIA AND PROBABLE AD

I) CRITERIA FOR ALL-CAUSE DEMENTIA: CORE CLINICAL CRITERIA

Dementia is diagnosed when there are cognitive or behavioural (neuropsychiatric) symptoms that:

i. Interfere with the ability to function at work or at usual activitiesii. Represent a decline from previous levels of functioning and performing

iii. Are not explained by delirium or major psychiatric disorderiv. Cognitive impairment is detected and diagnosed through a combination of (1)

history-taking from the patient and a knowledgeable informant and (2) an objective cognitive assessment

v. The cognitive or behavioural impairment involves a minimum of two of the following domains:

a. Impaired ability to acquire and remember new informationb. Impaired reasoning and handling of complex tasks, poor judgmentc. Impaired visuospatial abilitiesd. Impaired language functions e. Changes in personality, behaviour, or comportment

II) PROBABLE AD DEMENTIA

A) Core clinical criteria:

Probable AD dementia is diagnosed when the patient meets criteria for dementia described earlier in the text, and in addition has the following characteristics:

i. Insidious onsetii. Clear-cut history of worsening of cognition by report or observation

iii. The initial and most prominent cognitive deficits in one of the following categories:

a. Amnestic presentationb. Nonamnestic presentations (Language, Visuospatial, Executive)

iv. No evidence of cerebrovascular disease, Dementia with Lewy bodies, frontotemporal dementia or other concurrent active neurological disease.

B) Increased level of certainty:

i. Documented cognitive decline on informant and cognitive testingii. Probable AD dementia in a carrier of a causative AD genetic mutation (APP, PS-

1, PS-2)

C) Evidence of AD pathophysiological process

i. Amyloid PET imagingii. Elevated CSF tau, both total tau and phosphorylated tau

iii. FDG uptake on PET in temporoparietal cortexiv. Disproportionate atrophy in medial, basal and lateral temporal lobe and

medial parietal cortex

156

APPENDIX 3: PROPOSED DIAGNOSTIC CRITERIA FOR PCA

I) MENDEZ ET AL. (2002) PROPOSED CLINICAL DIAGNOSTIC CRITERIA

A) Core diagnostic features (all must be present)

i. Insidious onset and gradual progressionii. Presentation with visual complaints with intact primary visual functions

iii. Evidence of predominant complex visual disorder on examination: elements of Balint’s syndrome, visual agnosia, dressing apraxia, environmental disorientation

iv. Proportionally less impaired deficits in memory and verbal fluencyv. Relatively preserved insight with or without depression

B) Supportive diagnostic features

i. Presenile onsetii. Alexia

iii. Elements of Gerstmann’s syndromeiv. Ideomotor apraxiav. Physical examination within normal limits

vi. Predominant impairment of visual functionvii. Predominantly occipitoparietal abnormality with relative sparing of frontal and

medial temporal regions

II) TANG-WAI ET AL. (2004) PROPOSED CLINICAL DIAGNOSTIC CRITERIA

A) Core features

i. Insidious onset and gradual progressionii. Presentation of visual complaints in the absence of significant primary ocular

disease explaining the symptomsiii. Relative preservation of anterograde memory and insight early in the disorderiv. Disabling visual impairment throughout the disorderv. Absence of stroke or tumour

vi. Absence of early parkinsonism and hallucinationsvii. Any of the following:

Simultanagnosia with or without optic ataxia or ocular apraxia Constructional dyspraxia Visual field defect Environmental disorientation Any of the elements of Gerstmann syndrome

B) Supportive features

i. Alexiaii. Presenile onset

iii. Ideomotor or dressing apraxiaiv. Prosopagnosiav. Neuropsychological deficits referable to parietal and/or occipital regions

vi. Focal or asymmetric atrophy in parietal and/or occipital regions on structural imaging

157

vii. Focal or asymmetric hypoperfusion/hypometabolism in parietal and/or occipital regions on functional imaging

158

APPENDIX 4: VISUAL ASSESSMENT NEUROPSYCHOLOGICAL EXAMPLE STIMULI

A) EARLY VISUAL PROCESSING

Figure A-ii Shape detection test (VOSP)

159

Figure A-i Visual acuity test (CORVIST) subset to scale (Snellen equivalent: 6/18- 6/9)

Figure A-iii Shape discrimination (Efron, 1968): three levels of difficulty

Figure A-iv Hue discrimination (CORVIST)

160

161

B) VISUOPERCEPTUAL PROCESSING

Figure A-v Object Decision (VOSP)

Figure A-vi Fragmented letter (VOSP)

162

Figure A-vii Unusual and usual views (Warrington and James, 1988)

C) VISUOSPATIAL PROCESSING

Figure A-viii Number location (VOSP)

Figure A-ix Dot counting (VOSP)

163

D A E E B A C C

B E D E A B E A

B B D B C B E A

B B A C D D B B

B C D A A A B D

E E C E B A E D

D D A A C C E B

B C D C D E D E

C A A A D A B C

D E E D E B B C

D D A C C A E AFigure A-x A Cancellation task

164

APPENDIX 5: READING CORPORA PERFORMANCE FOR FOL AND CLA

i. BROWN AND URE CORPUS (Brown & Ure, 1969)

FOL: While FOL did not make any errors at baseline, she made one error at first follow-up

and one error at second follow-up: there was a trend towards FOL being less accurate at both

follow-up assessments (both t=-1.8; p=.086). Despite not being significantly slower than

controls at baseline, FOL was significantly slower at both follow-up assessments (FU1: t=12.1,

p<.005; FU2: t=25.7, p<.001).

CLA: While CLA did not make any errors at baseline, she made two errors at first follow-up

and three errors at second follow-up. The control group made no errors; consequently it was

not possible to use a modified t-test for error analysis. While CLA was not significantly slower

than controls at baseline, she was significantly slower at first follow-up assessment (t=5.2,

p<.005). Reading latency data were missing for second follow-up due to technical difficulties.

ii. SCHONELL READING LIST (Schonell & Goodacre, 1971)

FOL: FOL made three errors at baseline assessment; she showed trends towards being

slower and less accurate than her control group. FOL made the same amount of errors at first

follow-up. FOL made eight errors at second follow-up, making her significantly less accurate

than her control group (t=-6.5, p<.005). There was a trend towards FOL reading slower than

controls at baseline: she was significantly slower at first (t=6.4, p<.005) and second (t=13.3,

p<.001) follow-up.

CLA: CLA did not make any errors at baseline, making her significantly more accurate than

her control group. However, she made six errors at first follow-up and twelve errors at second

follow-up, making her significantly less accurate than her control group (FU1: t=3.8, p<.05;

FU2: t=8.4, p<.005). CLA was significantly slower than controls at baseline and both follow-up

assessments (FU1: t=4.9, p<.005; FU2: t=90.1, p<.001).

iii. COLTHEART REGULAR/IRREGULAR WORDS (Coltheart et al., 1979)

FOL: FOL made one visual error at baseline, three errors at first follow-up and nine errors

at second follow-up assessment: it was not possible to use a modified t-test for error analysis

as controls did not make any errors. FOL’s latencies did not significantly differ from her control

group’s at baseline; however, she was significantly slower than controls at both follow-up

assessments (FU1: t=11.2, p<.005; FU2: t=18.8, p<.001). While there was no significant 165

difference between FOL and her control group in the size of regularity effect on latencies at

baseline (p>.4) or first follow-up (p>.2), irregular words were read disproportionately slower at

second follow-up relative to controls (Revised Standardized Difference Test: t=2.61, p<.05).

CLA: CLA did not make any errors at baseline assessment but made six errors at first

follow-up and nine errors at second follow-up: it was not possible to use a modified t-test for

error analysis as controls did not make any errors. CLA was significantly slower than her

control group at baseline and follow-up assessments (FU1: t=8.2, p<.005; FU2: t=122.4,

p<.001). CLA was disproportionately slower at reading irregular words relative to her control

group at baseline and both follow-up assessments (Revised Standardized Difference Test: FU1:

t=5.68, p<.005; FU2: t=15.0, p<.0005).

iv.

166

GLOSSARY

AD: Alzheimer’s Disease

ADL: Activities of Daily Living

AIDS: Acquired Immunodeficiency Syndrome

ANOVA: Analysis of Variance

AoA: Age of Acquisition

APOE: Apolipoprotein E

APP: Amyloid Precursor Protein

CBD: Corticobasal Degeneration

CJD: Creutzfeld Jakob Disease

CORVIST: Cortical Visual Screening Test

CSF: Cerebrospinal Fluid

DLB: Dementia with Lewy Bodies

EEG: Electroencephalography

FDG: Fluorodeoxyglucose

FDR: False Discovery Rate

FTLD: Frontotemporal Dementia

FEW: Family-Wise Error

GDA: Graded Difficulty Arithmetic

GDST: Graded Difficulty Spelling Test

IQ: Intelligence Quotient

LBL: Letter-by-Letter

LPA: Logopenic Progressive Aphasia

MIDAS: Medical information Display and Analysis System

MMSE: Mini-Mental State Examination

MNI: Montreal Neurological Institute

MP-RAGE: Magnetization Prepared Rapid Gradient Echo

MRI: Magnetic Resonance Image

NFT: Neurofibrillary Tangles

NHNN: National Hospital for Neurology and Neurosurgery

NINCDS-ADRDA: National Institute of Neurological and Communicative Disorders and Stroke-

Alzheimer’s Disease and Related Disorders Association

Nsize: Orthographic Neighbourhood Size

PCA: Posterior Cortical Atrophy167

PET: Positron Emission Tomography

PiB: Pittsburgh compound-B

PS: Presenilin

RSDT: Revised Standardised Difference Test

RSVP: Rapid Serial Visual Presentation

SD: Standard Deviation

SPECT: Single Photon Emission Computed Tomography

SPM: Statistical Parametric Mapping

tAD: typical Alzheimer’s Disease

VaD: Vascular Dementia

VBM: Voxel-Based Morphometry

VOSP: Visual Object and Space Perception Battery

VWFA: Visual Word Form Area

168

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discovery.ucl.ac.uk · Web view2015-07-21 · The most common cause of dementia is Alzheimer’s disease (AD), defining features of which are the presence of senile plaques, neurofibrillary