visual scanning and attentional biases in alzheimer’s disease: … · 2019-03-13 · ii visual...

Visual Scanning and Attentional Biases in Alzheimer’s Disease: Assessing

Symptoms and Outcomes

by

Sarah An Chau

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Graduate Department of Pharmacology and Toxicology

University of Toronto

© Copyright by Sarah An Chau 2017

ii

Visual Scanning and Attentional Biases in Alzheimer’s Disease: Assessing

Symptoms and Outcomes

Sarah An Chau

Doctor of Philosophy

Graduate Department of Pharmacology and Toxicology

University of Toronto

2017

ABSTRACT

Current assessments of cognition and behaviour in Alzheimer’s disease (AD) rely on indirect

evaluations and are complicated by communication deficits, hampering ability to effectively

prescribe and monitor pharmacotherapy. The purpose of this thesis was to determine whether

direct measurements of visual scanning behaviour can optimize symptom and outcome

assessments in this patient population. In the four studies conducted, a non-verbal eye tracking

technique was used to characterize visual scanning behaviour in order to quantify methods of

measuring cognition, behaviour and pharmacotherapy-induced changes in AD patients.

To evaluate selective attention towards novel stimuli or novelty preference in AD, mild-to-

moderate AD patients (n=41) and elderly controls (n=24) viewed novel and repeated images

simultaneously. Compared with controls, AD patients spent less time on novel than repeated

images (F1,63=11.18, p=0.001). Reduced novelty preference was associated with worse

cognition (Standardized Mini-Mental State Examination, sMMSE, r63=0.29, p<0.05) and

attention (Digit Span, DS, r63=0.27, p<0.05). For 32 AD patients, sMMSE was re-assessed

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every 6 months for up to 2 years. Linear regressions showed that lower baseline fixation time

on novel images (t=2.78, p=0.010) predicted greater cognitive decline (R2=0.41, F3,28=6.51,

p=0.002).

To examine attention towards emotional-themed stimuli, AD patients (apathetic and non-

apathetic) were presented slides containing 2 neutral, 1 social and 1 dysphoric image.

Seventeen of the 36 AD patients had significant apathy (Neuropsychiatric Inventory, NPI

apathy≥4). Repeated-measures analysis of covariance showed that compared to non-apathetic,

apathetic patients demonstrated reduced attention towards social but not dysphoric images

(fixation time: F1,32=4.31, p=0.046; frequency: F1,32=11.34, p=0.002). Eight apathetic patients

entered an open-label trial of methylphenidate (MTP, 5-10mg BID) for treatment of apathy for

at least 4 weeks. Neuropsychological tests and visual scanning measurements were completed

at baseline and follow-up. Spearman correlations showed that improvement on Apathy

Evaluation Scale (AES) was associated with increased novelty preference (ρ6=0.79, p<0.05).

Lower baseline attention towards social images was associated with improvement on AES

(fixation time: ρ6=0.79, p<0.05; frequency: ρ6=0.86, p<0.01)

Eye tracking techniques to measure visual scanning behaviour provide an objective, non-

invasive, non-verbal and less cognitively-demanding method, compared to traditional tests,

which can improve assessment of symptoms and optimize treatment outcomes in AD patients.

iv

Acknowledgements

I would like to gratefully acknowledge the Sunnybrook/Glaxo Drug Safety Graduate

Fellowship and the Consortium of Canadian Centres for Clinical Cognitive Research for

supporting this work.

First and foremost, I would like to extend my deepest gratitude to Dr. Krista Lanctôt and Dr.

Nathan Herrmann for their mentorship. You have been great teachers, challenging me every

step of the way and inspiring me to think more critically. You have encouraged independence

and provided me with opportunities to transform ideas into concrete plans. I will carry the

important lessons you have taught me and the wealth of wisdom you have imparted forward

into the next chapter of my life.

I would like to thank Dr. Moshe Eizenman for providing the technical resources and

engineering expertise as both committee member and project collaborator. I have been

extremely fortunate to work with you and your lab. I would like to thank Kai-Ho Fok for

helping to initiate the studies. I would like to thank Jonathan Chung for his substantial

contributions to the projects as collaborator. Your effort and support has made every step of

this process easier. I would also like to thank Dr. Laurie Zawertailo for helping to guide my

progress through the program as a committee member. I would like to thank Dr. Alex Kiss for

lending his statistical expertise. I would like to thank Dr. Larry Grupp for his input and advice

during the early development stages of my projects. I would like to thank Dr. Neil Shear for his

encouragement and support of the projects.

The excellent teamwork within the Neuropsychopharmacology group has made it a great

environment to learn and grow. The camaraderie within the team have helped maintain the

sanity during times of high pressure. I would like to thank all the research associates and fellow

students, past and present, with whom I have had the pleasure of working. Thank you for your

friendship and I hope our paths will cross again in the future. Special thanks goes to Abby Li,

Romeo Penheiro, Myuri Ruthirakuhan, Marly Isen, Julia Hussman, Chelsea Sherman and

Janelle Bradley for helping with the studies and ensuring everything ran smoothly. I would like

to thank Dr. Walter Swardfager for his advice throughout the years on all matters, including

science, career and personal. Thank you for kindly sharing your wisdom to help me navigate

the twists and turns of academia.

Finally, I would like to thank my family for their endless support. My parents were my first

teachers. They inspired intellectual curiosity in me at a young age and taught me the work ethic

necessary to realize my goals. Thank you for the sacrifices you made to open these doors for

me. This work is dedicated to you.

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TABLE OF CONTENTS

LIST OF TABLES ................................................................................................................ VIII

LIST OF FIGURES ................................................................................................................. IX

LIST OF ABBREVIATIONS ................................................................................................... X

CHAPTER 1 ................................................................................................................................ 1

INTRODUCTION ...................................................................................................................... 1

1.1 Statement of Problem .......................................................................................................... 1

1.2 Purpose and Objectives ....................................................................................................... 2

1.2.1 Overall Purpose .......................................................................................................... 2

1.2.2 Study Objectives ........................................................................................................ 3

1.3 Review of the Literature ..................................................................................................... 4

1.3.1 Alzheimer’s Disease ................................................................................................... 4

1.3.2 Attention and Working Memory in AD ..................................................................... 6

1.3.3 Interactions between Attention and Working Memory .............................................. 8

1.3.4 Processing of Novel and Repeat Stimuli .................................................................... 9

1.3.5 Visual Scanning and Novelty Preference in AD ...................................................... 12

1.3.6 Apathy in AD ........................................................................................................... 14

1.3.7 Pathological Correlates of Apathy and Treatment ................................................... 16

1.3.8 Attention and Apathy ............................................................................................... 20

1.3.9 Visual Scanning in Apathy ....................................................................................... 23

1.4 Research Hypotheses and Rationale ................................................................................. 25

1.4.1 Study 1...................................................................................................................... 25

1.4.2 Study 2...................................................................................................................... 26

1.4.3 Study 3...................................................................................................................... 27

1.4.4 Study 4...................................................................................................................... 28

CHAPTER 2 .............................................................................................................................. 29

SELECTIVE ATTENTION TOWARDS NOVEL STIMULI IN AD ................................. 29

2.1 Materials and Methods ...................................................................................................... 29

2.1.1 Participants ............................................................................................................... 29

2.1.2 Procedures ................................................................................................................ 30

2.1.3 Neuropsychological Assessments ............................................................................ 31

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2.1.4 Recording and estimating visual scanning parameters ............................................ 32

2.1.5 Visual Stimuli ........................................................................................................... 33

2.1.6 Visual Scanning Parameters ..................................................................................... 34

2.1.7 Statistical Analyses .................................................................................................. 35

2.2 Results ………………………………………………………………………………...37

CHAPTER 3 .............................................................................................................................. 42

SELECTIVE ATTENTION TOWARDS NOVEL STIMULI AS A PREDICTOR OF

COGNITIVE DECLINE ..................................................................................................... 42


3.1.1 Participants ............................................................................................................... 42

3.1.2 Procedures ................................................................................................................ 42

3.1.3 Neuropsychological Tests ........................................................................................ 43

3.1.4 Visual Scanning Parameter ...................................................................................... 44


3.2 Results ………………………………………………………………………………...45

CHAPTER 4 .............................................................................................................................. 51

APATHY AND SELECTIVE ATTENTION TOWARDS POSITIVE STIMULI ............. 51


4.1.1 Participants ............................................................................................................... 51

4.1.2 Procedures ................................................................................................................ 51

4.1.3 Neuropsychological Assessments ............................................................................ 53

4.1.4 Visual Stimuli ........................................................................................................... 54



4.2 Results ………………………………………………………………………………...57

CHAPTER 5 .............................................................................................................................. 63

EFFECT OF METHYLPHENIDATE FOR APATHY ON VISUAL SCANNING

BEHAVIOUR ....................................................................................................................... 63


5.1.1 Participants ............................................................................................................... 63

5.1.2 Procedures ................................................................................................................ 64

5.1.3 Visual Stimuli ........................................................................................................... 65

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5.2 Results ………………………………………………………………………………...68

CHAPTER 6 .............................................................................................................................. 73

DISCUSSION ............................................................................................................................ 73

6.1 Selective Attention towards Novel Stimuli in AD ............................................................ 73

6.1.1 Discussion of Findings ............................................................................................. 73

6.1.2 Limitations ............................................................................................................... 77

6.1.3 Significance .............................................................................................................. 78

6.2 Selective Attention towards Novel Stimuli as a Predictor of Cognitive Decline ............. 79


6.2.2 Limitations ............................................................................................................... 83

6.2.3 Significance .............................................................................................................. 84

6.3 Apathy and Selective Attention towards Positive Stimuli ................................................ 85


6.3.2 Limitations ............................................................................................................... 88

6.3.2 Significance .............................................................................................................. 90

6.4 Effect of Methylphenidate Treatment for Apathy on Visual Scanning Behaviour .......... 91


6.4.2 Limitations ............................................................................................................... 95

6.4.3 Significance .............................................................................................................. 96

6.5 Recommendations for Future Studies and Clinical Implications ..................................... 97

6.6 Conclusions ..................................................................................................................... 101

CHAPTER 7 ............................................................................................................................ 102

REFERENCES ....................................................................................................................... 102

PUBLICATIONS AND ABSTRACTS ................................................................................. 130

APPENDIX A: STUDY FLOWCHART ............................................................................. 132

viii

List of Tables

Table 1: Clinical diagnostic criteria for apathy ......................................................................... 15

Table 2: Clinical trials assessing methylphenidate for the treatment of apathy in patients with

dementia . ............................................................................................................................... 22

Table 3: Eligibility criteria ......................................................................................................... 30

Table 4: Participant characteristics ............................................................................................ 37

Table 5: Visual scanning behaviour for controls and AD ......................................................... 39

Table 6: Pearson correlation coefficients between parameters .................................................. 41

Table 7: Patient baseline characteristics .................................................................................... 46

Table 8: Linear regression model of relative fixation time as a predictor of sMMSE change ... 47

Table 9: Cut-off values of relative fixation time with sensitivity and specificity ...................... 49

Table 10: Eligibility criteria ....................................................................................................... 52

Table 11: Clinical and demographic characteristics ................................................................... 58

Table 12: Linear regression model of predictors of fixation frequency within social images ... 61

Table 13: Linear regression model of predictors of fixation frequency within social images ... 62

Table 14: Clinical and demographic characteristics ................................................................... 68

Table 15: Spearman correlation coefficients between changes in novelty preference parameters

and apathy .............................................................................................................................. 69

Table 16: Spearman correlation coefficients between changes in novelty preference parameters,

apathy and attention ............................................................................................................... 70

Table 17: Spearman correlation coefficients between changes in attentional bias towards social

stimuli and apathy ................................................................................................................. 72

Table 18: Spearman correlation coefficients between baseline attentional bias towards social

stimuli and changes in apathy ................................................................................................ 72

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List of Figures

Figure I: Cholinesterase inhibitors mechanism of action .......................................................... 18

Figure II: Methylphenidate mechanims of action ...................................................................... 20

Figure III: Sample slide structure and sequence of the novelty preference paradigm ............... 33

Figure IV: Relative fixation time difference (%) per slide for 1- and 2-back conditions .......... 40

Figure V: sMMSE change from baseline versus relative fixation time (novel - repeat) ........... 48

Figure VI: Receiver operating characteristic curve for relative fixation time ............................ 49

Figure VII: Sample format of social/dysphoric paradigm slide ................................................ 54

Figure VIII: Mean relative fixation time for social and dysphoric themed images ................... 60

Figure IX: Mean fixation frequency within for social and dysphoric themed images ............... 60

Figure XI: Mean relative fixation time on novel images for apathy remitters and non-remitters

................................................................................................................................................ 71

x

List of Abbreviations

5-HT serotonin

ACh acetylcholine

AD Alzheimer's disease

ADAS-Cog Alzheimer's Disease Assessment Scale - Cognitive subscale

ADHD attention deficit hyperactive disorder

ADMET Apathy in Dementia Methylphenidate Trial

AES Apathy Evaluation Scale

AI Apathy Inventory

ANCOVA analysis of covariance

ANOVA analysis of variance

APP amyloid precursor protein

AS Apathy Scale

BOLD blood-oxygen-level dependent

ChEI cholinesterase inhibitor

CPT Conners’ Continuous Performance Test

CT computer-assisted tomography

DA dopamine

DAT dopamine transporter

DSB Digit Span Backward

DSF Digit Span Forward

DSM-IV-TR Diagnostic and Statistical Manual of Mental Disorders, 4th Edition

EEG electroencephalography

ERP event-related potential

FDG fludeoxyglucose

fMRI functional magnetic resonance imaging

GABA γ-aminobutyric acid

IAPS International Affective Picture System

IR infrared

MCI mild cognitive impairment

MINI Mini International Neuropsychiatric Interview

MMS Modified Mini Screen

MMSE Mini-mental State Exam

MRI magnetic resonance imaging

MTP methylphenidate

NE norepinephrine

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NET norepinephrine transporter

NINCDS-

ADRDA

National Institute of Neurological and Communicative Disorders and

Stroke - Alzheimer's Disease and Related Disorders Association

NPI Neuropsychiatric Inventory

NPS neuropsychiatric symptoms

PET positron emission tomography

PiB Pittsburgh compound B

RCT randomized controlled trial

ROC receiver operating characteristic

SCID Structured Clinical Interview for Diagnosis

SHSC Sunnybrook Health Sciences Centre

SRI Sunnybrook Research Institute

sMMSE Standardized Mini-mental State Exam

SPECT single-photon emission computed tomography

VAST visual attention scanning technology

VPC visual paired comparison task

WAIS-DS Wechsler Adult Intelligence Scale - Digit Span

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Chapter 1

Introduction

1.1 Statement of Problem

Alzheimer's disease (AD) is associated with several serious adverse health and economic

outcomes. Few medications are currently approved for the treatment of AD. One issue that has

hampered development of medications in this patient population is the lack of reliable tools to

assess symptoms. Cognitive evaluations become increasingly difficult due to progressive

communication and language deficits, particularly in the later stages of the disease.

Additionally, the complexity of experimental tasks and high reliance on verbal communication

represent obstacles in the assessment of cognitively impaired people. Even with relatively

intact communication capacity, standard cognitive tests, including the Mini-mental State Exam

(MMSE) [1] and the Alzheimer's Disease Assessment Scale - Cognitive subscale (ADAS-Cog)

[2] are inherently confounded by education. This represents a huge limitation given that AD is

prevalent across a broad range of socioeconomic backgrounds and low socioeconomic status

may in fact be associated with greater risk of developing AD [3]. Most assessments of

behaviour and function depend heavily on input from caregivers (often a family member), who

have high rates of emotional distress, depression and poorer physical health [4]. The burden of

care placed on caregivers may constrain their ability to accurately judge neuropsychiatric

symptoms (NPS) and functional impairments. In the clinical setting, these challenges limit the

ability to monitor disease progression and treatment response. In particular, apathy, the most

frequently occurring NPS in dementia [5], is difficult to assess and treat due to its overlap in

symptoms with depression [6-8]. However, these two NPS have divergent neurobiology [9-12]

2

and may require different classes of drugs for treatment. Thus, more direct, non-verbal, less

cognitively-demanding assessment tools would be of value in exploring the brain functions and

effects of pharmacotherapy in dementia patients.

1.2 Purpose and Objectives

1.2.1 Overall Purpose

Eye tracking procedures can be used to measure attentional biases towards visual stimuli in a

passive, non-verbal manner. The overall purpose of this work was to apply visual attention

scanning technology (VAST) to optimize methods of assessment and treatment monitoring in

AD patients by quantifying visual scanning behaviour. This thesis contains four studies where

this paradigm was applied to investigate visual scanning behaviour in the context of novel

stimuli to measure memory and attention and emotionally-charged stimuli to measure apathy in

AD patients. The first two studies focused on the cognitive aspect of AD. In the next two

studies, the theme shifted to the behavioural component of AD. First, Study 1 was designed to

establish the nature of deficits in visual scanning behaviour in an AD sample and its

associations with memory and attention tests. Next, Study 2 examined whether this parameter

could predict longitudinal outcomes in cognition. Study 3 was developed to characterize the

unique visual attention patterns associated with apathy in an AD sample. Finally, Study 4

examined pharmacotherapy-induced shifts in these visual parameters. Visual attention

paradigms may be of value in measuring symptoms, predicting longitudinal changes and

monitoring treatment outcomes. Visual attention may be a non-invasive, non-verbal and less

cognitively-demanding measurement that can improve assessment of symptoms and thereby

optimize treatment outcomes.

3

1.2.2 Study Objectives

Study 1: Selective Attention towards Novel Stimuli in AD

The objective of this study was to observe the spontaneous visual scanning patterns of AD

patients in the presence of novel and repeated visual stimuli in order to characterize selective

attention towards novelty in a naturalistic setting. By comparing visual scanning patterns of

AD patients and cognitively intact elderly controls, novelty processing deficits associated with

the disease state can be identified.

Study 2: Selective Attention towards Novel Stimuli as a Predictor of Cognitive Decline

The objective of this study was to explore whether attentional bias towards novel stimuli or

novelty preference can predict the degree of cognitive decline in patients with cognitive

impairments. Measuring early dysfunction in novelty preference can help identify patients who

are at greater risk of rapid decline. Determining factors associated with aggressive

deterioration would have relevant implications for treatment decisions at earlier stages of the

disease, where neurodegeneration is less global and AD-related pathological events may be

more modifiable.

Study 3: Apathy and Selective Attention towards Positive Stimuli

The objective of this study was to quantify attentional biases towards stimuli with positive and

negative emotional themes in apathetic AD patients. While mood-congruent biases (away from

positive and towards negative stimuli) have been found in depression, little is known about the

4

attentional bias patterns associated with apathy. By comparing apathetic and non-apathetic AD

patients, this study will determine the specific biases associated with the apathy phenotype.

Study 4: Effect of Methylphenidate for Apathy on Visual Scanning Behaviour

The objective of this study was to determine changes in attentional bias parameters associated

with methylphenidate treatment for apathetic symptoms. The pattern of visual scanning

behaviour associated with AD, established in the previous studies, may be sensitive to

pharmacological manipulations and be used to monitor and predict treatment response.

1.3 Review of the Literature

1.3.1 Alzheimer’s Disease

Alzheimer’s disease is characterized by severe deficits in cognition (most prominently memory

and attention) and function, as well as disturbances in behaviour or NPS [13]. AD is the most

common form of dementia, accounting for approximately 50-75% of cases [14]. Alzheimer’s

Disease International estimated that 46.8 million people worldwide were living with dementia

in 2015. The prevalence is expected to reach 74.7 million by 2030 [15]. Furthermore, the total

economic burden in 2015 was estimated at $818 billion USD and is projected to be $2 trillion

USD by 2030. In Canada, there are an estimated 564,000 people currently living with

dementia, with 25,000 new cases diagnosed every year [16]. This number was projected to

increase by 66% by 2031. With regard to economic burden, the annual estimated direct costs of

dementia in 2016 was $10.4 billion CAD and was projected to reach $16.6 billion CAD by

2031 [17]. The risk of mortality in people with dementia is more than doubled compared with

5

age-matched elderly without dementia [15], further underscoring the global impact of this

disease.

AD may be the consequence of many different aberrant changes in the brain. A large

proportion of research has focused on the amyloid hypothesis. Several disease-modifying drug

candidates currently under clinical development target this pathway. In the non-disease

condition, the transmembrane amyloid precursor protein (APP) is cleaved by the enzyme α-

secretase to produce a non-toxic soluble protein (sAPPα) [18]. Under the disease state,

cleavage of APP by β-secretase and γ-secretase generates two major species of hydrophobic

Aβ fragments [19, 20], with the species containing 42 amino acids (Aβ42) having greater

propensity for harm than the fragments containing 40 amino acids (Aβ40). Aβ42 monomers

have been found to be more susceptible to further aggregation and formation of toxic

extracellular amyloid plaques [21-23]. Evidence also suggests that increases in Aβ42/Aβ40

ratio and elevation of soluble Aβ oligomers, rather than the polymeric plaques, may be key to

the disease pathology [24-27]. Another prominent pathway linked with AD pathogenesis

involves intra-neuronal changes, particularly axonal microtubule instability.

Hyperphosphorylation of tau, which function to stabilize microtubules during polymerization,

can lead to protein aggregation, causing the proliferation of cytotoxic neurofibrillary tangles

and loss of cytoskeletal structural integrity [28-31]. Both amyloid and tau pathology have been

observed in the up-regulation of inflammatory cytokines, cyclo-oxygenase and activated

microglia, providing evidence for the neuro-inflammation theory of AD [32-34]. Moreover,

toxic amyloid species may also interfere with regular mitochondrial activity by propagating the

synthesis of reactive oxygen species, leading to further neuronal damage [35, 36]. Overall,

these aberrant changes to neuronal synaptic functioning, alone or combined, can result in the

marked global neurodegeneration observed in AD.

6

1.3.2 Attention and Working Memory in AD

The pattern of structural and functional changes in the brain, precipitated by upstream

pathophysiological events, may account for deficits in memory and attention typical in AD.

Computer-assisted tomography (CT) and magnetic resonance imaging (MRI) results indicate

atrophy of the temporal and parietal lobes early on in AD and progress to the frontal lobes in

the later stages [37]. Greater rates of atrophy have also been strongly correlated with neuron

loss [38] and progression of cognitive impairment [39, 40]. Consistent with these results,

hypometabolism or hypoperfusion in the temporoparietal cortex have been observed in

fludeoxyglucose positron emission tomography (FDG-PET) and single-photon emission

computed tomography (SPECT) [41, 42]. Visualization of amyloid plaque burden using PET

and the tracer Pittsburgh compound B (PiB) has demonstrated high retention in cortical

regions, including the prefrontal, cingulate, parietal and temporal cortex [43-45].

The role of both the temporoparietal and frontoparietal networks in visuospatial attention have

been well-established [46-49]. The subsystems of attention can be anatomically and

functionally studied in isolation and may have unique susceptibilities to the effects of AD

pathology [50]. In order to study these processes in AD, Perry and Hodges [50] classified

attention into 3 categories: selective, sustained and divided. Selective attention, or focus and

concentration, is defined as the process by which salient stimuli are encoded while irrelevant

distractors are filtered out. Sustained attention refers to the ability to maintain concentration on

a stimulus over extended periods of time. Finally, Perry and Hodges defined divided attention

as the capacity to allocate cognitive resources towards several stimuli simultaneously.

Evidence indicates that deficits in selective and divided attention are associated with the early

stages of AD while sustained attention remains relatively intact until the later stages [50-58].

7

The Stroop task is the most widely used test and current gold standard for evaluating selective

attention [59]. In this paradigm, participants are instructed to focus on one aspect of a stimulus

while ignoring other features. Interference occurs when non-relevant task components conflict

and impede processing of target components, causing increased reaction time, the primary

outcome of this task. Large interference effects have been found in early AD compared with

healthy elderly controls [60]. However, in a meta-analysis of Stroop studies in dementia

patients, Ben-David et al [61] contended that this effect could be partially explained by disease

and age-related decreases in processing speed and sensory degradation, specifically in colour

perception, and may not accurately reflect selective attention deficits [61].

Another construct of cognition that is important to consider in the context of AD is working

memory, defined as the temporary maintenance of information for quick access to facilitate

efficient updating in pursuit of goal-directed behaviours [62]. This is considered separate,

though related, to short-term memory, which is simply the limited capacity system for

temporary storage of information [63]. Working memory includes the coordination of different

faculties to utilize for temporarily maintenance and manipulation of information. There is

evidence to suggest that compared with controls, patients with mild cognitive impairment

(MCI) and AD perform worse on digit, word and spatial span tests [64-66]. MCI is defined by

decline in cognitive functioning beyond the normal changes associated with healthy aging and

has been linked with increased risk of conversion to AD [67, 68]. The prevailing model of

working memory, proposed by Baddeley and colleagues [62], posits that working memory is

composed of two subordinate systems responsible for temporary storage of verbal and

visuospatial information, the phonological loop and visuospatial sketchpad, respectively. A

high order central executive, an attentional component, exerts control over and coordination of

the two lower systems. A fourth component, the episodic buffer, was included in later revisions

8

to clarify the process by which information is integrated from different sources chronologically

[69].

The neural basis of working memory has been studied using the n-back continuous

performance task, a test that requires responding to a particular letter when the letter appeared

on the screen n letters previous [70, 71]. Memory loads tested were typically 1 to 3 spans back.

A meta-analysis of pooled neuroimaging results concluded that there were largely consistent

data showing frontal and parietal activation associated with performance on different variations

of the n-back test [71]. AD patients show lower accuracy on this task and had decreased frontal

activation during performance compared with controls [72]. However, studies evaluating the

psychometric properties of the n-back suggested that it is not a measure of working memory as

a single construct but may involve other processes [73-75].

1.3.3 Interactions between Attention and Working Memory

Interactions between working memory and attention, particularly visual selective attention, is

crucial during the many steps of information processing [76-78]. As suggested in the previous

section, many neuroscientists now view selective attention and working memory as

overlapping constructs with common neural substrates [78-82]. Indeed, evidence supports the

notion that working memory deficits in AD patients may reflect damage to the frontal cortex

[83, 84]. Furthermore, the posterior parietal lobe may also play a role in working memory [80,

85]. Acting as the central executive, selective attention can modulate or bias encoding of

relevant sensory information for working memory during the initial phases of visual stimulus

processing [78, 86]. Attention continues to exert top-down modulatory influence throughout

9

the encoding, maintenance and retrieval stages of working memory as a means to optimize

performance [76].

The relationship between working memory and selective attention also flows in the direction of

working memory representations guiding visual selective attention. Visual search tasks, where

subjects are required to hold information in working memory while performing a goal-directed

search task, are typically employed to study this relationship. Items congruent with

representations in working memory have been shown to capture attention [87, 88]. Increasing

memory load resulted in decreasing the attentional guidance effect, measured by response time

and accuracy during a discrimination task [89-92]. Furthermore, individuals with greater

working memory capacity were better at selectively attending to relevant information and were

less influenced by distracters [92]. However, studies have also demonstrated that attention can

also be directed away from memory-congruent contents in the visual field [93, 94]. These

authors suggest that many factors are involved in orienting attention towards or away from

memory-matching inputs, including the goals of the task. Thus, working memory can be

utilized in a flexible top-down, goal-dependent manner to guide attention.

1.3.4 Processing of Novel and Repeat Stimuli

Another interpretation of why attention can be biased away from working memory-congruent

items in the visual environment may lay in the natural salience of novel stimuli. Evolutionarily

speaking, identifying and ascribing attentional resources to processing novel inputs allow for

the exploration of new opportunities and aids in adaptation to changing environments. Novel

stimuli can facilitate enhanced sensory perception [95], strengthen reward processing [96, 97]

and visual working memory encoding [98]. The stages of novelty processing include: 1)

10

detection of novel stimuli guided by top-down memory inputs, 2) allocation of attentional

resources and finally, 3) sustained processing of the stimuli. Studies using

electroencephalography (EEG) to measure brain electrical activity suggest that the N2, an early

event-related potential (ERP) localized in the frontal cortex, may be associated with automatic

detection of novelty and may not require great demands on attention [99, 100]. Many

researchers believe the later P3 ERP component, measured over the parietal lobes, indexes the

orientation of attention towards novel stimuli [100, 101]. The oddball paradigm has been used

widely in these EEG studies [102]. Brain ERPs were measured while subjects are presented

with both repeated and infrequent novel stimuli. Results consistently suggest reduced

amplitude and prolonged peak latency of the P3 wave in AD patients compared with controls

in response to novel stimuli [103, 104].

Selective attention to novel stimuli or novelty preference has been studied as a means to assess

declarative memory or explicit memory [105-107]. The models of working memory and

selective attention described above presupposes that these constructs operate within the

framework of conscious awareness [108]. Snyder et al [107] have suggested that preference for

novelty is strongly linked with attention and implicit memory processes. Traditionally, explicit

and implicit memory retrieval (recognition and familiarity, respectively) have been deemed to

operate under distinct neural mechanisms [109, 110]. However, an emerging alternative view

contends that recognition and familiarity are not so easily separable, may share neural

mechanisms and function in an integrated manner [111, 112]. Recent research using priming

paradigms suggests that non-conscious inputs can be encoded and maintained in working

memory and utilized for task-relevant responses [113, 114]. Additionally, functional

neuroimaging results suggest that the prefrontal cortex may be involved in mediating working

memory operations beyond the confines of conscious awareness [115].

11

Bias towards novelty might reflect more efficient processing of contents that have already been

viewed and encoded into working memory. Some researchers [107, 116, 117] have attributed

the underlying mechanism of enhanced novelty signalling to repetition suppression or priming,

defined as reduced neural activation within visual processing pathways following repeated

exposure. Three models have been proposed to account for this phenomenon [118]. The fatigue

model posits that the amplitude of all stimulus-responsive neurons decrease during repeated

exposure as a result of synaptic depression. In the sharpening model, neurons coding irrelevant

features will demonstrate decreased firing, resulting in sparse representation of the stimuli. The

facilitation model predicts that while firing amplitude remains unchanged, duration of neural

firing is shortened in order to support quick processing. Functional MRI (fMRI) results have

demonstrated repetition effects, called fMRI adaptation, in both the temporal [119] and frontal

cortices [120], areas associated with attention and working memory. It is important to note that

effects of repetition on blood-oxygen-level dependent (BOLD) signals varied depending on the

experimental paradigm. Findings from an fMRI study suggested reduced repetition suppression

activity in the medial temporal lobe of AD patients compared with controls [121]. The

researchers also found that decreasing fMRI adaptation was correlated with poorer

performance on tests of recognition and episodic memory. Given that processing of novel and

repeat stimuli operate within the domains of attention and memory, further examination of

novelty preference may provide interesting insights into the specific cognitive consequences of

dementia.

12

1.3.5 Visual Scanning and Novelty Preference in AD

Selective attention towards novel stimuli can be quantified using visual attention scanning

methods that work by tracking eye movements during stimulus viewing. The direction of eye

movement is considered to be a good indicator of attention allocation [122-124]. Furthermore,

eye movement research has advanced understanding of the visual as well as cognitive deficits

expressed in AD. With respect to changes in basic ocular function, AD patients appear to have

disordered prosaccades (eye movement directed towards a target) and antisaccades (eye

movements directed away from a target). Patients demonstrate slower saccade velocity and

increased latency to initiate movement [125, 126]. With regard to more complex viewing

behaviours, which involve top-down goal-driven processes, AD patients demonstrated longer

fixation durations during visual search tasks and difficulties disengaging attention from targets

[127-129]. In scene exploration tasks, AD patients showed eye movement patterns which were

distinct from controls [130, 131]. Specifically, patients viewed fewer regions in the scene and

spent less time fixating on novel regions [131]. The researchers hypothesized that declines in

motivation and curiosity or apathy, a prevalent syndrome in AD, may account for these

observations. Apathy will be discussed further below.

The visual paired comparison (VPC) task, which involves monitoring spontaneous eye

movements while subjects are simultaneously presented with both novel and repeated images

following a delay, showed that cognitively intact monkeys and healthy infants had longer

viewing durations on novel images [132, 133]. Greater time spent on images have been

associated with larger P3 amplitudes, indicating increased attentional orientation towards

novelty [100]. Patients with MCI have demonstrated diminished attentional bias towards novel

stimuli compared with controls [105, 106, 134]. Interestingly, significant difference between

13

MCI and controls occurred when the delay between stimuli was 2 minutes and not 2 seconds.

Novelty preference was similar when the delay between first and second stimuli presentation

was almost immediate. This raises some question about the memory duration limits of

cognitively impaired people and the types of memory accessed with different delay times.

Atkinson and Shiffrin's model posits that information held in short-term memory decays within

15-30 seconds [63]. Diminished attentional bias towards novel stimuli has also been associated

with increased risk of converting to dementia in MCI patients and to MCI in cognitively intact

elderly [106]. These findings suggest that novelty preference may represent a less cognitively

and physically demanding tool to assess memory and selective attention capacity in non-verbal

populations.

Novelty signals in the brain have been associated with activity in neurotransmitter systems, in

particular, acetylcholine (ACh) and dopamine (DA) [135]. Further discussion of these two

systems, specifically in the context of apathy and attention, is provided below. Cholinesterase

inhibitors (ChEIs), including donepezil, galantamine and rivastigmine, work to enhance ACh

tone in the central nervous system and are currently prescribed for the treatment of AD.

Pharmacologic studies in cognitively intact young participants have shown that ChEIs can

modulate response to novel stimuli [136, 137]. However, to date, no studies have examined the

effect of ChEIs on novelty preference behaviour in the dementia population. Additionally, the

DAergic system is most prominently implicated in processing novelty [138, 139].

Pharmacological manipulations of this system have resulted in changes in fMRI adaptation

[136] and EEG novelty signals [140]. Given that the global neurodegeneration characteristic of

AD is accompanied by altered neurotransmitter function [141-143], the study of novelty

preference can also advance understanding of attention and memory in dementia.

14

1.3.6 Apathy in AD

Given the pathophysiological, structural and functional changes in the brain, the cognitive

deficits characteristic of AD commonly occur in conjunction with behavioural disruptions. One

NPS that has been linked with attention deficits is apathy. Apathy has been described as a

syndrome characterized by lack of motivation that is not due to diminished levels of

consciousness, cognitive impairments or emotional distress [144]. A validated diagnostic

criteria [145, 146] for apathy specific to AD has been proposed [147] (Table 1). According to

the criteria, diagnosis requires the presence of reduced motivation, initiation and environmental

responsiveness - all of which are rooted in behaviour, cognition and emotion. Importantly,

these symptoms must considerably affect daily functioning and not be explained by

physical/mental disabilities or the effect of a substance.

Apathy is the most frequently reported symptom, occurring throughout the spectrum of

dementia severity as well as in the MCI population. Several studies indicate a point prevalence

in the range of 32% and 93% in outpatients using the Neuropsychiatric Inventory (NPI) [12,

148-154]. Studies using more specific tests of apathy, such as the Apathy Evaluation Scale

(AES), Apathy Scale (AS) and Apathy Inventory (AI) report rates of 24% to 86% in AD

patients within the community [149, 150, 155-158]. Prevalence rates of apathy in residents of

nursing homes and long-term care facilities with dementia were similar [159-161]. Concerning

MCI, studies have reported a prevalence of over 35% in this population [162-164]. The wide

range of rates of apathy reported across studies may be accounted for by variations in the

clinical evaluative tools and cut-off values used.

15

Table 1. Clinical diagnostic criteria for apathy [147].

Domain A

Loss of or diminished motivation in comparison to previous level of

functioning and not consistent with age or culture. These changes may be

reported by the patient or by the observations of others.

Domain B

Goal-directed

Behaviour:

Goal-directed

Cognition:

Goal-directed

Emotion:

Presence of at least 1 symptom in at least 2 of the following domains for a

period of at least 4 weeks and present most of the time.

Initiation: loss of self-initiated behaviour (eg: starting conversation, doing

basic tasks of day-to-day living, seeking social activities, communicating

choices)

Responsiveness: loss of environment-stimulated behaviour (eg:

responding to conversation, participating in social activities)

Initiation: loss of spontaneous ideas and curiosity for routine and new

events (ie, challenging tasks, recent news, social opportunities,

personal/family and social affairs).

Responsiveness: loss of environment-stimulated ideas and curiosity for

routine and new events (ie, in the person’s residence, neighbourhood or

community).

Initiation: loss of spontaneous emotion, observed or self-reported (eg:

subjective feeling of weak or absent emotions, or observation by others of

a blunted affect).

Responsiveness: loss of emotional responsiveness to positive or negative

stimuli or events (eg: observer-reports of unchanging affect, or of little

emotional reaction to exciting events, personal loss, serious illness,

emotional-laden news).

Domain C

Symptoms (A - B) cause clinically significant impairment in personal,

social, occupational, or other important areas of functioning.

Domain D

Symptoms (A - B) not exclusively explained or due to physical disabilities

(eg: blindness and loss of hearing), motor disabilities, diminished level of

consciousness or physiological effects of a substance.

16

Though there have been some debate about the relationship between apathy and depression,

apathy is now regarded as a distinct syndrome or set of frequently co-occurring symptoms [6,

7, 144, 165]. However, apathy is still often misdiagnosed as depression due to the overlap in

some symptoms such as decreased energy, social withdrawal and emotional blunting [6-8].

Depression in AD consists of an additional emotional component and has a distinct prevalence

of 30-50% [166-168]. While there are now separate diagnostic criteria proposed for apathy

[147] and depression [169] in those with cognitive impairment, decreasing communication

skills may make diagnosis more difficult as the disease progresses.

Evidence suggests that apathy, independent of depression, is associated with deficits in global

functioning, cognition, executive functioning, as well as instrumental and basic activities of

daily living [170-174]. High burden and distress levels have been reported in caregivers of

patients with apathy [174-176]. Apathy was also correlated with poorer insight into personal

problems [177-179] and higher mortality rate in a cohort of nursing home patients [180].

According to longitudinal analyses, apathetic symptoms can contribute to more rapid

progression of cognitive and functional decline in AD [155, 170, 181]. Furthermore, MCI

patients diagnosed with apathy had a higher risk of developing AD [182-184]. Given these

factors, apathy in AD can lead to higher rates of institutionalization [185] and consequently,

greater economic burden [186].

1.3.7 Pathological Correlates of Apathy and Treatment

Factors considered key to AD pathogenesis, including amyloid plaque formation [23, 187-189]

and hyperphosphorylated tau aggregation [28, 190-192], might contribute to neuronal damage,

giving rise to both cognitive deterioration and apathy. Post-mortem AD brain analyses showed

17

that greater apathy was associated with increased presence of neurofibrillary tangles in the

frontal, parietal and anterior cingulate cortex [193-195]. Further, studies using PET amyloid

imaging with PiB found that greater Aβ plaque deposition in the prefrontal cortex of AD [196]

and cortical regions of MCI patients [197] were associated with more severe apathy. However,

one study [198] conducting cerebral spinal fluid evaluations found greater apathy associated

with total and phosphorylated tau but not Aβ42 concentrations.

Aberrant neurotransmission systems such as DA [199], NE [200], serotonin (5-HT) [200], γ-

aminobutyric acid (GABA) [201] and ACh [202] have all been implicated in the manifestation

of NPS. With respect to apathy, evidence suggests dysfunction in ACh and DA activity. Using

a nicotinic cholinergic receptor ligand with PET imaging, Sultzer et al [203] found an

association between more severe apathy and reduced binding in the anterior cingulate,

orbitofrontal cortex and hippocampus. A SPECT study using a dopamine transporter (DAT)

radioligand to measure DA reuptake provides clearer evidence for the role of DA in apathy

[204]. Decreased DAT availability in the striatum, particularly the putamen and caudate of the

basal ganglia, was associated with lack of initiative and interest on a measure of apathy,

suggesting loss of DAergic neurons and involvement of subcortical systems in the expression

of apathy.

18

Figure I. Cholinesterase inhibitors mechanism of action. ChEIs block AChE from breaking

down ACh, prolonging its activity in the synapse. AChE=acetylcholinesterase, A=acetate,

C=choline, ACh=acetylcholine, ChEI=cholinesterase inhibitor

Pharmacotherapies targeting the DA and ACh systems have demonstrated positive effects in

ameliorating apathy symptoms. Cholinesterase inhibitors (ChEIs), including donepezil,

rivastigmine, galatamine, approved for the symptomatic treatment of the cognitive symptoms

in AD have demonstrated some benefit for apathy, as a secondary outcome [205-207]. ChEIs

increase central ACh neurotransmission by blocking the hydrolyzing activity of the enzyme

acetylcholinesterase [208-210] (Figure I). Methylphenidate (MTP) has thus far been the most

studied psychostimulant in apathy treatment (See Table 2 for a summary of the trials). Its mode

19

of action includes inhibition of DA and norepinephrine (NE) reuptake in the synapse through

the DAT and norepinephrine transporter (NET) on the presynaptic membrane [211-213]

(Figure II). The use of MTP to manage symptoms of apathy in dementia has shown promise in

case reports [214, 215], open label trials [216, 217] and randomized placebo-controlled trials

[218, 219]. The first randomized placebo-controlled crossover trial of MTP in 13 apathetic AD

patients found modest but significant improvements in the active treatment group following 5

weeks (2 weeks in each treatment arm with a 1-week wash-out period) [218]. Further evidence

for the use of psychostimulants in the AD population was established in the Apathy in

Dementia Methylphenidate Trial (ADMET). In this phase 2 parallel-group, double-blind,

randomized controlled trial (RCT) to evaluate the efficacy and safety of MTP for the treatment

of apathy in 60 AD patients, significant improvements were observed in the active drug group

compared with placebo following 6 weeks [219]. A larger 6-month trial of MTP (ADMET2),

aiming to randomize 200 patients is currently underway. Evidence of the benefit of other

psychostimulants, including dextroamphetamine and modafinil, is less consistent and have not

been studied extensively in RCTs [220]. DA agonists, such as amantadine [221-225] and

bromocriptine [226-229], have not been studied specifically in AD patients but demonstrated

promise in other clinical populations.

20

Figure II. Methylphenidate mechanism of action. MTP binds DAT and NET to block reuptake

of DA and NE into the presynaptic terminal, resulting in increased concentrations of these

neurotransmitters in the synapse.

1.3.8 Attention and Apathy

The link between apathy and attention is apparent considering that the pharmacotherapies for

apathy described above also modulate attention. Restoring cholinergic activity via ChEIs has

produced modest improvements in memory and overall function in AD patients [230-232].

Interestingly, galantamine, a ChEI that additionally stimulates the nicotinic receptors, showed

positive effects on attention in RCTs of AD patients [233-235]. As nicotinic ACh receptor

activity has been shown to regulate DA release [236-238], it was then proposed that

21

galantamine’s additional association with the DA system may contribute to its benefit for

behaviour symptoms such apathy [239-241].

Psychostimulants are the preferred pharmacotherapy for the treatment of attention deficit

hyperactive disorder (ADHD) and has been shown to improve attention in younger populations

[242-244]. The DA and NE systems have connections throughout the prefrontal and limbic

areas and are important in modulating attention, arousal and emotion [245-247], components

which may be disordered and subsequently lead to apathy. In particular, DAergic neurons

make projections to attention networks in the brain, including apathy associated regions in the

frontal lobe [248-250]. Thus, increasing neurotransmission of DA and NE in these brain

regions through MTP may mitigate apathy in AD patients by modulating their attention.

Specifically, the interaction between midbrain DAergic limbic pathways and the motor

striatum, occurring indirectly through several circuits, work to link motivation with motor

outcomes [251].

The regulation of both motivation and attention through the actions of DA [252, 253] may

signify that attention and apathy are operating under similar mechanisms. In a RCT of AD

patients, MTP improved both apathy and selective attention [219]. Lanctôt et al [254] used a

dextroamphetamine drug challenge to probe the function of the DA system in apathetic AD

patients. Compared with non-apathetic patients, patients with apathy had reduced feelings of

positive effects after a single dose of drug, suggesting response to DAergic agents was blunted

by apathy. Interestingly, the patients who demonstrated inattention in response to

dextroamphetamine showed improvements in apathy in a trial of MTP to treat their apathy

symptoms [218]. This suggests that attention may be a predictor of apathy treatment response.

In other words, attention, as an indicator of intrinsic DA functioning, might be telling of a

patient’s ability to respond to treatment outcomes.

22

Table 2. Clinical trials assessing methylphenidate for the treatment of apathy in patients with

dementia.

Study Population Intervention Duration Outcomes

Double-blind randomized controlled trial

*Rosenberg &

Lanctôt et al [219]

n=60

mild to

moderate AD

Methylphenidate (20

mg/day) or placebo

6 weeks

NPI apathy ↓ (in

MTP compared

with placebo)

*Herrmann et al

[218]

n=13

AD

Methylphenidate (20

mg/day) or placebo

2 weeks

AES ↓ (in MTP

compared with

placebo)

Open label studies

*Padala et al [216] n=23

AD

Methylphenidate (20

mg/day)

12 weeks

AES ↓

Galynker et al [217] n=27

AD & VaD

Methylphenidate

(10-20 mg/day)

3-14 days

SANS ↓

Abbreviations: AD=Alzheimer`s disease, VaD=vascular dementia, NPI=neuropsychiatric

inventory, AES=apathy evaluation scale, SANS=scale for the assessment of negative

symptoms

↓ indicates decrease in apathy scores, * indicates studies using apathy as the primary outcome

Apathy may reflect deficits in attention networks that interact closely with frontal cortex

functioning. For example, studies have reported links between executive dysfunction and

apathy in AD patients [173, 255, 256]. Thus, apathy and attention may have similar neural

substrates and mechanisms. Evidence for this premise can be linked with the findings of

increased amyloid and tau burden in key frontal areas discussed above [193-198]. Similarly,

structural and functional imaging provide data to support this relationship. Results from

SPECT studies consistently show hypoactivity in the anterior cingulate gyrus and orbitofrontal

cortex of apathetic AD patients [157, 257-262]. Moreover, many studies have controlled for

the effects of depression and found distinct anatomical and functional patterns in depressed

23

compared with apathetic subjects [9-12]. A PET study found similar results using a negative

symptoms scale [263]. Additionally, structural MRI studies showed that reduced gray matter

volume and white matter (axonal) integrity were correlated with greater apathy severity in the

anterior cingulate and frontal regions [264-267]. Neuroimaging data in AD patients also

suggests that the different components of apathy are each associated with unique patterns of

brain activation and structural changes [11, 268]. Using SPECT, Benoit et al. [11] found that

lack of initiative (behavioural apathy), lack of interest (cognitive apathy) and emotional

blunting were correlated with hypoperfusion in the anterior cingulate cortex, orbitofrontal

cortex and dorsolateral prefrontal cortex, respectively. As such, understanding the apathy

domains is a current area of research [269]. Despite these underlying links, little is known of

the connection between apathy and attention in AD. Thus, a closer examination of the

processes associated with apathy in AD and the involvement of attention will lead to better

understanding of this syndrome and its treatment.

1.3.9 Visual Scanning in Apathy

As with selective attention, mood symptoms can be studied using eye tracking techniques.

Specifically, research in psychiatry have investigated mood congruent attentional biases for

emotional images in affective disorders. Eizenman et al [270-273] developed an eye tracking

system that monitors real-time point-of-gaze while allowing for free head movements. The

paradigm involves presentation of multiple visual stimuli that compete for the viewer’s

attention while monitoring the viewer’s point-of-gaze. Analysis of visual scanning patterns

provide a set of measurements that can be used to characterize attentional biases. They applied

this paradigm to study the visual scanning behaviour of depressed patients in the presence of

24

dysphoric (negatively valenced) and social (positively valenced) images [270]. The results

showed that depressed patients fixated more on dysphoric images compared with their non-

depressed counterparts, indicating a clear attentional bias associated with the disorder.

Additionally, the data suggested that healthy controls spent more time on social images

compared with patients. More recent studies employing the same methodology observed

similar results [274-276]. A meta-analysis [276] found that compared with non-depressed

controls, depressed patients maintained longer total fixations on dysphoric and shorter

durations on positive images, with medium to large effect sizes reported. The pooled analyses

also found greater bias for threatening stimuli in patients with anxiety. Interestingly, a study

that additionally examined treatment outcomes found that remitted depressed people had gaze

durations for positive stimuli which were comparable to non-depressed healthy controls [275].

Alternatively, attentional biases towards dysphoric images were higher in both currently and

remitted depressed subjects compared with controls.

The effect of apathy was not considered in those studies. Symptoms of social disinterest and

emotional blunting, the defining components of apathy, suggest that apathy may be a factor in

influencing the visual response to social or positive-themed stimuli. With regard to AD, where

apathy is particularly prevalent, few studies have sought to characterize visual scanning

behaviours associated with these symptoms. While viewing emotional faces, AD patients spent

less time fixating on regions of interest, particularly the face and eyes, and were more focused

on irrelevant aspects of the images compared with elderly controls [277]. In another eye

tracking study, Daffner et al [278] found that apathetic AD patients demonstrated less interest

in novel (incongruous) visual stimuli and distributed their overall viewing time evenly among

all types of stimuli compared with non-apathetic patients. Furthermore, greater apathy was

correlated with reduced novelty P3 amplitude [279]. These findings suggest that visual

25

scanning methods can be applied not only to measure cognitive domains, such as selective

attention and working memory, but also neuropsychiatric symptoms in dementia. A key

advantage of employing passive viewing tasks is the ability to overcome stress related to

cognitively-demanding tests, [280, 281] as well as physical limitations typical in elderly

populations. This technology represents a tool not only for direct measurements of visual

scanning behaviour, but also allows the study of attentional bias in a more naturalistic manner.

1.4 Research Hypotheses and Rationale

1.4.1 Study 1

Hypothesis 1: Alzheimer's disease patients will demonstrate reduced attentional bias towards

novel stimuli compared with cognitively healthy elderly controls.

Hypothesis 2: Increased attentional bias towards novel stimuli will be associated with higher

scores on standard tests of attention.

Rationale: Evidence has pointed to impairments in selective attention, the ability to focus on a

target stimulus while filtering out distractions, in the early stages of AD, which continue to

worsen linearly with disease severity [52, 53, 55-57]. Specific impairments in visual attention

have been observed in mild AD [50, 282, 283] as well as those in the pre-dementia stages [284,

285]. Finke et al [81] proposed a brain mechanism-based account of visual selective attention

deficits, where damage within parietal regions and intrinsic fronto-parietal networks in early

and prodromal AD may reduce the ability to prioritize relevant over irrelevant visual inputs.

Selective attention towards novel stimuli, referred to as novelty preference, has been associated

with memory and attention [105-107]. Implicit tasks of novelty preference using eye tracking

26

technology have been investigated in nonhuman primates and human infants. The VPC task,

which involves monitoring spontaneous eye movements while subjects are simultaneously

presented with both novel and previously displayed images following a delay, showed that

cognitively intact monkeys and healthy infants spent more time viewing novel images [132,

133, 286, 287]. In contrast, patients with MCI have demonstrated diminished novelty

preference [105, 106, 134]. Thus far, the degree of deficits in novelty preference specific to AD

have yet to be quantified. In an earlier study, Daffner et al [278] found that a subset of AD

patients spent less time viewing irregular (novel) line drawings compared with age-matched

controls [131, 278]. Additionally, the novelty P3 event-related potential, described as the brain

response associated with allocation of attention to novel events [288], is significantly reduced

in AD patients [279]. Thus, AD patients will likely demonstrate reduced time spent on novel

items as a function of their memory and attention deficits.

1.4.2 Study 2

Hypothesis 3: Reduced attentional bias for novel stimuli will be associated with greater

deterioration in cognition over two years in Alzheimer’s disease patients.

Rationale: Evidence suggests that the assessment of attention may be of value in predicting

disease progression in the early stages of dementia [289-291]. Using the VPC task, reduced

novelty preference was found to be associated with increased risk of converting to dementia in

MCI patients, as well as conversion to MCI in cognitively intact elderly [106]. Novelty signals

in the brain have been associated with activity in neurotransmitter systems, in particular, ACh

and DA [135]. Neurotransmitter dysfunction is also characteristic of the Alzheimer's disease

pathology [141-143]. Early dysfunction in novelty preference may reflect underlying

27

pathophysiological events, including neurotransmitter dysfunction, which would accelerate the

deterioration process. Thus, a novelty preference visual attention paradigm can be applied to

predict longitudinal changes in cognition.

1.4.3 Study 3

Hypothesis 4: Attentional biases toward social or positively themed stimuli will be reduced in

apathetic compared with non-apathetic Alzheimer’s disease patients.

Rationale: Apathy, characterized by reduced motivation, social disinterest and emotional

blunting in the absence of mood-related changes [145, 146], is the most frequently occurring

symptom in AD [150, 154, 292]. Several imaging studies showed that brain regions associated

with attention, particularly the anterior cingulate and frontal cortices, have reduced activity and

increased atrophy in apathetic compared with non-apathetic AD patients [9, 267]. Eizenman et

al [270] developed a non-verbal methodology to determine attentional biases in depressed

patients through the measurement of visual scanning behaviour. They found that, compared

with non-depressed controls, young depressed patients fixated longer on dysphoric or

negatively valenced images, but spent less time fixating on social images. Furthermore, non-

depressed controls fixated on social images more compared with depressed patients, indicating

a clear attentional bias associated with mood disorders. Another research group applied the

same methodology and observed that strong biases for dysphoric images were sustained for a

30-second duration [293]. However, the effect of apathy was not considered in those studies.

Symptoms of social disinterest and emotional blunting, the defining components of apathy,

suggest that apathetic patients may not demonstrate the attentional bias for social-themed

images seen in non-apathetic people.

28

1.4.4 Study 4

Hypothesis 5: Improvement in apathy symptoms will be associated with increased attentional

bias towards novel stimuli.

Hypothesis 6: Improvement in apathy symptoms will be associated with increased attentional

bias towards social themed stimuli.

Rationale: Apathy has been associated with the mesocorticolimbic DAergic pathway [294] and

psychostimulants, which work by increasing DA and NE levels, have demonstrated efficacy in

improving symptoms [216, 218, 219]. Clinically, methylphenidate, a DAergic agent, improved

apathy as well as selective attention in a randomized placebo-controlled trial of dementia

patients with apathy [295]. Psychostimulants such as methylphenidate may be modulating both

apathy and attention via a common pathway. Thus, improvements in apathy symptoms may

accompany improvements in novelty processing. Additionally, increased bias towards social

stimuli with methylphenidate, suggesting greater social interest, may be a marker of apathy

improvement.

29

Chapter 2

Selective Attention towards Novel Stimuli in AD

2.1 Materials and Methods

2.1.1 Participants

Participants with AD were recruited from outpatient clinics at Sunnybrook Health Sciences

Centre (SHSC). Eligibility for AD patients included: diagnosis of possible or probable AD

based on the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV-TR)

[13] and National Institute of Neurological and Communicative Disorders and Stroke-

Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) criteria [296],

minimum age of 65, no change in anti-dementia medications for at least 1 month prior to study

day and mild to moderate impairment (sMMSE ≥ 10). Elderly controls were either caregivers

accompanying patients (frequently a spouse) or recruited from the community. Inclusion

criteria for elderly controls consisted of a minimum age of 65, no current diagnosis of

dementia, sMMSE ≥ 26 and no evidence of a psychiatric disorder according to the Modified

Mini Screen [297] (MMS < 6).

30

2.1.2 Procedures

This was a cross-sectional study. Participants and caregivers provided information regarding

age, ethnicity, level of education, current medications, medical and psychiatric history. All

participants were administered the Wechsler Adult Intelligence Scale (WAIS) - Digit Span

(DS) [298] and sMMSE [299] and the controls were administered the MMS [297]. Following

these tests, both controls and dementia patients underwent the VAST procedures. Before

initiation of procedures, patients were screened according to the eligibility criteria. The study

purpose, procedures, risks and benefits were verbally explained to participants and caregivers.

Participants were given time to read the informed consent form and ask questions to the study

personnel. Written informed consent was provided by all participants or in the case that

patients could not provide consent, a legally authorized representative (caregiver) signed the

Table 3. Eligibility criteria

Control

Minimum 65 years of age

No indication of psychological disturbances (MMS < 6)

No diagnosis of AD or other cognitive impairments (sMMSE ≥ 26)

No presence/history of neurological illnesses or traumatic brain injury

No significant eye pathology

Alzheimer’s Disease

Minimum 65 years of age

Diagnosis of possible/probable AD (DSM-IV-TR and NINCDS-ADRDA criteria)

sMMSE ≥ 10

No presence/history of other neurological illnesses or traumatic brain injury

No change in anti-dementia medications less than 1 month prior to study day


31

forms. This study was approved by the Sunnybrook Research Institute (SRI) research ethics

board.

2.1.3 Neuropsychological Assessments

Standardized Mini-Mental State Examination

The sMMSE [299], a more systematic and reliable version of the original MMSE [300], was

used to describe severity of cognitive impairment. This 10-item scale, administered directly to

AD patients, measured domains such as orientation, memory, attention and language abilities.

Total scores may range from 0-30, with higher values indicating better cognitive function.

Wechsler Adult Intelligence Scale - Digit Span

The WAIS-DS [298] is valid tool for the assessment of working memory and auditory

attention. Sequences of digits were read and patients were required to repeat them in the same

(forward, DSF) or reverse (backward, DSB) order. DSF is thought to be a measure of selective

attention while it has been suggested that DSB may reflect executive functioning [301]. One

point was given for each correct sequence and the test was terminated when patients were

unsuccessful on two trials of the same sequence length. The forward task consisted of 8 items,

2 trials of the same digit length for each item, with a maximum score of 16. The backward task

consisted of 7 items, giving a possible maximum score of 14. Forward and backward scores

were combined to establish a total score, which was converted to a scaled score based on

standardized age norms.

32

Modified Mini Screen

The MMS [297] is a 22-item scale used to identify individuals whom may exhibit symptoms of

mood, anxiety and psychotic disorders. Questions were based on assessment tools such as the

Structured Clinical Interview for Diagnosis (SCID) and the Mini International

Neuropsychiatric Interview (MINI).

2.1.4 Recording and estimating visual scanning parameters

The VAST developed by EL-MAR Inc. (Toronto, Ontario, Canada) was used to record and

estimate visual scanning parameters. The technology incorporated a binocular eye tracking

system [273] that recorded eye gaze positions and pupil sizes, a display to present visual

stimuli, real-time processing algorithms to estimate visual scanning parameters [270, 302] and

a monitoring station to control and supervise the progress of the test [272]. The eye tracking

system, mounted on the display (a 23” computer monitor with a resolution of 1920 *1080

pixels), consisted of infrared (IR) light sources, IR video cameras and a processing unit that

estimated binocular gaze position 30 times/sec with an accuracy of ± 0.5º [273]. The gaze data

was processed by algorithms that segmented the data into saccades and fixations on images in

each slide and estimated visual scanning parameters [271, 272]. During the test, subjects were

allowed to move their heads freely within a relatively large volume (25x25x25 cm3) which

supported natural viewing of the visual stimuli.

The VAST procedures started with a 9-point eye tracking calibration procedure in which

participants followed a moving target on the computer screen. Following the short calibration

routine (less than 30 seconds) participants looked at a series of slides that were presented on

the VAST display and their visual scanning patterns and pupil-sizes were recorded. Refer to

33

Figure III for slide structure. Each slide contained four images that were similar in complexity.

Participants sat at a distance of approximately 65 centimetres from the monitor so that the

visual angle subtended by each of the four images on each slide was approximately 15.5º x

12.2º. The horizontal and vertical separation between any two images was greater than 2.5º.

Figure III. Sample slide structure and sequence of the novelty preference paradigm. The first

slide of each set (start) contained 4 novel images. The slide following (1-back) contained 2

novel and 2 repeated images. The final slide of the set (2-back) contained the other 2 images

repeated from the start slide and 2 novel images.

2.1.5 Visual Stimuli

In this paradigm, each slide contained 4 images, arranged in a 2 by 2 configuration, that were

similar in complexity and neutral in content. All images generally contained one or two simple

items with a similar theme for each slide in order to maintain task simplicity and minimize

attentional bias based on deviance (mismatched items), respectively. For example, one slide

series contained images of different varieties of fruit and another included images of different

furniture. Neutral images were similar to those found in the International Affective Picture

System (IAPS) database with medium ratings for valence (feelings of pleasure versus

34

displeasure) and low ratings for arousal (feelings of excitement versus calm). The series of

slides included 16 sets of test slides and 58 filler slides. Each set of test slides were comprised

of three slides that were presented consecutively. The start slide of each set contained 4 novel

images and the 2 subsequent slides contained 2 novel images and 2 images that were repeats of

images on the start slide (See Figure III). Repeated images were presented in the same

positions on the start slide and on subsequent slides. Each slide was displayed for 10.5 seconds

and was followed by 1 second of a uniform grey screen. Thus, the delay between presentations

of repeated images was 1 second when the repeated images were presented immediately

following the start slide (1-back condition) and 12.5 seconds when they were presented on the

second slide that followed the start slide (2-back condition). The 1-second blank screen acted

to mask repeated images, which occurred in the same position within each slide set. This

presentation structure maintained both spatial and stimulus familiarity for the repeated images,

in order to simplify the task for the study participants. The positions of repeated images on the

slides (top-left, top-right, bottom left and bottom right) were uniformly distributed between the

16 test sets. A total of 48 test slides were presented (16 start, 16 1-back and 16 2-back slides).

Ten filler slides were used at the beginning of the presentation to familiarize subjects with the

presentation set-up and 48 filler slides were inserted randomly between test-sets (1-4 filler

slides between two consecutive test sets) to mask the structure of the sets. A total of 106 slides

were presented but only the 48 test-slides were analyzed.

2.1.6 Visual Scanning Parameters

Relative fixation time was the primary outcome measure. This parameter has been used

previously to characterize visual scanning behaviour of patients with eating disorders [272] and

35

depression [270] and was calculated by dividing the fixation time on novel/repeated images on

a slide by the total fixation time for all four images on a slide. The bias towards novel images

(novelty preference) was characterized by the difference between the relative fixation times on

novel and repeated images on a slide. Greater biases (larger differences in relative fixation

time) indicate stronger novelty preferences. Additionally, to obtain further insight into

differences between the visual scanning behaviour of AD patients and controls in this modified

VPC task, the two basic components of relative fixation time: the number of discrete fixations

(fixation frequency within images) and the average duration of discrete fixations (average

fixation duration in milliseconds) on novel/repeated images for each slide were analyzed. For

each participant, relative fixation time (novel – repeat), average fixation duration and fixation

frequency within images (for the 1- and 2-back conditions, separately) were determined by

calculating the means of these parameters on the corresponding 16 test slides.

2.1.7 Statistical Analyses

Demographic, neuropsychological and visual scanning data were summarized using

proportions or mean ± standard deviation (SD). Analyses were conducted using IBM SPSS

Statistics 20.0 for Windows (IBM Corp., Armonk, NY). Sample size calculations for all

hypotheses were conducted using G*Power 3.1 [303, 304].

Relative fixation time (novel – repeat) was summarized using mean ± standard error of the

mean (SEM). Clinical and demographic characteristics were compared between AD and

controls using analysis of variance (ANOVA) for continuous variables and χ2 for categorical

variables. To compare the basic component parameters of the relative fixation time of AD and

36

controls at baseline (i.e., slides with only novel images), one-factorial ANOVAs were

performed on average fixation duration and fixation frequency within images per image for

start slides. Two-factorial repeated-measures ANOVA models were performed to explore

within-subject effects of image type (novel, repeat), between-group effects (control, AD) and

interaction between factors for average fixation duration and fixation frequency within images

per image in both the 1- and 2-back conditions. Paired Student’s t-tests were performed to

determine specific differences between novel and repeated images within each study group. A

two-factorial repeated-measures ANOVA was performed to determine the effect between

groups (AD, control) and within-subject conditions (1-back, 2-back), as well as interaction

between factors for relative fixation time difference (Hypothesis 1). This model, with up to 2

covariates included, required 38 participants (2 groups of 19) in order to achieve a power of 0.8

and detect medium to large effect size at an α of 0.05. The proportion of participants in each

group who displayed any novelty preference behaviour in the paradigm was also calculated.

Novelty preference in individual participants was defined as relative fixation time (novel -

repeat) greater than 0 in either the 1- and 2-back conditions, representing longer fixation times

on novel compared with repeated images. Pearson correlations were conducted to explore

associations between visual scanning behaviour outcomes and neuropsychological test scores,

including the sMMSE, DS Total, DSF and DSB (Hypothesis 2). This analysis required a

sample size of 44 participants to detect medium to large effect sizes (power = 0.80, α = 0.05).

All analyses were considered significant at an α of 0.05 with no corrections made for multiple

comparisons.

37

2.2 Results

The results of this study were published in Dementia and Geriatric Cognitive Disorders Extra

(Chau SA, Herrmann N, Eizenman M, Chung J, Lanctôt KL. Exploring visual selective

attention towards novel stimuli in Alzheimer’s disease patients. Dement Geriatr Cogn Disord

Extra 2015; 5(3):492-502) [305].

Forty-one AD patients and twenty-four elderly controls participated. See Appendix A for

patient recruitment flow chart. Fifty-two AD patients consented and 11 were excluded (2

cataracts, 5 calibration issues, 4 refused to continue). Twenty-nine healthy elderly provided

Table 4. Participant Characteristics. Values are mean ± SD or n (%). One-factorial

analysis of variance tests were completed for age, sMMSE, DS Total, DSF and DSB

scores. χ2 tests were performed for sex and education. Digit span total were age-

corrected scaled scores.

Measure Control AD p

Age, years 76.2 (6.4) 79.2 (6.7) 0.090

Sex, female 50.0% 46.3% 0.776

Education

Grade school

High school

Post-secondary

12.5%

41.7%

45.8%

24.4%

31.7%

43.9%

0.138

Standardized Mini-Mental State

Examination 28.1 (2.0) 22.2 (4.0) <0.001

Digit Span Total

Forward

Backward

12.1 (4.1)

9.9 (3.2)

7.3 (2.4)

9.9 (2.5)

9.2 (1.9)

5.2 (2.1)

0.009

0.279

<0.001

AD = Alzheimer’s disease

38

consent and 5 were excluded in the final analyses (1 cataracts, 2 psychiatric disorder, 1

calibration issues, 1 refused to continue). Groups were comparable in age, education and sex.

Controls performed better on tests of attention and cognition (sMMSE, DS Total, See Table 4).

During presentation of the start slides, when all four images were novel, control and AD

participants had similar average fixation duration (F1,63=1.39, p = 0.2) and fixation frequency

within images (F1,63 = 0.23, p = 0.6) per image (See Table 5). However, in the presence of

repeated stimuli, there were significant differences between the visual scanning behaviour of

control and AD participants (See Table 5). There was a significant effect of image type (F1,63 =

78.10, p < 0.001) and group by image type interaction (F1,63 = 27.03, p < 0.001) but no

between group effects (F1,63 = 0.92, p = 0.3) for fixation frequency within images in the 1-back

condition. Post-hoc analysis revealed greater fixation frequency within images on novel

compared with repeated images in both the control and AD groups. Similar results were

observed in the 2-back condition. Note that for both the 1-back and 2-back conditions there

were no significant main effect of group for fixation frequency within images. The total

number of discrete fixations on all the images on 1-back and 2-back slides are similar for the

two groups. For average fixation duration in the 1-back condition, there was a significant main

effect of image type (F1,63 = 18.30, p < 0.001) and group (F1,63 = 5.92, p = 0.018) but no

interaction between factors (F1,63 = 0.37, p = 0.5). Higher average fixation duration occurred on

novel compared with repeated images in both groups. Results for average fixation duration in

the 2-back condition were similar.

39

Table 5. Visual scanning behaviour for controls and AD. Values are mean ± SD. Two-

factorial analysis of variance tests were conducted using between-group (control, AD)

and within-subject (novel, repeat) factors.

Parameter Control (n = 24) AD (n = 41) p

Start Slide

Fixation Frequency Within

Images 5.4 (0.6) 5.3 (0.7) 0.632

Average Fixation Duration

(msec) 431.8 (66.8) 454.6 (79.8) 0.242

1-back Slide

Fixation Frequency

Within Images

novel

repeat

6.3 (0.9)

4.4 (0.9)

5.4 (0.8)

4.9 (0.9)

0.342 (group)

<0.001 (image type)

<0.001 (group X image)

Average Fixation

Duration (msec)

novel

repeat

443.5 (51.6)

401.2 (56.4)

498.7 (120.3)

442.4 (85.2)

0.018 (group)

<0.001 (image type)

0.544 (group X image)

2-back Slide

Fixation Frequency

Within Images

novel

repeat

5.9 (1.1)

4.6 (0.8)

5.3 (1.0)

5.2 (1.0)

0.921 (group)

<0.001 (image type)

<0.001 (group X image)

Average Fixation

Duration (msec)

novel

repeat

442.3 (72.1)

403.2 (63.0)

484.1 (130.6)

444.9 (101.4)

0.088 (group)

<0.001 (image type)

0.999 (group X image)

AD = Alzheimer’s disease

Analyses of difference in relative fixation time between novel and repeated images (a

composite of average fixation duration and fixation time within images) also suggested

significant differences between groups. Elderly controls spent 12.0 ± 2.4 % more time fixating

on novel compared with 1-back repeat images and 9.7 ± 2.2 % more time fixating on novel

40

compared with 2-back repeat images (Figure IV). AD patients spent 5.3 ± 1.6 % more time on

novel compared with 1-back and 3.7 ± 1.5 % more time on novel compared with 2-back

images. The ANOVA model revealed significant group main effect (F1,63 = 11.18, p = 0.001)

but no condition main effect (F1,63 = 1.03, p = 0.315) or interaction between factors (F1,63 =

0.037, p = 0.848). The relative fixation time difference between 1- and 2-back was comparable

for all participants. Overall, within individuals, 100% of controls and 92.3% of patients

displayed novelty preference behaviour (relative fixation time mean difference for either 1- or

2-back greater than 0).

Figure IV. Relative fixation time difference (%) per slide for 1- and 2-back conditions (mean

difference between novel and repeat ± standard error of the mean). Higher values represent

preference for novel over repeated images.

41

Pearson correlations for differences in relative fixation times on novel and repeated images and

other neuropsychological measures were performed for all 65 participants (Table 6). Overall,

sMMSE and DS total scaled scores were significantly correlated with relative fixation time

difference for both 1- and 2-back conditions. In the 1-back condition, sMMSE accounted for

7.9% (r63 = 0.281, p = 0.023) and DS Total accounted for 6.7% (r63 = 0.258, p = 0.038) of the

variance in relative fixation time difference. In the 2-back condition, sMMSE accounted for

8.3% (r63 = 0.288, p = 0.020) and DS Total accounted for 7.2% (r63 = 0.269, p = 0.030) of the

variance in relative fixation time mean difference. When considering the DSF and DSB

subscores separately, DSF scores were correlated with relative fixation time differences (i.e.

larger biases towards novel images) in the 1-back condition, accounting for 7.3% of the

variance (r63 = 0.272, p = 0.029), but not in the 2-back condition (r63 = 0.092, p = 0.5).

Interestingly, DSB scores were correlated with relative fixation time differences in the 2-back

condition, accounting for 14.7% of the variance (r63 = 0.383, p = 0.002), but not in the 1-back

condition (r63 = 0.123, p = 0.330).

Table 6. Pearson correlation coefficients between parameters (n = 65).

Measure sMMSE DS Total DSF DSB

Relative Fixation Time

Difference (1-back) 0.281* 0.258* 0.271* 0.123

Relative Fixation Time

Difference (2-back) 0.288* 0.269* 0.092 0.383**

Significant correlations: *p < 0.05; **p < 0.01

sMMSE = Standardized Mini-Mental State Examination; DS Total = Digit Span

Total (age-corrected scaled score); DSF = Digit Span Forward; DSB = Digit Span

Backward

42

Chapter 3

Selective Attention towards Novel Stimuli as a Predictor of Cognitive

Decline


3.1.1 Participants

AD participants were recruited from an outpatient memory clinic at SHSC. This study included

patients diagnosed with possible or probable AD based on the DSM-IV-TR [13] and NINCDS-

ADRDA criteria [296]. Eligibility criteria included: no change in anti-dementia medications

for at least 1 month prior to study day and mild to moderate cognitive impairment (sMMSE ≥

10). Furthermore, all eligible patients had no significant eye pathology, severe impairments in

communication or diagnosis of other neurological illnesses. Specifically, patients were

excluded if they developed any concurrent neurological illnesses, such as stroke, which would

result in cognitive decline not related to AD

3.1.2 Procedures

This was a longitudinal study. At the baseline study visit, all participants were administered the

sMMSE [299] and the Conners’ Continuous Performance Test (CPT) [306]. Follow-up scores

on the sMMSE, administered by the study psychiatrist, were then collected from patient charts

every 6 months for up to 2 years. As in Study 1, patients were screened according to the

eligibility criteria before initiation of VAST procedures. Detailed descriptions of the VAST

43

procedures and novelty preference visual stimuli paradigm were provided in the previous

sections (2.1.4 and 2.1.5). The study purpose, procedures, risks and benefits were verbally

explained to participants and caregivers. Participants were given time to read the informed

consent form and ask questions to the study personnel. Written informed consent was provided

by all participants or in the case that patients could not provide consent, a legally authorized

representative (caregiver) signed the forms. This study was approved by the institution’s

research ethics board.

3.1.3 Neuropsychological Tests

Standardized Mini-Mental State Examination

The sMMSE [299], a more systematic and reliable version of the original MMSE [300], was

used to describe severity of cognitive impairment. Total scores may range from 0-30, with

higher values indicating better cognitive function. This 10-item scale, administered directly to

AD patients, measured domains such as orientation, memory, attention and language abilities.

Conners’ Continuous Performance Test

The CPT [306] is a 15-minute computerized test of attention, used widely in attention deficit

hyperactivity disorder research. A significant association between higher CPT inattention and

greater improvements in apathy in a clinical trial of methylphenidate in apathetic AD patients

was previously reported [218]. This supports the usefulness of this test in probing attention

abilities in the present study population. Test-takers were instructed to press a space bar

44

whenever letters other than X appeared on the screen. Scores summarizing inattention,

vigilance and disinhibition were calculated, with higher scores indicating greater deficits.

3.1.4 Visual Scanning Parameter

Relative fixation time, defined as the ratio of total duration of discrete fixations on novel

images relative to total duration of fixations on all images of a slide, was calculated from two

basic visual parameters: average fixation duration and the fixation frequency (described in

detail above in section 2.1.6). Novelty preference was estimated by subtracting the values of

relative fixation time for repeat images from novel images on each 1-back and 2-back slide

(novel - repeat). The average of all 16 test slides were calculated for each patient. The mean

values for the 1-back and 2-back conditions were then added in order to obtain a single value

for relative fixation time to represent novelty preference for each subject. Higher values

indicated stronger preferences for the novel images.


Baseline demographic, neuropsychological and visual scanning data were summarized using

proportions or mean ± standard deviation (SD). Analyses were conducted using IBM SPSS



A multivariate linear regression model with backward elimination was used to test the novelty

preference parameter, relative fixation time, as a predictor of sMMSE change (follow-up -

baseline), controlling for baseline sMMSE, age, education and attention (CPT Inattention). For

45

this model, 43 subjects were needed to achieve a power of 0.80, α of 0.05 and R2 = 0.25

(Hypothesis 3). Covariates were chosen based on findings suggesting interactions with

cognitive abilities or sMMSE scores. Age and education are known to have an effect on

sMMSE scores, with older age and lower education associated with lower sMMSE [1, 307].

Overall attention, a domain of cognition, may have an effect on changes in sMMSE scores and

could interact with visual scanning parameters associated with attentional bias. For exploratory

analyses, all patients were divided into those who declined significantly or reliably on the

sMMSE (defined as a decrease of 3 or more points [308]) within the 2 year period. Receiver

operating characteristic (ROC) analyses were used to test whether relative fixation time could

correctly classify or predict significant decline versus no decline. Patients on different classes

of anti-dementia medications (ChEIs versus memantine) were compared in ANOVA models to

explore potential associations with novelty preference at baseline.

3.2 Results

The results of this study were published in the Journal of Alzheimer’s Disease (Chau SA,

Herrmann N, Sherman C, Chung J, Eizenman M, Kiss A, Lanctôt KL. Visual selective

attention towards novel stimuli predicts cognitive decline in Alzheimer’s disease patients. J

Alzheimers Dis 2017; 55(4): 1339-49.) [309].

46

Thirty-two patients with mild to moderate AD were included in this analysis (See Table 7 for

baseline data). Appendix A provides the patient recruitment flow chart. Follow-up sMMSE

scores for 34 AD patients were available - 2 were excluded in the final analyses due to

occurrence of stroke during the follow-up period. The primary analysis included the most

recent score available within the 2-year period for each participant. The mean length of follow-

Table 7. Patient baseline characteristics (n = 32). Values are mean ±

standard deviation or proportions.

Measure Value

Age 77.9 (7.8)

Standardized Mini-mental State Examination 22.2 (4.4)

Gender (%)

Male

Female

56.3%

43.8%

Education (%)

Grade school

High school

Post-secondary

Graduate

18.8%

34.4%

21.9%

25.0%

Cognitive Enhancers (%)

Cholinesterase inhibitors

Memantine

75.0%

25.0%

Conners’ Continuous Performance Test

Inattention 534.2 (175.3)

Difference between Frequency of Fixations on

novel and repeat images 0.61 (1.39)

Difference between Average Fixation Duration on

novel and repeated images (msec) 79.8 (108.5)

Difference between Relative Fixation Times on

novel and repeated images (%) 7.0 (8.7)

47

up was 1.7 ± 0.4 years. Mean sMMSE score significantly decreased from 22.3 ± 4.5 at baseline

to 19.6 ± 5.4 at follow-up (t31 = 4.86, p < 0.001).

In the linear regression model, relative fixation time (t = 2.78, p = 0.010, tolerance = 0.95,

variance inflation factor = 1.05), CPT Inattention (t = -2.88, p = 0.008, tolerance = 0.61,

variance inflation factor = 1.63) and baseline sMMSE (t = -2.20, p = 0.036, tolerance = 0.63,

variance inflation factor = 1.58) were significant predictors of sMMSE change scores. Age and

education were not significant predictors and therefore removed from the final model in the

backward elimination method. Reduced time spent on novel compared with repeat images,

controlling for overall attention and cognition at baseline, predicted greater decline in

cognition (See Figure V). This model accounted for 41% of the variance in sMMSE change

scores (R2 = 0.41, Adjusted R2 = 0.35, F3,28 = 6.51, p = 0.002, Table 8). A model that included

only baseline sMMSE and CPT Inattention as predictors of sMMSE change accounted for 25%

of the variance (R2 = 0.25, Adjusted R2 = 0.20, F2,29 = 4.80, p = 0.016). Thus, relative fixation

time accounted for an additional 16% of the variance in sMMSE change scores.

Table 8. Linear regression model of relative fixation time as a predictor of sMMSE

change (R2 = 0.41, Adjusted R2 = 0.35, F3,28 = 6.51, p = 0.002, n = 32).

Predictors β t p-value

Relative Fixation Time 0.41 2.78 0.010

Conners’ CPT Inattention -0.53 -2.88 0.008

sMMSE Baseline -0.40 -2.20 0.036

CPT = Continuous Performance Test, sMMSE = Standardized Mini-mental State

Examination

48

Figure V. sMMSE change from baseline versus relative fixation time (novel - repeat). Lower

baseline novel relative fixation time predicted greater decline in sMMSE scores (unadjusted

values).

Of the 32 AD patients included in the study, 14 had a significant decline in sMMSE scores (≥ 3

points decrease) while 18 did not. ROC analyses performed on all 32 patients indicated that

relative fixation time had an area under the curve of 0.72 (95% confidence interval = 0.55 to

0.90, p = 0.033, Figure VI), indicating moderate ability of this single visual scanning parameter

to classify patients into those who did and did not decline. The sensitivity and specificity of

various cut-off values for relative fixation time are given in Table 9.

49

Figure VI. Receiver operating characteristic (ROC) curve for relative fixation time (Area

under the curve = 0.72, 95% confidence interval = 0.55 to 0.90, p = 0.033, n = 32).

Table 9. Cut-off values of relative fixation time with sensitivity and specificity.

Cut-off Value Sensitivity Specificity

-0.26 0.214 0.944

1.05 0.429 0.889

5.05 0.714 0.722

8.67 0.786 0.556

9.63 0.857 0.444

11.08 0.929 0.339

50

There were 24 patients on ChEIs, 4 on memantine monotherapy and 4 not taking any anti-

dementia medications. Overall, AD patients on ChEIs had higher, though non-significant (F1,30

= 2.93, p = 0.097), relative fixation time on novel images compared to those not on any ChEIs

(7.9 ± 7.7% versus 2.3 ± 9.0%, respectively). Within the ChEI group, the three patients taking

galantamine had a mean relative fixation time of 15.9 ± 6.7% compared with 7.9 ± 8.7% in

patients on donepezil and rivastigmine (F1,22 = 4.34, p = 0.049).

51

Chapter 4

Apathy and Selective Attention towards Positive Stimuli


4.1.1 Participants

Participants were recruited from outpatient clinics at SHSC. Eligibility for AD patients

included: diagnosis of possible or probable AD based on the DSM-IV-TR [13] and NINCDS-

ADRDA criteria [296], minimum age of 65, no change in anti-dementia medications for at

least 1 month prior to study day and mild to moderate cognitive impairment (sMMSE ≥ 10).

Apathetic AD patients were additionally required to have significant apathy (NPI apathy

subscore ≥ 4 for at least 4 weeks) and no significant depression (NPI depression subscore < 4).

The NPI apathy cut-off score of ≥ 4 has consistently been used to define clinically significant

apathy [219, 310-312] and represents “often”, “frequent” or “very frequent” apathy of

“moderate” or “marked” severity. See Table 10 for full criteria. Patients were excluded if they

had significant eye pathology, communicative impairments or other neurological illnesses.

4.1.2 Procedures

This was a cross-sectional study. Participants and caregivers provided information regarding

age, ethnicity, level of education, current medications, medical and psychiatric history. The

sMMSE [299] was administered to assess cognitive status and the Conners’ CPT [306] was

used to evaluate attention. Behaviour disturbances, including apathy were assessed through

52

interviews with caregivers using the NPI and Apathy Evaluation Scale (AES) [313]. Following

these tests, patients underwent the VAST procedures with the social/dysphoric stimuli

paradigm. Section 2.1.4 provided a detailed description of the VAST recording procedures.

Before initiation of procedures, patients were screened according to the eligibility criteria. The

study purpose, procedures, risks and benefits were verbally explained to participants and

caregivers. Participants were given time to read the informed consent form and ask questions

to the study personnel. Written informed consent was provided by all participants or in the case

that patients could not provide consent, a legally authorized representative (caregiver) signed

the forms. This study was approved by the SRI research ethics board.

Table 10. Eligibility criteria

Alzheimer’s Disease – No Apathy


sMMSE ≥ 10

No significant apathy or depression (NPI subscore < 4)




Alzheimer’s Disease - Apathy


sMMSE ≥ 10

Significant apathy (NPI apathy ≥ 4)

No significant depression (NPI depression < 4)




53

4.1.3 Neuropsychological Assessments

See Section 3.1.3 for full description of the sMMSE [299] and Conners’ CPT [306].

Neuropsychiatric Inventory

The NPI [314] is a widely used assessment of behaviour disturbances in dementia, including:

apathy, agitation, delusions, hallucinations, depression, euphoria, aberrant motor behaviour,

irritability, disinhibition, anxiety, sleeping, and eating. Caregivers were asked to judge the

frequency and severity of all existing symptoms in the past 4 weeks and in relation to the

patient’s behaviour before development of dementia. The frequency score was judged on a 4-

point scale from “Occasionally” (1) to “Often” (2) to “Frequently” (3) to “Very Frequently”

(4). The severity score was judged on a 3-point scale from “Mild” (1) to “Moderate” (2) to

“Marked” (3). The frequency and severity scores were multiplied to generate a composite

score, ranging from 0 to 12, for each domain. Total scores range from 1 to 144 with higher

scores indicating greater behavioural disturbances.

Apathy Evaluation Scale

The AES [315] informant version is a reliable and valid measure of apathy widely used in

clinical research. Severity on each of the 18 items was characterized with a 4-point Likert scale

using the following descriptions: “Not at All”, “Slightly”, “Somewhat” and “Very Much”.

Scores ranged from 18 to 72. Higher values were allocated for stronger presence of specific

symptoms and thus, a higher total score indicated greater apathy. As an additional advantage,

54

this scale also provided subscores for each of the domains of apathy, including behaviour,

cognition and emotion [313].


The visual stimuli for this paradigm consisted of a series of slides, each displayed for 10.5

seconds, followed by 1 second of a uniform grey screen. Each slide contained four images of

different emotional themes: 1 dysphoric, 1 social and 2 neutral images (Figure VII). Dysphoric

and social images were similar in complexity. Images were selected from the International

Affective Picture System (IAPS), a standardized database of images used to study emotion and

attention. Images were chosen based on IAPS ratings for valence (feelings of pleasure vs.

displeasure): neutral images had an approximate valence of 5, social images ranged from 6 to 8

while dysphoric images had valence ratings of 2 to 4. Additionally, the dysphoric and social

images on each slide had IAPS ratings of arousal (feelings of excitement vs. calm) between 4

and 6, with a maximum rating difference of 2 to maintain consistency within slides. Neutral

images had low arousal ratings between 2 and 4. The four images on each slide were arranged

in a 2 by 2 configuration, with the positions (top-left, top-right, bottom left and bottom right)

of the different emotional themes uniformly distributed between the 16 test slides. The sets of

images used in the present study are similar to those used by Eizenman et al [270] to

differentiate depressed and non-depressed younger adults in a previous study. However, they

have yet to be validated in the apathetic AD patient population. These slides were a component

of a battery of tests that included measurements for novelty preference (described above) and

depression. The slides for the different tests were intermixed and a total of 106 slides were

presented. To analyze apathy, only data from the 16 test slides with 1 dysphoric, 1 social and 2

55

neutral images were used. The total testing procedure, including novelty preference slides, was

divided into 2 sessions of approximately 10 minutes each. In between the 2 sessions the

subjects were given a 5-minute break.

Figure VII. Sample format of social/dysphoric paradigm slide. Each slide contained 1 social

(bottom left), 1 dysphoric (bottom right) and 2 neutral (top left and right) images. The position

of image types were randomly intermixed between slides.


Relative fixation time and fixation frequency within images, parameters used previously to

characterize visual scanning behaviour in young patients with eating disorders [272] and

depression [270], were the chosen outcome measures. Relative fixation time is the ratio of time

spent on a particular image over the total time spent on all four images of a slide, expressed as

a percent. This measure is an indicator of the relative interest in an image, taking into account

all other images on the slide that are competing for the viewer’s attention. Thus, it was used

56

rather than real time because it allowed for better comparisons of preferences between specific

images within a slide. Fixation frequency described the total count or number of discrete

fixations within an image. This was a basic eye movement parameter used in combination with

average fixation duration to calculate relative fixation time. Biases were summarized by

subtracting mean relative fixation time and fixation frequency on neutral from social and

dysphoric images (bias towards social = social – neutral; bias towards dysphoric = dysphoric –

neutral). For each participant, biases for social and dysphoric images were determined by

calculating the means of these parameters on the 16 relevant test slides.


Baseline demographic, neuropsychological and visual scanning data were summarized using

counts or mean ± standard deviation (SD). Analyses were conducted using IBM SPSS



Clinical and demographic characteristics were compared between apathetic and non-apathetic

groups using ANOVA for continuous variables and χ2 for categorical variables. Two-factorial

repeated-measures analysis of covariance (ANCOVA) models, with up to 2 covariates, were

conducted to explore within-subject effects of image type (social bias, dysphoric bias),

between-group effects (apathetic, non-apathetic) and interaction between factors for relative

fixation time and fixation frequency (Hypothesis 4). A sample size of 38 (2 groups of 19) was

required to detect medium to large effect sizes (power = 0.80, α = 0.05). Covariates were

chosen based on the group comparison analyses in order to control for significant differences

in clinical and neuropsychological parameters between apathetic and non-apathetic groups.

57

Hierarchical multiple regression analyses were used to test the contribution of overall apathy

(AES Total), controlling for cognition (sMMSE) and attention (CPT Inattention), in predicting

social biases (both relative fixation time and fixation frequency). sMMSE and CPT Inattention

were entered in Step 1 and AES Total was entered in Step 2 of the model. To further explore

the contributions made by specific AES subdomains, the regression models were repeated with

sMMSE and CPT Inattention entered in Step 1 and the AES subscores entered in Step 2 using

a stepwise procedure. All analyses were considered significant at an α of 0.05 with no

corrections made for multiple comparisons.

4.2 Results

This work was published in the Journal of Alzheimer’s Disease (Chau SA, Chung J, Herrmann

N, Eizenman M, Lanctôt KL. Apathy and attentional biases in Alzheimer’s disease. J

Alzheimers Dis 2016; 51: 837-46.) [316].

Thirty-six (19 non-apathetic and 17 apathetic) AD patients with a mean (±SD) age of 78.2 ±

7.8 and a mean MMSE score of 22.4 ± 3.5 were included (Table 11). Appendix A provides the

patient recruitment flow chart. Groups were comparable in age, gender, education, concomitant

medications use, cognition (sMMSE) and attention (Conners’ CPT). Apathetic patients had

higher scores on the NPI Total, NPI apathy, AES Total and all AES domain scores (behaviour,

cognition and emotion). All participants, including those with significant apathy, had low

scores on the NPI depression.

58

Table 11. Clinical and demographic characteristics. Values are mean ± standard

deviation or proportions.

Non-apathetic

n=19

Apathetic

n=17 p-value

Age, years 77.6 (8.6) 78.8 (6.9) 0.639

Gender, % female 52.6% 29.4% 0.335

Education, %

Grade school

High school

Post-secondary

Graduate

11.1%

27.8%

38.9%

22.2%

29.4%

35.3%

5.9%

29.4%

0.113

Concomitant medications, %

Cholinesterase Inhibitors

Memantine

Anti-depressants

Methylphenidate

84.2%

21.1%

47.4%

10.5%

88.2%

5.9%

47.1%

23.5%

0.727

0.189

0.985

0.296

Standardized Mini-mental State

Exam 22.8 (2.9) 22.0 (4.2) 0.507


Apathy subscore

Depression subscore

5.7 (6.6)

0.6 (1.0)

0.7 (1.2)

22.2 (10.1)

6.8 (2.2)

0.4 (0.8)

<0.001*

<0.001*

0.422


Behaviour

Cognition

Emotion

34.1 (6.6)

8.4 (2.0)

15.3 (3.8)

3.8 (1.2)

55.5 (8.0)

14.2 (3.3)

25.0 (3.3)

6.2 (1.6)

<0.001*

<0.001*

<0.001*

<0.001*

Conners’ Continuous Performance

Test

Inattention

Vigilance

Impulsivity

544.0 (194.5)

102.0 (17.4)

201.6 (84.7)

567.5 (248.8)

108.7 (43.0)

189.0 (56.3)

0.763

0.770

0.203

On average, apathetic patients spent 10.5 ± 10.7 % more time on social images than on neutral

images while non-apathetic patients spent 20.0 ± 17.0 % more time on social images than on

neutral images. Mean relative fixation times on dysphoric images were 13.3 ± 10.5 % higher

59

than that on neutral images for apathetic and 14.3 ± 8.5 % higher for non-apathetic patients.

Controlling for differences in overall neuropsychiatric symptoms (NPI), an image (dysphoric

vs. social) by apathy (non-apathetic vs. apathetic) interaction (F1,32 = 4.31, p = 0.046, ηp2 =

0.12, power = 0.52) for relative fixation time was found (Figure VIII). No significant group

(F1,32 = 0.93, p = 0.341) or image (F1,32 = 0.28, p = 0.598) main effects emerged. Post-hoc

analyses showed no differences between apathetic and non-apathetic patients in relative

fixation time on social images (F1,32 = 3.06, p = 0.090) or dysphoric images (F1,32 = 0.62, p =

0.437).

Mean fixation frequency on social images was higher than that on neutral images by 2.0 ± 1.9

fixations for apathetic patients and by 3.4 ± 1.7 fixations for non-apathetic patients. On

average, fixation frequency on dysphoric images was higher than that on neutral images by 2.4

± 1.6 fixations for apathetic patients and 2.7 ± 1.6 fixations for non-apathetic patients. There

was a significant image by apathy interaction (F1,32 = 11.34, p = 0.002, ηp2 = 0.26, power =

0.91, See Figure IX) but no group (non-apathetic vs. apathetic, F1,32 = 0.97, p = 0.333) or image

(dysphoric vs. social, F1,32 = 0.05, p = 0.831) main effects, controlling for group differences in

overall neuropsychiatric symptoms (NPI). Compared with non-apathetic patients, those with

apathy had reduced fixation frequency on social images (F1,32 = 5.83, p = 0.021) but no

difference on dysphoric images (F1,32 = 0.81, p = 0.374). To explore the effect of gender

imbalances between the non-apathetic and apathetic groups, ANCOVA models were repeated

with gender as an additional covariate. Results indicated that gender did not significantly affect

the outcome of models for relative fixation time or frequency.

60

Figure VIII. Mean relative fixation time with standard deviation error bars for social and

dysphoric (minus neutral) themed images for non-apathetic (n = 19) and apathetic (n = 17) AD

patients.

Figure IX. Mean fixation frequency within images with standard deviation error bars for social

and dysphoric (minus neutral) themed images for non-apathetic (n = 19) and apathetic (n = 17)

AD patients.

61

Linear regressions with AES Total, CPT Inattention and sMMSE as predictors were performed

separately for fixation frequency and relative fixation time on social images. For fixation

frequency within social images, AES Total (t = -2.09, p = 0.044, tolerance = 0.85, variance

inflation factor = 1.18) and sMMSE (t = -3.09, p = 0.004, tolerance = 0.58, variance inflation

factor = 1.73) were significant predictors of fixation frequency on social images (Table 12).

The total model accounted for 26% of the variance (R2 = 0.26, Adjusted R2 = 0.19, F3,32 = 3.65,

p = 0.023). There were no significant predictors of relative fixation time on social images or

fixation frequency and relative fixation time on dysphoric images.

Table 12. Linear regression model of predictors of fixation frequency within social

images (R2 = 0.26, Adjusted R2 = 0.19, F3,32 = 3.65, p = 0.023, n = 36). AES Total and

sMMSE were significant predictors.


sMMSE -0.62 -3.09 0.004*


AES Total -0.35 -2.09 0.044*

sMMSE = Standardized Mini-mental State Exam; CPT = Continuous Performance Test;

AES = Apathy Evaluation Scale

62

Hierarchical regression models with AES behaviour, cognition and emotion entered at Step 2

in the stepwise method, following CPT Inattention and sMMSE entered at Step 1 (described

above), showed the distinct contribution of each domain score on fixation frequency within

social images (Table 13). AES emotion (t = -2.61, p = 0.014, tolerance = 0.89, variance

inflation factor = 1.12) and sMMSE (t = -3.02, p = 0.005, tolerance = 0.64, variance inflation

factor = 1.57) were significant predictors of fixation frequency on social images. Higher AES

emotion subscores (more severe symptoms) were associated with a decreased number of

fixations on social images. The overall model accounted for 30% of the variance (R2 = 0.30,

Adjusted R2 = 0.24, F3,32 = 4.62, p = 0.009). Models for relative fixation time on social images

and fixation frequency and relative fixation time on dysphoric images as dependent variables

were not significant.

Table 13. Linear regression model of predictors of fixation frequency within social

images (R2 = 0.30, Adjusted R2 = 0.24, F3,32 = 4.62, p = 0.009, n = 36). AES Emotion and

sMMSE were significant predictors.


sMMSE -0.56 -3.02 0.005*


AES Emotion -0.41 -2.61 0.014*

AES Cognition -0.16 -0.62 0.537

AES Behaviour -0.06 -0.30 0.763

sMMSE = Standardized Mini-mental State Exam; CPT = Continuous Performance Test;


63

Chapter 5

Effect of Methylphenidate for Apathy on Visual Scanning Behaviour


5.1.1 Participants

Participants were recruited from outpatient clinics at SHSC. Patients with clinically significant

apathy in which a medication was deemed beneficial by the study physician were recruited for

this open label trial of MTP. Eligibility included: diagnosis of possible or probable AD based

on the DSM-IV-TR [13] and NINCDS-ADRDA criteria [296] in the mild to moderate stage

(sMMSE ≥ 10), no change in anti-dementia medications for at least 1 month prior to study day

and significant apathy (NPI apathy subscore ≥ 4 for at least 4 weeks). The NPI apathy cut-off

score of ≥ 4 has consistently been used to define clinically significant apathy [219, 310-312]

and represents “often”, “frequent” or “very frequent” apathy of “moderate” or “marked”

severity. Exclusion for the trial included: current use of antipsychotics, failure of treatment

with MTP for apathy in the past, treatment with a medication that would prohibit the safe

concurrent use of MTP such as monoamine oxidase inhibitors and tricyclic antidepressants.

Patients were also excluded if they had significant eye pathology, communicative impairments

or other neurological illnesses.

64

5.1.2 Procedures

This was an open label trial. Participants and caregivers provided information regarding age,

level of education, current medications, medical and psychiatric history. Eligible apathetic

patients were prescribed MTP (immediate release formulation, 5-10 mg BID) following their

baseline visit. MTP is known to reach peak plasma concentrations after 2 hours and has a

pharmacokinetic half-life of 2-3 hours [317]. VAST and neuropsychological assessments were

repeated following approximately 4 weeks of treatment. A double-blinded placebo controlled

cross-over trial of MTP found benefit for apathy after 2 weeks [218]. In a randomized double-

blind placebo controlled trial [219] of MTP in AD patients, improvements in apathy were

observed after 6 weeks of treatment. Additionally, increases in selective attention (measured by

the WAIS-DS) emerged following 4 weeks and improved further at week 6 in the active

treatment group compared with placebo [295]. Thus, 4 weeks of treatment was deemed a

suitable duration to observe changes on both apathy and attention measures.

At each study visit, the sMMSE [299] was administered to assess cognitive status. The WAIS-

DS was used to assess working memory and auditory attention [298]. Sequences of digits were

read and patients were required to repeat them in the same (forward, DSF) or reverse

(backward, DSB) order. Behaviour disturbances, including apathy were assessed through

interviews with caregivers using the NPI and AES [313]. Details of these tests are available in

Section 2.1.3 (sMMSE, WAIS-DS) and 4.1.3 (NPI, AES). Following this battery of tests, both

controls and dementia patients underwent the VAST procedures with both the novelty

preference and social/dysphoric stimuli paradigm. Section 2.1.4 provides a detailed description

of the VAST recording procedures.

65

Before initiation of procedures, patients were screened according to the eligibility criteria. The

study purpose, procedures, risks and benefits were verbally explained to participants and

caregivers. Participants were given time to read the informed consent form and ask questions.

Written informed consent was provided by all participants or in the case that patients could not

provide consent, a legally authorized representative (caregiver) signed the forms. This study

was approved by the SRI research ethics board.


Specific details of the novelty preference and social/dysphoric stimuli were provided in section

2.1.5 and 4.1.4, respectively. The visual stimuli consisted of slides displayed for 10.5 seconds

each, followed by 1 second of a uniform grey screen. Ten filler slides were used at the

beginning of the presentation to familiarize subjects with the presentation set-up. The novelty

preference stimuli comprised of a series of slides each containing four images simultaneously

presented. All four images had similar complexity and IAPS rating for valence and arousal.

Two images on each slide were novel and two were repeats of images that were shown

previously - 1-back and 2-back repeats were tested. The four images on each slide were

arranged 2 by 2, with the position of the novel stimuli and previously shown stimuli randomly

intermixed. There were 16 of these slide series for a total of 48 slides presented in this portion

of the test. The visual stimuli for the social/dysphoric paradigm comprised of a series of slides

each containing four images (1 dysphoric, 1 social and 2 neutral images), selected from the

IAPS collection. A total of 16 slides were presented. In this slide series, images were chosen

from the IAPS ratings for valence (feelings of pleasure vs. displeasure): neutral images had

medium valence, social images had high valence and dysphoric images had low valence

66

ratings. Additionally, the social and dysphoric images on each slide had similar IAPS ratings of

arousal (feelings of excitement vs. calm). The four images on each slide were arranged 2 by 2,

with the specific spatial position of each theme randomly inter-mixed. There were 16 test slides

presented in this portion of the test. The total testing procedure, including both novelty

preference and social/dysphoric slides (106 total slides), was divided into 2 sessions of

approximately 10 minutes each. In between the two sessions the subjects were given a 5-

minute break.


The visual scanning parameters used in this study were the number of discrete fixations on

each image (fixation frequency within images) and the ratio of total duration of discrete

fixations on novel images relative to total duration of fixations on all images of a slide (relative

fixation time). These parameters were investigated in previous sections of this thesis. Novelty

preference was estimated by subtracting the values of relative fixation time and frequency for

repeat images from novel images on each 1-back and 2-back slide (novel - repeat). The average

of all 16 test slides were calculated for each patient. The mean values (both duration and

frequency parameters) for the 1-back and 2-back conditions were then added in order to obtain

a single value to represent novelty preference for each subject. Higher values indicated

stronger preferences for the novel images. Social biases were summarized by subtracting mean

relative fixation time and fixation frequency on neutral from social images (bias towards social

= social – neutral). For each participant, biases for social images were determined by

calculating the means of these parameters on the 16 relevant test slides.

67


Baseline and treatment scores on neuropsychological and psychiatric tests were compared

using two-tailed paired Student’s t-tests. Spearman correlations were conducted to explore

associations between change in visual scanning behaviour (treatment - baseline) and AES

scores (treatment - baseline). Relative fixation time and frequency on novel images were used

to examine the effect of MTP on novelty preference (Hypothesis 5). To explore the relationship

between change in apathy and novelty preference with standard tests of attention/working

memory, further correlations were conducted for AES, fixation parameters and DS scores

(Total, Forward and Backward). Relative fixation time and frequency on social images were

used to analyze treatment effects on processing of positive stimuli (Hypothesis 6). As in Study

3, exploratory analyses were performed to examine the distinct relationship between each AES

subdomain and attentional bias for positive stimuli. A sample size of 20 would be required to

detect large effect sizes (power = 0.80, α = 0.05). Spearman correlations were also conducted

to explore baseline predictors of change in apathy (AES). To examine whether apathy

remission was associated with different attentional bias patterns, patients were categorized into

groups based on change in NPI apathy scores. Remitters or treatment responders were defined

as patients who had an NPI apathy score decrease below 4 following treatment and non-

remitters were those who maintained scores above or at 4. Two-factorial repeated-measures

ANOVA models were employed to explore within-subject effects of MTP (baseline, follow-

up), between group effects (remitter, non-remitter) and interaction between factors for

attentional bias parameters. All analyses were considered significant at an α of 0.05 with no

corrections made for multiple comparisons.

68

5.2 Results

Eight AD patients with clinically significant apathy, as determined by the study psychiatrist,

were treated with methylphenidate (5-10 mg BID). Appendix A provides the patient

recruitment flowchart. In this study, all eligible candidates screened for the open label trial

participated and were included in the analyses. The mean follow-up length was 6.1 ± 4.6 weeks

Table 14. Clinical and demographic characteristics (n=8). Values are mean ±

standard deviation or proportions.

Baseline Treatment p-value

Age, years 76.0 (6.5)

Standardized Mini-mental State

Exam 21.8 (6.3) 21.0 (7.5) 0.378

Gender 2 Females

6 Males

Education, n

Grade school

High school

Post-secondary

Graduate

1

2

2

3

Concomitant medications, n

Cholinesterase Inhibitors

Memantine

Anti-depressants

6

0

2


Apathy subscore

Depression subscore

22.9 (6.5)

7.1 (2.0)

3.8 (4.1)

19.9 (13.1)

4.8 (3.9)

2.0 (3.7)

0.524

0.137

0.247


Behaviour

Cognition

Emotion

55.8 (8.3)

14.6 (2.0)

25.3 (4.1)

6.1 (1.0)

46.6 (18.1)

13.8 (2.2)

24.3 (2.9)

6.3 (1.6)

0.265

0.195

0.465

0.732

Digit Span Total Scaled

Forward

Backward

9.8 (2.9)

9.3 (2.2)

5.0 (2.1)

10.1 (4.2)

9.1 (2.4)

5.6 (2.9)

0.612

0.879

0.217

69

and the average dose of the 8 patients was 15 mg per day. No patients experienced adverse

events during treatment. On average, patients had numerical decreases in apathy test scores

(AES and NPI apathy), though these changes were not statistically significant. Four patients

remitted based on NPI apathy scores (baseline NPI apathy at least 4 and follow-up scores

decreased to below 4) and 4 patients did not respond to treatment (no change in scores). There

were also small decreases in overall behaviour and depressive symptoms (Table 14).

Spearman correlations showed that baseline to follow-up improvements in apathy on the AES

was significantly associated with increase in fixation frequency on novel images (ρ6 = -0.786, p

= 0.021, Table 15). Additional correlations were conducted to examine the relationship

between changes in selective attention/working memory, novelty preference and apathy.

Changes in DSB scores trended towards significant correlations with change in relative

fixation time (r6 = 0.660, p = 0.075), fixation frequency (ρ6 = 0.672, p = 0.068) and apathy (ρ6 =

-0.669, p = 0.070, Table 16). Specifically, increase in fixations (both duration and frequency)

on novel images was associated with improved DSB scores. Improvements in apathy on the

Table 15. Spearman correlation coefficients between changes in novelty preference

parameters and apathy (n = 8). Attentional bias values were novel minus repeat for both

1- and 2-back images.

Measure

Change in Relative

Fixation Time on

Novel Images

Change in Fixation

Frequency Within Novel

Images

Change in AES

Total -0.583 -0.786*

Significant correlations: *p < 0.05


70

AES were also associated with improvements in DSB. There were no significant or trending

relationships with DSF scores.

Exploratory analysis showed that remitters had a numerically larger, though statistically non-

significant, increase in time spent on novel images compared with the non-remitted group

(group main effect: F1,6 = 1.70, p = 0.240, treatment main effect: F1,6 = 1.77, p = 0.231, group x

treatment interaction: F1,6 = 3.42, p = 0.114, Figure X). Remitters had a relative fixation time

on novel images of 10.5 ± 11.6 % at baseline and 12.0 ± 16.6 % following treatment. Non-

remitters had a relative fixation time on novel images of 6.3 ± 7.6 % at baseline, which

decreased to -3.1 ± 6.3 % following treatment. Thus, patients who did not respond to apathy

treatment switched their attention towards repeated images post treatment.

Table 16. Spearman correlation coefficients between changes in novelty preference

parameters, apathy and attention (n = 8). Attentional bias values were novel minus repeat

for both 1- and 2-back images.

Measure

Change in

Relative

Fixation Time

on Novel

Images

Change in

Fixation

Frequency

Within Novel

Images

Change in

AES

Total

Change in DS

Total 0.417 0.135 -0.340

Change in DSF 0.099 -0.284 0.079

Change in DSB 0.660† 0.672† -0.669†

Trending correlations: †p < 0.1

AES = Apathy Evaluation Scale; DS Total = Digit Span Total (age-corrected scaled

score); DSF = Digit Span Forward ; DSB = Digit Span Backward

71

Figure X. Mean relative fixation time on novel images with standard deviation bars for apathy

remitters (n=4) and non-remitters (n=4) following methylphenidate treatment.

Spearman correlation analyses were also used to determine associations between changes in

apathy scores and attentional bias towards positive or social themed images. There were no

significant correlations between changes in relative fixation time or fixation frequency on

social images (social – neutral) and improvements in apathy scores (Table 17). However,

exploratory analyses suggested that improvement in apathy symptoms on the AES was

associated with lower baseline attentional bias for social images, measured by both relative

fixation time (ρ6 = 0.786, p = 0.021) and frequency (ρ6 = 0.872, p = 0.006, Table 18). Further,

there was a trending correlation between lower baseline attentional bias for social images, both

relative fixation time (ρ6 = 0.665, p = 0.072) and frequency (ρ6 = 0.665, p = 0.072), and

72

improvements in AES emotion domain scores. There were no other baseline predictors of

improvement in apathy or attention.

Table 17. Spearman correlation coefficients between changes in attentional bias towards

social stimuli and changes in apathy (n = 8). Attentional bias values were social minus

neutral images.

Measure

Change in Relative

Fixation Time for

Social Images

Change in Fixation

Frequency Within

Social Images

Change in AES Total -0.571 -0.167

Change in AES Behaviour -0.073 0146

Change in AES Cognition -0.279 -0.012

Change in AES Emotion -0.548 -0.456

Significant correlations: *p < 0.05 †p < 0.1

AES = Apathy Evaluation Scale; DS Total = Digit Span Total (age-corrected scaled score)

Table 18. Spearman correlation coefficients between baseline attentional bias towards

social stimuli and changes in apathy (n = 8).

Measure

Baseline Relative

Fixation Time for

Social Images

Baseline Fixation

Frequency Within

Social Images

Change in AES Total 0.786* 0.862**

Change in AES Behaviour 0.244 0.146

Change in AES Cognition 0.424 0.582

Change in AES Emotion 0.665† 0.665†

Significant correlations: *p < 0.05 **p < 0.01 †p < 0.1

AES = Apathy Evaluation Scale; DS Total = Digit Span Total (age-corrected scaled score)

73

Chapter 6

Discussion

6.1 Selective Attention towards Novel Stimuli in AD

6.1.1 Discussion of Findings

The goal of this study was to determine whether AD patients demonstrate reduced attention

bias towards novel stimuli compared with healthy elderly controls. The associations between

attentional bias towards novel stimuli and scores on standard tests of attention were also tested.

The data showed that cognitively impaired participants had decreased bias towards novel

images compared with elderly controls. Specifically, in the presence of novel images and

images repeated from 1 and 2 slides previous, patients with mild to moderate AD spent less

time fixating on novel images compared with elderly volunteers. This is in line with an earlier

eye tracking study of novelty preference in patients with cognitive deficits [131], which found

that AD patients exhibited reduced exploration of novel stimuli. Disrupted neural processing of

novelty has been reported in other neurological and psychiatric disease populations.

Parkinson's patients have shown increased frontal P3 latency and diminished amplitude in

response to novel stimuli [318, 319]. In schizophrenic patients presenting with symptoms of

delusions, alterations in frontolimbic functional connectivity were observed during processing

of novel stimuli in a visual target detection task [320]. Patients with frontal lobe damage, due

to both cortical and subcortical strokes, also show reduced P3 [321, 322] and N2 [323]

response to novel stimuli.

74

The findings in this study were similar to results reported by Crutcher et al [105] using the

VPC task. However, there were some key discrepancies that are important to highlight. In that

study, the authors found significantly lower fixation times on novel images in MCI compared

with controls when the delays between the familiarization and test slides were long (2 minutes)

[105]. MCI patients performed similar to controls when the delay was shorter (2 seconds). In

the present study, short delays of 1 second and 12.5 seconds invoked differences in novelty

preference behaviour between mild to moderate AD patients and healthy elderly controls. This

inconsistency might be explained by differences between the patient populations of the two

studies and by differences between the testing paradigms. Patients in the present study were

more cognitively impaired (MMSE for MCI patients in that study was 27.5 ± 2.8 compared

with 22.2 ± 4.0 for AD patients in the present study) and thus, working memory capacity may

decay within a shorter period of time. A 2-second delay may not be sufficient to detect

limitations in the working memory capacity of MCI patients, who would have more preserved

cognitive functions than AD patients. With regard to the methodology, the paradigm used in

this study allotted longer viewing times (10.5 seconds) in contrast to the 5 seconds allowed in

Crutcher et al [105]. This was done in order to integrate differences in visual scanning

behaviour over longer time periods and improve the signal to noise ratio of the estimated visual

scanning behaviour parameters. This, thereby, enhanced ability to detect differences between

groups. Visual scanning patterns of MCI patients may begin to differentiate between healthy

controls with extended observation durations. As it has been suggested that short-term and

working memory are limited to 12-30 second durations [63], the 2-minute delay period used by

Crutcher et al may in fact be accessing more long-term based memory capacities.

Interestingly, the data suggest that AD patients do retain some capacity for novelty preference

and selective attention. Specifically, patients spent 5.3% and 3.7% more time fixating on novel

75

compared with 1- and 2-back repeat images, respectively. Even though novelty preference

behaviour was reduced when compared with age-matched controls, the paradigm described in

this paper was sensitive enough to detect selective attention towards novel stimuli in 92% of

the AD patients. Furthermore, as the 1-back repeats immediately preceded the 2-back repeats,

1-back stimuli functioned as a distractor for the 2-back stimuli. As there was no significant

condition main effects, relative fixation times on novel images were comparable for both 1-

back and 2-back. Thus, novelty preference was largely preserved in controls even in the

presence of distracters within the 12.5-second time-frame (2-back condition). While AD

patients had lower novelty preference compared with controls, presence of distracters did not

significantly reduce their performance further. While the VPC paradigm typically contained

slides with only 2 images simultaneously presented, VAST displayed 4 different images

simultaneously, heightening the competition for the participant’s attention. As AD patients

tend to have deficits in disengaging and shifting attention between images [50], displaying

more images on a slide will increase the differences between the visual scanning behaviour of

cognitively impaired patients and cognitively healthy controls. The more complex stimulus

structure in this paradigm may be more reflective of real-world conditions where the brain

must continually process and filter several competing visual inputs in the visual environment.

The underlying mechanisms of novelty preference may involve both selective attention and

working memory. EEG studies have found ERP signals in the frontal and parietal lobes during

the stages of novelty processing [99-101]. fMRI studies have found reduced neural activation

in frontal and temporal regions following repeated exposure to a particular stimuli, now

referred to as repetition suppression [46-49, 107, 116, 117]. Both temporoparietal and

frontoparietal networks have been linked with visuospatial attention [46-49] and working

memory functions [80, 83-85]. Furthermore, these brain regions have been found to have

76

greater atrophy [37, 39, 40] and reduced activity [41-45] in AD patients. The results from the

correlation analyses provide further support for the links between novelty preference and

selective attention and working memory. Specifically, significant associations were found

between greater relative fixation time on novel images and higher cognitive status, as well as

selective attention and working memory. Furthermore, the 1- and 2-back conditions were

correlated with different subtests. The results showed significant associations between novelty

preference and higher DSF scores in the 1-back condition, while in the 2-back condition,

novelty preference was associated with better performance on DSB. As the DSF subtest has

been characterized as a measure of selective attention while the DSB subtest has been thought

to tap into additional executive functions and working memory [301, 324], different

neurological correlates may be involved in processing the 1-back and 2-back conditions.

Response to immediately repeated stimuli in the 1-back condition may require simple selective

attention. Longer duration and the presence of distractors in the 2-back condition may involve

more executive functioning in order to process visual inputs.

Novelty seeking has been studied in rodent models of drug addiction using variations of a free-

choice place-preference task. Typically, control animals demonstrated a natural preference for

novel over familiar environments while exposure to psychostimulants, known to increase

attention and reward salience via the DA and NE systems [295, 325], disrupted this behaviour

[326, 327]. Similarly, a study of primates in an explicit decision-making task, using an eye

tracking system to determine preference, showed bias towards novel images in untreated

monkeys and further exacerbation of this bias under conditions of augmented DAergic tone

[328]. Aberrant neurotransmitter activity associated with AD may mediate reductions in the

salient quality of novel visual inputs, thereby impairing the inherent tendencies to explore

novel objects in the environment. For example, modulation of DA and NE activity, via

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methylphenidate, improved selective attention and symptoms of apathy in AD patients [295].

Furthermore, ChEIs, the first-line pharmacotherapy for treatment of cognitive symptoms in

AD, have been shown to modulate visual selective attention [329, 330].

6.1.2 Limitations

There are some limitations to consider when interpreting the results of this study. Although the

novelty preference paradigm used multiple presentations of novel and repeated stimuli of

neutral content, personal interest or attraction towards particular images within each individual

may compete with the natural preference for novel stimuli. Similarly, individual variability in

novelty preference behaviour, which may have genetic underpinnings [331, 332], might also be

a factor in the expression of bias towards novel images. These characteristics could have

confounded results and likely accounted for the relatively large standard deviations in the mean

visual scanning parameters. Although the healthy elderly controls recruited in this study were

not diagnosed with dementia or any cognitive impairment, pathophysiological events involved

in the expression of symptomatic AD are thought to begin well before official diagnosis [333].

Thus, the potential effects prodromal dementia may have on visual scanning behaviour in the

control sample could not be accounted for in this case.

Some considerations should also be given to the strengths and weaknesses of the standard tests

of attention and cognition used in this study. Given that the WAIS-DS and sMMSE

independently accounted for up to only a small amount of the variance in the Pearson

correlation analyses, other domains of attention and cognition may affect novelty preference.

More comprehensive neuropsychological tests might be employed in future studies to explore

the neurological processes associated with visual attention bias towards novel stimuli. The

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sMMSE and WAIS-DS were administered by several research staff, which may introduce

confounding factors due to inter-rater variability. The DS required administrators to read aloud

a series of digits, adhering to a rhythm of 1 digit per second. Tempo may differ between raters

and may even differ within each rater at different time points in the study. This may influence

patient performance. For example, random short and long delays between clusters of digits

might facilitate better attention and memory for a select cluster. However, it should be noted

that in this study, all research staff received training and practice on administering this test in a

standardized manner. Additionally, within an elderly population, auditory perception may not

be optimal, resulting in difficulty hearing and concentration on the task. Thus, poor

performance might be due to physical disability and would not truly reflect attention deficits.

6.1.3 Significance

In summary, reductions in biases towards novel images differentiated cognitively intact from

mild to moderate AD patients, and was associated with standard measures of attention and

cognitive status. This study established that measurements of visual scanning behaviour can be

used to differentiate cognitively impaired patients and cognitively intact elderly. As

communication becomes increasingly difficult as the disease progresses, non-verbal methods

will provide an alternative means of evaluating symptoms. Other measures of visual selective

attention and working memory, such as the Stroop [59] and n-back [71] respectively, require

specific instructions and button presses as a measure of performance. In addition to deficits in

cognition, reduced motor function typical in the elderly [334] may further amplify the

difficulty of these tasks. The methodology in this study may offer a less cognitively-

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demanding, non-verbal and more naturalistic method of assessing visual selective attention in

the dementia population.

6.2 Selective Attention towards Novel Stimuli as a Predictor of Cognitive Decline


In this study, selective attention towards novel stimuli as a predictor of longitudinal changes in

cognition was investigated. The results suggest that novelty preference, measured by visual

scanning behaviour, can predict significant decline in cognitively impaired elderly people.

Specifically, less time spent on novel compared with repeat images predicted decrease in

sMMSE scores within 6 to 24 months. Attention, measured by the CPT, and baseline sMMSE

contributed significantly to the regression models. These finding were not unexpected as

deficits in attention and executive function have been associated with greater cognitive

deterioration [50, 289]. Furthermore, studies have found higher rates of disease progression in

patients with greater initial cognitive impairment, indicated by the lower MMSE scores [335-

337]. Results of the present study suggest that overall attention capabilities may be modulating

the relationship between novelty preference and cognitive changes.

Together, these findings suggest that deficits in processing novelty (reduced time allocated to

novel compared with repeat stimuli) were associated with greater decline in cognitive ability.

Using the VPC, Zola et al [106] showed that reduced novelty preference was associated with

greater risk of conversion to MCI in healthy elderly or to AD in MCI patients. The analyses

combined both groups and the authors did not report subgroup analyses for conversion to MCI

and AD separately. Significant effects were found for percent fixation time on novel images

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using a 2-minute delay interval between familiarization and test phases. As discussed above,

the 2-minute delay may be accessing more long-term memory processes compared with the

short delay paradigm used in this study. However, those results largely agree with findings

from the present on relative fixation time. Visual scanning can provide important information

with regard to disease progression.

Altered activity of neurotransmitter systems, elements of the AD pathogenesis, may generate

early deficits in selective attention towards novel stimuli which in turn, could signal advancing

cognitive deterioration. Detection of novelty could be disrupted by altered ACh

neurotransmission, the pathway most associated with the cognitive symptoms of AD.

Cholinergic neurons in the basal forebrain project to areas associated with memory and

cognition, including the hippocampus and orbitofrontal cortex [338]. Recordings from a group

of cells in the basal forebrain of primates have shown increased neural response to novel visual

stimuli, which decreased with repetition [339]. Impairments in spatial and object recognition

were induced by targeted ablation of different groups of neurons in the basal forebrain of mice

and rescued with ChEI administration [340]. Pharmacological manipulation of the cholinergic

system, including the use of ChEIs, has been shown to modulate novelty processing. For

example, rivastigmine was found to enhance the novelty P3 ERP in response to novel sounds

[137], while the anticholinergic agent scopolamine reduced frontal P3 response to both

infrequently occurring visual [341] and auditory stimuli [342]. Furthermore, scopolamine

attenuated repetition suppression effects, reducing differences between the fMRI hemodynamic

responses towards novel and repeated stimuli in frontal and extrastriate brain regions [343].

Deficits in attention and memory through cholinergic dysfunction may reduce capacity to

recognize old information and result in attenuated repetition suppression and novelty signals.

As such, in this study, patients on ChEIs had numerically greater novelty preference compared

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with those on memantine monotherapy or no anti-dementia medications. Furthermore, the

three patients on galantamine demonstrated greater novelty preference compared to those on

rivastigmine or donepezil. Galantamine has been shown to improve different domains of

attention in AD patients, possibly through its specific interaction with nicotinic cholinergic

receptors [233, 235]. Consistent with these results, other groups have found that galantamine

blunted repetition suppression in mesolimbic areas of healthy adults in a fMRI study [136] and

shifted novelty signals from the medial temporal lobe to the prefrontal cortex in a

magnetoencephalography study [140]. These findings suggest that the effect of ACh on

novelty processing may vary across different attention-related regions in the brain.

The DAergic system is most prominently implicated in encoding novelty [138, 139]. With

regard to dementia, reduced expression of DA receptors in the cortex and hippocampus have

been observed in AD patients compared with age-matched controls [138, 139]. There is also

evidence that DA can modulate ACh neurotransmission in AD patients, establishing a

functional relationship between two systems associated with dementia [344]. Furthermore,

psychostimulants, drugs which amplify DA and NE, have been shown to improve cognitive

functions, including attention in patients with AD [295], Parkinson’s disease [345] and ADHD

[242]. It has been proposed that novel inputs elicit phasic firing of DA neurons in the ventral

tegmental area, projecting to the hippocampus, in order to motivate exploratory behaviour

[346]. fMRI studies in humans [97, 347, 348] have linked novelty processing with mesolimbic

structures, integral components of DA-associated pathways. Bunzeck et al [136] combined a

pharmacologic challenge with fMRI to explore the effect of DA on blood oxygen level-

dependent signals related to viewing of novel and familiar/repeated images. The administration

of the DA precursor levodopa to healthy adults attenuated repetition suppression effects in the

hippocampus, parahippocampal cortex and the substantia nigra/ventral tegmental area.

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Levodopa also increased the onset of ERPs in the medial temporal lobe in response to novel

scenery [140]. An event-related potential associated with early response to novelty, the N2b

component, was also observed to increase following administration of apomorphine, a D1/D2

receptor agonist [135]. In contrast, inconsistent results have been found regarding the

connection between DA and the later processing of novel stimuli. Pharmacological

manipulation of DA activity does not appear to affect the P3 ERP, a later component of the

novelty signal [349, 350]. In an EEG experiment, Mikell et al [351] observed increases in N2

amplitude but no change in P3 in Parkinson’s patients ON versus OFF medication. Thus,

Rangel-Gomez and Meeter [135] suggested that dopamine may exert stronger influences over

early response and detection of novelty. Furthermore, the effect of DA on different cognitive

domains may adhere to an inverted U-shaped response curve. Overall, this suggests that less

active scanning on novel images may be a consequence of aberrant DA functioning and could

signify increase risk of further cognitive deterioration.

There has been less focus on the role of other neurotransmitter systems in novelty encoding.

Modulation of glutamate and GABA does exert an effect on novelty processing, though results

appear to be variable. Inhibition of glutamatergic activity with ketamine led to attenuation of

the P3 and N2 amplitude in response to novel auditory and visual stimuli [352, 353]. However,

facilitation of GABAergic activity via the GABA-A agonist thiopental also led to attenuation

of these event-related potentials in healthy adults [352]. The role of 5-HT in novelty

processing, which has mainly been addressed in genetic studies, is also ambiguous. Lower 5-

HT availability, linked with expression of the 5-HT transporter, was associated with enhanced

P3 component [354]. However, decreased 5-HT 2a receptor function was associated with a

weaker response to novel stimuli in the hippocampus [355]. Given that NE is a direct analog of

DA as well as its strong reciprocal interaction with ACh [356], NE may also be an important

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factor in novelty preference behaviour [357]. Additionally, NE is known to mediate selective

attention [358, 359] and may be an important factor of the neural basis of the P3 component

[360]. Overall, deficits in the processing of novel stimuli may signify neurochemical changes

related to underlying pathophysiological events typical of AD.

6.2.2 Limitations

Several limiting factors should be taken into consideration when interpreting the findings of

this study. The analyses were limited by sample size. A priori power calculations for a linear

regression with up to 5 covariates indicated that 43 subjects were required to detect large effect

sizes (power = 0.80, α = 0.05). However, in the backward regression models, no more than 3

covariates survived significance. Post hoc sample size calculations in this case indicated that

32 subjects are sufficient to detect large effect sizes with a power of 0.80 and α of 0.05. This

study was exploratory and findings should be considered preliminary. Future studies should be

conducted with larger patient sample sizes in order to confirm these results as well as allow for

more covariates to be considered.

While the sMMSE, the dependent variable in this study, is a widely used screen for cognitive

impairments [361, 362], it is not a comprehensive test of cognition [363, 364]. Typically, floor

effects are associated with lower education [307] and gender [365] while ceiling effects are

observed in MCI [363, 364] and highly educated individuals [366]. Furthermore, variability in

scores within individuals represents another confounder [367]. Adding more extensive

cognitive batteries would be an interesting next step in this line of research. The ADAS-Cog

[368], the standard outcome measure in clinical trials of AD treatment, should be considered.

However, because of recent failures of drugs to improve ADAS-cog scores in RCTs,

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researchers have questioned whether this scale is sensitive to subtle changes in the mild range

of disease severity. Indeed, studies have demonstrated ceiling effects in MCI and mild AD

patients [369, 370]. This is relevant as the population under study is AD patients with mild to

moderate disease severity. Thus, more difficult tests such as the CANTAB can also be included

when assessing patients at the mild and prodromal stages. The CANTAB is a computerized

visual cognitive battery which assesses domains such as reaction time, spatial working memory

and rapid visual information processing. There are batteries assessing core cognition, in

addition to ones specific for AD, MCI and ADHD. The overall battery and its subtests are

sensitive to age-related decline [371] and has been shown to differentiate MCI and mild AD

[372, 373]. This can provide useful information with regard to the relationship between novelty

preference and specific domains of cognition.

6.2.3 Significance

Early deficits in selective attention and ability to process or explore salient novel stimuli may

be a valuable marker of risk of rapid disease progression. In summary, reduced visual attention

towards novel stimuli was associated with greater decline in cognition in AD patients

following 2 years. These findings provide further insight into the attentional deficits associated

with AD. Novelty preference measurements using visual attention scanning technology might

be a less cognitively-demanding tool to help clinicians identify those most at risk of decline in

order to adapt treatment and management plans. Furthermore, these findings may have relevant

implications with regard to clinical trial designs. Heterogeneity in disease progression,

suggesting different underlying disease mechanisms, can produce variable and unreliable

changes on the chosen cognitive outcomes. This may have contributed to the largely

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unsuccessful development of disease-modifying treatments for AD. Drug candidates might be

exerting distinct effects on the different progression subtypes. Future clinical trials of AD can

use various tools, including visual attention, to account for these patient subtypes in order to

more accurately evaluate drug efficacy.

6.3 Apathy and Selective Attention towards Positive Stimuli


In this study, visual attentional biases associated with apathy in dementia patients were

examined. The results suggest that apathetic patients had decreased attentional bias for social

stimuli compared with non-apathetic patients. This behaviour is consistent with the symptoms

of social disinterest and emotional blunting characteristic of apathy [145, 146]. Findings from

previous eye tracking studies have also shown reduced bias towards social images in young

adults with clinical depression compared with age-matched controls [270, 293]. While apathy

was not investigated in those studies, the principle features of apathy that overlap with

depression may be a factor in driving attentional biases away from social stimuli in depressed

patients. It has been proposed that the mesocorticolimbic DAergic system, thought to mediate

incentive salience [374, 375] and reward-motivated behaviours [245, 252, 253], is involved in

the expression of apathetic symptoms in patients with AD [294]. A SPECT study found that

reduced striatal DA transporter uptake was correlated with greater apathy in dementia patients

[204]. Thus, disruptions in this pathway may dampen the salient and rewarding qualities of

positive stimuli, prompting patients to orient away from social images. The results of the

present study also showed no differences between groups on biases towards dysphoric images.

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The patient sample used here had low levels of depression based on the NPI depression

subscore, which would account for this lack of bias towards dysphoric images.

The linear regression analysis suggested that more severe apathy (measured by AES total)

predicted reduced attentional bias towards social images, based on the fixation frequency

parameter. Additionally, better cognition (measured by the sMMSE) and reduced attention

(measured by the CPT Inattention) were also significant predictors in the model. As the

variance inflation factors for each covariate were relatively low, there was little concern for

multicollinearity. The linear regression analyses also indicated that cognition and attention

influenced visual scanning behaviour. Given that greater deficits in cognition and attention

have been associated with apathy in AD [155, 170, 181, 282], the interplay between apathy,

cognition and attention may function to direct visual scanning behaviour in the presence of

social or positive stimuli. Interestingly, there were no significant predictors for relative fixation

time on social images. Attentional biases based on discrete number of fixations or exploratory

eye movements within social images may be more sensitive to differences in levels of apathy

than total time allocated to social images.

Exploratory analyses of the subtypes of apathy suggested that, in particular, emotional blunting

is more relevant with regard to attentional bias towards social images. Reduced emotional

responsivity (higher AES emotion subscores) was associated with lower fixation frequencies

on social images. The social stimuli used in this study had both higher valence and arousal

compared with neutral images. In a previous study with similar visual stimuli [270], fixation

frequency was higher on images with higher valence and arousal. For social stimuli, the

increase in fixation frequency due to higher arousal was amplified by the increase in fixation

frequency due to higher valence. The combined effect for non-apathetic AD patients was an

increased bias towards social images; the mean fixation frequency on social images was 3.4

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fixations/image higher on social images than on neutral images. As emotional blunting

decreases the effects of valence and arousal on visual scanning parameters, the bias towards

social images in patients with apathy was decreased to 2.0 fixations/image. Further, there were

no significant predictors of attentional bias towards dysphoric images. The dysphoric stimuli in

this study had higher arousal and lower valence compared with neutral images. For dysphoric

stimuli, the increase in fixation frequency due to higher arousal are reduced by the decrease in

fixation frequency due to lower valence. The combined effect for non-apathetic AD patients

was a lower attention bias towards dysphoric stimuli (compared with the bias towards social

stimuli). The mean fixation frequency on dysphoric images was 2.7 fixations/image higher

than on neutral images. For dysphoric images, emotional blunting attenuated the effects of both

higher arousal (i.e. the increase in fixation frequency due to higher arousal was reduced) and

lower valence on fixation frequency (i.e. the reduction in fixation frequency due to lower

valence was reduced). Thus, the net effect of emotional blunting for dysphoric images was

rather small and the bias towards dysphoric images of apathetic AD patients was similar to that

of non-apathetic AD patients. For apathetic AD patients the average fixation frequency on

dysphoric images was 2.4 fixations/image higher than on neutral images. Thus, diminished

response to high valence (positive) stimuli may be an important manifestation of the emotional

blunting feature of apathy.

This provides further insight into the reduced bias towards social stimuli previously observed

in clinically depressed patients [270, 274-276]. Strong biases for dysphoric stimuli were

observed and can be explained by mood-specific symptoms such as sadness, hopelessness and

worthlessness. However, apathy was not considered in those studies. By investigating a

population with prevalent apathy and reducing the effect of depression in the patient sample,

the distinct effect of apathy could be studied. Flat affect, a component of both depression and

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apathy, may be the key factor in driving attention away from positive stimuli with both high

arousal and valence (i.e. social images). These results are consistent with previous imaging

findings of different neural activation patterns and structural changes associated with each

apathy domain [11, 268]. The domains of apathy may have different pathological mechanisms

and influence both cognitive and attentional abilities.

6.3.2 Limitations

Several factors should be considered when interpreting the results of this study. Although this

paradigm used many test slides with different items, personal attraction towards particular

images within each individual may compete for attentional resources. For example, strong

personal interest or preference for particular neutral images may act as distractors and interfere

with biases towards images with emotional content. Additionally, patients with cognitive

deficits may not have the capacity to interpret the complex emotional themes depicted in the

images. Several studies [376-378] have reported impairments in processing and decoding of

information with emotional content, including facial expression and scenes, in AD patients.

Again, apathy and other non-cognitive features of AD were not considered in those studies.

This represents a possible alternative interpretation of the underlying basis of reduced

attentional biases for social images found in apathetic compared with non-apathetic patients.

Inability to effectively process positive and negative stimuli as well as overall disinterest, may

be a component of apathy.

The data might also be limited by sample size. A priori power calculations for the repeated-

measures ANCOVA with up to 2 covariates (primary analysis) indicated that 38 patients (19 in

each group) were required to detect medium to large effect sizes (power = 0.80, α = 0.05).

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However, the repeated-measures ANCOVA model with the current sample size of 36 yielded

large effect sizes and power of 0.91 for the fixation frequency parameter. This study was

exploratory and findings should be considered preliminary. Future studies should be conducted

in order to determine whether these results hold in larger patient sample sizes.

In this study, results for two outcome variables (relative fixation time and fixation frequency),

which have been used successfully in past studies, were presented [270, 272, 293]. However,

these parameters might not fully elucidate the process of visual attention bias associated with

apathy. Future studies should focus on developing other parameters, which may provide further

insight into visual scanning behaviour.

The NPI cut-off scores used to define patients have yet to be psychometrically validated in

clinical and research settings. Although NPI depression subscores were low and comparable

between the study groups, the cut-off score of 4 has not been validated and may not be

clinically relevant. However, this value has previously been used to screen out significant

psychosis, delusions and agitation/aggression [219, 295]. Additionally, the NPI apathy

subscore ≥ 4 was used to define patients with significant apathy in clinical trials investigating

treatments for apathy [219, 295, 310, 312]. A study [145] aimed at estimating the concurrent

validity of the proposed diagnostic criteria for apathy used the NPI apathy as a comparator. It

was reported that those defined as apathetic under the criteria had a mean NPI apathy of 6.9 ±

3.3, indicating that a cut-off of 4 would fall within this standard deviation range. Overall, these

factors could have confounded observations and may have contributed to the relatively large

standard deviations in the mean visual scanning parameters.

It should be noted that although apathetic and non-apathetic patients were not matched based

on levels of cognition and attention, only participants in the mild to moderate range were

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recruited. As a result, groups had comparable scores on the sMMSE and all CPT subscores.

Similar to these findings, others [9, 258, 267, 379, 380] have also observed non-significant,

though numerically lower MMSE scores in apathetic compared with non-apathetic patients in

the mild to moderate AD stage. One study [263] did find significantly lower MMSE scores in

apathetic compared with non-apathetic patients. In general, higher levels of apathy are

associated with more severe cognitive and functional deficits [155, 170, 181, 381]. Thus, future

studies with larger target sample sizes should further explore visual scanning behaviour and

apathy in more severely impaired patient populations.

6.3.2 Significance

Currently, there are several barriers to the assessment of apathy in the dementia population. In

addition to associations with more rapid decline [155, 170, 181, 381], apathy is also linked

with higher risk of conversion to AD from MCI [182, 184]. Experts in the field have

emphasized the need to identify biomarkers and risk factors of AD in the early and pre-

symptomatic stage [382]. As such, assessments of apathy may provide a potential avenue for

prevention and early treatment of dementia. As discussed above, methods of assessment in

research and clinical environments rely heavily on informant interview, which may be

subjective. Current recommendations advocate the use of a clinician’s objective evaluation in

corroboration with separate interviews with caregivers and patients [145, 383], which may

nevertheless be ambiguous and time-consuming. Furthermore, apathy can often be

misdiagnosed as depression due to the overlap in symptoms.

In summary, apathetic AD patients demonstrated reduced attentional bias towards social

images compared with non-apathetic patients and more severe apathy was associated with

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decreased preference for social images. This study provides insight into the visual scanning

behaviour of apathetic AD patients in the presence of emotional-themed stimuli as well as the

distinct effects of the different apathy subtypes. Given the high prevalence of apathy in

dementia and assessment problems arising from memory and communicative difficulties, a

focus on more studies to develop objective technologies to evaluate apathy in both the clinical

and research environment should be considered.

6.4 Effect of Methylphenidate Treatment for Apathy on Visual Scanning Behaviour


This pilot study explored the effect of MTP treatment for apathy on processing of novel and

positive-themed stimuli in AD patients. For novelty preference, it was found that

improvements in apathy were significantly associated with increased number of fixations on

novel images compared with repeat images. Furthermore, patients who achieved remission,

had increases in time spent on novel images following treatment compared with the decrease in

novelty preference observed in those whom did not remit. However, this model did not reach

statistical significance. These findings are in line with findings of associations between novelty

processing and apathy. Daffner et al [278] found that apathetic AD patients demonstrated

decreased exploration of novel visual stimuli and distributed their overall viewing time evenly

among all types of stimuli. Additionally, more severe apathy was associated with reduced P3

ERP amplitudes [279]. Daffner et al [322] also reported similar results in cortical stroke

patients with frontal lobe lesions. Prolonged latency and reduced amplitude of the novelty P3

ERP over the frontal lobes were observed in subcortical stroke patients with apathy compared

to those without apathy [321]. More severe apathy was also correlated with both longer latency

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and decreased amplitude. Reduced novelty P3 amplitude has been associated with apathy in

patients with Parkinson's, a disease characterized by depletion of DAergic neurons in the

nigrostriatal pathway [318, 384].

As discussed in Study 2, the DAergic system is most prominently implicated in encoding

novelty [138, 139]. Pharmacological manipulations of DA activity can affect early N2 ERP

amplitudes in EEG studies [135, 351] as well as repetition suppression in fMRI investigations

[136] in response to novel stimuli. In healthy adults, the administration of the DA precursor

levodopa to healthy adults attenuated fMRI repetition suppression effects in the hippocampus,

parahippocampal cortex and the substantia nigra/ventral tegmental area [136]. One study

reported increases in the early N2 ERP amplitude but no change in P3 in Parkinson’s patients

ON versus OFF medication [351]. Another EEG study showed increases in the N2b response

to novelty following administration of apomorphine, a D1/D2 receptor agonist [135]. However,

pharmacological manipulations of DA activity do not appear to affect the P3 ERP, the later

component of novelty signals [349, 350]. Given these findings, it was suggested that DA may

play a key role in mediating early but not later EEG responses to novel stimuli [135]. No

pharmacological studies of novelty preference have given special consideration to the effect of

apathy.

In a randomized placebo-controlled clinical trial of MTP for the treatment of apathy in AD,

patients on the active treatment for 6 weeks improved on tests of both apathy [219] and

attention [295]. However changes in apathy, measured by the NPI apathy and AES, and

attention, measured by the DS Total, were not significantly correlated [295]. In the present

study, no significant correlations between change in DS Total scores and change in AES or

visual scanning parameters were found. Interestingly, when DS Total scores were parsed into

its subscores, associations between change in DSB scores and novelty preference as well as

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apathy trended towards significance. Improvements in DSB were associated with increased

novelty preference and improvements in apathy. This was not observed with DSF scores. As

discussed briefly in Study 1, DSF is purported to be reliant on the subordinate phonological

loop and visuospatial sketchpad described in Baddeley’s model of working memory [324]. The

DSB is thought to encompass additional executive and working memory functions [301]. Thus,

the DSB and not DSF, may be accessing frontoparietal attention and working memory

networks that are common with apathy. MTP may be mitigating the effects of apathy through

these circuits. This analysis provides additional evidence to support the involvement of higher-

order attention and working memory processes in novelty preference.

While results from Study 3 suggested lower attentional bias towards social images in apathetic

compared with non-apathetic AD patients, no significant correlations were found between

change in apathy and visual scanning on social stimuli following MTP treatment in this study.

A sample size of 8 was likely not sufficiently powered to detect a relationship between these

two variables with large effect sizes. However, baseline attentional bias towards social images

(both relative fixation time and frequency) was significantly correlated with change in apathy

on the AES. Lower baseline bias towards positive images predicted greater improvements in

apathy after MTP treatment. Thus, patients with greater dysfunction in the processing of social

or positive themed stimuli may benefit more from apathy treatment. Post hoc analyses further

suggested that improvements in the emotional domain of apathy may be the key contributor in

this relationship. In Study 3, greater AES emotion subscore emerged as the main predictor of

reduced attentional bias toward social images. Visual scanning behaviour in the presence of

social stimuli may be sensitive to MTP-induced changes in the emotional blunting aspect of

apathy.

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Overall, scores on tests of apathy (AES and NPI) decreased from baseline to follow-up, though

this did not reach statistical significance. Interestingly, depressive symptoms, measured by NPI

subscore, also decreased. Psychostimulants are not recommended for the treatment for

depression. However, MTP has shown to ameliorate depressive symptoms in treatment

resistant depression [385], post-stroke [386], palliative care and terminal cancer patients [387,

388]. In an open label study of MTP for treatment of apathy in 23 patients, significant

improvements in both apathetic and depressive symptoms were observed after 12 weeks [216].

In that study, 78% of patients were reported to have comorbid depression at the start of the

trial. The mean baseline NPI depression score in this study was 3.8 ± 4.1, signifying presence

of depressive symptoms in these patients. These findings collectively highlight the difficulties

with separating these two syndromes. Interestingly, clinicians have noted the development of

apathy associated with the use of a selective-serotonin reuptake inhibitor (SSRI) in cases of

non-AD psychiatric patients [389-392] and geriatric patients [393]. It was also noted that

symptoms were improved or resolved upon discontinuation or reduction of SSRI medication

[389-393]. Though the mechanism of action is unclear, it has been suggested that SSRIs

indirectly modulate DA neurotransmission via its effect on the frontal cortex [390]. Evidence

indicates that serotonergic neurotransmission can inhibit [394] and excite [395] endogenous

DA release in both the forebrain and midbrain. Alternatively, the underlying presence of

apathy may be unmasked following successful treatment of the depressed symptoms. Thus,

methods to separate depression from apathy and predictors of response to different treatments

will be essential to improving patient outcomes.

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6.4.2 Limitations

The results of this study should be very carefully interpreted in light of the small sample size. A

priori power calculations suggest that a sample size of 20 would be needed to achieve a power

of 0.80 to detect a large effect size (ρ = 0.5) in the correlation analyses. However, significant

correlations with very large effect sizes (ρ = 0.8) were detected. A robust relationship may

exist between changes in visual scanning parameters and apathy, suggesting that a lower

sample size may be sufficient in this case. This was a pilot study conducted in order to inform a

larger and more comprehensive investigation. With larger sample sizes, covariates can be

included in a multivariate linear regression to control for several clinical covariates, such as

cognition, age and gender.

In the present study, the AES informant version was used as the apathy outcome variable for

the correlation analyses. Variability leading to confounders might exist due to differences in

each informant’s interpretation of questions on the AES, which may be ambiguous. The higher

recommended cut-off score for the caregiver version (41.5) suggests that compared to self

(36.5) and clinician (40.5) reports, informants might be overestimating apathy symptoms in

patients [315]. In order to standardize this method of assessment, the AES clinician version can

be used. This version of the AES is administered as a semi-structured interview in which a

trained rater utilizes both verbal and non-verbal data to determine the patient’s apathy level on

each item of the test. The AES-Clinician, though a reliable and valid scale for apathy [313,

396], is psychometrically less robust with regard to diagnostic performance than the AES-

Informant, according to Clarke et al [396]. The researchers proposed that the AES-Informant

was a better quick screen of apathy symptoms and training level of raters may have lowered

performance ability of the clinician version. The 4-6 hours experience with the scale suggested

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by the test developers [313] might not be sufficient for reliability. Thus, a caveat to using the

AES-Clinician in future studies is that it should be administered by a trained clinician who

knows the patients well, such as the primary physician, and who has had opportunities to

observe their behaviours.

All patients tolerated MTP well with no serious adverse events reported. Medication

compliance was determined through interviews with caregivers. Based on these evaluations

compliance was not reported to be an issue. However, patients with memory impairments may

neglect to follow the dose schedule, particularly if not aided by the caregivers. Patients may

fail to properly store drugs in dry and room temperature conditions, degrading the drugs and

rendering them less effective. These events would confound results as the true effect of MTP

would not be measured in each case.

Remission status in the comparison of responders analysis was defined as those patients who

dropped below a 4 on their NPI apathy subscore at the follow-up visit. As described above, this

cut-off score has been used in previous trials of methylphenidate to defined clinically

significant apathy [219, 295, 310, 312]. However, the clinical relevance of this score has yet to

be determined.

6.4.3 Significance

In summary, improvement in apathy symptoms was associated with increase in attentional bias

towards novel stimuli in AD patients treated with MTP for apathy. Additionally, lower baseline

attentional bias for social stimuli predicted better response to apathy treatment. This study

provided preliminary data to suggest that visual scanning behaviour and attentional biases may

be sensitive to pharmacological manipulation. Treatments for apathy and depression differ.

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Clinicians have observed the development of apathy following SSRI treatment for depression

in psychiatric and geriatric patients [389-393]. These points highlight the significance of

exploring more precise methods of evaluation in order to better measure symptoms, inform

treatment decisions and prevent the prescription of ineffective or even detrimental courses of

therapy. The measurement of attentional biases may represent a reliable marker of

pharmacotherapy-induced behavioural changes.

6.5 Recommendations for Future Studies and Clinical Implications

Study 1 and 2 demonstrated the utility of novelty preference in differentiating dementia and

cognitively intact elderly as well as temporally predicting cognitive decline. Eye tracking

measures have been used to study novelty processing in infants [286, 287, 397]. The novelty

preference paradigm explored in these studies may be of value in probing selective attention

and working memory across all ages and cognitive capacities, in order to advance

understanding of information processing throughout the human developmental stages. In future

studies, the novelty preference of MCI, prodromal AD patients and individuals with high

dementia risk factors can be monitored longitudinally to better understand the trajectory of

cognitive decline and identify potential predictors of conversion. Individuals can be tracked

throughout development, from infancy and beyond, to explore the effects of age and cognitive

impairments on the different cognitive domains. The impact of AD may appear earlier on tests

of visual scanning behaviour than on the traditional neuropsychological assessments. These

patients can then be enrolled in clinical trials of disease-modifying treatments. Early

interference of underlying pathological events, including amyloid, hyperphosphorylated tau

and neuroinflammation, may better preserve cognition and function in these patients.

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Several modifications to the current paradigm can also be implemented to explore factors that

might influence novelty preference. One modification would be to increase the number of

repetitions. In 3- and 6-month old infants, preference shifted from familiar to novel images

with increasing exposure [397]. This might also be the case for cognitively impaired patients.

The limits of novelty preference can be studied further by increasing the duration between first

and second exposure to an image. At different ages and cognitive capacities, memory for items

will decay at different rates. This would also allow for the study of implicit long-term memory.

To further investigate the neurobiology of attentional biases associated with apathy, in addition

to monitoring the effects of MTP, a drug challenge can be used to investigate whether response

immediately following a single dose of psychostimulant can predict apathy outcome in the

open label trial. Herrmann et al [218] used a drug challenge to probe the function of the DA

system in apathetic AD patients. Greater inattention scores on the Conners’ CPT induced by

another psychostimulant, dextroamphetamine, was predictive of better response to subsequent

MTP treatment for apathy. The observed inattention or increase in susceptibility to distracters

may signify activation of the DA system in response to dextroamphetamine, suggesting that

these patients had a more intact neurotransmitter system. This may be a better approach to

speculating status of neurotransmitter systems and underlying neurobiological mechanisms

than assessments of baseline visual scanning patterns to predict response to pharmacotherapy.

In Study 4, a total of 4 out of 8 patients experienced remission (decreases in NPI apathy score

below 4), indicating that response to MTP was variable. If shifts in visual attention patterns can

be detected following a single dose of MTP, responders can be identified before starting a

course of treatment for apathy. Given that psychostimulants have been associated with

cardiovascular events [398] in addition to behaviourally activating effects such irritability,

agitation and psychosis [216-218], information on a patient’s response likelihood will help

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physicians make better risk-benefit evaluations of treatment options. In frail populations where

concurrent medical conditions and polypharmacy are common, identifying reliable predictors

is of value toward guiding treatment decisions.

Steps should also be taken to develop drug challenge studies that specifically target depression

in AD. The studies described in this thesis did not focus explicitly on depression. However,

overlaps between depression and apathy do occur. For instance, the apathetic patients recruited

had low levels of depression, according to their NPI depression subscores. The distinction

between apathy and depression is important as there is evidence that antidepressants are not

effective for treating apathy and may indeed worsen this condition [389-393]. Neuronal

damage and consequent dysfunction of the amygdala and frontoparietal pathways, thought to

regulate the attentional bias towards emotionally salient information within the environment

[399], may propel some AD patients towards depression. Antidepressants, specifically those

that alter the 5-HT system, have been shown to normalize fMRI signals in the amygdala and

frontoparietal circuitry during exposure to negatively valenced stimuli [400]. This was

observed together with improvements in mood. A single dose of an antidepressant can alter the

processing of emotional stimuli in both depressed and healthy individuals only a few hours

following drug administration [401, 402]. Eizenman et al [403] found that fixation shifts away

from dysphoric images within 1 week of duloxetine treatment for major depression predicted

better response to treatment following 6 weeks. Thus, an antidepressant challenge can also be

applied to determine those most likely to benefit from the drug and more importantly, identify

patients who might develop apathy. Furthermore, as there are several different antidepressants

with distinct mechanisms currently indicated for depression, visual scanning can be used to

pinpoint the most effective drug. The best course of treatment may necessitate targeting one

syndrome independently or both apathy and depression in conjunction.

100

The large standard deviation for visual scanning parameters indicate that patients had unique

responses to the many stimuli presented. Additionally, as discussed in Study 3, AD patients

may have difficulty interpreting the emotional content in complex scenes. The results might be

improved if images that elicited strong responses from patients with dementia were included in

the stimulus paradigm. Thus, the image presentation should be optimized according to the

intention of its application. For example, images that robustly discriminate apathetic and

depressed patients should be selected for development of a diagnostic tool to differentiate these

two syndromes. Some images may be more sensitive to drug manipulations and should be

included in pharmacotherapy paradigms.

Relative fixation time and fixation frequency within images were used in these studies to

summarize attentional biases toward novel and emotional stimuli. These parameters were also

presented in previous eye tracking studies [107, 270, 274-276, 286, 287, 397]. Relative fixation

time provided a description of how much time was spent on specific images. Fixation

frequency within images, a basic component used to calculate relative fixation time, described

how much subjects are moving their eyes within the confines of an image. These parameters

have provided valuable information on the visual attention patterns of AD patients.

Examination of other parameters, as well as different methods of analyzing the visual scanning

data would be the next steps to progress understanding of attentional biases. Visual scanning

data can be segmented to explore whether response to images change during the entire 10.5

seconds allotted per slide. Reduced cognitive capacity or behaviour symptoms such as apathy

may be associated with different patterns during early and late processing. The visual attention

scanning technology generates a wealth of information about different parameters of eye

movements, allowing for enormous opportunity to further explore visual information

processing.

101

6.6 Conclusions

Overall, in these studies, the visual scanning patterns associated with attention, memory and

behaviour deficits in AD patients were investigated. Dementia patients and cognitively intact

elderly controls demonstrated divergent eye movement patterns in the presence of novel and

repeated visual stimuli. This reduced ability to process novel stimuli in AD patients predicted

temporal declines in cognition. Within the AD group, apathetic patients demonstrated a

different pattern of visual scanning in the presence of emotionally-charged stimuli compared

with non-apathetic patients. Shifts in attentional biases within apathetic patients were induced

through modulating DA and NE activity. Thus, attentional bias paradigms may be an effective

and more naturalistic method of tracking cognitive deficits and neuropsychiatric symptoms in

dementia patients, as well as detecting pharmacotherapy-induced changes. This is of particular

significance in disease populations where currently available assessments of symptoms are

complicated by progressive communication and language deficits, as well as barriers due to

varying education levels. Attention bias paradigms such as these can circumvent the need to

rely on subjective information provided by caregivers for evaluation. The measure of visual

scanning behaviour using eye tracking techniques provide an objective, non-invasive, non-

verbal and less cognitively-demanding method that can improve assessment of symptoms and

optimize treatment outcomes.

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Chapter 7

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Publications and Abstracts

Peer-Reviewed Journals

Chau SA, Herrmann N, Sherman C, Chung J, Eizenman M, Kiss A, Lanctôt KL. Visual

selective attention towards novel stimuli predicts cognitive decline in Alzheimer’s disease

patients. J Alzheimers Dis 2017; 55(4): 1339-49.

Liu CS, Ruthirakuhan M, Chau SA, Carvalho AF, Herrmann N, Lanctôt KL. Pharmacological

management of agitation and aggression in Alzheimer’s disease: a review of current and novel

treatments. Current Alzheimer Research 2016; 13(10): 1134-44.

Chau SA, Chung J, Herrmann N, Eizenman M, Lanctôt KL. Apathy and attentional biases in

Alzheimer’s disease. J Alzheimers Dis 2016; 51: 837-46.

Chau SA, Herrmann N, Eizenman M, Chung J, Lanctôt KL. Exploring visual selective

attention towards novel stimuli in Alzheimer’s disease patients. Dement Geriatr Cogn Disord

Extra 2015; 5(3):492-502.

Liu CS, Chau SA, Ruthirakuhan M, Lanctôt KL, Herrmann N. Cannabinoids for the treatment

of agitation and aggression in Alzheimer’s disease. CNS Drugs 2015; 29: 615-23.

Chau S, Herrmann N, Ruthirakuhan MT, Chen JJ, Lanctôt KL. Latrepirdine for Alzheimer’s

disease. Cochrane Database Syst Rev 2015; 4:CD009524.

Chung J, Chau SA, Lanctôt KL, Herrmann N, Eizenman M. A novel test of implicit memory;

an eye tracking study. International Journal of Applied Mathematics, Electronics and

Computers 2014; 2(4): 45-8.

Lanctôt KL, Chau SA, Herrmann N, Drye LT, Rosenberg PB, Scherer RW, Black SE, Vaidya

V, Bachman DL, Mintzer J. for the ADMET investigators. Effect of methylphenidate on

attention in apathetic AD patients in a randomized, placebo controlled trial. Int Psychogeriatr

2014; 26(2): 239-46.

Mazereeuw G, Lanctôt KL, Chau SA, Swardfager W, Herrmann N. Effects of omega-3 fatty

acids on cognitive performance: a meta-analysis. Neurobiol Aging 2012; 33(7): 1482.e17-29.

Herrmann N, Chau SA, Kircanski I, Lanctôt KL. Current and emerging treatment options for

Alzheimer’s disease: A systematic review. Drugs 2011; 71(15): 2031-65.

131

Book Chapters

Chau SA, Liu CS, Ruthirakuhan M, Lanctôt KL, Herrmann N. Pharmacotherapy of Dementia.

In: Chiu H, Shulman K & Ames D, editors. Mental Health and Illness Worldwide: Mental

Health and Illness of the Elderly. Singapore: Springer 2017. DOI: 10.1007/978-981-10-0370-

7_20-1

Lanctôt KL, Kircanski I, Chau SA, Herrmann N. The Current Status of Alzheimer Disease

Treatment: Why we need better therapies and how we will develop them. In: Wegrzyn RD,

Rudolph AS, editors. Frontiers in Neuroscience series, Alzheimer's Disease: Targets for New

Clinical Diagnostic and Therapeutic Strategies. Boca Raton (FL): CRC Press 2012: 117-62.

Published Abstracts

Chau SA, Herrmann N, Eizenman M, Chung J, Lanctôt KL. Methylphenidate and attentional

bias for novel stimuli in Alzheimer’s disease. Biol Psychiatry 2017; In press

Chau S, Herrmann N, Eizenman M, Chung J, Lanctôt KL. Attentional bias towards dysphoric

stimuli in geriatric patients with depressive symptoms. Alzheimers Dement 2016; 12(7 Suppl):

P487.

Chau S, Herrmann N, Eizenman M, Grupp L, Chung J, Ruthirakuhan M, Lanctôt KL.

Exploring visual attention in cognitively impaired patients. Ann Neurol 2014; 76(18 Suppl):

S14.

Chau S, Herrmann N, Eizenman M, Grupp L, Isen M, Lanctôt KL. Visual scanning as a

biomarker for assessing symptoms of apathy and depression in Alzheimer’s disease. Can

Geriatr J 2013; 16(4): 206.

Li A, Chau S, Herrmann N, Eizenman M, Grupp L, Isen M, Lanctôt KL. Using visual

scanning as a biomarker for working memory in patients with Alzheimer’s disease. Can

Geriatr J 2013; 16(4): 223.

Lanctôt KL, Chau S, Herrmann N, Drye L, Rosenberg P, Scherer R, Vaidya V, Black SE,

Mintzer J. Effects of methylphenidate on attention and association with apathy in AD patients.

Am J Geriatr Psychiatry 2013; 21(3 Suppl): S158-9.

Lanctôt KL, Chau S, Eizenman M, Herrmann N. Using visual attention to detect apathy in

Alzheimer’s disease. Alzheimers Dement 2013; 9(4 Suppl): P524.

132

Appendix A: Study Flowchart

81 Screened

Study 1: 41 AD Study 1: 24 Control

52 Alzheimer’s

Disease

5 Excluded

1 Cataracts

2 Psychiatric Disorder

1 Could not Calibrate

1 Refused to Continue

29 Cognitively

Healthy

11 Excluded

4 Refused to Continue

2 Cataracts

5 Could not Calibrate

Patient recruitment flowchart for Study 1-4

Study 2: 32 AD

Study 4:

8 Apathetic

Study 3: 36 AD

17 Apathetic

19 Non-apathetic

9 Excluded

2 Stroke

7 No Follow-up

5 Excluded

5 Depressive Symptoms

(NPI depression>3)

visual scanning and attentional biases in alzheimer’s disease: … · 2019-03-13 · ii visual...

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