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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
iii
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.
v
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
vi
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 Materials and Methods ...................................................................................................... 42
3.1.1 Participants ............................................................................................................... 42
3.1.2 Procedures ................................................................................................................ 42
3.1.3 Neuropsychological Tests ........................................................................................ 43
3.1.4 Visual Scanning Parameter ...................................................................................... 44
3.1.5 Statistical Analyses .................................................................................................. 44
3.2 Results ………………………………………………………………………………...45
CHAPTER 4 .............................................................................................................................. 51
APATHY AND SELECTIVE ATTENTION TOWARDS POSITIVE STIMULI ............. 51
4.1 Materials and Methods ...................................................................................................... 51
4.1.1 Participants ............................................................................................................... 51
4.1.2 Procedures ................................................................................................................ 51
4.1.3 Neuropsychological Assessments ............................................................................ 53
4.1.4 Visual Stimuli ........................................................................................................... 54
4.1.5 Visual Scanning Parameters ..................................................................................... 55
4.1.6 Statistical Analyses .................................................................................................. 56
4.2 Results ………………………………………………………………………………...57
CHAPTER 5 .............................................................................................................................. 63
EFFECT OF METHYLPHENIDATE FOR APATHY ON VISUAL SCANNING
BEHAVIOUR ....................................................................................................................... 63
5.1 Materials and Methods ...................................................................................................... 63
5.1.1 Participants ............................................................................................................... 63
5.1.2 Procedures ................................................................................................................ 64
5.1.3 Visual Stimuli ........................................................................................................... 65
vii
5.1.4 Visual Scanning Parameters ..................................................................................... 66
5.1.5 Statistical Analyses .................................................................................................. 67
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
6.2.1 Discussion of Findings ............................................................................................. 79
6.2.2 Limitations ............................................................................................................... 83
6.2.3 Significance .............................................................................................................. 84
6.3 Apathy and Selective Attention towards Positive Stimuli ................................................ 85
6.3.1 Discussion of Findings ............................................................................................. 85
6.3.2 Limitations ............................................................................................................... 88
6.3.2 Significance .............................................................................................................. 90
6.4 Effect of Methylphenidate Treatment for Apathy on Visual Scanning Behaviour .......... 91
6.4.1 Discussion of Findings ............................................................................................. 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
ix
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
xi
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
1
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
No significant eye pathology
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 Materials and Methods
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.
3.1.5 Statistical Analyses
Baseline 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].
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 Materials and Methods
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
Diagnosis of possible/probable AD (DSM-IV-TR and NINCDS-ADRDA criteria)
sMMSE ≥ 10
No significant apathy or depression (NPI subscore < 4)
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
No significant eye pathology
Alzheimer’s Disease - Apathy
Diagnosis of possible/probable AD (DSM-IV-TR and NINCDS-ADRDA criteria)
sMMSE ≥ 10
Significant apathy (NPI apathy ≥ 4)
No significant depression (NPI depression < 4)
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
No significant eye pathology
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].
4.1.4 Visual Stimuli
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.
4.1.5 Visual Scanning Parameters
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.
4.1.6 Statistical Analyses
Baseline demographic, neuropsychological and visual scanning data were summarized using
counts 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].
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
Neuropsychiatric Inventory
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
Apathy Evaluation Scale
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.
Predictors β t p-value
sMMSE -0.62 -3.09 0.004*
Conners’ CPT Inattention -0.36 -1.93 0.063
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.
Predictors β t p-value
sMMSE -0.56 -3.02 0.005*
Conners’ CPT Inattention -0.28 -1.51 0.140
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;
AES = Apathy Evaluation Scale
63
Chapter 5
Effect of Methylphenidate for Apathy on Visual Scanning Behaviour
5.1 Materials and Methods
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.
5.1.3 Visual Stimuli
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.
5.1.4 Visual Scanning Parameters
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
5.1.5 Statistical Analyses
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
Neuropsychiatric Inventory
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
Apathy Evaluation Scale
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
AES = Apathy Evaluation Scale
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
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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
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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
6.2.1 Discussion of Findings
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
6.3.1 Discussion of Findings
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
6.4.1 Discussion of Findings
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.
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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.
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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
References
[1] Ishizaki J, Meguro K, Ambo H, Shimada M, Yamaguchi S, Hayasaka C, Komatsu H,
Sekita Y, Yamadori A (1998) A normative, community-based study of Mini-Mental
State in elderly adults: the effect of age and educational level. J Gerontol B Psychol Sci
Soc Sci 53, P359-363.
[2] Doraiswamy PM, Krishen A, Stallone F, Martin WL, Potts NL, Metz A, DeVeaugh-
Geiss J (1995) Cognitive performance on the Alzheimer's Disease Assessment Scale:
effect of education. Neurology 45, 1980-1984.
[3] Evans DA, Hebert LE, Beckett LA, Scherr PA, Albert MS, Chown MJ, Pilgrim DM,
Taylor JO (1997) Education and other measures of socioeconomic status and risk of
incident Alzheimer disease in a defined population of older persons. Arch Neurol 54,
1399-1405.
[4] Kuzuya M, Enoki H, Hasegawa J, Izawa S, Hirakawa Y, Shimokata H, Akihisa I
(2011) Impact of caregiver burden on adverse health outcomes in community-dwelling
dependent older care recipients. Am J Geriatr Psychiatry 19, 382-391.
[5] van Reekum R, Stuss DT, Ostrander L (2005) Apathy: why care? J Neuropsychiatry
Clin Neurosci 17, 7-19.
[6] Starkstein SE, Ingram L, Garau ML, Mizrahi R (2005) On the overlap between apathy
and depression in dementia. J Neurol Neurosurg Psychiatry 76, 1070-1074.
[7] Tagariello P, Girardi P, Amore M (2009) Depression and apathy in dementia: same
syndrome or different constructs? A critical review. Arch Gerontol Geriatr 49, 246-
249.
[8] Starkstein SE, Leentjens AF (2008) The nosological position of apathy in clinical
practice. J Neurol Neurosurg Psychiatry 79, 1088-1092.
[9] Lanctôt KL, Moosa S, Herrmann N, Leibovitch FS, Rothenburg L, Cotter A, Black SE
(2007) A SPECT study of apathy in Alzheimer's disease. Dement Geriatr Cogn Disord
24, 65-72.
[10] Kang JY, Lee JS, Kang H, Lee HW, Kim YK, Jeon HJ, Chung JK, Lee MC, Cho MJ,
Lee DS (2012) Regional cerebral blood flow abnormalities associated with apathy and
depression in Alzheimer disease. Alzheimer Dis Assoc Disord 26, 217-224.
[11] Benoit M, Clairet S, Koulibaly PM, Darcourt J, Robert PH (2004) Brain perfusion
correlates of the apathy inventory dimensions of Alzheimer's disease. Int J Geriatr
Psychiatry 19, 864-869.
[12] Holthoff VA, Beuthien-Baumann B, Kalbe E, Ludecke S, Lenz O, Zundorf G, Spirling
S, Schierz K, Winiecki P, Sorbi S, Herholz K (2005) Regional cerebral metabolism in
early Alzheimer's disease with clinically significant apathy or depression. Biol
Psychiatry 57, 412-421.
[13] (2013) Diagnostic and Statistical Manual of Mental Disorders American Psychiatric
Association, Arlington, VA.
103
[14] World Health Organization (2012) Dementia: a public health priority.
http://www.alzheimer.ca/en/sk/Get-involved/Raise-your-
voice/~/media/Files/national/External/WHO_ADI_dementia_report_final.ashx.
[15] Alzheimer's Disease International (2015) World Alzheimer Report 2015: The global
impact of dementia: An analysis of prevalence, incidence, cost and trends.
http://www.alz.co.uk/research/files/WorldAlzheimerReport.pdf.
[16] Alzheimer Society Canada (2016) Prevalence and Monetary Costs of Dementia in
Canada.
http://www.alzheimer.ca/~/media/Files/national/Statistics/PrevalenceandCostsofDemen
tia_EN.pdf.
[17] Alzheimer Society Canada (2010) Rising tide: the impact of dementia on Canadian
society. http://www.alzheimer.ca/en/Get-involved/Raise-your-voice/Rising-
Tide/~/media/Files/national/Advocacy/ASC_Rising%20Tide_Full%20Report_Eng.ash
x.
[18] Selkoe DJ (1996) Amyloid beta-protein and the genetics of Alzheimer's disease. J Biol
Chem 271, 18295-18298.
[19] Glenner GG, Wong CW (1984) Alzheimer's disease: initial report of the purification
and characterization of a novel cerebrovascular amyloid protein. Biochem Biophys Res
Commun 120, 885-890.
[20] Glenner GG, Wong CW (1984) Alzheimer's disease and Down's syndrome: sharing of a
unique cerebrovascular amyloid fibril protein. Biochem Biophys Res Commun 122,
1131-1135.
[21] Cappai R, Barnham KJ (2008) Delineating the mechanism of Alzheimer's disease A
beta peptide neurotoxicity. Neurochem Res 33, 526-532.
[22] Selkoe DJ (1991) The molecular pathology of Alzheimer's disease. Neuron 6, 487-498.
[23] Hardy JA, Higgins GA (1992) Alzheimer's disease: the amyloid cascade hypothesis.
Science 256, 184-185.
[24] Kumar-Singh S, Theuns J, Van Broeck B, Pirici D, Vennekens K, Corsmit E, Cruts M,
Dermaut B, Wang R, Van Broeckhoven C (2006) Mean age-of-onset of familial
alzheimer disease caused by presenilin mutations correlates with both increased
Abeta42 and decreased Abeta40. Hum Mutat 27, 686-695.
[25] Suzuki N, Cheung TT, Cai XD, Odaka A, Otvos L, Jr., Eckman C, Golde TE, Younkin
SG (1994) An increased percentage of long amyloid beta protein secreted by familial
amyloid beta protein precursor (beta APP717) mutants. Science 264, 1336-1340.
[26] Pimplikar SW (2009) Reassessing the amyloid cascade hypothesis of Alzheimer's
disease. Int J Biochem Cell Biol 41, 1261-1268.
[27] Younkin SG (1995) Evidence that A beta 42 is the real culprit in Alzheimer's disease.
Ann Neurol 37, 287-288.
[28] Goedert M, Klug A, Crowther RA (2006) Tau protein, the paired helical filament and
Alzheimer's disease. J Alzheimers Dis 9, 195-207.
[29] Thal DR, Holzer M, Rub U, Waldmann G, Gunzel S, Zedlick D, Schober R (2000)
Alzheimer-related tau-pathology in the perforant path target zone and in the
104
hippocampal stratum oriens and radiatum correlates with onset and degree of dementia.
Exp Neurol 163, 98-110.
[30] Schneider A, Mandelkow E (2008) Tau-based treatment strategies in neurodegenerative
diseases. Neurotherapeutics 5, 443-457.
[31] Lee VM, Trojanowski JQ (2006) Progress from Alzheimer's tangles to pathological tau
points towards more effective therapies now. J Alzheimers Dis 9, 257-262.
[32] Swardfager W, Lanctot K, Rothenburg L, Wong A, Cappell J, Herrmann N (2010) A
meta-analysis of cytokines in Alzheimer's disease. Biol Psychiatry 68, 930-941.
[33] Cagnin A, Brooks DJ, Kennedy AM, Gunn RN, Myers R, Turkheimer FE, Jones T,
Banati RB (2001) In-vivo measurement of activated microglia in dementia. Lancet 358,
461-467.
[34] Floyd RA (1999) Neuroinflammatory processes are important in neurodegenerative
diseases: an hypothesis to explain the increased formation of reactive oxygen and
nitrogen species as major factors involved in neurodegenerative disease development.
Free Radic Biol Med 26, 1346-1355.
[35] Reddy PH, Beal MF (2008) Amyloid beta, mitochondrial dysfunction and synaptic
damage: implications for cognitive decline in aging and Alzheimer's disease. Trends
Mol Med 14, 45-53.
[36] Hansson Petersen CA, Alikhani N, Behbahani H, Wiehager B, Pavlov PF, Alafuzoff I,
Leinonen V, Ito A, Winblad B, Glaser E, Ankarcrona M (2008) The amyloid beta-
peptide is imported into mitochondria via the TOM import machinery and localized to
mitochondrial cristae. Proc Natl Acad Sci U S A 105, 13145-13150.
[37] Jack CR, Jr., Dickson DW, Parisi JE, Xu YC, Cha RH, O'Brien PC, Edland SD, Smith
GE, Boeve BF, Tangalos EG, Kokmen E, Petersen RC (2002) Antemortem MRI
findings correlate with hippocampal neuropathology in typical aging and dementia.
Neurology 58, 750-757.
[38] de Leon MJ, DeSanti S, Zinkowski R, Mehta PD, Pratico D, Segal S, Clark C,
Kerkman D, DeBernardis J, Li J, Lair L, Reisberg B, Tsui W, Rusinek H (2004) MRI
and CSF studies in the early diagnosis of Alzheimer's disease. J Intern Med 256, 205-
223.
[39] Jack CR, Jr., Shiung MM, Gunter JL, O'Brien PC, Weigand SD, Knopman DS, Boeve
BF, Ivnik RJ, Smith GE, Cha RH, Tangalos EG, Petersen RC (2004) Comparison of
different MRI brain atrophy rate measures with clinical disease progression in AD.
Neurology 62, 591-600.
[40] Fox NC, Scahill RI, Crum WR, Rossor MN (1999) Correlation between rates of brain
atrophy and cognitive decline in AD. Neurology 52, 1687-1689.
[41] Silverman DH, Small GW, Chang CY, Lu CS, Kung De Aburto MA, Chen W, Czernin
J, Rapoport SI, Pietrini P, Alexander GE, Schapiro MB, Jagust WJ, Hoffman JM,
Welsh-Bohmer KA, Alavi A, Clark CM, Salmon E, de Leon MJ, Mielke R, Cummings
JL, Kowell AP, Gambhir SS, Hoh CK, Phelps ME (2001) Positron emission
tomography in evaluation of dementia: Regional brain metabolism and long-term
outcome. JAMA 286, 2120-2127.
105
[42] Herholz K, Schopphoff H, Schmidt M, Mielke R, Eschner W, Scheidhauer K, Schicha
H, Heiss WD, Ebmeier K (2002) Direct comparison of spatially normalized PET and
SPECT scans in Alzheimer's disease. J Nucl Med 43, 21-26.
[43] Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP, Bergstrom M,
Savitcheva I, Huang GF, Estrada S, Ausen B, Debnath ML, Barletta J, Price JC, Sandell
J, Lopresti BJ, Wall A, Koivisto P, Antoni G, Mathis CA, Langstrom B (2004) Imaging
brain amyloid in Alzheimer's disease with Pittsburgh Compound-B. Ann Neurol 55,
306-319.
[44] Rowe CC, Ng S, Ackermann U, Gong SJ, Pike K, Savage G, Cowie TF, Dickinson KL,
Maruff P, Darby D, Smith C, Woodward M, Merory J, Tochon-Danguy H, O'Keefe G,
Klunk WE, Mathis CA, Price JC, Masters CL, Villemagne VL (2007) Imaging beta-
amyloid burden in aging and dementia. Neurology 68, 1718-1725.
[45] Mintun MA (2005) Utilizing advanced imaging and surrogate markers across the
spectrum of Alzheimer's disease. CNS Spectr 10, 13-16.
[46] Rapoport SI (1991) Positron emission tomography in Alzheimer's disease in relation to
disease pathogenesis: a critical review. Cerebrovasc Brain Metab Rev 3, 297-335.
[47] Brown DR, Hunter R, Wyper DJ, Patterson J, Kelly RC, Montaldi D, McCullouch J
(1996) Longitudinal changes in cognitive function and regional cerebral function in
Alzheimer's disease: a SPECT blood flow study. J Psychiatr Res 30, 109-126.
[48] Jagust WJ, Eberling JL, Reed BR, Mathis CA, Budinger TF (1997) Clinical studies of
cerebral blood flow in Alzheimer's disease. Ann N Y Acad Sci 826, 254-262.
[49] Corbetta M, Shulman GL (2002) Control of goal-directed and stimulus-driven attention
in the brain. Nat Rev Neurosci 3, 201-215.
[50] Perry RJ, Hodges JR (1999) Attention and executive deficits in Alzheimer's disease. A
critical review. Brain 122 ( Pt 3), 383-404.
[51] McLaughlin PM, Anderson ND, Rich JB, Chertkow H, Murtha SJ (2014) Visual
selective attention in amnestic mild cognitive impairment. J Gerontol B Psychol Sci Soc
Sci 69, 881-891.
[52] Levinoff EJ, Li KZ, Murtha S, Chertkow H (2004) Selective attention impairments in
Alzheimer's disease: evidence for dissociable components. Neuropsychology 18, 580-
588.
[53] Pignatti R, Rabuffetti M, Imbornone E, Mantovani F, Alberoni M, Farina E, Canal N
(2005) Specific impairments of selective attention in mild Alzheimer's disease. J Clin
Exp Neuropsychol 27, 436-448.
[54] Johannsen P, Jakobsen J, Bruhn P, Gjedde A (1999) Cortical responses to sustained and
divided attention in Alzheimer's disease. Neuroimage 10, 269-281.
[55] Perry RJ, Watson P, Hodges JR (2000) The nature and staging of attention dysfunction
in early (minimal and mild) Alzheimer's disease: relationship to episodic and semantic
memory impairment. Neuropsychologia 38, 252-271.
[56] Belleville S, Chertkow H, Gauthier S (2007) Working memory and control of attention
in persons with Alzheimer's disease and mild cognitive impairment. Neuropsychology
21, 458-469.
106
[57] McGuinness B, Barrett SL, Craig D, Lawson J, Passmore AP (2010) Attention deficits
in Alzheimer's disease and vascular dementia. J Neurol Neurosurg Psychiatry 81, 157-
159.
[58] Baddeley AD, Baddeley HA, Bucks RS, Wilcock GK (2001) Attentional control in
Alzheimer's disease. Brain 124, 1492-1508.
[59] MacLeod CM (1992) The Stroop task: The ‘gold standard’ of attentional measures. Exp
Psychol Gen 121, 12-14.
[60] Hutchison KA, Balota DA, Duchek JM (2010) The utility of Stroop task switching as a
marker for early-stage Alzheimer's disease. Psychol Aging 25, 545-559.
[61] Ben-David BM, Tewari A, Shakuf V, Van Lieshout PH (2014) Stroop effects in
Alzheimer's disease: selective attention speed of processing, or color-naming? A meta-
analysis. J Alzheimers Dis 38, 923-938.
[62] Baddeley AD, Hitch GJ (1974) Working Memory In The Psychology of Learning and
Motivation: Advances in Research and Theory, Bower GH, ed. Academic Press, New
York.
[63] Atkinson RC, Shiffrin RM (1968) Human memory: a proposed system and its control
processes In The Psychology of Learning and Motivation: Advances in Research and
Theory, Spence KW, Spence JT, eds. Academic Press, New York, pp. 89–195.
[64] Huntley JD, Howard RJ (2010) Working memory in early Alzheimer's disease: a
neuropsychological review. Int J Geriatr Psychiatry 25, 121-132.
[65] Stopford CL, Thompson JC, Neary D, Richardson AM, Snowden JS (2012) Working
memory, attention, and executive function in Alzheimer's disease and frontotemporal
dementia. Cortex 48, 429-446.
[66] Baddeley AD, Bressi S, Della Sala S, Logie R, Spinnler H (1991) The decline of
working memory in Alzheimer's disease. A longitudinal study. Brain 114 ( Pt 6), 2521-
2542.
[67] de Bruijn RF, Akoudad S, Cremers LG, Hofman A, Niessen WJ, van der Lugt A,
Koudstaal PJ, Vernooij MW, Ikram MA (2014) Determinants, MRI correlates, and
prognosis of mild cognitive impairment: the Rotterdam Study. J Alzheimers Dis 42
Suppl 3, S239-249.
[68] Visser PJ, Kester A, Jolles J, Verhey F (2006) Ten-year risk of dementia in subjects
with mild cognitive impairment. Neurology 67, 1201-1207.
[69] Baddeley AD (2000) The epiosodic buffer: A new component of working memory?
Trends Cogn Sci 4, 417-423.
[70] Gevins AS, Cutillo BC (1993) Neuroelectric evidence for distributed processing in
human working memory. Electroencephalogr Clin Neurophysiol 87, 128-143.
[71] Owen AM, McMillan KM, Laird AR, Bullmore E (2005) N-back working memory
paradigm: a meta-analysis of normative functional neuroimaging studies. Hum Brain
Mapp 25, 46-59.
[72] Rombouts SA, Barkhof F, Goekoop R, Stam CJ, Scheltens P (2005) Altered resting
state networks in mild cognitive impairment and mild Alzheimer's disease: an fMRI
study. Hum Brain Mapp 26, 231-239.
107
[73] Miller KM, Price CC, Okun MS, Montijo H, Bowers D (2009) Is the n-back task a
valid neuropsychological measure for assessing working memory? Arch Clin
Neuropsychol 24, 711-717.
[74] Kane MJ, Conway AR, Miura TK, Colflesh GJ (2007) Working memory, attention
control, and the N-back task: a question of construct validity. J Exp Psychol Learn
Mem Cogn 33, 615-622.
[75] Jaeggi SM, Buschkuehl M, Perrig WJ, Meier B (2010) The concurrent validity of the
N-back task as a working memory measure. Memory 18, 394-412.
[76] Gazzaley A, Nobre AC (2012) Top-down modulation: bridging selective attention and
working memory. Trends Cogn Sci 16, 129-135.
[77] Theeuwes J, Belopolsky A, Olivers CN (2009) Interactions between working memory,
attention and eye movements. Acta Psychol (Amst) 132, 106-114.
[78] Awh E, Vogel EK, Oh SH (2006) Interactions between attention and working memory.
Neuroscience 139, 201-208.
[79] Awh E, Jonides J (2001) Overlapping mechanisms of attention and spatial working
memory. Trends Cogn Sci 5, 119-126.
[80] Mayer JS, Bittner RA, Nikolic D, Bledowski C, Goebel R, Linden DE (2007) Common
neural substrates for visual working memory and attention. Neuroimage 36, 441-453.
[81] Finke K, Myers N, Bublak P, Sorg C (2013) A biased competition account of attention
and memory in Alzheimer's disease. Philos Trans R Soc Lond B Biol Sci 368,
20130062.
[82] LaBar KS, Gitelman DR, Parrish TB, Mesulam M (1999) Neuroanatomic overlap of
working memory and spatial attention networks: a functional MRI comparison within
subjects. Neuroimage 10, 695-704.
[83] Yetkin FZ, Rosenberg RN, Weiner MF, Purdy PD, Cullum CM (2006) FMRI of
working memory in patients with mild cognitive impairment and probable Alzheimer's
disease. Eur Radiol 16, 193-206.
[84] Kalpouzos G, Eustache F, de la Sayette V, Viader F, Chetelat G, Desgranges B (2005)
Working memory and FDG-PET dissociate early and late onset Alzheimer disease
patients. J Neurol 252, 548-558.
[85] Berryhill ME (2012) Insights from neuropsychology: pinpointing the role of the
posterior parietal cortex in episodic and working memory. Front Integr Neurosci 6, 31.
[86] Rutman AM, Clapp WC, Chadick JZ, Gazzaley A (2010) Early top-down control of
visual processing predicts working memory performance. J Cogn Neurosci 22, 1224-
1234.
[87] Downing PE (2000) Interactions between visual working memory and selective
attention. Psychol Sci 11, 467-473.
[88] Olivers CN, Nieuwenhuis S (2006) The beneficial effects of additional task load,
positive affect, and instruction on attentional blink. Journal of Experimental
Psychology: Human Perception and Performance 32, 364-379.
[89] Zhang B, Zhang JX, Huang S, Kong L, Wang S (2011) Effects of load on the guidance
of visual attention from working memory. Vision Res 51, 2356-2361.
108
[90] de Fockert JW, Rees G, Frith CD, Lavie N (2001) The role of working memory in
visual selective attention. Science 291, 1803-1806.
[91] Carlisle NB, Woodman GF (2011) Automatic and strategic effects in the guidance of
attention by working memory representations. Acta Psychol (Amst) 137, 217-225.
[92] Ahmed L, de Fockert JW (2012) Focusing on attention: the effects of working memory
capacity and load on selective attention. PLoS One 7, e43101.
[93] Woodman GF, Luck SJ (2007) Do the contents of visual working memory
automatically influence attentional selection during visual search? J Exp Psychol Hum
Percept Perform 33, 363-377.
[94] Carlisle NB, Woodman GF (2011) When memory is not enough: electrophysiological
evidence for goal-dependent use of working memory representations in guiding visual
attention. J Cogn Neurosci 23, 2650-2664.
[95] Schomaker J, Meeter M (2012) Novelty enhances visual perception. PLoS One 7,
e50599.
[96] Guitart-Masip M, Bunzeck N, Stephan KE, Dolan RJ, Duzel E (2010) Contextual
novelty changes reward representations in the striatum. J Neurosci 30, 1721-1726.
[97] Krebs RM, Heipertz D, Schuetze H, Duzel E (2011) Novelty increases the mesolimbic
functional connectivity of the substantia nigra/ventral tegmental area (SN/VTA) during
reward anticipation: Evidence from high-resolution fMRI. Neuroimage 58, 647-655.
[98] Mayer JS, Kim J, Park S (2011) Enhancing visual working memory encoding: The role
of target novelty. Vis cogn 19, 863-885.
[99] Tarbi EC, Sun X, Holcomb PJ, Daffner KR (2011) Surprise? Early visual novelty
processing is not modulated by attention. Psychophysiology 48, 624-632.
[100] Chong H, Riis JL, McGinnis SM, Williams DM, Holcomb PJ, Daffner KR (2008) To
ignore or explore: top-down modulation of novelty processing. J Cogn Neurosci 20,
120-134.
[101] Friedman D, Cycowicz YM, Gaeta H (2001) The novelty P3: an event-related brain
potential (ERP) sign of the brain's evaluation of novelty. Neurosci Biobehav Rev 25,
355-373.
[102] Herrmann CS, Knight RT (2001) Mechanisms of human attention: event-related
potentials and oscillations. Neurosci Biobehav Rev 25, 465-476.
[103] Polich J, Corey-Bloom J (2005) Alzheimer's disease and P300: review and evaluation
of task and modality. Curr Alzheimer Res 2, 515-525.
[104] Ashford JW, Coburn KL, Rose TL, Bayley PJ (2011) P300 energy loss in aging and
Alzheimer's disease. J Alzheimers Dis 26 Suppl 3, 229-238.
[105] Crutcher MD, Calhoun-Haney R, Manzanares CM, Lah JJ, Levey AI, Zola SM (2009)
Eye tracking during a visual paired comparison task as a predictor of early dementia.
Am J Alzheimers Dis Other Demen 24, 258-266.
[106] Zola SM, Manzanares CM, Clopton P, Lah JJ, Levey AI (2013) A behavioral task
predicts conversion to mild cognitive impairment and Alzheimer's disease. Am J
Alzheimers Dis Other Demen 28, 179-184.
109
[107] Snyder KA, Blank MP, Marsolek CJ (2008) What form of memory underlies novelty
preferences? Psychon Bull Rev 15, 315-321.
[108] Baars BJ, Franklin S (2003) How conscious experience and working memory interact.
Trends Cogn Sci 7, 166-172.
[109] Squire LR (2004) Memory systems of the brain: a brief history and current perspective.
Neurobiol Learn Mem 82, 171-177.
[110] Tulving E, Schacter DL (1990) Priming and human memory systems. Science 247, 301-
306.
[111] Yonelinas AP, Aly M, Wang WC, Koen JD (2010) Recollection and familiarity:
examining controversial assumptions and new directions. Hippocampus 20, 1178-1194.
[112] Dew IT, Cabeza R (2011) The porous boundaries between explicit and implicit
memory: behavioral and neural evidence. Ann N Y Acad Sci 1224, 174-190.
[113] Soto D, Mantyla T, Silvanto J (2011) Working memory without consciousness. Curr
Biol 21, R912-913.
[114] Hassin RR, Bargh JA, Engell AD, McCulloch KC (2009) Implicit working memory.
Conscious Cogn 18, 665-678.
[115] Dutta A, Shah K, Silvanto J, Soto D (2014) Neural basis of non-conscious visual
working memory. Neuroimage 91, 336-343.
[116] Grill-Spector K, Kushnir T, Edelman S, Avidan G, Itzchak Y, Malach R (1999)
Differential processing of objects under various viewing conditions in the human lateral
occipital complex. Neuron 24, 187-203.
[117] Desimone R (1996) Neural mechanisms for visual memory and their role in attention.
Proc Natl Acad Sci U S A 93, 13494-13499.
[118] Grill-Spector K, Henson R, Martin A (2006) Repetition and the brain: neural models of
stimulus-specific effects. Trends Cogn Sci 10, 14-23.
[119] Stern CE, Corkin S, Gonzalez RG, Guimaraes AR, Baker JR, Jennings PJ, Carr CA,
Sugiura RM, Vedantham V, Rosen BR (1996) The hippocampal formation participates
in novel picture encoding: evidence from functional magnetic resonance imaging. Proc
Natl Acad Sci U S A 93, 8660-8665.
[120] Buckner RL, Koutstaal W (1998) Functional neuroimaging studies of encoding,
priming, and explicit memory retrieval. Proc Natl Acad Sci U S A 95, 891-898.
[121] Pihlajamaki M, O'Keefe K, O'Brien J, Blacker D, Sperling RA (2011) Failure of
repetition suppression and memory encoding in aging and Alzheimer's disease. Brain
Imaging Behav 5, 36-44.
[122] Godijn R, Theeuwes J, Godijn R (2004) The relationship between inhibition of return
and saccade trajectory deviations. J Exp Psychol Hum Percept Perform 30, 538-554.
[123] Hoffman JE, Subramaniam B (1995) The role of visual attention in saccadic eye
movements. Percept Psychophys 57, 787-795.
[124] Deubel H, Schneider WX (1996) Saccade target selection and object recognition:
evidence for a common attentional mechanism. Vision Res 36, 1827-1837.
[125] Shafiq-Antonacci R, Maruff P, Masters C, Currie J (2003) Spectrum of saccade system
function in Alzheimer disease. Arch Neurol 60, 1272-1278.
110
[126] Yang Q, Wang T, Su N, Xiao S, Kapoula Z (2013) Specific saccade deficits in patients
with Alzheimer's disease at mild to moderate stage and in patients with amnestic mild
cognitive impairment. Age (Dordr) 35, 1287-1298.
[127] Crawford TJ, Higham S, Renvoize T, Patel J, Dale M, Suriya A, Tetley S (2005)
Inhibitory control of saccadic eye movements and cognitive impairment in Alzheimer's
disease. Biol Psychiatry 57, 1052-1060.
[128] Crawford TJ, Devereaux A, Higham S, Kelly C (2015) The disengagement of visual
attention in Alzheimer's disease: a longitudinal eye-tracking study. Front Aging
Neurosci 7, 118.
[129] Rosler A, Mapstone ME, Hays AK, Mesulam MM, Rademaker A, Gitelman DR,
Weintraub S (2000) Alterations of visual search strategy in Alzheimer's disease and
aging. Neuropsychology 14, 398-408.
[130] Moser A, Kompf D, Olschinka J (1995) Eye movement dysfunction in dementia of the
Alzheimer type. Dementia 6, 264-268.
[131] Daffner KR, Scinto LF, Weintraub S, Guinessey JE, Mesulam MM (1992) Diminished
curiosity in patients with probable Alzheimer's disease as measured by exploratory eye
movements. Neurology 42, 320-328.
[132] Bachevalier J, Brickson M, Hagger C (1993) Limbic-dependent recognition memory in
monkeys develops early in infancy. Neuroreport 4, 77-80.
[133] Nemanic S, Alvarado MC, Bachevalier J (2004) The hippocampal/parahippocampal
regions and recognition memory: insights from visual paired comparison versus object-
delayed nonmatching in monkeys. J Neurosci 24, 2013-2026.
[134] Lagun D, Manzanares C, Zola SM, Buffalo EA, Agichtein E (2011) Detecting
cognitive impairment by eye movement analysis using automatic classification
algorithms. J Neurosci Methods 201, 196-203.
[135] Rangel-Gomez M, Meeter M (2016) Neurotransmitters and Novelty: A Systematic
Review. J Psychopharmacol 30, 3-12.
[136] Bunzeck N, Guitart-Masip M, Dolan RJ, Duzel E (2014) Pharmacological dissociation
of novelty responses in the human brain. Cereb Cortex 24, 1351-1360.
[137] Klinkenberg I, Blokland A, Riedel WJ, Sambeth A (2013) Cholinergic modulation of
auditory processing, sensory gating and novelty detection in human participants.
Psychopharmacology (Berl) 225, 903-921.
[138] Kumar U, Patel SC (2007) Immunohistochemical localization of dopamine receptor
subtypes (D1R-D5R) in Alzheimer's disease brain. Brain Res 1131, 187-196.
[139] Kemppainen N, Laine M, Laakso MP, Kaasinen V, Nagren K, Vahlberg T, Kurki T,
Rinne JO (2003) Hippocampal dopamine D2 receptors correlate with memory functions
in Alzheimer's disease. Eur J Neurosci 18, 149-154.
[140] Eckart C, Bunzeck N (2013) Dopamine modulates processing speed in the human
mesolimbic system. Neuroimage 66, 293-300.
[141] Cross AJ, Crow TJ, Ferrier IN, Johnson JA, Bloom SR, Corsellis JA (1984) Serotonin
receptor changes in dementia of the Alzheimer type. J Neurochem 43, 1574-1581.
111
[142] Cross AJ, Crow TJ, Ferrier IN, Johnson JA, Markakis D (1984) Striatal dopamine
receptors in Alzheimer-type dementia. Neurosci Lett 52, 1-6.
[143] Reinikainen KJ, Soininen H, Riekkinen PJ (1990) Neurotransmitter changes in
Alzheimer's disease: implications to diagnostics and therapy. J Neurosci Res 27, 576-
586.
[144] Marin RS (1991) Apathy: a neuropsychiatric syndrome. J Neuropsychiatry Clin
Neurosci 3, 243-254.
[145] Mulin E, Leone E, Dujardin K, Delliaux M, Leentjens A, Nobili F, Dessi B, Tible O,
Aguera-Ortiz L, Osorio RS, Yessavage J, Dachevsky D, Verhey FR, Jentoft AJ, Blanc
O, Llorca PM, Robert PH (2011) Diagnostic criteria for apathy in clinical practice. Int J
Geriatr Psychiatry 26, 158-165.
[146] Zhao H, Zhao Z, Huang L, Preter M (2012) Diagnosis and assessment of apathy in
Chinese patients with Alzheimer's disease. J Psychosom Res 72, 405-407.
[147] Robert P, Onyike CU, Leentjens AF, Dujardin K, Aalten P, Starkstein S, Verhey FR,
Yessavage J, Clement JP, Drapier D, Bayle F, Benoit M, Boyer P, Lorca PM, Thibaut
F, Gauthier S, Grossberg G, Vellas B, Byrne J (2009) Proposed diagnostic criteria for
apathy in Alzheimer's disease and other neuropsychiatric disorders. Eur Psychiatry 24,
98-104.
[148] Mirakhur A, Craig D, Hart DJ, McLlroy SP, Passmore AP (2004) Behavioural and
psychological syndromes in Alzheimer's disease. Int J Geriatr Psychiatry 19, 1035-
1039.
[149] Thomas P, Clement JP, Hazif-Thomas C, Leger JM (2001) Family, Alzheimer's disease
and negative symptoms. Int J Geriatr Psychiatry 16, 192-202.
[150] Tatsch MF, Bottino CM, Azevedo D, Hototian SR, Moscoso MA, Folquitto JC, Scalco
AZ, Louza MR (2006) Neuropsychiatric symptoms in Alzheimer disease and
cognitively impaired, nondemented elderly from a community-based sample in Brazil:
prevalence and relationship with dementia severity. Am J Geriatr Psychiatry 14, 438-
445.
[151] Levy ML, Cummings JL, Fairbanks LA, Masterman D, Miller BL, Craig AH, Paulsen
JS, Litvan I (1998) Apathy is not depression. J Neuropsychiatry Clin Neurosci 10, 314-
319.
[152] Mega MS, Cummings JL, Fiorello T, Gornbein J (1996) The spectrum of behavioral
changes in Alzheimer's disease. Neurology 46, 130-135.
[153] Landes AM, Sperry SD, Strauss ME (2005) Prevalence of apathy, dysphoria, and
depression in relation to dementia severity in Alzheimer's disease. J Neuropsychiatry
Clin Neurosci 17, 342-349.
[154] Srikanth S, Nagaraja AV, Ratnavalli E (2005) Neuropsychiatric symptoms in dementia-
frequency, relationship to dementia severity and comparison in Alzheimer's disease,
vascular dementia and frontotemporal dementia. J Neurol Sci 236, 43-48.
[155] Starkstein SE, Jorge R, Mizrahi R, Robinson RG (2006) A prospective longitudinal
study of apathy in Alzheimer's disease. J Neurol Neurosurg Psychiatry 77, 8-11.
112
[156] Starkstein SE, Petracca G, Chemerinski E, Kremer J (2001) Syndromic validity of
apathy in Alzheimer's disease. Am J Psychiatry 158, 872-877.
[157] Robert PH, Darcourt G, Koulibaly MP, Clairet S, Benoit M, Garcia R, Dechaux O,
Darcourt J (2006) Lack of initiative and interest in Alzheimer's disease: a single photon
emission computed tomography study. Eur J Neurol 13, 729-735.
[158] Lyketsos CG, Steinberg M, Tschanz JT, Norton MC, Steffens DC, Breitner JC (2000)
Mental and behavioral disturbances in dementia: findings from the Cache County Study
on Memory in Aging. Am J Psychiatry 157, 708-714.
[159] Zuidema SU, Derksen E, Verhey FR, Koopmans RT (2007) Prevalence of
neuropsychiatric symptoms in a large sample of Dutch nursing home patients with
dementia. Int J Geriatr Psychiatry 22, 632-638.
[160] Pitkala KH, Laurila JV, Strandberg TE, Tilvis RS (2004) Behavioral symptoms and the
administration of psychotropic drugs to aged patients with dementia in nursing homes
and in acute geriatric wards. Int Psychogeriatr 16, 61-74.
[161] Margallo-Lana M, Swann A, O'Brien J, Fairbairn A, Reichelt K, Potkins D, Mynt P,
Ballard C (2001) Prevalence and pharmacological management of behavioural and
psychological symptoms amongst dementia sufferers living in care environments. Int J
Geriatr Psychiatry 16, 39-44.
[162] Lyketsos CG, Lopez O, Jones B, Fitzpatrick AL, Breitner J, DeKosky S (2002)
Prevalence of neuropsychiatric symptoms in dementia and mild cognitive impairment:
results from the cardiovascular health study. JAMA 288, 1475-1483.
[163] Copeland MP, Daly E, Hines V, Mastromauro C, Zaitchik D, Gunther J, Albert M
(2003) Psychiatric symptomatology and prodromal Alzheimer's disease. Alzheimer Dis
Assoc Disord 17, 1-8.
[164] Hwang TJ, Masterman DL, Ortiz F, Fairbanks LA, Cummings JL (2004) Mild
cognitive impairment is associated with characteristic neuropsychiatric symptoms.
Alzheimer Dis Assoc Disord 18, 17-21.
[165] Ishii S, Weintraub N, Mervis JR (2009) Apathy: a common psychiatric syndrome in the
elderly. J Am Med Dir Assoc 10, 381-393.
[166] Zubenko GS, Zubenko WN, McPherson S, Spoor E, Marin DB, Farlow MR, Smith GE,
Geda YE, Cummings JL, Petersen RC, Sunderland T (2003) A collaborative study of
the emergence and clinical features of the major depressive syndrome of Alzheimer's
disease. Am J Psychiatry 160, 857-866.
[167] Karttunen K, Karppi P, Hiltunen A, Vanhanen M, Valimaki T, Martikainen J, Valtonen
H, Sivenius J, Soininen H, Hartikainen S, Suhonen J, Pirttila T (2011) Neuropsychiatric
symptoms and Quality of Life in patients with very mild and mild Alzheimer's disease.
Int J Geriatr Psychiatry 26, 473-482.
[168] Modrego PJ (2010) Depression in Alzheimer's Disease. Pathophysiology, Diagnosis,
and Treatment. J Alzheimers Dis.
[169] Olin JT, Schneider LS, Katz IR, Meyers BS, Alexopoulos GS, Breitner JC, Bruce ML,
Caine ED, Cummings JL, Devanand DP, Krishnan KR, Lyketsos CG, Lyness JM,
Rabins PV, Reynolds CF, 3rd, Rovner BW, Steffens DC, Tariot PN, Lebowitz BD
113
(2002) Provisional diagnostic criteria for depression of Alzheimer disease. Am J
Geriatr Psychiatry 10, 125-128.
[170] Wadsworth LP, Lorius N, Donovan NJ, Locascio JJ, Rentz DM, Johnson KA, Sperling
RA, Marshall GA (2012) Neuropsychiatric Symptoms and Global Functional
Impairment along the Alzheimer's Continuum. Dement Geriatr Cogn Disord 34, 96-
111.
[171] Senanarong V, Siwasariyanon N, Washirutmangkur L, Poungvarin N, Ratanabunakit C,
Aoonkaew N, Udomphanthurak S (2012) Alzheimer's disease dementia as the diagnosis
best supported by the cerebrospinal fluid biomarkers: difference in cut-off levels from
thai experience. Int J Alzheimers Dis 2012, 212063.
[172] Boyle PA, Malloy PF, Salloway S, Cahn-Weiner DA, Cohen R, Cummings JL (2003)
Executive dysfunction and apathy predict functional impairment in Alzheimer disease.
Am J Geriatr Psychiatry 11, 214-221.
[173] McPherson S, Fairbanks L, Tiken S, Cummings JL, Back-Madruga C (2002) Apathy
and executive function in Alzheimer's disease. J Int Neuropsychol Soc 8, 373-381.
[174] Benoit M, Andrieu S, Lechowski L, Gillette-Guyonnet S, Robert PH, Vellas B (2008)
Apathy and depression in Alzheimer's disease are associated with functional deficit and
psychotropic prescription. Int J Geriatr Psychiatry 23, 409-414.
[175] Kaufer DI, Cummings JL, Christine D, Bray T, Castellon S, Masterman D, MacMillan
A, Ketchel P, DeKosky ST (1998) Assessing the impact of neuropsychiatric symptoms
in Alzheimer's disease: the Neuropsychiatric Inventory Caregiver Distress Scale. J Am
Geriatr Soc 46, 210-215.
[176] Pang FC, Chow TW, Cummings JL, Leung VP, Chiu HF, Lam LC, Chen QL, Tai CT,
Chen LW, Wang SJ, Fuh JL (2002) Effect of neuropsychiatric symptoms of
Alzheimer's disease on Chinese and American caregivers. Int J Geriatr Psychiatry 17,
29-34.
[177] Robert PH, Clairet S, Benoit M, Koutaich J, Bertogliati C, Tible O, Caci H, Borg M,
Brocker P, Bedoucha P (2002) The apathy inventory: assessment of apathy and
awareness in Alzheimer's disease, Parkinson's disease and mild cognitive impairment.
Int J Geriatr Psychiatry 17, 1099-1105.
[178] Derouesne C, Thibault S, Lagha-Pierucci S, Baudouin-Madec V, Ancri D, Lacomblez
L (1999) Decreased awareness of cognitive deficits in patients with mild dementia of
the Alzheimer type. Int J Geriatr Psychiatry 14, 1019-1030.
[179] Petracca G, Teson A, Chemerinski E, Leiguarda R, Starkstein SE (1996) A double-
blind placebo-controlled study of clomipramine in depressed patients with Alzheimer's
disease. J Neuropsychiatry Clin Neurosci 8, 270-275.
[180] van Dijk PT, Dippel DW, Habbema JD (1994) A behavioral rating scale as a predictor
for survival of demented nursing home patients. Arch Gerontol Geriatr 18, 101-113.
[181] Lechowski L, Benoit M, Chassagne P, Vedel I, Tortrat D, Teillet L, Vellas B (2009)
Persistent apathy in Alzheimer's disease as an independent factor of rapid functional
decline: the REAL longitudinal cohort study. Int J Geriatr Psychiatry 24, 341-346.
114
[182] Palmer K, Di Iulio F, Varsi AE, Gianni W, Sancesario G, Caltagirone C, Spalletta G
(2010) Neuropsychiatric predictors of progression from amnestic-mild cognitive
impairment to Alzheimer's disease: the role of depression and apathy. J Alzheimers Dis
20, 175-183.
[183] Richard E, Schmand B, Eikelenboom P, Yang SC, Ligthart SA, Moll van Charante EP,
van Gool WA (2012) Symptoms of apathy are associated with progression from mild
cognitive impairment to Alzheimer's disease in non-depressed subjects. Dement Geriatr
Cogn Disord 33, 204-209.
[184] Vicini Chilovi B, Conti M, Zanetti M, Mazzu I, Rozzini L, Padovani A (2009)
Differential impact of apathy and depression in the development of dementia in mild
cognitive impairment patients. Dement Geriatr Cogn Disord 27, 390-398.
[185] Banerjee S, Murray J, Foley B, Atkins L, Schneider J, Mann A (2003) Predictors of
institutionalisation in people with dementia. J Neurol Neurosurg Psychiatry 74, 1315-
1316.
[186] Herrmann N, Lanctôt KL, Sambrook R, Lesnikova N, Hebert R, McCracken P,
Robillard A, Nguyen E (2006) The contribution of neuropsychiatric symptoms to the
cost of dementia care. Int J Geriatr Psychiatry 21, 972-976.
[187] Terry RD, Gonatas NK, Weiss M (1964) Ultrastructural Studies in Alzheimer's
Presenile Dementia. Am J Pathol 44, 269-297.
[188] Blessed G, Tomlinson BE, Roth M (1968) The association between quantitative
measures of dementia and of senile change in the cerebral grey matter of elderly
subjects. Br J Psychiatry 114, 797-811.
[189] Cohen ML, Golde TE, Usiak MF, Younkin LH, Younkin SG (1988) In situ
hybridization of nucleus basalis neurons shows increased beta-amyloid mRNA in
Alzheimer disease. Proc Natl Acad Sci U S A 85, 1227-1231.
[190] Vincent IJ, Davies P (1992) A protein kinase associated with paired helical filaments in
Alzheimer disease. Proc Natl Acad Sci U S A 89, 2878-2882.
[191] Hanger DP, Betts JC, Loviny TL, Blackstock WP, Anderton BH (1998) New
phosphorylation sites identified in hyperphosphorylated tau (paired helical filament-
tau) from Alzheimer's disease brain using nanoelectrospray mass spectrometry. J
Neurochem 71, 2465-2476.
[192] Luterman JD, Haroutunian V, Yemul S, Ho L, Purohit D, Aisen PS, Mohs R, Pasinetti
GM (2000) Cytokine gene expression as a function of the clinical progression of
Alzheimer disease dementia. Arch Neurol 57, 1153-1160.
[193] Forstl H, Burns A, Luthert P, Cairns N, Lantos P, Levy R (1992) Clinical and
neuropathological correlates of depression in Alzheimer's disease. Psychol Med 22,
877-884.
[194] Marshall GA, Fairbanks LA, Tekin S, Vinters HV, Cummings JL (2006)
Neuropathologic correlates of apathy in Alzheimer's disease. Dement Geriatr Cogn
Disord 21, 144-147.
115
[195] Tekin S, Mega MS, Masterman DM, Chow T, Garakian J, Vinters HV, Cummings JL
(2001) Orbitofrontal and anterior cingulate cortex neurofibrillary tangle burden is
associated with agitation in Alzheimer disease. Ann Neurol 49, 355-361.
[196] Mori T, Shimada H, Shinotoh H, Hirano S, Eguchi Y, Yamada M, Fukuhara R,
Tanimukai S, Zhang MR, Kuwabara S, Ueno S, Suhara T (2014) Apathy correlates
with prefrontal amyloid beta deposition in Alzheimer's disease. J Neurol Neurosurg
Psychiatry 85, 449-455.
[197] Marshall GA, Donovan NJ, Lorius N, Gidicsin CM, Maye J, Pepin LC, Becker JA,
Amariglio RE, Rentz DM, Sperling RA, Johnson KA (2013) Apathy is associated with
increased amyloid burden in mild cognitive impairment. J Neuropsychiatry Clin
Neurosci 25, 302-307.
[198] Skogseth R, Mulugeta E, Jones E, Ballard C, Rongve A, Nore S, Alves G, Aarsland D
(2008) Neuropsychiatric correlates of cerebrospinal fluid biomarkers in Alzheimer's
disease. Dement Geriatr Cogn Disord 25, 559-563.
[199] Tanaka Y, Meguro K, Yamaguchi S, Ishii H, Watanuki S, Funaki Y, Yamaguchi K,
Yamadori A, Iwata R, Itoh M (2003) Decreased striatal D2 receptor density associated
with severe behavioral abnormality in Alzheimer's disease. Ann Nucl Med 17, 567-573.
[200] Lanctôt KL, Herrmann N, Mazzotta P (2001) Role of serotonin in the behavioral and
psychological symptoms of dementia. J Neuropsychiatry Clin Neurosci 13, 5-21.
[201] Lanctôt KL, Herrmann N, Mazzotta P, Khan LR, Ingber N (2004) GABAergic function
in Alzheimer's disease: evidence for dysfunction and potential as a therapeutic target
for the treatment of behavioural and psychological symptoms of dementia. Can J
Psychiatry 49, 439-453.
[202] Pinto T, Lanctot KL, Herrmann N (2011) Revisiting the cholinergic hypothesis of
behavioral and psychological symptoms in dementia of the Alzheimer's type. Ageing
Res Rev 10, 404-412.
[203] Sultzer DL, Melrose RJ, A. NT, Veliz J, Harwood DG, Juarez KO, Wong LT, Brody
AL, Mandelkern MA (2016) Apathy and regional cholinergic receptor binding in
Alzheimer’s disease: 2-FA PET imaging. Am J Geriatr Psychiatry 24, S135-136.
[204] David R, Koulibaly M, Benoit M, Garcia R, Caci H, Darcourt J, Robert P (2008)
Striatal dopamine transporter levels correlate with apathy in neurodegenerative diseases
A SPECT study with partial volume effect correction. Clin Neurol Neurosurg 110, 19-
24.
[205] Mega MS, Masterman DM, O'Connor SM, Barclay TR, Cummings JL (1999) The
spectrum of behavioral responses to cholinesterase inhibitor therapy in Alzheimer
disease. Arch Neurol 56, 1388-1393.
[206] Kaufer D (1998) Beyond the cholinergic hypothesis: the effect of metrifonate and other
cholinesterase inhibitors on neuropsychiatric symptoms in Alzheimer's disease. Dement
Geriatr Cogn Disord 9 Suppl 2, 8-14.
[207] Rodda J, Morgan S, Walker Z (2009) Are cholinesterase inhibitors effective in the
management of the behavioral and psychological symptoms of dementia in Alzheimer's
116
disease? A systematic review of randomized, placebo-controlled trials of donepezil,
rivastigmine and galantamine. Int Psychogeriatr 21, 813-824.
[208] Bartus RT (2000) On neurodegenerative diseases, models, and treatment strategies:
lessons learned and lessons forgotten a generation following the cholinergic hypothesis.
Exp Neurol 163, 495-529.
[209] Bowen DM, Smith CB, White P, Davison AN (1976) Neurotransmitter-related enzymes
and indices of hypoxia in senile dementia and other abiotrophies. Brain 99, 459-496.
[210] Davies P, Maloney AJ (1976) Selective loss of central cholinergic neurons in
Alzheimer's disease. Lancet 2, 1403.
[211] Ferris RM, Tang FLM, Maxwell RA (1972) A comparison of the capacities of isomers
of amphetamine, deoxypipradol and methylphenidate to inhibit the uptake of tritiated
catecholamines into rat cerebral cortex slices, synaptosomal preparationsof rat cerebral
cortex, hypothalamus and striatum and into adrenergic nerves of rabbit aorta. J
Pharmacol Exp Ther 181, 407-416.
[212] Andersen PH (1987) Biochemical and pharmacological characterization of
[3H]GBR12935 binding in vitro to rat striatal membranes: labeling of dopamine uptake
complex. J Neurochem 48, 1887-1896.
[213] Wall SC, Gu H, Rudnick G (1995) Biogenic amine flux mediated by clones transporters
stably expressed in cultured cell lines: amphetamine specificity for inhibition and
efflux. Mol Pharmacol 47, 544-550.
[214] Padala PR, Burke WJ, Bhatia SC (2007) Modafinil therapy for apathy in an elderly
patient. Ann Pharmacother 41, 346-349.
[215] Padala PR, Burke WJ, Bhatia SC, Petty F (2007) Treatment of apathy with
methylphenidate. J Neuropsychiatry Clin Neurosci 19, 81-83.
[216] Padala PR, Burke WJ, Shostrom VK, Bhatia SC, Wengel SP, Potter JF, Petty F (2010)
Methylphenidate for apathy and functional status in dementia of the Alzheimer type.
Am J Geriatr Psychiatry 18, 371-374.
[217] Galynker I, Ieronimo C, Miner C, Rosenblum J, Vilkas N, Rosenthan R (1997)
Methylphenidate treatment of negative symptoms in patients with dementia. J
Neuropsychiatry Clin Neurosci 9, 231-239.
[218] Herrmann N, Rothenburg LS, Black SE, Ryan M, Liu BA, Busto UE, Lanctôt KL
(2008) Methylphenidate for the treatment of apathy in Alzheimer disease: prediction of
response using dextroamphetamine challenge. J Clin Psychopharmacol 28, 296-301.
[219] Rosenberg PB, Lanctot KL, Drye LT, Herrmann N, Scherer RW, Bachman DL,
Mintzer JE, Investigators A (2013) Safety and efficacy of methylphenidate for apathy
in Alzheimer's disease: a randomized, placebo-controlled trial. J Clin Psychiatry 74,
810-816.
[220] Frakey LL, Salloway S, Buelow M, Malloy P (2012) A randomized, double-blind,
placebo-controlled trial of modafinil for the treatment of apathy in individuals with
mild-to-moderate Alzheimer's disease. J Clin Psychiatry 73, 796-801.
117
[221] Drayton SJ, Davies K, Steinberg M, Leroi I, Rosenblatt A, Lyketsos CG (2004)
Amantadine for executive dysfunction syndrome in patients with dementia.
Psychosomatics 45, 205-209.
[222] Kraus MF, Maki PM (1997) Effect of amantadine hydrochloride on symptoms of
frontal lobe dysfunction in brain injury: case studies and review. J Neuropsychiatry
Clin Neurosci 9, 222-230.
[223] Van Reekum R, Bayley M, Garner S, Burke IM, Fawcett S, Hart A, Thompson W
(1995) N of 1 study: amantadine for the amotivational syndrome in a patient with
traumatic brain injury. Brain Inj 9, 49-53.
[224] Cohen RA, Fisher M (1989) Amantadine treatment of fatigue associated with multiple
sclerosis. Arch Neurol 46, 676-680.
[225] Erkulwater S, Pillai R (1989) Amantadine and the end-stage dementia of Alzheimer's
type. South Med J 82, 550-554.
[226] Catsman-Berrevoets CE, von Harskamp F (1988) Compulsive pre-sleep behavior and
apathy due to bilateral thalamic stroke: response to bromocriptine. Neurology 38, 647-
649.
[227] Crismon ML, Childs A, Wilcox RE, Barrow N (1988) The effect of bromocriptine on
speech dysfunction in patients with diffuse brain injury (akinetic mutism). Clin
Neuropharmacol 11, 462-466.
[228] Powell JH, al-Adawi S, Morgan J, Greenwood RJ (1996) Motivational deficits after
brain injury: effects of bromocriptine in 11 patients. J Neurol Neurosurg Psychiatry 60,
416-421.
[229] Marin RS, Fogel BS, Hawkins J, Duffy J, Krupp B (1995) Apathy: a treatable
syndrome. J Neuropsychiatry Clin Neurosci 7, 23-30.
[230] Lanctôt KL, Herrmann N, Yau KK, Khan LR, Liu BA, LouLou MM, Einarson TR
(2003) Efficacy and safety of cholinesterase inhibitors in Alzheimer's disease: a meta-
analysis. CMAJ 169, 557-564.
[231] Birks J (2006) Cholinesterase inhibitors for Alzheimer's disease. Cochrane Database
Syst Rev, CD005593.
[232] Hansen RA, Gartlehner G, Lohr KN, Kaufer DI (2007) Functional outcomes of drug
treatment in Alzheimer's disease: A systematic review and meta-analysis. Drugs Aging
24, 155-167.
[233] Gorus E, Lambert M, De Raedt R, Mets T (2007) The influence of galantamine on
reaction time, attention processes, and performance variability in elderly Alzheimer
patients. J Clin Psychopharmacol 27, 182-187.
[234] Vellas B, Cunha L, Gertz HJ, De Deyn PP, Wesnes K, Hammond G, Schwalen S
(2005) Early onset effects of galantamine treatment on attention in patients with
Alzheimer's disease. Curr Med Res Opin 21, 1423-1429.
[235] Galvin JE, Cornblatt B, Newhouse P, Ancoli-Israel S, Wesnes K, Williamson D, Zhu
Y, Sorra K, Amatniek J (2008) Effects of galantamine on measures of attention: results
from 2 clinical trials in Alzheimer disease patients with comparisons to donepezil.
Alzheimer Dis Assoc Disord 22, 30-38.
118
[236] Marshall DL, Redfern PH, Wonnacott S (1997) Presynaptic nicotinic modulation of
dopamine release in the three ascending pathways studied by in vivo microdialysis:
comparison of naive and chronic nicotine-treated rats. J Neurochem 68, 1511-1519.
[237] Zhou FM, Liang Y, Dani JA (2001) Endogenous nicotinic cholinergic activity regulates
dopamine release in the striatum. Nat Neurosci 4, 1224-1229.
[238] Johnson JH, Zhao C, James JR, Rosecrans JA (2000) Individual variability of dopamine
release from nucleus accumbens induced by nicotine. Brain Res Bull 51, 249-253.
[239] Blesa R (2000) Galantamine: therapeutic effects beyond cognition. Dement Geriatr
Cogn Disord 11 Suppl 1, 28-34.
[240] Erkinjuntti T (2002) Treatment options: the latest evidence with galantamine
(Reminyl). J Neurol Sci 203-204, 125-130.
[241] Zhang L, Zhou FM, Dani JA (2004) Cholinergic drugs for Alzheimer's disease enhance
in vitro dopamine release. Mol Pharmacol 66, 538-544.
[242] Tucha O, Prell S, Mecklinger L, Bormann-Kischkel C, Kubber S, Linder M, Walitza S,
Lange KW (2006) Effects of methylphenidate on multiple components of attention in
children with attention deficit hyperactivity disorder. Psychopharmacology (Berl) 185,
315-326.
[243] Tucha O, Mecklinger L, Laufkotter R, Klein HE, Walitza S, Lange KW (2006)
Methylphenidate-induced improvements of various measures of attention in adults with
attention deficit hyperactivity disorder. J Neural Transm 113, 1575-1592.
[244] Lopez J, Lopez V, Rojas D, Carrasco X, Rothhammer P, Garcia R, Rothhammer F,
Aboitiz F (2004) Effect of psychostimulants on distinct attentional parameters in
attentional deficit/hyperactivity disorder. Biol Res 37, 461-468.
[245] Nieoullon A, Coquerel A (2003) Dopamine: a key regulator to adapt action, emotion,
motivation and cognition. Curr Opin Neurol 16 Suppl 2, S3-9.
[246] Robbins TW (1984) Cortical noradrenaline, attention and arousal. Psychol Med 14, 13-
21.
[247] Harley CW (1987) A role for norepinephrine in arousal, emotion and learning?: limbic
modulation by norepinephrine and the Kety hypothesis. Prog Neuropsychopharmacol
Biol Psychiatry 11, 419-458.
[248] Ikemoto S (2007) Dopamine reward circuitry: two projection systems from the ventral
midbrain to the nucleus accumbens-olfactory tubercle complex. Brain Res Rev 56, 27-
78.
[249] Albanese A, Altavista MC, Rossi P (1986) Organization of central nervous system
dopaminergic pathways. J Neural Transm Suppl 22, 3-17.
[250] Watanabe M, Kodama T, Hikosaka K (1997) Increase of extracellular dopamine in
primate prefrontal cortex during a working memory task. J Neurophysiol 78, 2795-
2798.
[251] Haber SN, Fudge JL, McFarland NR (2000) Striatonigrostriatal pathways in primates
form an ascending spiral from the shell to the dorsolateral striatum. J Neurosci 20,
2369-2382.
119
[252] Koob GF (1996) Hedonic valence, dopamine and motivation. Mol Psychiatry 1, 186-
189.
[253] Wise RA (2004) Dopamine, learning and motivation. Nat Rev Neurosci 5, 483-494.
[254] Lanctôt KL, Herrmann N, Black SE, Ryan M, Rothenburg LS, Liu BA, Busto UE
(2008) Apathy associated with Alzheimer disease: use of dextroamphetamine
challenge. Am J Geriatr Psychiatry 16, 551-557.
[255] Drijgers RL, Verhey FR, Leentjens AF, Kohler S, Aalten P (2011) Neuropsychological
correlates of apathy in mild cognitive impairment and Alzheimer's disease: the role of
executive functioning. Int Psychogeriatr 23, 1327-1333.
[256] Senanarong V, Poungvarin N, Jamjumras P, Sriboonroung A, Danchaivijit C,
Udomphanthuruk S, Cummings JL (2005) Neuropsychiatric symptoms, functional
impairment and executive ability in Thai patients with Alzheimer's disease. Int
Psychogeriatr 17, 81-90.
[257] Benoit M, Dygai I, Migneco O, Robert PH, Bertogliati C, Darcourt J, Benoliel J,
Aubin-Brunet V, Pringuey D (1999) Behavioral and psychological symptoms in
Alzheimer's disease. Relation between apathy and regional cerebral perfusion. Dement
Geriatr Cogn Disord 10, 511-517.
[258] Benoit M, Koulibaly PM, Migneco O, Darcourt J, Pringuey DJ, Robert PH (2002)
Brain perfusion in Alzheimer's disease with and without apathy: a SPECT study with
statistical parametric mapping analysis. Psychiatry Res 114, 103-111.
[259] Craig AH, Cummings JL, Fairbanks L, Itti L, Miller BL, Li J, Mena I (1996) Cerebral
blood flow correlates of apathy in Alzheimer disease. Arch Neurol 53, 1116-1120.
[260] Lopez OL, Zivkovic S, Smith G, Becker JT, Meltzer CC, DeKosky ST (2001)
Psychiatric symptoms associated with cortical-subcortical dysfunction in Alzheimer's
disease. J Neuropsychiatry Clin Neurosci 13, 56-60.
[261] Migneco O, Benoit M, Koulibaly PM, Dygai I, Bertogliati C, Desvignes P, Robert PH,
Malandain G, Bussiere F, Darcourt J (2001) Perfusion brain SPECT and statistical
parametric mapping analysis indicate that apathy is a cingulate syndrome: a study in
Alzheimer's disease and nondemented patients. Neuroimage 13, 896-902.
[262] Ott BR, Noto RB, Fogel BS (1996) Apathy and loss of insight in Alzheimer's disease: a
SPECT imaging study. J Neuropsychiatry Clin Neurosci 8, 41-46.
[263] Marshall GA, Monserratt L, Harwood D, Mandelkern M, Cummings JL, Sultzer DL
(2007) Positron emission tomography metabolic correlates of apathy in Alzheimer
disease. Arch Neurol 64, 1015-1020.
[264] Ota M, Sato N, Nakata Y, Arima K, Uno M (2012) Relationship between apathy and
diffusion tensor imaging metrics of the brain in Alzheimer's disease. Int J Geriatr
Psychiatry 27, 722-726.
[265] Starkstein SE, Mizrahi R, Capizzano AA, Acion L, Brockman S, Power BD (2009)
Neuroimaging correlates of apathy and depression in Alzheimer's disease. J
Neuropsychiatry Clin Neurosci 21, 259-265.
120
[266] Kim JW, Lee DY, Choo IH, Seo EH, Kim SG, Park SY, Woo JI (2011) Microstructural
alteration of the anterior cingulum is associated with apathy in Alzheimer disease. Am J
Geriatr Psychiatry 19, 644-653.
[267] Apostolova LG, Akopyan GG, Partiali N, Steiner CA, Dutton RA, Hayashi KM, Dinov
ID, Toga AW, Cummings JL, Thompson PM (2007) Structural correlates of apathy in
Alzheimer's disease. Dement Geriatr Cogn Disord 24, 91-97.
[268] Stanton BR, Leigh PN, Howard RJ, Barker GJ, Brown RG (2013) Behavioural and
emotional symptoms of apathy are associated with distinct patterns of brain atrophy in
neurodegenerative disorders. J Neurol 260, 2481-2490.
[269] Geda YE, Schneider LS, Gitlin LN, Miller DS, Smith GS, Bell J, Evans J, Lee M,
Porsteinsson A, Lanctot KL, Rosenberg PB, Sultzer DL, Francis PT, Brodaty H, Padala
PP, Onyike CU, Ortiz LA, Ancoli-Israel S, Bliwise DL, Martin JL, Vitiello MV, Yaffe
K, Zee PC, Herrmann N, Sweet RA, Ballard C, Khin NA, Alfaro C, Murray PS, Schultz
S, Lyketsos CG, Neuropsychiatric Syndromes Professional Interest Area of Istaart
(2013) Neuropsychiatric symptoms in Alzheimer's disease: past progress and
anticipation of the future. Alzheimers Dement 9, 602-608.
[270] Eizenman M, Yu LH, Grupp L, Eizenman E, Ellenbogen M, Gemar M, Levitan RD
(2003) A naturalistic visual scanning approach to assess selective attention in major
depressive disorder. Psychiatry Res 118, 117-128.
[271] Sturm V, Cassel D, Eizenman M (2011) Objective estimation of visual acuity with
preferential looking. Invest Ophthalmol Vis Sci 52, 708-713.
[272] Pinhas L, Fok KH, Chen A, Lam E, Schachter R, Eizenman O, Grupp L, Eizenman M
(2014) Attentional biases to body shape images in adolescents with anorexia nervosa:
an exploratory eye-tracking study. Psychiatry Res 220, 519-526.
[273] Guestrin ED, Eizenman M (2007) Remote point-of-gaze estimation with free head
movements requiring a single-point calibration. Conf Proc IEEE Eng Med Biol Soc
2007, 4556-4560.
[274] Duque A, Vazquez C (2015) Double attention bias for positive and negative emotional
faces in clinical depression: evidence from an eye-tracking study. J Behav Ther Exp
Psychiatry 46, 107-114.
[275] Isaac L, Vrijsen JN, Rinck M, Speckens A, Becker ES (2014) Shorter gaze duration for
happy faces in current but not remitted depression: evidence from eye movements.
Psychiatry Res 218, 79-86.
[276] Armstrong T, Olatunji BO (2012) Eye tracking of attention in the affective disorders: a
meta-analytic review and synthesis. Clin Psychol Rev 32, 704-723.
[277] Ogrocki PK, Hills AC, Strauss ME (2000) Visual exploration of facial emotion by
healthy older adults and patients with Alzheimer disease. Neuropsychiatry
Neuropsychol Behav Neurol 13, 271-278.
[278] Daffner KR, Mesulam MM, Cohen LG, Scinto LF (1999) Mechanisms underlying
diminished novelty-seeking behavior in patients with probable Alzheimer's disease.
Neuropsychiatry Neuropsychol Behav Neurol 12, 58-66.
121
[279] Daffner KR, Rentz DM, Scinto LF, Faust R, Budson AE, Holcomb PJ (2001)
Pathophysiology underlying diminished attention to novel events in patients with early
AD. Neurology 56, 1377-1383.
[280] Vargas-Lopez V, Torres-Berrio A, Gonzalez-Martinez L, Munera A, Lamprea MR
(2015) Acute restraint stress and corticosterone transiently disrupts novelty preference
in an object recognition task. Behav Brain Res 291, 60-66.
[281] Eagle AL, Fitzpatrick CJ, Perrine SA (2013) Single prolonged stress impairs social and
object novelty recognition in rats. Behav Brain Res 256, 591-597.
[282] Vasquez BP, Buck BH, Black SE, Leibovitch FS, Lobaugh NJ, Caldwell CB,
Behrmann M (2011) Visual attention deficits in Alzheimer's disease: relationship to
HMPAO SPECT cortical hypoperfusion. Neuropsychologia 49, 1741-1750.
[283] Parasuraman R, Nestor P (1993) Attention and driving. Assessment in elderly
individuals with dementia. Clin Geriatr Med 9, 377-387.
[284] Alescio-Lautier B, Michel BF, Herrera C, Elahmadi A, Chambon C, Touzet C, Paban V
(2007) Visual and visuospatial short-term memory in mild cognitive impairment and
Alzheimer disease: role of attention. Neuropsychologia 45, 1948-1960.
[285] Bonney KR, Almeida OP, Flicker L, Davies S, Clarnette R, Anderson M,
Lautenschlager NT (2006) Inspection time in non-demented older adults with mild
cognitive impairment. Neuropsychologia 44, 1452-1456.
[286] Fagan JF, 3rd (1990) The paired-comparison paradigm and infant intelligence. Ann N Y
Acad Sci 608, 337-357; discussion 358-364.
[287] Rose SA, Feldman JF, Jankowski JJ (2003) Infant visual recognition memory:
independent contributions of speed and attention. Dev Psychol 39, 563-571.
[288] Courchesne E, Hillyard SA, Galambos R (1975) Stimulus novelty, task relevance and
the visual evoked potential in man. Electroencephalogr Clin Neurophysiol 39, 131-143.
[289] Rapp MA, Reischies FM (2005) Attention and executive control predict Alzheimer
disease in late life: results from the Berlin Aging Study (BASE). Am J Geriatr
Psychiatry 13, 134-141.
[290] Silveri MC, Reali G, Jenner C, Puopolo M (2007) Attention and memory in the
preclinical stage of dementia. J Geriatr Psychiatry Neurol 20, 67-75.
[291] Zhang Z, Zheng H, Liang K, Wang H, Kong S, Hu J, Wu F, Sun G (2015) Functional
degeneration in dorsal and ventral attention systems in amnestic mild cognitive
impairment and Alzheimer's disease: an fMRI study. Neurosci Lett 585, 160-165.
[292] Chase TN (2011) Apathy in neuropsychiatric disease: diagnosis, pathophysiology, and
treatment. Neurotox Res 19, 266-278.
[293] Kellough JL, Beevers CG, Ellis AJ, Wells TT (2008) Time course of selective attention
in clinically depressed young adults: an eye tracking study. Behav Res Ther 46, 1238-
1243.
[294] Mitchell RA, Herrmann N, Lanctôt KL (2010) The Role of Dopamine in Symptoms
and Treatment of Apathy in Alzheimer's Disease. CNS Neurosci Ther 17, 411-427.
[295] Lanctôt KL, Chau SA, Herrmann N, Drye LT, Rosenberg PB, Scherer RW, Black SE,
Vaidya V, Bachman DL, Mintzer JE (2014) Effect of methylphenidate on attention in
122
apathetic AD patients in a randomized, placebo-controlled trial. Int Psychogeriatr 26,
239-246.
[296] McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM (1984)
Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group
under the auspices of Department of Health and Human Services Task Force on
Alzheimer's Disease. Neurology 34, 939-944.
[297] Alexander MJ, Haugland G, Lin SP, Bertollo DN, McCorry FA (2008) Mental health
screening in addiction, corrections and social service settings: validating the MMS. Int
J Ment Health 6, 105-119.
[298] Wheeler D (1981) Wechsler Adult Intelligence Scale - Revised, Manual, Psychological
Corporation, New York.
[299] Molloy DW, Alemayehu E, Roberts R (1991) Reliability of a Standardized Mini-
Mental State Examination compared with the traditional Mini-Mental State
Examination. Am J Psychiatry 148, 102-105.
[300] Folstein MF, Folstein SE, McHugh PR (1975) "Mini-mental state". A practical method
for grading the cognitive state of patients for the clinician. J Psychiatr Res 12, 189-198.
[301] St Clair-Thompson H, Sykes S (2010) Scoring methods and the predictive ability of
working memory tasks. Behav Res Methods 42, 969-975.
[302] Hannula DE, Althoff RR, Warren DE, Riggs L, Cohen NJ, Ryan JD (2010) Worth a
glance: using eye movements to investigate the cognitive neuroscience of memory.
Front Hum Neurosci 4, 166.
[303] Faul F, Erdfelder E, Buchner A, Lang A-G (2009) Statistical power analyses using
G*Power 3.1: Tests for correlation and regression analyses. Behavior Research
Methods 41, 1149-1160.
[304] Faul F, Erdfelder E, Lang A-G, Buchner A (2007) G*Power 3: A flexible statistical
power analysis program for the social, behavioral, and biomedical sciences. Behavior
Research Methods 39, 175-191.
[305] Chau SA, Herrmann N, Eizenman E, Chung J, Lanctot KL (2015) Exploring visual
selective attention towards novel stimuli in Alzheimer's disease patients. Dement
Geriatr Cogn Disord Extra 5, 492-502. (Reprinted with permission from Karger. The
final, published version of this article is available at
http://www.karger.com/Article/FullText/442383)
[306] Conners CK (2000) Multi-Health Systems, Inc, North Tonawanda, NY.
[307] Ostrosky-Solis F, Lopez-Arango G, Ardila A (2000) Sensitivity and specificity of the
Mini-Mental State Examination in a Spanish-speaking population. Appl Neuropsychol
7, 25-31.
[308] Hensel A, Angermeyer MC, Riedel-Heller SG (2007) Measuring cognitive change in
older adults: reliable change indices for the Mini-Mental State Examination. J Neurol
Neurosurg Psychiatry 78, 1298-1303.
[309] Chau SA, Herrmann N, Sherman C, Chung J, Eizenman M, Kiss A, Lanctot KL (2017)
Visual Selective Attention Toward Novel Stimuli Predicts Cognitive Decline in
Alzheimer's Disease Patients. J Alzheimers Dis 55, 1339-1349. (Reprinted with
123
permission from IOS Press. The final published version of this article is available at
http://content.iospress.com/articles/journal-of-alzheimers-disease/jad160641)
[310] Vilalta-Franch J, Calvo-Perxas L, Garre-Olmo J, Turro-Garriga O, Lopez-Pousa S
(2013) Apathy syndrome in Alzheimer's disease epidemiology: prevalence, incidence,
persistence, and risk and mortality factors. J Alzheimers Dis 33, 535-543.
[311] Onyike CU, Sheppard JM, Tschanz JT, Norton MC, Green RC, Steinberg M, Welsh-
Bohmer KA, Breitner JC, Lyketsos CG (2007) Epidemiology of apathy in older adults:
the Cache County Study. Am J Geriatr Psychiatry 15, 365-375.
[312] Waldemar G, Gauthier S, Jones R, Wilkinson D, Cummings J, Lopez O, Zhang R, Xu
Y, Sun Y, Knox S, Richardson S, Mackell J (2011) Effect of donepezil on emergence
of apathy in mild to moderate Alzheimer's disease. Int J Geriatr Psychiatry 26, 150-
157.
[313] Marin RS, Biedrzycki RC, Firinciogullari S (1991) Reliability and validity of the
Apathy Evaluation Scale. Psychiatry Res 38, 143-162.
[314] Cummings JL, Mega M, Gray K, Rosenberg-Thompson S, Carusi DA, Gornbein J
(1994) The Neuropsychiatric Inventory: comprehensive assessment of psychopathology
in dementia. Neurology 44, 2308-2314.
[315] Clarke DE, Reekum R, Simard M, Streiner DL, Freedman M, Conn D (2007) Apathy in
dementia: an examination of the psychometric properties of the apathy evaluation scale.
J Neuropsychiatry Clin Neurosci 19, 57-64.
[316] Chau SA, Chung J, Herrmann N, Eizenman M, Lanctot KL (2016) Apathy and
Attentional Biases in Alzheimer's Disease. J Alzheimers Dis 51, 837-846. (Reprinted
with permission from IOS Press. The final published version of this article is available
at http://content.iospress.com/articles/journal-of-alzheimers-disease/jad151026)
[317] Kimko HC, Cross JT, Abernethy DR (1999) Pharmacokinetics and clinical
effectiveness of methylphenidate. Clin Pharmacokinet 37, 457-470.
[318] Kaufman DA, Bowers D, Okun MS, Van Patten R, Perlstein WM (2016) Apathy,
Novelty Processing, and the P3 Potential in Parkinson's Disease. Front Neurol 7, 95.
[319] Tsuchiya H, Yamaguchi S, Kobayashi S (2000) Impaired novelty detection and frontal
lobe dysfunction in Parkinson's disease. Neuropsychologia 38, 645-654.
[320] Schott BH, Voss M, Wagner B, Wustenberg T, Duzel E, Behr J (2015) Fronto-limbic
novelty processing in acute psychosis: disrupted relationship with memory performance
and potential implications for delusions. Front Behav Neurosci 9, 144.
[321] Yamagata S, Yamaguchi S, Kobayashi S (2004) Impaired novelty processing in apathy
after subcortical stroke. Stroke 35, 1935-1940.
[322] Daffner KR, Mesulam MM, Scinto LF, Acar D, Calvo V, Faust R, Chabrerie A,
Kennedy B, Holcomb P (2000) The central role of the prefrontal cortex in directing
attention to novel events. Brain 123 ( Pt 5), 927-939.
[323] Knight RT (1984) Decreased response to novel stimuli after prefrontal lesions in man.
Electroencephalogr Clin Neurophysiol 59, 9-20.
[324] Baddeley A (1996) The fractionation of working memory. Proc Natl Acad Sci U S A
93, 13468-13472.
124
[325] Berridge CW, Devilbiss DM (2011) Psychostimulants as Cognitive Enhancers: The
Prefrontal Cortex, Catecholamines, and Attention-Deficit/Hyperactivity Disorder. Biol
Psychiatry 69, e101-111.
[326] Adriani W, Chiarotti F, Laviola G (1998) Elevated novelty seeking and peculiar d-
amphetamine sensitization in periadolescent mice compared with adult mice. Behav
Neurosci 112, 1152-1166.
[327] Laviola G, Adriani W (1998) Evaluation of unconditioned novelty-seeking and d-
amphetamine-conditioned motivation in mice. Pharmacol Biochem Behav 59, 1011-
1020.
[328] Costa VD, Tran VL, Turchi J, Averbeck BB (2014) Dopamine modulates novelty
seeking behavior during decision making. Behav Neurosci 128, 556-566.
[329] Bentley P, Driver J, Dolan RJ (2008) Cholinesterase inhibition modulates visual and
attentional brain responses in Alzheimer's disease and health. Brain 131, 409-424.
[330] Foldi NS, White RE, Schaefer LA (2005) Detecting effects of donepezil on visual
selective attention using signal detection parameters in Alzheimer's disease. Int J
Geriatr Psychiatry 20, 485-488.
[331] Schinka JA, Letsch EA, Crawford FC (2002) DRD4 and novelty seeking: results of
meta-analyses. Am J Med Genet 114, 643-648.
[332] Bardo MT, Donohew RL, Harrington NG (1996) Psychobiology of novelty seeking and
drug seeking behavior. Behav Brain Res 77, 23-43.
[333] Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, Iwatsubo T, Jack
CR, Jr., Kaye J, Montine TJ, Park DC, Reiman EM, Rowe CC, Siemers E, Stern Y,
Yaffe K, Carrillo MC, Thies B, Morrison-Bogorad M, Wagster MV, Phelps CH (2011)
Toward defining the preclinical stages of Alzheimer's disease: recommendations from
the National Institute on Aging-Alzheimer's Association workgroups on diagnostic
guidelines for Alzheimer's disease. Alzheimers Dement 7, 280-292.
[334] Stelmach GE, Nahom A (1992) Cognitive-motor abilities of the elderly driver. Hum
Factors 34, 53-65.
[335] Kennedy RE, Cutter GR, Wang G, Schnieder LS (2015) Using baseline cognitive
severity for enriching Alzheimer's disease clinical trials: How doesn Mini-mental State
Examination predict rate of change? Alzheimer's & Dementia: Translational Research
& Clinical Interventions 1, 46-52.
[336] Mendiondo MS, Ashford JW, Kryscio RJ, Schmitt FA (2000) Modelling mini mental
state examination changes in Alzheimer's disease. Stat Med 19, 1607-1616.
[337] Wilkosz PA, Seltman HJ, Devlin B, Weamer EA, Lopez OL, DeKosky ST, Sweet RA
(2010) Trajectories of cognitive decline in Alzheimer's disease. Int Psychogeriatr 22,
281-290.
[338] Auld DS, Kornecook TJ, Bastianetto S, Quirion R (2002) Alzheimer's disease and the
basal forebrain cholinergic system: relations to beta-amyloid peptides, cognition, and
treatment strategies. Prog Neurobiol 68, 209-245.
125
[339] Wilson FA, Rolls ET (1990) Neuronal responses related to the novelty and familarity of
visual stimuli in the substantia innominata, diagonal band of Broca and periventricular
region of the primate basal forebrain. Exp Brain Res 80, 104-120.
[340] Okada K, Nishizawa K, Kobayashi T, Sakata S, Kobayashi K (2015) Distinct roles of
basal forebrain cholinergic neurons in spatial and object recognition memory. Sci Rep
5, 13158.
[341] Potter DD, Pickles CD, Roberts RC, Rugg MD (2000) Scopolamine impairs memory
performance and reduces frontal but not parietal visual P3 amplitude. Biol Psychol 52,
37-52.
[342] Potter DD, Pickles CD, Roberts RC, Rugg MD (2000) The effect of cholinergic
receptor blockade by scopolamine on memory performance and the auditory P3. J
Psychophysiol 14, 11-23.
[343] Thiel CM, Henson RN, Morris JS, Friston KJ, Dolan RJ (2001) Pharmacological
modulation of behavioral and neuronal correlates of repetition priming. J Neurosci 21,
6846-6852.
[344] Martorana A, Di Lorenzo F, Esposito Z, Lo Giudice T, Bernardi G, Caltagirone C,
Koch G (2013) Dopamine D(2)-agonist rotigotine effects on cortical excitability and
central cholinergic transmission in Alzheimer's disease patients. Neuropharmacology
64, 108-113.
[345] Auriel E, Hausdorff JM, Herman T, Simon ES, Giladi N (2006) Effects of
methylphenidate on cognitive function and gait in patients with Parkinson's disease: a
pilot study. Clin Neuropharmacol 29, 15-17.
[346] Duzel E, Bunzeck N, Guitart-Masip M, Duzel S (2010) NOvelty-related motivation of
anticipation and exploration by dopamine (NOMAD): implications for healthy aging.
Neurosci Biobehav Rev 34, 660-669.
[347] Bunzeck N, Duzel E (2006) Absolute coding of stimulus novelty in the human
substantia nigra/VTA. Neuron 51, 369-379.
[348] Bunzeck N, Schutze H, Stallforth S, Kaufmann J, Duzel S, Heinze HJ, Duzel E (2007)
Mesolimbic novelty processing in older adults. Cereb Cortex 17, 2940-2948.
[349] Albrecht MA, Martin-Iverson MT, Price G, Lee J, Iyyalol R, Waters F (2011)
Dexamphetamine effects on separate constructs in the rubber hand illusion test.
Psychopharmacology (Berl) 217, 39-50.
[350] Gabbay FH, Duncan CC, McDonald CG (2010) Brain potential indices of novelty
processing are associated with preference for amphetamine. Exp Clin Psychopharmacol
18, 470-488.
[351] Mikell CB, Sheehy JP, Youngerman BE, McGovern RA, Wojtasiewicz TJ, Chan AK,
Pullman SL, Yu Q, Goodman RR, Schevon CA, McKhann GM, 2nd (2014) Features
and timing of the response of single neurons to novelty in the substantia nigra. Brain
Res 1542, 79-84.
[352] Watson TD, Petrakis IL, Edgecombe J, Perrino A, Krystal JH, Mathalon DH (2009)
Modulation of the cortical processing of novel and target stimuli by drugs affecting
glutamate and GABA neurotransmission. Int J Neuropsychopharmacol 12, 357-370.
126
[353] Gunduz-Bruce H, Reinhart RM, Roach BJ, Gueorguieva R, Oliver S, D'Souza DC,
Ford JM, Krystal JH, Mathalon DH (2012) Glutamatergic modulation of auditory
information processing in the human brain. Biol Psychiatry 71, 969-977.
[354] Heitland I, Kenemans JL, Oosting RS, Baas JM, Bocker KB (2013) Auditory event-
related potentials (P3a, P3b) and genetic variants within the dopamine and serotonin
system in healthy females. Behav Brain Res 249, 55-64.
[355] Schott BH, Seidenbecher CI, Richter S, Wustenberg T, Debska-Vielhaber G, Schubert
H, Heinze HJ, Richardson-Klavehn A, Duzel E (2011) Genetic variation of the
serotonin 2a receptor affects hippocampal novelty processing in humans. PLoS One 6,
e15984.
[356] Zaborszky L, Cullinan WE, Luine VN (1993) Catecholaminergic-cholinergic
interaction in the basal forebrain. Prog Brain Res 98, 31-49.
[357] Ranganath C, Rainer G (2003) Neural mechanisms for detecting and remembering
novel events. Nat Rev Neurosci 4, 193-202.
[358] Arnsten AF, Steere JC, Hunt RD (1996) The contribution of alpha 2-noradrenergic
mechanisms of prefrontal cortical cognitive function. Potential significance for
attention-deficit hyperactivity disorder. Arch Gen Psychiatry 53, 448-455.
[359] Aston-Jones G, Chiang C, Alexinsky T (1991) Discharge of noradrenergic locus
coeruleus neurons in behaving rats and monkeys suggests a role in vigilance. Prog
Brain Res 88, 501-520.
[360] Nieuwenhuis S, Aston-Jones G, Cohen JD (2005) Decision making, the P3, and the
locus coeruleus-norepinephrine system. Psychol Bull 131, 510-532.
[361] Pezzotti P, Scalmana S, Mastromattei A, Di Lallo D (2008) The accuracy of the MMSE
in detecting cognitive impairment when administered by general practitioners: a
prospective observational study. BMC Fam Pract 9, 29.
[362] Galasko D, Klauber MR, Hofstetter CR, Salmon DP, Lasker B, Thal LJ (1990) The
Mini-Mental State Examination in the early diagnosis of Alzheimer's disease. Arch
Neurol 47, 49-52.
[363] van Gorp WG, Marcotte TD, Sultzer D, Hinkin C, Mahler M, Cummings JL (1999)
Screening for dementia: comparison of three commonly used instruments. J Clin Exp
Neuropsychol 21, 29-38.
[364] Xu G, Meyer JS, Thornby J, Chowdhury M, Quach M (2002) Screening for mild
cognitive impairment (MCI) utilizing combined mini-mental-cognitive capacity
examinations for identifying dementia prodromes. Int J Geriatr Psychiatry 17, 1027-
1033.
[365] Rosselli M, Matute E, Pinto N, Ardila A (2006) Memory abilities in children with
subtypes of dyscalculia. Dev Neuropsychol 30, 801-818.
[366] Crum RM, Anthony JC, Bassett SS, Folstein MF (1993) Population-based norms for
the Mini-Mental State Examination by age and educational level. JAMA 269, 2386-
2391.
[367] Clark CM, Sheppard L, Fillenbaum GG, Galasko D, Morris JC, Koss E, Mohs R,
Heyman A (1999) Variability in annual Mini-Mental State Examination score in
127
patients with probable Alzheimer disease: a clinical perspective of data from the
Consortium to Establish a Registry for Alzheimer's Disease. Arch Neurol 56, 857-862.
[368] Rosen WG, Mohs RC, Davis KL (1984) A new rating scale for Alzheimer's disease.
Am J Psychiatry 141, 1356-1364.
[369] Cano SJ, Posner HB, Moline ML, Hurt SW, Swartz J, Hsu T, Hobart JC (2010) The
ADAS-cog in Alzheimer's disease clinical trials: psychometric evaluation of the sum
and its parts. J Neurol Neurosurg Psychiatry 81, 1363-1368.
[370] Benge JF, Balsis S, Geraci L, Massman PJ, Doody RS (2009) How well do the ADAS-
cog and its subscales measure cognitive dysfunction in Alzheimer's disease? Dement
Geriatr Cogn Disord 28, 63-69.
[371] Robbins TW, James M, Owen AM, Sahakian BJ, McInnes L, Rabbitt P (1994)
Cambridge Neuropsychological Test Automated Battery (CANTAB): a factor analytic
study of a large sample of normal elderly volunteers. Dementia 5, 266-281.
[372] Owen AM, Sahakian BJ, Semple J, Polkey CE, Robbins TW (1995) Visuo-spatial
short-term recognition memory and learning after temporal lobe excisions, frontal lobe
excisions or amygdalo-hippocampectomy in man. Neuropsychologia 33, 1-24.
[373] Egerhazi A, Berecz R, Bartok E, Degrell I (2007) Automated Neuropsychological Test
Battery (CANTAB) in mild cognitive impairment and in Alzheimer's disease. Prog
Neuropsychopharmacol Biol Psychiatry 31, 746-751.
[374] Berridge KC, Robinson TE (1998) What is the role of dopamine in reward: hedonic
impact, reward learning, or incentive salience? Brain Res Brain Res Rev 28, 309-369.
[375] Alcaro A, Huber R, Panksepp J (2007) Behavioral functions of the mesolimbic
dopaminergic system: an affective neuroethological perspective. Brain Res Rev 56,
283-321.
[376] Koff E, Zaitchik D, Montepare J, Albert MS (1999) Emotion processing in the visual
and auditory domains by patients with Alzheimer's disease. J Int Neuropsychol Soc 5,
32-40.
[377] Spoletini I, Marra C, Di Iulio F, Gianni W, Sancesario G, Giubilei F, Trequattrini A,
Bria P, Caltagirone C, Spalletta G (2008) Facial emotion recognition deficit in amnestic
mild cognitive impairment and Alzheimer disease. Am J Geriatr Psychiatry 16, 389-
398.
[378] Klein-Koerkamp Y, Beaudoin M, Baciu M, Hot P (2012) Emotional decoding abilities
in Alzheimer's disease: a meta-analysis. J Alzheimers Dis 32, 109-125.
[379] Nakaaki S, Murata Y, Sato J, Shinagawa Y, Tatsumi H, Hirono N, Furukawa TA
(2007) Greater impairment of ability in the divided attention task is seen in Alzheimer's
disease patients with depression than in those without depression. Dement Geriatr
Cogn Disord 23, 231-240.
[380] Tunnard C, Whitehead D, Hurt C, Wahlund LO, Mecocci P, Tsolaki M, Vellas B,
Spenger C, Kloszewska I, Soininen H, Lovestone S, Simmons A (2011) Apathy and
cortical atrophy in Alzheimer's disease. Int J Geriatr Psychiatry 26, 741-748.
128
[381] You SC, Walsh CM, Chiodo LA, Ketelle R, Miller BL, Kramer JH (2015)
Neuropsychiatric Symptoms Predict Functional Status in Alzheimer's Disease. J
Alzheimers Dis 48, 863-869.
[382] Hampel H, Frank R, Broich K, Teipel SJ, Katz RG, Hardy J, Herholz K, Bokde AL,
Jessen F, Hoessler YC, Sanhai WR, Zetterberg H, Woodcock J, Blennow K (2010)
Biomarkers for Alzheimer's disease: academic, industry and regulatory perspectives.
Nat Rev Drug Discov 9, 560-574.
[383] Weiser M, Garibaldi G (2015) Quantifying motivational deficits and apathy: A review
of the literature. Eur Neuropsychopharmacol 25, 1060-1081.
[384] Mathis S, Neau JP, Pluchon C, Fargeau MN, Karolewicz S, Iljicsov A, Gil R (2014)
Apathy in Parkinson's disease: an electrophysiological study. Neurol Res Int 2014,
290513.
[385] Parker G, Brotchie H (2010) Do the old psychostimulant drugs have a role in managing
treatment-resistant depression? Acta Psychiatrica Scand 121, 308-314.
[386] Grade C, Redford B, Chrostowski J, Toussaint L, Blackwell B (1998) Methylphenidate
in early poststroke recovery: a double-blind, placebo-controlled study. Arch Phys Med
Rehabil 79, 1047-1050.
[387] Ng CG, Boks MP, Roes KC, Zainal NZ, Sulaiman AH, Tan SB, de Wit NJ (2014)
Rapid response to methylphenidate as an add-on therapy to mirtazapine in the treatment
of major depressive disorder in terminally ill cancer patients: a four-week, randomized,
double-blinded, placebo-controlled study. Eur Neuropsychopharmacol 24, 491-498.
[388] Kerr CW, Drake J, Milch RA, Brazeau DA, Skretny JA, Brazeau GA, Donnelly JP
(2012) Effects of methylphenidate on fatigue and depression: a randomized, double-
blind, placebo-controlled trial. J Pain Symptom Manage 43, 68-77.
[389] Hoehn-Saric R, Harris GJ, Pearlson GD, Cox CS, Machlin SR, Camargo EE (1991) A
fluoxetine-induced frontal lobe syndrome in an obsessive compulsive patient. J Clin
Psychiatry 52, 131-133.
[390] Hoehn-Saric R, Lipsey JR, McLeod DR (1990) Apathy and indifference in patients on
fluvoxamine and fluoxetine. J Clin Psychopharmacol 10, 343-345.
[391] George MS, Trimble MR (1992) A fluvoxamine-induced frontal lobe syndrome in a
patient with comorbid Gilles de la Tourette's syndrome and obsessive compulsive
disorder. J Clin Psychiatry 53, 379-380.
[392] Barnhart WJ, Makela EH, Latocha MJ (2004) SSRI-induced apathy syndrome: a
clinical review. J Psychiatr Pract 10, 196-199.
[393] Padala PR, Padala KP, Monga V, Ramirez DA, Sullivan DH (2012) Reversal of SSRI-
associated apathy syndrome by discontinuation of therapy. Ann Pharmacother 46, e8.
[394] Kapur S, Remington G (1996) Serotonin-dopamine interaction and its relevance to
schizophrenia. Am J Psychiatry 153, 466-476.
[395] Benloucif S, Keegan MJ, Galloway MP (1993) Serotonin-facilitated dopamine release
in vivo: pharmacological characterization. J Pharmacol Exp Ther 265, 373-377.
129
[396] Clarke DE, Van Reekum R, Patel J, Simard M, Gomez E, Streiner DL (2007) An
appraisal of the psychometric properties of the Clinician version of the Apathy
Evaluation Scale (AES-C). Int J Methods Psychiatr Res 16, 97-110.
[397] Rose SA, Gottfried AW, Melloy-Carminar P, Bridger WH (1982) Familiarity and
novelty preferences in infant recognition memory: Implications for information
processing. Developmental Psychology 18, 704-713.
[398] Schelleman H, Bilker WB, Kimmel SE, Daniel GW, Newcomb C, Guevara JP, Cziraky
MJ, Strom BL, Hennessy S (2012) Methylphenidate and risk of serious cardiovascular
events in adults. Am J Psychiatry 169, 178-185.
[399] Vuilleumier P, Driver J (2007) Modulation of visual processing by attention and
emotion: windows on causal interactions between human brain regions. Philos Trans R
Soc Lond B Biol Sci 362, 837-855.
[400] Fu CH, Williams SC, Cleare AJ, Brammer MJ, Walsh ND, Kim J, Andrew CM, Pich
EM, Williams PM, Reed LJ, Mitterschiffthaler MT, Suckling J, Bullmore ET (2004)
Attenuation of the neural response to sad faces in major depression by antidepressant
treatment: a prospective, event-related functional magnetic resonance imaging study.
Arch Gen Psychiatry 61, 877-889.
[401] Harmer CJ, O'Sullivan U, Favaron E, Massey-Chase R, Ayres R, Reinecke A, Goodwin
GM, Cowen PJ (2009) Effect of acute antidepressant administration on negative
affective bias in depressed patients. Am J Psychiatry 166, 1178-1184.
[402] Browning M, Reid C, Cowen PJ, Goodwin GM, Harmer CJ (2007) A single dose of
citalopram increases fear recognition in healthy subjects. J Psychopharmacol 21, 684-
690.
[403] Giacobbe P, Fok K-H, Tang W, Sanh W, Grupp L, Eizenman M (2011) in 37th Annual
Harvey Stancer Research Day, Toronto, ON.
130
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)
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