Investigating the Effect of Mild Cognitive Impairment on Brain Activity During Completion of the Clock-Drawing Test

Investigating the Effect of Mild Cognitive Impairment on Brain Activity during completion of the Clock-Drawing Test

Natasha Arti Talwar

A thesis submitted in conformity with the requirements for the degree of Master of Science Institute of Medical Science University of Toronto

Investigating the Effect of Mild Cognitive Impairment on Brain Activity during completion of the Clock-Drawing Test

Natasha Arti Talwar

Master of Science

Institute of Medical Science University of Toronto

2019

Abstract

There is a lack of quick cognitive assessments for general practitioners to use to screen for MCI, resulting in missed diagnoses. The CDT is essential in assessment of dementia, therefore has potential as a screening tool for MCI. Studies have identified MCI-related behavioural impairment on the CDT, however there is less knowledge regarding the effect of MCI on CDT- related brain activity. This study combined fMRI and an fMRI-compatible tablet to measure brain activity during a naturalistic version of the CDT in an MCI and control cohort. Although patients with MCI performed worse on the CDT, the test did not have adequate sensitivity and specificity to MCI. Patients with MCI exhibited less extensive CDT-related activity in the frontal and parietal lobes relative to controls. Both areas have important cognitive functions for CDT completion, suggesting that reduced activity in these regions may cause the behavioural impairment observed in patients with MCI.

Acknowledgments

First, I would like to thank my supervisor, Dr. Tom Schweizer for his support and guidance throughout my experience in his lab. He has provided me with valuable advice and knowledge, which has helped me grow to become a better aspiring scientist. I am truly grateful for the amazing opportunity to be his graduate student.

I would also like to extend my gratitude to the other members of my PAC for their help and encouragement. Thank you to Dr. Corinne Fischer for taking time out of her busy schedule as a clinician to provide valuable feedback as a committee member, as well for her instrumental role in patient recruitment. Thank you to Dr. Simon Graham for offering his expertise and advise throughout multiple new versions of analyses and paper drafts. I would also like to thank both Dr. Graham and Fred Tam as well as the lab at Sunnybrook Research Institute for their construction of the fMRI-compatible tablet, which played a vital role in my thesis project.

Thank you to the entire Schweizer Lab, both past and present, for their constant support and endless laughs, which have made this experience truly unforgettable. I would like to give a special thanks to Dr. Nathan Churchill teaching me how to use PRONTO/OPPNI and about the intricacies of fMRI analysis. He was always patient and willing to answer my questions, making him both a great teacher and mentor. A huge thank you to Megan Hird who was essential in development of the project and continued to give me guidance and advice even after leaving to attend medical school. Thank you to Iryna Pshonyak for being more than just a co-worker, but a friend. Completion of my thesis would not have been possible without her help with the project and destress sessions chatting about TV shows, food or life in general. My gratitude to everyone else who has helped with running scans, in particular Tahira Tasneem and Breanna Jessop. I would also like to thank Anthony Sheen and Cindy Hamid, the research technologists at SMH for their help, patience and fun conversations while running the MRI.

Finally, and most importantly, I would like to thank my family and friends who have given me endless love and support. Thank you for your patience when I am stressed out and emotional. Thank you for believing in me and giving me the strength to continue to pursue my passions. I would not have made it this far without you. In particular, thank you to my sisters and my parents. Since the day we were born, my parents have given us every opportunity possible to help us achieve our dreams. I share this degree and every other accomplishment with them. iii

Contributions

Dr. Tom Schweizer (1) provided the funding for the current study, (2) supervised the development of the study protocol, participant recruitment, participant testing, data analysis and interpretation, (3) provided detailed feedback and revisions of the thesis throughout the writing process, (4) provided guidance on my academic progress.

Dr. Corinne Fischer referred all of the patients with MCI included in the current investigation from the Memory Disorders Clinic at SMH, helped with development of the protocol, and provided detailed revisions of the thesis. Dr. Simon Graham helped develop the fMRI- compatible tablet, assisted with development of the study protocol and provided detailed revisions of the thesis.

Megan Hird was essential in the development and initiation of this project. She was thoroughly involved in applying for funding, creating the tablet tasks and early stages of recruitment and participant testing. She trained me on the study protocol and how to extract and analyze the data.

Dr. Nathan Churchill taught me how to use the PRONTO/OPPNI software, which I used to analyze the fMRI data. He helped develop an analysis plan, supervised my analysis, addressed any issues I encountered, assisted with interpretations of the results and provided detailed revisions of the thesis.

Iryna Pshonyak was involved in participant testing throughout the duration of the current study. Co-op students in the lab, including Tahira Tasneem, Breanna Jessop, Eden Shaul and Maryam Yossofzai, also helped with participant recruitment, participant testing and double scoring of the CDT.

This work was supported by an Alzheimer’s Association Research Grant from the Alzheimer’s Association awarded to Dr. Tom Schweizer.

Table of Contents

Abstract ...... ii

Acknowledgments...... iii

Contributions...... iv

Table of Contents ...... v

List of Tables ...... ix

List of Figures ...... x

List of Appendices ...... xi

List of Abbreviations ...... xii

Chapter 1 Introduction ...... 1

1.1 Overview ...... 1

1.2 Background ...... 3

1.2.1 Age-Related Effects on Cognition ...... 3

1.2.2 Alzheimer’s Disease (AD) ...... 3

1.2.3 Mild Cognitive Impairment (MCI) ...... 4

1.2.4 Importance of Detecting MCI ...... 7

1.2.5 Limitations of Testing for MCI...... 8

1.2.6 Current Cognitive Assessments for MCI ...... 9

1.3 The Clock-Drawing Test (CDT) ...... 12

1.3.1 History and Use of the CDT ...... 12

1.3.2 Administration of the CDT ...... 13

1.3.3 Scoring Systems and Common Errors on the CDT ...... 13

1.3.4 CDT and Dementia ...... 14

1.3.5 CDT in the context of MCI ...... 15

1.4 Neuroimaging of AD and MCI ...... 17

1.4.1 fMRI and Blood Oxygen Level Dependent (BOLD) Signal ...... 19 v

1.4.2 Strengths and Limitations of fMRI Compared to Other Neuroimaging Modalities ...... 20

1.4.3 Studying Neural Correlates of Cognitive Tasks ...... 22

1.4.4 Neuroimaging of the CDT ...... 24

1.5 Knowledge Gaps ...... 27

Chapter 2 Specific Research Questions and Hypotheses ...... 29

2.1 Summary and Rationale ...... 29

2.2 Research Objectives and Hypotheses ...... 31

2.2.1 Investigating the Effect of MCI on Behavioral CDT Performance ...... 31

2.2.2 Investigating the Effect of MCI on Brain Activation Patterns Associated with CDT Completion ...... 32

Experimental Materials and Methods ...... 34

3.1 Statement of Ethical Approval ...... 34

3.2 Participants ...... 34

3.2.1 Recruitment ...... 34

3.2.2 Screening...... 35

3.2.3 Inclusion Criteria ...... 36

3.2.4 Exclusion Criteria ...... 36

3.2.5 Consent ...... 37

3.3 Experimental Procedures ...... 37

3.3.1 Clinical Data Collection ...... 38

3.3.2 Pre-Imaging Procedure ...... 38

3.3.3 Cognitive Tests and Questionnaires...... 39

3.3.4 Tablet-Based CDT ...... 42

3.3.5 fMRI-Compatible Tablet Technology ...... 43

3.3.6 fMRI Protocol and Data Acquisition ...... 46

3.4 Data Extraction and Analysis...... 47 vi

3.4.1 CDT Data Extraction ...... 47

3.4.2 CDT Scoring ...... 47

3.4.3 Cognitive and Demographic Data Extraction ...... 53

3.4.4 Demographic and Behavioral Statistical Analysis ...... 53

3.4.5 fMRI Data Extraction ...... 54

3.4.6 Pre-Processing of fMRI Data ...... 55

3.4.7 Post-Hoc Group-Level Analyses of fMRI Data...... 57

Investigating the Effect of MCI on Behavioral CDT Performance ...... 59

4.1 Results ...... 59

4.1.1 Participant Demographics ...... 59

4.1.2 Cognitive Testing ...... 60

4.1.3 Tablet-Based CDT Performance ...... 63

4.2 Discussion ...... 70

4.2.1 Hypothesis 1: Difference in CDT Performance between the Groups ...... 71

4.2.2 Hypothesis 2: Effect of MCI on Specific CDT Components ...... 71

4.2.3 Hypothesis 3: Efficacy of CDT Scoring Systems to Detect MCI ...... 73

4.2.4 Validity of the CDT as a Cognitive Assessment Tool ...... 74

4.2.5 Validity of the Tablet-Based CDT ...... 75

4.3 Chapter Summary ...... 76

Investigating the Effect of MCI on Brain Activation Patterns associated with CDT Completion ...... 77

5.1 Results ...... 77

5.1.1 Brain Activity Patterns of the CDT ...... 77

5.1.2 Brain Activity Patterns of the CDT Components ...... 81

5.1.3 Differences in Brain Activity in Specific Regions of Interest (ROIs) ...... 94

5.1.4 Differences in Functional Connectivity Patterns during the CDT ...... 95

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5.2 Discussion ...... 105

5.2.1 Hypothesis 1: Brain Activity associated with CDT Completion ...... 105

5.2.2 Hypothesis 2: Brain Activity associated with Completion of the CDT Components (R1, R2 and R3) ...... 109

5.2.3 Differences in Functional Connectivity Patterns during the CDT ...... 111

5.3 Chapter Summary ...... 114

General Discussion ...... 116

6.1 Significance...... 118

6.2 Limitations ...... 118

6.3 Future Directions ...... 122

6.3.1 Confirming and Validating Areas and Degree of CDT Impairment Characteristic of MCI ...... 122

6.3.2 Identifying the Effect of the Sub-Types of MCI on CDT Performance and Brain Activity...... 122

6.3.3 Developing Better Performance Measures Specific to MCI...... 123

6.3.4 Identifying the Effect of AD on CDT-Related Brain Activity ...... 124

Summary and Conclusions ...... 126

References ...... 128

Appendices ...... 154

viii

List of Tables

Table 3.1: Shulman Scoring System……………………………………………………………..48

Table 3.2: Sunderland Scoring System…………………………………………………………..49

Table 3.3: Rouleau Scoring System……………………………………………………….……..50

Table 3.4: Cahn Scoring System…………………………………..……………………………..52

Table 4.1: Demographic Characteristics…………………………..……………………………..59

Table 4.2: MoCA Scores…………………………………………..……………………………..61

Table 4.3: Paper-Based CDT Scores….…………………………..……………………………..62

Table 4.4: Tablet-Based CDT Scores….…………………………..…………………………….64

Table 4.5: Sensitivity and Specificity of the Tablet-Based CDT..……………………………….66

Table 4.6: Correlation between Tablet-Based CDT and MoCA...……………………………….67

Table 4.7: Correlation between Tablet-Based CDT and Demographics...………………...…….68

Table 5.1: Clusters of Activation for CDT vs Fixation……………….....………………...…….80

Table 5.2: Clusters of Activation for R1 vs Fixation………………...... ………………...…….84

Table 5.3: Clusters of Activation for R2 vs Fixation………………...... ………………...…….88

Table 5.4: Clusters of Activation for R3 vs Fixation………………….....………………...…….92

Table 5.5: Group Differences in CDT-Related Activation in Regions of Interest……....…...….94

Table 5.6: Clusters of Group Differences in CDT-Related Functional Connectivity....……...….96

List of Figures

Figure 3.1: Experimental Design………………………………………………………………...43

Figure 3.2: Tablet Set-Up……...………………………………………………………………...44

Figure 3.3: Tablet Set-Up on MRI Table……...………………………………………………...45

Figure 3.4: Tablet Visual Feedback during CDT…...…………………………………………...45

Figure 5.1: Group Activation Patterns during CDT completion…...…………………….……...79

Figure 5.2: Group Activation Patterns during R1 completion…...……………………………...83

Figure 5.3: Group Activation Patterns during R2 completion…...……………………………...87

Figure 5.4: Group Activation Patterns during R3 completion…...……………………………...91

Figure 5.5: CDT-related Functional Connectivity from the middle frontal gyrus….....…..…....100

Figure 5.6: CDT-related Functional Connectivity from the middle temporal gyrus….....……..102

Figure 5.7: CDT-related Functional Connectivity from the supramarginal gyrus….....…...…...104

List of Appendices

Appendix 1: List of Medications taken by Participants………………………………………..154

List of Abbreviations

AAL Automated Anatomical Labelling AAN American Academy of Neurology ABCS AB Cognitive Screen AD Alzheimer’s Disease AFNI Analysis of Functional Neuroimages BNA-R Behavioural Neurology Assessment – Revised BOLD Blood-Oxygen Level Dependent BW Bandwidth CAMCOG Cambridge Cognitive Test CBF Cerebral blood flow CDT Clock-Drawing Test CI Confidence Interval CT Computerized Tomography DICOM Digital Imaging and Communications in Medicine EEG Electroencephalography EPI Echo Planar Imaging FA Flip Angle FDR False-discovery rate fMRI Functional Magnetic Resonance Imaging fNIRS Functional Near-Infrared Spectroscopy FOV Field of View FSL FMRIB Software Library FWHM Full width at half maximum GLM Generalized Linear Model GM Gray matter GP General Practitioner MCI Mild Cognitive Impairment MEG Magnetoencephalography MMSE Mini-Mental Status Examination MNI Montreal Neurological Institute MoCA Montreal Cognitive Assessment xii

MPRAGE Magnetization Prepared Rapid Acquisition Gradient Echo MRI Magnetic Resonance Imaging MTO Ministry of Transportation Ontario n Number of participants NIfTI Neuroimaging Informatics Technology Initiative OPPNI Optimization of Preprocessing Pipelines for Neuroimaging PET Positron Emission Tomography PRONTO Preprocessing OptimizatioN Toolkit R1 Clock face score (per Rouleau scoring method) R2 Clock numbers score (per Rouleau scoring method) R3 Clock hands score (per Rouleau scoring method) RA Research Assistant REB Research Ethics Board ROI Region of Interest SIS Six Item Screener SMH St. Michael’s Hospital SPECT Single-Photon Emission Computed Tomography STMS Short Test of Mental Status TE Echo Time TI Inversion Time TR Repetition Time

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Chapter 1 Introduction

1.1 Overview

Longer life expectancy and consequently a larger elderly population has led to the rise of cognitive impairment in our society. The scale of cognitive impairment can range from expected cognitive decline due to normal age-related changes, to mild deficits seen in early stages classified as mild cognitive impairment (MCI), to more severe deficits that are characteristic of forms of dementia, such as Alzheimer’s Disease (AD). Approximately 50 million people are living with dementia worldwide in 2018 with projections that the number will double in 20 years (Patterson, 2018). Due to the significant impact of dementia on our population, there have been increased efforts put towards developing effective strategies for treating this disease. There is considerable interest in intervening at milder stages of impairment to slow progression of the disease. In particular, MCI is an early stage of impairment that presents itself as a promising target for interventions. Patients with MCI exhibit mild deficits in cognitive function and memory that are suggestive of underlying neurodegenerative processes (R. C. Petersen, 2004; Ronald C. Petersen et al., 2001; Ronald C Petersen, 2006). Patients with MCI have a higher relative risk of developing dementia (3.3) compared to healthy age-matched controls (Ronald C. Petersen et al., 2018), further supporting the notion that MCI may be a critical point for staging interventions.

MCI can have subtle effects on cognition, making it difficult to differentiate from normal aging (R. C. Petersen, 2004). Furthermore, the disease presentation and consequent symptoms are variable between cases of MCI, complicating the development of simple, effective diagnostic tools. Currently, diagnosing MCI relies on cognitive batteries, which are collections of individual assessments used to measure cognitive abilities in various domains. However, many of these batteries are time-consuming and require additional expertise and training to administer. As a result, only approximately 24% of general practitioners (GPs) regularly complete cognitive screening for dementia, leading to a large number of missed MCI diagnoses (Bush, Kozak, &

1 2

Elmslie, 1997; Scanlan, Brush, Quijano, & Borson, 2002). Quick and simple cognitive assessments need to be developed, which can be conducted in a general practice setting to identify older adults with signs of cognitive impairment.

The clock-drawing test (CDT) is an assessment that is commonly included in cognitive batteries to measure executive function and visuospatial ability. However, the task can also be used as a global assessment of brain function due to its integration of multiple cognitive domains. The CDT is a well-established, widely used cognitive test, which has been historically employed as a screening tool for various forms of dementia (Pinto & Peters, 2009; Rouleau, Salmon, Butters, Kennedy, & McGuire, 1992; K.I. Shulman, 2000; Yamamoto et al., 2004). Due to its utility in screening for more severe forms of cognitive impairment, the CDT presents as a potential screening tool that may be sensitive to cognitive changes associated with MCI. Previous literature has investigated the effect of MCI on performance on the CDT, showing impaired performance on the task in patients with MCI (Babins, Slater, Whitehead, & Chertkow, 2008; Donnelly, Donnelly, & Cory, 2008; Ehreke et al., 2011; Mazancova, Nikolai, Stepankova, Kopecek, & Bezdicek, 2017; Thomann et al., 2008; Yamamoto et al., 2004). However, across the literature there is still debate whether it is an effective screening tool for MCI because of limitations in sensitivity and specificity (Chiu, Li, Lin, Chiu, & Liu, 2008; Ehreke, Luppa, König, & Riedel-Heller, 2010; Pinto & Peters, 2009; Ricci et al., 2016). To obtain a better understanding of the effect of MCI on CDT performance, it is essential to examine the underlying brain activity of the task and how that changes in patients with MCI compared to healthy controls. This is the first study to explore the effect of MCI on the neural correlates of the CDT using novel MRI-compatible tablet technology to deliver a realistic version of the CDT during functional MRI (fMRI) of brain activity. The results of this investigation may help pinpoint neuropathological changes in brain function that are associated with impaired CDT performance in patients with MCI. Overall, this information will contribute to a more comprehensive understanding of the efficacy of the CDT as a screening tool for MCI.

1.2 Background

1.2.1 Age-Related Effects on Cognition

There are normal changes in cognition that occur as a result of the aging process. Healthy aging has been associated with a decline in fluid intelligence, which is the ability to problem-solve in novel situations without referencing pre-existing knowledge (Horn & Cattell, 2002). Meanwhile, crystallized intelligence, which is the ability to use existing, previously acquired knowledge, is maintained or improved with increased age (Horn & Cattell, 2002). The age-associated changes in fluid intelligence can affect various cognitive domains, resulting in declines in processing speed, conceptual reasoning, attention, language, visuospatial abilities and executive functioning (Harada, Natelson Love, & Triebel, 2013). The changes are also variable amongst the elderly population, such that it can be difficult to distinguish between normal declines in cognitive function and those observed in mild forms of impairment. The spectrum of age-related cognitive decline spans from healthy aging to mild deficits seen in MCI to forms of dementia, such as AD (R. C. Petersen, 2004). Progressing along the spectrum leads to increased cortical atrophy and associated effects on brain structure and function, with resulting symptoms and degrees of cognitive impairment (Bäckman, Jones, Berger, Laukka, & Small, 2005). Understanding the key attributes of each stage is necessary to distinguish between the different levels of impairment, as well as to provide an accurate diagnosis and proper treatment.

1.2.2 Alzheimer’s Disease (AD)

Dementia is a progressive neurodegenerative disease that has devastating effects on patients and imposes a significant burden on their family and the health-care system. There are approximately 10 million new cases of dementia diagnosed each year, adding to the current total of 50 million older individuals living with dementia in 2018 (Patterson, 2018). With a growing aging population, this number is expected to significantly increase, with a doubling time of 20 years (Patterson, 2018). Dementia is associated with deterioration in cognitive functioning, characteristically in the memory domain, which impedes abilities to perform daily life activities and is commonly accompanied by a change in behaviour and emotions. Although there is an incomplete understanding of the pathology underlying dementia, there are a number of

4 established biological hallmarks that have been associated with dementia, such as the accumulation of -amyloid plaques, neurofibrillary tangles and gross frontotemporal cortical atrophy (Braak & Braak, 1991; H. J. Rosen et al., 2002; Wiltfang et al., 2005).

Dementia is not a singular disorder, but instead encompasses many different diseases associated with cognitive decline, including AD, vascular dementia, frontotemporal dementia and dementia with Lewy bodies. Amongst these types of dementia, AD is the most common and well-known, comprising of 60-70% of all dementia cases (World Health Organization, 2017).

AD was first defined in 1906, however it took approximately 70 years before its severity and impact was recognized leading to a surge of AD research (Alzheimer’s Association, 2017). AD is broken into stages from mild to moderate to severe AD. Mild AD is associated with deficits in cognitive function, personality changes and signs of AD-related biomarkers, but it has less impact on activities and daily living tasks (Morris et al., 2012; Skurla, Rogers, & Sunderland, 1988). As the disease progresses, ability to function deteriorates drastically until the point where full-time care is required. AD is ultimately fatal. Research on AD has been ongoing and has revealed important information about the disease, however there is a still a lack of knowledge on the specific biological underpinnings and mechanism of action. Without true understanding of how the disease develops and progresses, it is difficult to effectively treat patients with hope of slowing or stopping AD progression.

1.2.3 Mild Cognitive Impairment (MCI)

MCI is an intermediate stage lying between healthy aging and dementia, that has gained increased attention in both clinical practice and research. Patients with MCI experience a decline in cognitive function and memory, which manifests as slight, but noticeable effects on their cognition (R. C. Petersen, 2004; Ronald C. Petersen et al., 2001; Ronald C Petersen, 2006). These mild deficits do not have a large impact on their day-to-day functioning, but can be indicative of an underlying neurodegenerative process (R. C. Petersen, 2004; Ronald C. Petersen et al., 2001; Ronald C Petersen, 2006). However, due to the subtle nature of MCI, it can be difficult to distinguish from healthy aging.

MCI is common in elderly populations and the prevalence exponentially grows with increasing age (Ronald C. Petersen et al., 2018). It was estimated that the prevalence of MCI for individuals 60-64 years old is 6.7%, 8.4% for individuals aged 65-69, 10.1% for individuals aged 70-74, 14.8% for individuals 75-79, 25.2% for individuals aged 80-84 and 38% in individuals over 85 years old (Ronald C. Petersen et al., 2018). However, there is noticeable variability in reports of MCI prevalence and incidence rates leading to large ranges found in published data (prevalence rates: 3 to 42% per 1,000 people/year, incidence rates: 21.5 to 71.3% per 1,000 people/year) (Ward, Arrighi, Michels, & Cedarbaum, 2012). This is largely due to inconsistent diagnosis criteria used by clinicians.

A limiting factor in diagnosis of MCI is the lack of clear diagnostic criteria for clinicians to follow (Christa Maree Stephan et al., 2013; R. C. Petersen, 2004; Ward et al., 2012). Although there are no universal criteria to guide MCI diagnosis, in 2011 the National Institute on Aging- Alzheimer’s Association proposed criteria, which have become the most accepted and widely implemented today (Albert et al., 2011). The National Institute on Aging-Alzheimer’s Association proposed both Core Clinical Criteria, which were intended for clinical settings, and Clinical Research Criteria, which employed biomarkers and were intended only for research settings (Albert et al., 2011).

The Core Clinical Criteria consist of four main criteria used by clinicians to use to establish a diagnosis of MCI (Albert et al., 2011). The criteria are as follows:

i) A concern regarding change in cognition: The patient displays evidence of a change in cognition from a previous state. This evidence can be presented through concern from the patient, an informant who is very familiar with the patient, or a skilled physician who has observed the patient (Albert et al., 2011).

ii) Impairment in one or more cognitive domains: There is evidence of impairment in at least one cognitive domain as represented by lower performance than would be expected based on the patient’s age and education level. Many different cognitive domains may be affected in MCI, including memory, attention, executive function, language and visuospatial skills. Impaired episodic memory is often observed in

patients with MCI who progress to develop AD-related dementia (Albert et al., 2011). Traditionally, MCI-related impaired performance is characterized by scores that are 1 to 1.5 standard deviations below the mean score of healthy individuals with the same approximate age and education background (Albert et al., 2011; Christa Maree Stephan et al., 2013; Peters, Villeneuve, & Belleville, 2014; R. C. Petersen, 2004). In the case that there are multiple assessments available, there should be a clear longitudinal decline in performance.

iii) Preservation of independence in functional abilities: Patients with MCI may present mild difficulties with complex tasks that they were previously able to complete, such as grocery shopping or paying bills. These difficulties can manifest as decreased efficiency, longer task completion time and more errors committed. However, despite these minor challenges, patients with MCI maintain their independence and regular function in daily life with minimal need for assistance. Though the information necessary to assess this criterion may be difficult to collect, the information is important for distinguishing between mild impairment and dementia (Albert et al., 2011).

iv) Not demented: There is no evidence that the patient meets the diagnostic criteria for dementia (Albert et al., 2011; R. C. Petersen, 2004). The cognitive changes observed must be mild without any substantial impairment on the individual’s ability to function in social or occupational settings. The assessment of MCI requires longitudinal information to understand if there have been any changes in the individual’s cognitive abilities. Repeated evaluations are preferred, but may not always be possible, therefore this information can be extracted from the individual’s clinical history (Albert et al., 2011).

Though the criteria outlined by the National Institute on Aging- Alzheimer’s Association provide a helpful guideline for MCI diagnosis, practical implementation still relies heavily on clinician judgement. Consequently, across studies and clinics, there is still a large amount of variability in the methods used to diagnose MCI, despite the use of the same Core Clinical Criteria (Christa Maree Stephan et al., 2013).

Identifying a valid and reproducible method for diagnosing MCI is important to ensure that no individuals are misdiagnosed, and patients receive the necessary treatment and support. Despite a low impact on daily functioning, MCI has considerable clinical significance due to the relatively high risk of development into AD (R C Petersen et al., 2001). Compared to healthy age-matched controls, the relative risk for patients with MCI of developing any form of dementia is 3.3 and the relative risk of developing AD is 3.0 (Ronald C. Petersen et al., 2018). Diagnosing MCI presents an opportunity for early-stage interventions, which target memory or other cognitive domains and can help slow or stop progression to AD (Yiannopoulou & Papageorgiou, 2013). However, there are no current interventions identified to effectively treat MCI and prevent progression to AD.

1.2.4 Importance of Detecting MCI

Significant efforts in many areas of research have been put towards developing effective treatments for AD, such as pharmacotherapy, lifestyle changes and hormone therapy (Henderson, 2014; Rolland, Abellan van Kan, & Vellas, 2008; Yiannopoulou & Papageorgiou, 2013). However, due to the complexity of the disease, many drug trials and attempts to cure AD have failed (Casey, Antimisiaris, & O’Brien, 2010). Recently, researchers have moved towards interventions that are aimed at slowing or stopping progression in early stages of impairment and high-risk populations (Yiannopoulou & Papageorgiou, 2013).

Patients with MCI suffer from mild cognitive changes that are not as severe as AD or other forms of dementia. Longitudinal observation has revealed that patients with MCI have a higher probability of developing AD and progress to AD at an accelerated rate compared to healthy age- matched individuals (Ronald C. Petersen et al., 2001). Taken together, these findings suggest that MCI is a potential therapeutic intervention target for AD. Consequently, MCI has attracted interest from researchers and clinicians as a focus for clinical trials and studies.

With all of the research interest around MCI, it has become increasingly important to identify clear standards for diagnosis. As mentioned in Section 1.2.2, it can be difficult to differentiate the slight impairment in MCI from the effects of healthy aging, and furthermore the diagnostic criteria for MCI provides limited guidelines and still relies heavily on clinician judgement. To

8 benefit from any early stage interventions, effective and standardized assessments for MCI diagnosis are crucial. This gap in knowledge has been identified leading to increased focus put towards identifying valid screening tools that can be widely used for detecting MCI.

1.2.5 Limitations of Testing for MCI

Mild forms of cognitive impairment are underdiagnosed in many clinical practice settings. Only an estimated 8% of patients with MCI are identified by their general practitioner (GP) (Scanlan et al., 2002), highlighting a large issue with current methodology for diagnosis. One of the contributing factors to this rate is the reluctance of GPs to routinely screen for dementia. In a study conducted in Ottawa, GPs were surveyed and although 82% endorsed dementia screening, only 24% routinely conducted dementia screening on their patients (Bush et al., 1997).

Time constraints of clinical appointments can have a large impact on quality of care resulting in less-thorough clinical examinations (Tsiga, Panagopoulou, Sevdalis, Montgomery, & Benos, 2013). In the case of dementia screening, GPs confirm that a large barrier preventing them from completing regular cognitive assessments is that they lack time and there are no adequate, easily administered and quick screening tests available (Bush et al., 1997). It becomes the responsibility of the patient to ask for cognitive assessments or to seek out additional care in specialized clinics if they notice any cognitive changes. In established dementia, there is a significant impact on daily life and basic cognitive functioning, therefore it is more noticeable and easier to catch, despite the lack of cognitive screening. In the case of MCI, the symptoms are mild and do not always have recognizable effects on daily life activities. If clinicians do not perform regular cognitive impairment screening, cases of MCI may be easily missed.

Valid and quick screening measures need to be developed, which can easily be conducted in general practice to identify patients who may be developing cognitive impairment and require more comprehensive screening in specialized clinics. Current methods for dementia screening rely heavily on cognitive batteries, which can be long and require additional expertise for administration and scoring. Establishing tests which are tailored to be used in general practice can help increase the rate of MCI diagnosis and allow for earlier interventions in this vulnerable population before progression to AD.

1.2.6 Current Cognitive Assessments for MCI

Cognitive assessments have been a staple in neurology clinics as helpful tools to support clinician diagnoses. The goal of cognitive testing is to assess the function of one or more targeted cognitive domains. Characteristics of normal, healthy performance on these tests have been established as a comparison for patient populations. With this information, these assessments can be used to quantify degree of cognitive impairment in patients, which is especially useful in cases without apparent functional or behavioural changes, such as patients with MCI (Folstein, Folstein, & McHugh, 1975; W. G. Rosen, Mohs, & Davis, 1984). However, for cognitive assessments to become clinical screening tools, they must be effective at both detecting impairment in patient populations and differentiating patients from healthy individuals.

Current clinical methods for testing for MCI include comprehensive batteries to assess different aspects of cognitive function, which consist of individual cognitive tests. These batteries have proven to be useful at detecting MCI, including instruments, such as the Cambridge Cognitive Test (CAMCOG) (Roth, Tym, & Mountjoy, 1986), the Montreal Cognitive Assessment (MoCA) (Nasreddine et al., 2005), and the Mini-Mental State Examination (MMSE) (Folstein et al., 1975). The MMSE in particular is a commonly-used battery for MCI diagnosis in elderly and memory clinics. These batteries are effective because they combine multiple individual tests for function of specific cognitive domains and provide a larger scale picture on cognitive functioning as a whole. However, a large drawback of using these batteries is that they are lengthy (approximately 10 minutes long) and consequently become difficult to administer routinely in general practice clinics, where appointments are short. Briefer (less than 10 minutes) non-comprehensive screening batteries have been developed, including the AB Cognitive Screen (ABCS) (Molloy, Standish, & Lewis, 2005), the Six Item Screener (SIS) (Callahan et al., 2002) and the Short Test of Mental Status (STMS) (Tang-Wai et al., 2003), however only the STMS has a good predictive value for patients with MCI (Lonie, Tierney, & Ebmeier, 2009; Tang-Wai et al., 2003). A significant disadvantage of both short and regular cognitive batteries is their heavy reliance on language abilities and IQ, causing level of education, age, ethnicity and social class to have an effect on test outcomes (Teng et al., 1994; Tombaugh & McIntyre, 1992).

Many studies have investigated the validity of cognitive batteries as screening tools for MCI (Ahmed, de Jager, & Wilcock, 2012; Bossers, van der Woude, Boersma, Scherder, & van

Heuvelen, 2012; Darvesh, Leach, Black, Kaplan, & Freedman, 2005; Folstein et al., 1975; Lonie et al., 2009; Nasreddine et al., 2005; Schweiger, Doniger, Dwolatzky, Jaffe, & Simon, 2003; Xu, Meyer, Thornby, Chowdhury, & Quach, 2002). However, the main focus of research is on the composite score, which summarizes the whole battery, and there is less extensive research on the individual tests within the battery, consequently creating a large gap in knowledge supporting the implementation of these tests. Each test is responsible for measuring function of one or more specific cognitive domains. Therefore, it is important to confirm that the individual tests are sensitive to the slight cognitive changes characteristic of MCI and are accurately assessing their associated domains.

In a study by De Jager et al., performance on several different cognitive tests was investigated in three different groups of varying levels of dementia and compared to a cognitively healthy control group (De Jager, Hogervorst, Combrinck, & Budge, 2003). However, only the Hopkins Verbal Learning Test, the Category Fluency Test and the Letter Comparison Speed Test were classified as effective at differentiating patients with MCI from healthy controls (De Jager et al., 2003). The CDT, described in detail in Section 1.3, is another commonly used test in cognitive batteries and its utility in MCI diagnosis has been investigated. However, because of the multiple options for administration and scoring of the CDT, there is debate over its efficacy as a screening tool (Chiu et al., 2008; Ehreke et al., 2010; Pinto & Peters, 2009; Ricci et al., 2016). Memory tests are of particular interest for MCI populations. The Enhanced Cue Recall Test does not have adequate sensitivity and specificity for detecting MCI (Saka, Mihci, Topcuoglu, & Balkan, 2006), the Memory Alteration Test has been shown to be effective at discriminating MCI from healthy controls (L. Rami, Molinuevo, Sanchez-Valle, Bosch, & Villar, 2007; Lorena Rami et al., 2007). Although there has been initial research on cognitive tests in MCI cohorts, much more research is necessary to better understand their utility and potential to administer as a stand-alone test.

As previously mentioned, time constraints are one of the main barriers to cognitive testing in general clinical practice (Bush et al., 1997). Though the established cognitive batteries are highly effective at classifying MCI, they require time and expertise for administration. This has created a need for the development of a quick and simple cognitive test that can be used to screen for MCI. There are many cognitive assessments that have been used historically to screen for cognitive impairment associated with all forms of dementia, including AD, such as the CDT and

11 the Delayed Word Recall Test. Because these tests are sensitive to severe forms of impairment, they may also have utility in milder forms, such as MCI.

Dementia has been studied and tested for much longer than MCI, resulting in a larger database of knowledge on useful screening tools. Similar to MCI, dementia screening tools are usually cognitive batteries, including the MMSE (Folstein et al., 1975) and the MoCA (Nasreddine et al., 2005). A Canada-wide survey of GPs investigated the common cognitive assessment protocol used in clinics and showed that 67% of practitioners use the MMSE (Folstein et al., 1975), 52% use the CDT, 52% use the Delayed Word Recall Test and 13% use Alternating Sequences (Iracleous et al., 2009). An international survey conducted by Shulman et al. found similar results with the MMSE (Folstein et al., 1975) being the most popular, followed by the CDT, the Delayed Word Recall Test, the Verbal Fluency Test, Similarities and the Trail-Making Test (Kenneth I. Shulman et al., 2006).

The use of cognitive batteries, such as the MMSE (Folstein et al., 1975), is popular, but they can be time intensive and require additional training for administration and scoring, deterring GPs from regularly using them (Brodaty, Howarth, Mant, & Kurrle, 1994; Bush et al., 1997; Glasser, 1993). Single-domain cognitive tests have potential to be used in general clinical practice as they are the briefest tools available. Although often referred to as “single-domain” tests, these individual assessments measure function of many cognitive domains. These tests were originally adapted to assess a specific, target domain, but in reality, test completion requires integration of multiple cognitive functions, suggesting that “single-domain” tests may be effective measures of global brain function.

A number of “single-domain” cognitive tests have been established to measure dementia-related impairment by focusing on cognitive domains commonly affected by dementia. Short-term memory deficits are a characteristic hallmark of AD-related dementia, which is usually tested using word recall tests (Storey, Slavin, & Kinsella, 2002). Studies have shown that the Delayed Recall Test is a useful tool for AD diagnosis, helping differentiate patients with AD from healthy adults and patients with MCI (Arnáiz et al., 2004; Devanand, Folz, Gorlyn, Moeller, & Stern, 1997; Welsh, Butters, Hughes, Mohs, & Heyman, 1991). Beyond memory domains, tests of executive functioning have also gained interest due to their ability to reveal dementia-related impairment, including tests such as the Verbal Fluency and the CDT (Bäckman et al., 2005;

Monsch et al., 1992). In a meta-analysis of screening tools for dementia, it was determined that in a primary care setting, the optimal “single-domain” tests are the Selective Reminding Test and the CDT (Mitchell, Psych, & Malladi, 2010). Compared to the MMSE, administering “single- domain” tests in a primary care environment had equal sensitivity, but inferior specificity (Mitchell et al., 2010). However, as a first-step in screening for dementia, “single-domain “tests are effective and provide GPs with quick and simple tool that can be regularly administered.

Given the literature presented above, the thesis focuses on studying a specific “single-domain” cognitive test, which has been validated in a dementia population and shows potential to be used a screening tool for MCI: the CDT.

1.3 The Clock-Drawing Test (CDT)

1.3.1 History and Use of the CDT

The CDT is a popular, well-established cognitive test used extensively to assess cognitive and mental status in both neurological and psychiatric populations (Freedman et al., 1994; Strauss, Sherman, Spreen, & Spreen, 2006). Historically, the CDT has been a part of mental status examinations dating back to the 1950s (Battersby, Bender, Pollack, & Kahn, 1956; Critchley, 1953). The test was originally developed to measure visuospatial abilities, however it was later discovered that correct task completion involved engagement of many other cognitive functions including semantic memory, planning, abstract thinking, executive control and attention (Freedman et al., 1994; Paula et al., 2013; Rouleau et al., 1992; D R Royall, Cordes, & Polk, 1998; Donald R Royall, Mulroy, Chiodo, & Polk, 1999; K.I. Shulman, 2000). The multi-domain nature of the task makes it a valuable screening tool for various neurocognitive disorders, such as AD, Huntington’s disease, Parkinson’s disease and AD (Pinto & Peters, 2009; Rouleau et al., 1992; K.I. Shulman, 2000; Yamamoto et al., 2004). The CDT is most commonly used to screen for AD and related dementias (Pinto & Peters, 2009; Rouleau et al., 1992; K.I. Shulman, 2000; Yamamoto et al., 2004). Unlike common screening tools for dementia, the CDT is largely non- verbal and relies on coordination of higher-order cognitive abilities and fine motor skills to effectively complete the task.

1.3.2 Administration of the CDT

The CDT encompasses all of the ideal characteristics of a cognitive assessment as it is fast (less than 2 minutes long), easily tolerated by patients, has simple scoring procedures, has low risk of language or education biases and has high reliability (K.I. Shulman, 2000). Administration is simple and only requires paper and a pencil making it easy to deliver in various settings where full cognitive batteries may not be possible. The original CDT instructed patients to draw the face of a clock, put all the numbers in and set it to the time specified (Strauss et al., 2006). This basic test has been adapted into multiple versions and many different scoring systems have been established to assess performance on the test. Commonly administered versions of the CDT include “command free-drawn”, which is most similar to the original version of the CDT and requires the patient to draw the entire clock on a blank piece of paper; “command pre-drawn”, where patients are provided a pre-drawn outline of a circle and must fill in the rest to create the face of a clock; and “copy”, where patients are provided a completed clock and must copy it (Lezak, Howieson, Loring, Hannay, & Fischer, 2004; Rouleau et al., 1992; D R Royall et al., 1998; K.I. Shulman, 2000; Strauss et al., 2006). The completion time may be recorded to help measure impaired task performance (Strauss et al., 2006).

1.3.3 Scoring Systems and Common Errors on the CDT

The main measurement of task performance is the CDT score, which is determined after task completion using guidance from a scoring system. There are more than a dozen different scoring systems that have been developed for the CDT. The scoring systems are divided into two main classifications: quantitative systems and qualitative systems, where qualitative systems provide more detailed evaluation of types of errors in addition to the numerical score given for the clock drawing (Strauss et al., 2006). Commonly used quantitative scoring systems for freehand drawing include the 10-point scales proposed by Sunderland et al. and Libon et al (Libon, Swenson, Barnoski, & Sands, 1993; Sunderland et al., 1989). The Shulman scoring system (4- point scale) is another popular choice, used due to its simplicity and validity (K.I. Shulman, 2000). Royall et al. developed the CLOX system, which has specific instructions for administration and scoring the CDT using the pre-drawn method (D R Royall et al., 1998).

There are a number of qualitative scoring systems that aim to quantify specific types of CDT errors (e.g. Cosentino et al., 2004; Rouleau et al., 1992; Tuokko et al., 1992). Common errors displayed in impaired CDT performance include: (1) Clock size errors: drawing the clock face too small or too big for the space provided; (2) Omissions: omitting numbers from the drawing; (3) Graphic difficulties: distorted or poorly drawn hands or numbers making it hard to understand/read; (4) Stimulus-bound errors: setting the hands to the wrong time; (5) Conceptual errors: writing the time on the clock, not drawing clock numbers or other errors demonstrating a loss of knowledge of clock features; (6) Perseverative errors: continuation of activity without stimulus, such as putting in numbers past “12” or drawing more than two hands; and (7) Spatial/Planning errors: hemispatial neglect, large gaps between numbers, numbers outside the clock face or on the clock line (Eknoyan, Hurley, & Taber, 2012; Strauss et al., 2006).

Each scoring system attributes each error type to levels on the grading scale. The general trends of the scoring systems are similar, however certain systems place higher significance upon different errors depending on what cognitive dysfunction the system is aiming to detect (Mainland, Amodeo, & Shulman, 2014; Donald R Royall et al., 1999).

1.3.4 CDT and Dementia

The CDT has been identified as a key screening tool for various forms of dementia (Agrell & Dehlin, 1998; Cahn-Weiner et al., 1999; Freedman et al., 1994; Rouleau et al., 1992; D R Royall et al., 1998; Tuokko et al., 1992). It is one of the most frequently used cognitive assessment tools for dementia (Reilly, Challis, Burns, & Hughes, 2004), used on its own and in batteries to measure the cognitive status of elderly individuals. It has proven to be valuable for detecting cases of AD with a sensitivity ranging between 75-87% and a specificity ranging between 78- 87% depending on scoring method used (Agrell & Dehlin, 1998; Brodaty & Moore, 1997; Kirby, Denihan, Bruce, Coakley, & Lawlor, 2001; Nishiwaki et al., 2004; D R Royall et al., 1998; Tuokko et al., 1992; Velayudhan et al., 2018; Watson, Arfken, & Birge, 1993). Research has suggested that patients with AD exhibit impaired performance on the command condition of the CDT, but not on the copy condition (Cosentino et al., 2004; Libon et al., 1993; Rouleau et al., 1992). Patients with AD showed better performance on the copy condition compared to the command condition, whereas this was not seen in other forms of dementia, such as vascular

15 dementia (Cosentino et al., 2004; Libon et al., 1993). These studies concluded that the difficulty with CDT completion in the command condition experienced by patients with AD reflected dysfunction in semantic memory as patients had a deficit in knowledge necessary to create an accurate representation of a clock (Cosentino et al., 2004; Libon et al., 1993; Rouleau et al., 1992). However, other studies show that patients with AD commit stimulus-bound errors, spatial/planning errors, perseveration errors and draw large clocks, indicating underlying cognitive dysfunction in many domains including semantic memory, visuospatial abilities and executive functioning (Eknoyan et al., 2012). The ability of the task to identify AD-related impairment has justified the use of the CDT in this population and suggests possible utility of the CDT to detect earlier stages of impairment in the AD pathological process, such as MCI.

1.3.5 CDT in the context of MCI

Although the CDT is considered a “single-domain” test, it actually spans multiple aspects of cognition. The CDT measures the function of many cognitive domains that may be impaired in MCI and early stages of dementia, including visuospatial ability, semantic memory, planning, concentration, verbal understanding, abstract thinking and visuoconstruction skills (K.I. Shulman, 2000; Tuokko et al., 1992). The promise of the CDT as a valid MCI screening tool has thus resulted in many investigations of the effect of MCI on CDT task performance. In some studies, comparing CDT performance between patients with MCI and cognitively healthy controls revealed statistically significant mean differences, with impaired performance in patients with MCI (Babins et al., 2008; Donnelly et al., 2008; Ehreke et al., 2011; Mazancova et al., 2017; Thomann et al., 2008). However, despite showing performance decrements in MCI cohorts, the majority of the studies have failed to identify a significant difference in behavioural performance between the two groups (Beinhoff, Hilbert, Bittner, Grön, & Riepe, 2005; Nunes et al., 2008; Parsey & Schmitter-Edgecombe, 2011; Powlishta et al., 2002; Rubínová et al., 2014; Sager, Hermann, La Rue, & Woodard, 2006). A major factor in the discrepancy in results across the literature involves the variability of CDT delivery and scoring methods. There is a lack of consistency in CDT methodology between the studies, making it difficult to determine the true efficacy of the task in screening for MCI (Ehreke et al., 2010; Pinto & Peters, 2009).

Reliable screening tools must be able to accurately discriminate between healthy and diseased individuals. Sensitivity and specificity are useful for assessing the ability of diagnostic tools to classify their intended population (Altman & Bland, 1994). Sensitivity is the proportion of true positives, which are correctly identified as positive (i.e. the percentage of patients with MCI who are correctly classified as having MCI), meanwhile specificity is the proportion of true negatives, which are correctly identified as negative (i.e. the percentage of patients without MCI who are correctly classified as cognitively healthy) (Altman & Bland, 1994). A sensitivity over 80% and a specificity over 60% are considered to be good standards for screening tools (Blake, McKinney, Treece, Lee, & Lincoln, 2002).

The sensitivity and specificity of the CDT provide valuable information to better assess the efficacy of this tool in the context of MCI. Across the different scoring systems, the average sensitivity of the CDT ranges between 50% and 80% and the specificity ranges between 65% and 90% (Ehreke et al., 2010; Gavett et al., 2010; Rubínová et al., 2014; Scanlan et al., 2002). The sensitivity and specificity results were variable among the studies even when the same scoring system was used, which is largely the consequence of inconsistent cut-off points being used to classify MCI (Ehreke et al., 2010). In a study by Connor et al., several different scoring methods were used to measure CDT performance in patients with mild impairment and the maximum test sensitivity was 60%, which is below the minimum sensitivity for reliability (Blake et al., 2002; Connor, Seward, Bauer, Golden, & Salmon, 2005). Meanwhile, Yamamoto et al. investigated CDT performance in patients with MCI for three different scoring methods (Cahn et al., 1996; Rouleau et al., 1992; Sunderland et al., 1989) and reported good sensitivity and specificity (sensitivity = 82.6% and specificity = 65.9%) for the Cahn scoring method, endorsing its use in MCI diagnosis (Yamamoto et al., 2004). In a more recent investigation by Mazancova et al., the Shulman scoring system (K I Shulman, Shedletsky, & Silver, 1986) was effective at differentiating healthy and MCI cohorts with a sensitivity of 93.8% and a specificity of 62.5% (Mazancova et al., 2017). The discrepancy in results makes it difficult to assess the true efficacy of the task. Multiple studies have suggested that more detailed scoring systems are more effective at detecting MCI (Ehreke et al., 2011; Parsey & Schmitter-Edgecombe, 2011; Rubínová et al., 2014).

Given the variability in CDT methodology and scoring, current literature has not reached a consensus on the efficacy of the CDT at detecting MCI (Chiu et al., 2008; Ehreke et al., 2010;

Pinto & Peters, 2009; Ricci et al., 2016). A large proportion of the research has focused on behavioural changes characteristic of patients with MCI when completing the CDT. However, there is limited knowledge regarding the brain areas engaged in both cognitively normal and cognitively impaired individuals during CDT completion. Characteristic changes in brain function and structure have been well-established in patients with MCI and these changes bring about the symptomatic cognitive deficits in MCI (Bakker, Albert, Krauss, Speck, & Gallagher, 2015; Dickerson et al., 2004; Lam, Masellis, Freedman, Stuss, & Black, 2013; Peters et al., 2014; Sperling et al., 2010). The CDT engages many cognitive domains, including those affected by MCI and dementia, suggesting that impaired CDT performance in patients with MCI is linked to the brain changes associated with the disease (K.I. Shulman, 2000; Tuokko et al., 1992). The main purpose of cognitive assessments is to detect behavioural changes that are associated with the underlying pathological changes in the brain. Therefore, to truly justify the use of the CDT in an MCI population, it is essential to determine whether the task is able to detect cognitive impairment related to structural or functional brain changes.

However, there are significant barriers to studying brain activity associated with cognitive tests like the CDT, mainly due to technological limitations associated with performing neuroimaging during CDT administration (see Section 1.4.3). The development of a novel, fMRI-compatible tablet has overcome these challenges and allowed delivery of a realistic replication of the CDT during fMRI (Karimpoor et al., 2015; Tam, Churchill, Strother, & Graham, 2012). This technology as used in this thesis, will provide an understanding of the brain areas that are engaged during CDT completion in both an MCI and healthy control cohort, thereby creating a better understanding of the efficacy of the CDT as a screening tool for MCI.

1.4 Neuroimaging of AD and MCI

Neuroimaging has become an important component in both clinical neurology and neuroscience research because of its ability to characterize brain function and structure in vivo. Clinicians and neuropsychologists rely on neuroimaging to help their diagnoses of various brain injuries and disorders. Researchers are able to identify pathological changes in brain physiology that are linked to clinical presentation.

Numerous technologies are used to image the brain, the imaging itself can be described in terms of two main classifications: structural imaging and functional imaging. Structural imaging creates images of brain anatomy and is commonly used for diagnostic purposes, providing visualization of physical brain changes, including atrophy, ventricular enlargement and microangiopathic changes as well as other alterations (Ferreira & Busatto, 2011; Joko et al., 2016; Krumm et al., 2016; Wirth et al., 2017). Structural imaging techniques include computerized tomography (CT) and MRI.

In contrast, functional imaging identifies brain activation patterns over a period of time through measurement of aspects of physiological function, such as glucose metabolism or cerebral perfusion. Functional imaging can be used to measure brain activity at rest or in response to stimuli. Measurements of electrophysiological activity in the brain are achieved using electroencephalography (EEG) and magnetoencephalography (MEG). Other techniques are available to record localized blood flow or metabolic activity as different physiologic parameters of brain function, including fMRI, functional near-infrared spectroscopy (fNIRS), positron emission tomography (PET) and single-photon emission computed tomography (SPECT).

Neuroimaging is an important tool in investigation of MCI, AD and other forms of dementia. Structural imaging, provided by CT and MRI, has helped identify structural biomarkers associated with MCI and AD including cortical thickness, white matter hyperintensities, cerebral atrophy and hippocampal atrophy. Functional imaging is also useful for detecting biomarkers of disease as previous PET and SPECT studies have identified characteristic reduced perfusion in areas of the temporal, parietal and frontal lobes in MCI and AD patients (Ferreira & Busatto, 2011; Moretti, 2015). High interest biomarkers in AD, such as beta amyloid and tau protein levels are also measured using PET (Barthel, Seibyl, & Sabri, 2015; Nordberg, 2004; Tateno et al., 2015). Combined with cognitive assessments and clinical history, biomarkers identified through neuroimaging can provide clinicians with a more informed picture of patient conditions for a more accurate diagnosis (Albert et al., 2011; McKhann et al., 2011). In research settings, both structural and functional neuroimaging have played an essential role in advancing knowledge on AD and MCI by identifying the neural correlates of cognitive symptoms, identifying any changes in brain structure or function with disease progression and identifying novel biomarkers associated with AD and MCI.

1.4.1 fMRI and Blood Oxygen Level Dependent (BOLD) Signal fMRI is a widely used functional neuroimaging method that provides detailed information on brain activity with spatial and temporal resolution on the order of millimeters and seconds, respectively, throughout the brain volume. Brain activity is indirectly measured using fMRI by detecting changes in blood oxygen levels in cerebrovasculature that are associated with fluctuations in neural activity, either at rest or in the presence of an externally-directed task (J. E. Chen & Glover, 2015). Task-based fMRI compares the changes in blood oxygen levels that occur during different cognitive states, meanwhile resting-state fMRI measures synchronization of blood oxygen level changes at rest between brain areas (J. E. Chen & Glover, 2015).

The method fMRI identifies changes in neural metabolism by measuring variation in the blood- oxygen dependent (BOLD) signal. Increased activity of neurons within a given brain region is coupled with an increase in consumption of glucose and oxygen, which is compensated for by increased blood delivery to that area in order to supply oxygen for neuron functioning in the form of oxygenated hemoglobin (J. E. Chen & Glover, 2015; Ogawa, Lee, Kay, & Tank, 1990). During the time of increased activity, the oxygen supply exceeds the oxygen demand, resulting in a period of several of seconds in which there is an elevated level of regional oxygenation (J. E. Chen & Glover, 2015). Both increased and decreased activity of specific brain areas will change the level of local oxygenation and these changes are measured by the BOLD signal (J. E. Chen & Glover, 2015; Ogawa et al., 1990).

When in the MRI, participants are immersed by a strong magnetic field, which aligns the spin of water protons in the body. The protons are then stimulated by a radio frequency current, causing them to flip their spin. When the field is turned off, the protons return to their normal spin and produce a signal that can be measured. The return signal amplitude depends on the magnetization of the local tissue. Oxygenated hemoglobin is diamagnetic, whereas deoxygenated hemoglobin is paramagnetic. The BOLD signal takes advantage of these magnetic properties to detect changes in the MRI signal that correspond to a difference in the ratio of oxygenated hemoglobin to deoxygenated hemoglobin, therefore measuring activation-related changes in blood oxygen.

In MCI and AD research, task-based fMRI has gained substantial attention because of its ability to identify functional cognitive changes in these impaired populations compared to healthy, age- matched controls (Bakker et al., 2015; H. J. Li et al., 2015).

1.4.2 Strengths and Limitations of fMRI Compared to Other Neuroimaging Modalities

Continuous advancement in the neuroimaging field has allowed for the development of diverse neuroimaging technology that can be used to investigate underlying brain activity associated with cognition. These methods have collectively contributed to the growth in knowledge about brain structure and function in many different healthy and diseased populations. Each neuroimaging method has both strengths and weaknesses. It is important to evaluate each option to determine which would best suit the experiment. Technical considerations include factors such as spatial resolution, temporal resolution and sensitivity to motion and noise. It is also necessary to consider the potential risks to the study population (e.g. exposure to radiation), the practicality (e.g. accessibility, cost), the reproducibility in future studies and the viability of the modality working with the study protocol.

To study brain activity, fMRI is a widely-used tool because it has multiple advantages and strengths, which make it preferable to other functional neuroimaging methods. A significant benefit of fMRI is that it is completely non-invasive, unlike other modalities, such as PET and SPECT, which involve injection of a radioactive agent and risks to study participants (Crosson et al., 2010). Additionally, MRI emits no ionizing radiation. Furthermore, fMRI has superior spatial resolution compared to other neuroimaging tools (Crosson et al., 2010). Spatial resolution is the accuracy in which a given technique is able to localize brain activity in space (Crosson et al., 2010; Lev & Grant, 2000). This benefit is enhanced by the fact that an MRI system is able to provide both structural and functional brain images, allowing the functional images to be superimposed on the structural images with minimal manipulation (Crosson et al., 2010). This provides a precise anatomical localization of brain activity and thus a more accurate depiction of what brain regions might be associated with the cognitive process being investigated.

Another technical asset of fMRI in comparison to other modalities is its superior imaging depth. In other imaging methods (e.g. fNIRS, EEG and MEG), neural activity is only measured primarily in surface-level cortical regions (e.g. parietal lobes, occipital lobes, etc.), whereas fMRI is able to measure activity in surface-level areas and deeper areas of the brain, including subcortical structures (e.g. hippocampus, cerebellum, thalamus, etc.). Many cognitive processes rely on the function of subcortical structures, making it essential to record brain activity from these regions when studying cognition.

Although there are various advantages of fMRI, there are some limitations that need to be considered. In terms of practicality, MRI has a larger cost burden in comparison to EEG, fNIRS or MEG, however it is cheaper than PET. Although MRI poses no significant risks to the health or safety of participants, it can affect patient comfort. The MRI procedure requires individuals to lie extremely still in a small, confined space for an extended time, which is not tolerated well by all populations (e.g. young children, individuals who are claustrophobic, etc.) and can prevent participants from completing MRI studies. In addition, the acquisition of MRI images is very sensitive to head motion, which can pose as a challenge in the certain populations. The MRI procedures produce a loud noise and though individuals are provided with headphones to block the noise, it can still be agitating and uncomfortable, which can potential confound the study results. Due to the magnetic nature of MRI, individuals with metallic implants, such as cardiac pacemakers, intravascular stents or drug infusion pumps, are also excluded from MRI studies. fMRI provides a moderate temporal resolution (i.e. resolution of seconds), which is greater than the temporal resolution observed in PET (i.e. resolution of tens of seconds). The temporal resolution is the main limitation of fMRI compared to other modalities, such as EEG and MEG, which have a temporal resolution of milliseconds (Crosson et al., 2010). The lower temporal resolution in MRI is mainly a consequence of the sluggish hemodynamic response of the BOLD signal in relation to electrophysiological recordings. Unlike EEG, fMRI is not a direct measure of neural spiking activity, but depends on vascular factors of the brain. BOLD fMRI is a composite measure, reflecting contributions from cerebral blood flow, blood volume and neurometabolism. This can cause ambiguity in clinical studies as changes in BOLD signal can be due to altered blood flow, neural activity or both. Different from other neuroimaging modalities, such as PET, fMRI does not have absolute units of measure. To overcome this limitation, it is necessary to carefully design fMRI experiments to focus on relative signal changes.

Despite this, fMRI provides valuable information and has significant benefits as a functional neuroimaging method. Although the temporal resolution is not as high as EEG or MEG, it is still a moderate resolution for studying brain activity over the timescale of neurocognitive test administration and its spatial resolution is far superior to any other modalities. Many robust preprocessing techniques have been established to overcome the sensitivities of MRI to motion and other confounding signals. As a result, fMRI has become very popular in cognitive neuroscience studies (J. E. Chen & Glover, 2015; Crosson et al., 2010; Ogawa et al., 1990).

1.4.3 Studying Neural Correlates of Cognitive Tasks

Cognitive tests are essential tools, which are implemented in many clinical and research settings to help distinguish between normal and abnormal brain function. Many of these standardized behavioural tasks have been developed to measure specific cognitive processes linked with brain structures. However, the relationship between test performance and underlying brain function remains unclear. Task completion requires recruitment and coordination of multiple different brain regions and although many tasks target specific cognitive domains, poor test scores can reflect damage in multiple areas or the interconnections between areas (Lezak et al., 2004; Strauss et al., 2006). To improve the understanding of what is being measured by these cognitive assessments, it is necessary to measure both brain activity and behavioural performance simultaneously. However, this has proven to be an ongoing challenge in neuroimaging research.

Traditionally, neural correlates of cognitive tasks have been investigated using lesion studies (Keller, Schindler, Kerkhoff, Rosen, & Golz, 2005; Levine et al., 1998; Tranel, Rudrauf, Vianna, & Damasio, 2008). Studying patients with brain lesions helps researchers characterize how components of the brain are involved in cognition. Lesion studies recruit patients with brain lesions and test for association between lesion location and deficits on specific cognitive tests. The goal of these studies is to understand the role of the lesioned brain region in task completion and hence its role in the underlying cognitive processes. However, there are many fundamental weaknesses of lesion studies, which have resulted in a large variability in results from these investigations (Lezak et al., 2004). Lesions are naturally occurring; therefore, the exact location and extent of the lesion is not experimentally controlled across the patient group. Additionally, lesions rarely conform to a single functionally homogenous brain area and often contribute to more than one type of deficit in the patients. Therefore, lesion studies can provide a preliminary understanding of relevant task-related brain regions, however it is essential to corroborate all results with further neuroimaging research. fMRI (see Section 1.4.2) has many advantages for studying whole-brain activity underlying cognitive processes and poses as a useful tool in this context. However, the MRI environment presents unique challenges that make it difficult to administer realistic versions of cognitive tasks during an imaging procedure. The MRI system includes a large magnet with access in the form of a narrow, long tube in which participants lie supine throughout the imaging procedure while

23 keeping their head still. Simple behaviours, such as writing and drawing, cannot be easily replicated in this environment as there is limited room to complete tasks and participants would not be able to view their hand during task completion. In addition, the strong magnetic field of the MRI system potentially hinders the implementation of behavioural response recording systems because any ferromagnetic components in the technology would undergo strong attractive forces with the MRI magnet. These limitations have consequently resulted in a large gap in knowledge regarding the brain activation patterns associated with cognitive tasks.

To overcome the limitations outlined above, researchers have attempted to use highly modified versions of cognitive tests to identify task-related brain activity. Common procedures in fMRI studies include button boxes to record responses (i.e. yes or no responses, or to measure reaction time); or instructing participants to trace with their finger either in the air or on a board placed in front of them, as a substitute for normal writing behaviour with paper and a pencil (De Rover et al., 2011; Diciotti et al., 2010; Gould, Brown, Owen, Ffytche, & Howard, 2003; Ino, Asada, Ito, Kimura, & Fukuyama, 2003; Katanoda, Yoshikawa, & Sugishita, 2001; Makuuchi, Kaminaga, & Sugishita, 2003). However, these methods are insufficient for measuring task-related brain activity that mimics what would occur in a clinical setting during administration of neurocognitive tests.

Other functional imaging methods, such as EEG and fNIRS, do not suffer from the same hardware limitations as fMRI and have been investigated as potential modalities for identifying clinically relevant task-related brain activity. Studies have implemented these techniques to study a variety of cognitive assessments, providing a valuable insight to neural correlates of these tasks (Hagen et al., 2014; Shoyama et al., 2011). However, as outlined in Section 1.4.2, the inferiority of the spatial resolution of these methods significantly limits the generalizability of the results. EEG and fNIRS are only able to measure surface-level cortical regions and consequently will miss any task-related brain activity in subcortical structures. The spatial resolution of fMRI allows for a comprehensive view of brain activity underlying cognitive tasks making fMRI a more appealing tool for investigating the topic of this thesis.

Over the past decade, a novel, fMRI-compatible tablet has been developed to overcome the spatial and technical limitations of the MRI environment, and to aid in the delivery of naturalistic versions of cognitive tests during fMRI (Karimpoor et al., 2015; Tam et al., 2012). The tablet has

24 gone through many stages of development, incorporating feedback from users to reach its current stage. It was developed to facilitate investigations of neural activation patterns associated with writing and drawing activities. It is equipped with a touch sensitive screen, stylus and real-time visual feedback that helps guide hand motions and provides a more realistic task sensation (Karimpoor et al., 2015; Tam et al., 2012). The tablet has been validated as a tool to administer various cognitive tests in an MRI environment (e.g. CDT, trail-making test, letter cancellation test) in multiple different populations (Deng et al., 2019; Karimpoor et al., 2017, 2018; Talwar et al., 2019). It is simple, user-friendly and provides a good replication of traditional paper and pencil cognitive tests. Accordingly, the tablet system is an essential element of the experimental methods used in this thesis.

1.4.4 Neuroimaging of the CDT

The CDT is a complex cognitive assessment that requires the recruitment and coordination of multiple different cognitive functions, including visuospatial ability, attention, executive function, planning and semantic memory. These cognitive domains can be affected by MCI; however, the effect of MCI on CDT completion remains unclear. Behaviourally, patients with MCI tend to perform worse on the task, yet current literature has not reached a consensus on whether the CDT is an effective screening tool for MCI. Identifying the underlying brain activation patterns associated with the CDT in both MCI and cognitively normal populations is important to provide further understanding of the ability of the CDT to detect neuropathological changes that are associated with MCI. Furthermore, this information can help inform clinicians on what brain regions are essential for the task as well as what brain areas may be pathologically impaired in patients who perform poorly on the task.

Early-stage research in the neuroimaging field has attempted to characterize the neural correlates of the CDT, identifying both cortical and sub-cortical regions that are important for CDT completion (Cahn-Weiner et al., 1999; Formisano et al., 2002; Ino et al., 2003; D. Y. Lee et al., 2008; Leyhe et al., 2009; Matsuoka et al., 2013, 2010; Nagahama, Okina, Suzuki, Nabatame, & Matsuda, 2005; Shoyama et al., 2011; Takahashi et al., 2008; Thomann et al., 2008; Tranel et al., 2008; Trojano et al., 2000; Ueda et al., 2002). This literature is outlined below.

Due to the complexity of the task and the limitations of neuroimaging (see Section 1.4.3), it is difficult to realistically replicate the CDT during fMRI. Consequently, primary research on the topic has used indirect methods to identify CDT-related brain regions. Tranel et al. evaluated how patients with brain lesions in different regions performed on the CDT and found that impaired CDT performance was most significantly correlated with structural damage in right parietal and left inferior frontal-parietal cortices (Tranel et al., 2008). They identified that visuospatial errors were more common in patients with right hemispheric lesions, while time setting errors were more common in patients with left hemispheric lesions (Tranel et al., 2008).

Other studies have used neuroimaging techniques to measure signals, such as regional cerebral blood flow (CBF) or gray matter (GM) volume, and investigated how these signals correlate with CDT performance (Cahn-Weiner et al., 1999; Matsuoka et al., 2013, 2010; Nagahama et al., 2005; Takahashi et al., 2008; Thomann et al., 2008; Ueda et al., 2002). Investigations of regional CBF in patients with AD and MCI identified altered CBF in the bilateral parietal lobes, posterior temporal lobes and hippocampus to be most commonly correlated with poor CDT performance (Matsuoka et al., 2013; Nagahama et al., 2005; Takahashi et al., 2008; Ueda et al., 2002). Meanwhile, investigations that have examined the relationship between local GM density and CDT performance have shown multiple cortical (e.g. temporal, parietal, frontal) and subcortical (e.g. thalamus, caudate) areas to be significantly associated with impairment in patients with AD and MCI (Cahn-Weiner et al., 1999; Matsuoka et al., 2010; Thomann et al., 2008). Kim et al. examined the correlations between structural brain changes characteristic to dementia and CDT performance in a cohort of patients with multiple forms of dementia, including AD, Parkinson’s Disease with dementia and vascular dementia. The results showed that the degree of both cerebral periventricular white matter hyperintensities and medial temporal lobe atrophy were highly associated with CDT performance, whereas cortical atrophy and ventricular enlargement did not reveal any significant correlations (Kim, Lee, Choi, Sohn, & Lee, 2009).

Correlation studies are useful at identifying areas with underlying structural pathology that are related to impaired task performance. On the other hand, functional imaging provides a view of brain activity throughout task performance, which more directly identifies brain regions involved in completion of the CDT. There is a gap in the existing literature as few studies have implemented functional imaging techniques to characterize brain activity during CDT completion. Ino et al. delivered a highly modified version of the traditional CDT during fMRI,

26 which required a group of healthy individuals (n = 18) to follow auditory instructions for a specific time setting condition and use their finger to trace the corresponding clock hands on a plastic board (Ino et al., 2003). The results identified CDT-related brain activity across the group in many regions, including the bilateral posterior parietal cortex, dorsal premotor areas and cerebellum and the left pre-supplementary motor area, ventral prefrontal cortex and precentral gyrus (Ino et al., 2003). Though this study provided valuable new insights to the brain activation patterns associated with the CDT, it only truly identified regions involved in the “hands” component instead of the whole task, as participants did not draw the entire clock. Unlike in a clinical setting, participants used their finger to complete the drawing and did not have any visual feedback of their hand movements.

Other neuroimaging techniques have been used to circumvent the challenges of implementing the CDT in an MRI environment. Shoyama et al. used fNIRS to measure brain activity through changes in hemoglobin concentration during completion of the CDT in a group of healthy individuals (n = 37) (Shoyama et al., 2011). The study reported a significant increase in hemoglobin levels in the prefrontal and temporal areas during task completion as well as a correlation between CDT completion time and hemoglobin changes in the prefrontal areas (Shoyama et al., 2011). fNIRS, similar to EEG, is a highly useful methodology because it does not require the scanning apparatus necessary for MRI and allows for a more traditional delivery of cognitive tasks. However, it has low spatial resolution, only measuring changes in surface- level brain structures. In particular, this study only measured hemoglobin changes in the superior temporal and prefrontal cortical surface areas, and was unable to provide knowledge on the whole-brain response to CDT completion (Shoyama et al., 2011).

A few studies have used mental imagery of clock-drawing as a surrogate for the task to probe CDT-related activation. In two fMRI studies, participants imagined two time setting conditions and judged which of the two conditions had a greater angle between the clock hands (Formisano et al., 2002; Trojano et al., 2000). These studies identified frontal and parietal brain networks to have significant task-related activity (Formisano et al., 2002; Trojano et al., 2000). Leyhe et al. instructed participants from three different cohorts (MCI, early AD and healthy controls) to mentally set clock hands to a time condition, measuring brain activity throughout the task with fMRI (Leyhe et al., 2009). The clock task recruited activity in the intraparietal, inferior temporal and occipital cortex in healthy controls and when deciding the minute hand location, patients

27 with MCI displayed reduced activity in the right medial temporal gyrus compared to controls (Leyhe et al., 2009). These studies identified brain areas associated with clock-drawing, however mental imagery does not involve the same mental processing as actual task completion, limiting the results of these investigations (Lacourse, Orr, Cramer, & Cohen, 2005).

Recently, we implemented an MRI-compatible tablet design (see Section 1.4.3) to provide a more realistic replication of the CDT in the scanning environment. Brain activity associated with CDT completion was investigated in a population of healthy older adults aged 52 to 85 (n = 33) using an MRI-compatible tablet to provide a realistic replication of the task during fMRI (Talwar et al., 2019). The study identified CDT-related activation bilaterally across the frontal, occipital and parietal lobes as well as motor regions, including the supplementary motor area and precentral gyri (Talwar et al., 2019). Decreased brain activity was associated with increased age in areas localized to the bilateral parietal and occipital lobes as well as the right temporal lobe and right motor regions (Talwar et al., 2019). The results of this study provide important information on the neural correlates of the CDT in a healthy older adult population, which can serve as a useful comparison to patient populations.

The current literature has provided a useful foundation of information on the neural correlates of the CDT, however to overcome the technical limitations of an MRI environment, many studies have used highly modified versions of the CDT. The alterations to the task make it dissimilar to the traditional CDT administered in clinical settings, causing an imprecise understanding of what brain areas are normally involved in CDT completion. More recently, we studied CDT-related brain activity with a more realistic version of the CDT using an MRI-compatible tablet, however findings were limited to older, healthy adults.

1.5 Knowledge Gaps

Currently, MCI is significantly underdiagnosed in the general clinic as GPs are resistant to performing dementia screening due to the time constraints (Bush et al., 1997; Scanlan et al., 2002). The cognitive batteries commonly used in memory and geriatric clinics are not suitable for general practice settings and unfortunately, there is a lack of quick and simple cognitive tasks which have been validated for screening in an MCI population.

The CDT is a widely-used screening tool in cases of dementia and has been investigated as a potential cognitive test for assessing MCI. However, most studies have focused on behavioural task performance and have not investigated the underlying CDT-related brain activity and how it is affected by MCI. There has been preliminary research attempting to characterize the brain activation patterns associated with the CDT, however most of these studies have used indirect methods and highly modified versions of the task. A recent study used an MRI-compatible tablet to realistically replicate the CDT during fMRI in an older adult population (Talwar et al., 2019). This study provided important groundwork to understand the brain activation patterns underlying the CDT in healthy older individuals. To our knowledge, there is no previous study that uses a naturalistic method of the CDT to study brain activity during fMRI in an MCI population. The knowledge gained from this study can help better inform clinicians on the ability of the CDT to detect MCI-related neuropathology.

Chapter 2 Specific Research Questions and Hypotheses

2.1 Summary and Rationale

With an increasingly prevalent aging population, cognitive impairment has become an important area of research. Making up 60-70% of all cases of dementia, AD is the most common form of dementia (World Health Organization, 2017). Despite extensive research and development in the field, there is no current cure for AD, leaving many people to suffer from this debilitating disease. Therefore, increased focus has been put on establishing effective methods to prevent AD onset in high risk populations (Yiannopoulou & Papageorgiou, 2013).

MCI has been highlighted as a valuable target group for interventions as approximately 10-15% of patients with MCI progress to develop AD, which is significantly higher than the rate in the general healthy aging population (1-2%) (R C Petersen et al., 2001). However, MCI has very slight effects on cognitive function, which may be easily confused with normal age-related cognitive changes, creating a large barrier for MCI diagnosis. Multiple cognitive batteries have been validated as effective screening tools for MCI (i.e. MMSE, MoCA, etc.), but these batteries require additional training for administration and take longer to deliver (~10 minutes) (Ahmed et al., 2012; Bossers et al., 2012; Darvesh et al., 2005; Folstein et al., 1975; Lonie et al., 2009; Nasreddine et al., 2005; Roth et al., 1986; Schweiger et al., 2003; Xu et al., 2002). In a general practice setting, there is a deficiency in cognitive testing because of the time constraints experienced by GPs, leading to a large proportion of missed MCI diagnoses (Brodaty et al., 1994; Bush et al., 1997). There is a significant need for a simple and quick cognitive screening tool that is sensitive to the cognitive changes that are characteristic of MCI.

After the MMSE, the CDT is the most commonly used cognitive assessment by GPs to screen for dementia (Iracleous et al., 2009; Kenneth I. Shulman et al., 2006). The CDT is a cognitive test which has become integral in assessing multiple forms of dementia, including AD (Pinto & Peters, 2009; Rouleau et al., 1992; K.I. Shulman, 2000; Yamamoto et al., 2004). This test measures executive function and visuospatial ability among many other cognitive functions,

29 30 making it a sensitive test for dementia-related impairment (Bäckman et al., 2005). As a first-step, the CDT is a promising tool for GPs to use when screening for cognitive impairment, including MCI, which can be referred to specialized clinics for further testing.

Behavioural studies of the CDT have reported impaired performance, reflected by lower scores, in patients with MCI compared to healthy controls (Babins et al., 2008; Beinhoff et al., 2005; Donnelly et al., 2008; Ehreke et al., 2011; Mazancova et al., 2017; Nunes et al., 2008; Parsey & Schmitter-Edgecombe, 2011; Powlishta et al., 2002; Rubínová et al., 2014; Sager et al., 2006; Thomann et al., 2008). However, there is a large variability in the reports of CDT sensitivity and specificity to MCI making it difficult to assess the true efficacy of the CDT as a screening tool for MCI (Chiu et al., 2008; Ehreke et al., 2010; Pinto & Peters, 2009; Ricci et al., 2016).

Despite the abundance of investigations on the effect of MCI on behavioural CDT performance, there is a significant gap in knowledge regarding the task-related brain activity. Pathological MCI-related changes in brain structure and function lead to characteristic cognitive deficits, suggesting that impaired function in these brain areas underlies CDT performance deficits in patients with MCI (Bakker et al., 2015; Dickerson et al., 2004; Lam et al., 2013; Peters et al., 2014; K.I. Shulman, 2000; Sperling et al., 2010; Tuokko et al., 1992). Therefore, to properly assess the efficacy of the CDT at detecting MCI-related impairment, it is essential to use neuroimaging tools to compare the brain activation patterns underlying CDT performance in an MCI and healthy control cohort.

Functional MRI (fMRI) is a useful tool for measuring task-related brain activity, however there are many technical and spatial limitations, which make it difficult to implement realistic versions of cognitive tasks in a scanning environment. Consequently, current literature uses highly modified versions of the CDT (i.e. visualizing setting the time on a clock) or indirect methods (i.e. lesion studies) to identify neural correlates of the CDT. The creation of a novel, fMRI- compatible tablet at Sunnybrook has advanced the potential for research in this area (Karimpoor et al., 2015; Tam et al., 2012). This tablet technology uses creative engineering solutions to overcome traditional challenges associated with MRI and create a naturalistic version of cognitive tests that replicates administration in clinical settings. Using the fMRI-compatible tablet to deliver the CDT will identify task-related brain activation patterns that are highly similar to the brain areas recruited during CDT administration in the clinical setting. Comparing

CDT-related patterns of brain activity between the MCI and healthy control cohort may identify brain regions where altered brain function is characteristic of MCI. This will provide new, valuable information on the effect of MCI on CDT performance that can guide a more valid judgement of the efficacy of the CDT to screen for MCI.

2.2 Research Objectives and Hypotheses

The primary aims of this thesis are to use the fMRI-compatible tablet to administer the CDT during fMRI and determine (1) the behavioural CDT performance in patients with MCI measured using multiple different scoring techniques and time setting conditions; and (2) the brain activation patterns characteristic of patients with MCI during CDT completion.

2.2.1 Investigating the Effect of MCI on Behavioral CDT Performance

The first set of objectives will use the fMRI-compatible tablet to deliver the CDT in order to investigate CDT performance in patients with MCI and compare performance to healthy matched controls. The objectives and hypotheses in this part will be addressed in Chapter 4 of the current thesis.

The objectives are as follows: 1) To determine whether there is a significant difference in CDT performance between patients with MCI and healthy age matched controls; 2) To determine which components of clock-drawing (e.g. clock face drawing, clock number drawing, clock hands drawing) patients with MCI have difficulty performing compared to healthy age matched controls; and 3) To determine if current scoring systems are effective at detecting impairment associated with MCI

The hypotheses associated with these objectives are: 1) Given that patients with MCI demonstrate cognitive impairment in domains important for CDT completion (i.e. visuospatial ability, executive function, attention, etc.), patients with MCI will display impaired performance on the CDT compared to age matched healthy controls as reflected by worse scores on the task and longer time for task completion. However, due to the mild nature of MCI, this impairment will not be statistically different from performance observed in matched healthy controls. 2) Because patients with MCI commonly experience subtle cognitive deficits, they will demonstrate more difficulty in complex components of the CDT (e.g. drawing the numbers and hands) compared to simple, more routine components (e.g. drawing the clock face). 3) Due to the mild nature of MCI, current CDT scoring methods that were developed for AD screening will not be effective at distinguishing MCI from age matched healthy controls. Semi-qualitative systems (i.e. Cahn, Rouleau) will be better at differentiating patients with MCI from healthy controls than purely quantitative systems (e.g. Sunderland, Shulman).

2.2.2 Investigating the Effect of MCI on Brain Activation Patterns Associated with CDT Completion

The second set of objectives combines the use of the MRI-compatible tablet with fMRI to characterize the underlying brain activation patterns during completion of the CDT in patients with MCI, and comparing the results to brain activity observed in matched healthy controls. The objectives and hypotheses associated with this part will be addressed in Chapter 5 of the thesis.

The objectives are as follows: 1) To compare brain activation patterns characteristic of patients with MCI to healthy age matched controls during completion of the CDT; and 2) To identify any differences in brain activity between patients with MCI and healthy age matched controls during each component of the CDT (e.g. clock face drawing, clock number drawing, clock hand drawing)

The hypotheses associated with these objectives are listed below: 1) Both patients with MCI and healthy age matched controls will display reliable activity across brain regions identified as being important for completion of the CDT (e.g. parietal lobe, frontal lobe, temporal lobe, cerebellum, motor areas). However, patients with MCI will exhibit reduced brain activity compared to matched controls, specifically in regions of the right parietal lobe and left temporal lobe. 2) Patients with MCI will show reduced brain activity during completion of the number and hand drawing components of the CDT, due to the increased complexity associated with these components. Given that this investigation is preliminary and exploratory in nature, no specific hypotheses are made regarding brain regions affected.

Experimental Materials and Methods

The experimental procedures developed for this study were made in collaboration between St. Michael’s Hospital’s Cognitive Neuroscience Lab and Sunnybrook Research Institute.

3.1 Statement of Ethical Approval

The current study obtained ethical approval from the Research Ethics Board (REB) at St. Michael’s Hospital (SMH), Toronto, Ontario, Canada under REB 14-356 titled, “Investigating the driving performance and underlying neural networks of aging cohorts and individuals with mild cognitive impairment, Alzheimer’s disease, multiple sclerosis, vascular cognitive impairment, traumatic brain injury, and stroke”. Before participating in the study, all participants freely provided their written informed consent.

3.2 Participants

3.2.1 Recruitment

The study aimed to recruit 20 patients with MCI and 20 healthy age- and sex-matched control participants.

The patients with MCI were recruited from the Memory Disorders Clinic at SMH. This clinic primarily evaluates older-aged individuals with memory concerns. All patients that participated in the study were clinically assessed by a geriatric psychiatrist (Dr. Corinne Fischer) in the Memory Disorders Clinic and received a formal MCI diagnosis. To provide a comprehensive assessment and informed diagnosis, all patients underwent a three-hour appointment at the clinic, which captured their full medical history and previous diagnoses, their current subjective concerns with their memory and/or cognition and objective cognitive testing, including the Behavioural Neurological Assessment-Revised (BNA-R) (Darvesh et al., 2005), the Mini-Mental Status Examination (MMSE) (Folstein et al., 1975) and the Montreal Cognitive Assessment

(MoCA) (Nasreddine et al., 2005). To provide a diagnosis, the geriatric psychiatrist considered the patient’s comprehensive history, the results of clinical neuroimaging (i.e. MRI, CT, SPECT) primarily to rule out other pathological processes and performance on the cognitive assessment batteries.

Healthy matched control participants were recruited from various community sources, including the Baycrest Geriatric Hospital Research Volunteer Database, the University of Toronto Alumni Association and the SMH Volunteer Association. All participants were screened to ensure they met the inclusion and did not meet the exclusion criteria.

3.2.2 Screening

Patients with MCI recruited from the Memory Disorders Clinic indicated whether they were interested in participating in research studies. Those interested in research were screened to determine their eligibility for the study. A transcription of the clinical appointment was available on the SMH online medical record system (Soarian Clinicals, Version 4.00 SP06). This report was used by the research assistant (RA) to screen the patients for eligibility according to the inclusion and exclusion criteria (See Section 3.2.3 and 3.2.4 below). If the patient met the criteria then the RA contacted the patient and explained the study protocol in detail. If the patient was interested in participating then the RA used the questions on the MRI Requisition form, provided by the SMH Medical Imaging Department, to ensure that the patient was eligible and able to undergo the MRI procedures. Once confirming that the patient met all the requirements (i.e. MCI diagnosis, study and MRI screening criteria), they were enrolled in the study.

Healthy control participants were contacted by the RA through information provided in the volunteer databases outlined in Section 3.2.1. The RA described the study protocol in detail. The RA screened interested individuals over the phone using a screening questionnaire that was developed for the study to ensure that the participants met the inclusion and exclusion criteria (See Section 3.2.3 and 3.2.4). Participants were also screened using the MRI Requisition form, provided by the SMH Medical Imaging Department. If they met all criteria, the participants were enrolled into the study.

3.2.3 Inclusion Criteria

All study participants (patients with MCI and healthy control participants): (1) held a valid driver’s license; (2) were right-handed as determined by the Edinburgh Handedness Inventory (Oldfield, 1971); (3) were age 45-85 years old; (4) were fluent in English; and (5) met the safety requirements for an MRI as defined by the MRI Requisition form, provided by the SMH Medical Imaging Department. Both patients with MCI and healthy controls with a history of clinical depression were included in the study if depressive symptoms were controlled through treatment.

All patients with MCI received a formal diagnosis from a geriatric psychiatrist through the Memory Disorders Clinic at SMH based on a comprehensive assessment outlined in Section 3.2.1. All patients with MCI met the criteria defined by the National Institute on Aging- Alzheimer’s Association (Albert et al., 2011). Specifically, patients with MCI demonstrated: (1) a concern regarding a change in cognitive abilities, either self-reported, reported by a caregiver or a clinician; (2) impairment in at least one cognitive domain; (3) preserved functional abilities and independence; and (4) no presence of dementia.

3.2.4 Exclusion Criteria

Study participants were excluded if they had any of the following: (1) history of severe neurological incidents (e.g. stroke, traumatic brain injury, brain tumour, etc.); (2) history of any neurological disorders (e.g. AD, Parkinson’s disease, multiple sclerosis etc.); (3) history of severe psychiatric conditions (e.g. bipolar disorder, schizophrenia, uncontrolled depression, etc.); (4) presence of a gross movement disorder or impairment; (5) history of substance abuse or dependency; (6) presence of visual abnormalities not corrected with lenses; (7) significant hearing loss; (8) diagnosed or treated learning disabilities; and (9) any condition that prevented them from meeting MRI screening criteria (e.g. metal implant, claustrophobic, etc.).

The inclusion and exclusion criteria were evaluated for patients with MCI clinically by the geriatric psychiatrist and through appointment transcriptions by the RA. The criteria were evaluated for healthy control participants by their self-report through the screening form questionnaire administered by the RA.

3.2.5 Consent

The geriatric psychiatrist at the SMH Memory Disorders Clinic determined whether the patients with MCI were interested in being approached for research studies. If interested, patients were screened for the study and approached to participate. Healthy control participants displayed interest in research by signing up for the volunteer databases listed in Section 3.2.1 and were contacted directly for recruitment to this study. At the beginning of their appointment to undergo imaging and behavioural testing, consent was obtained for all participants by the RA.

3.3 Experimental Procedures

The experimental procedures aimed to use an fMRI-compatible tablet to characterize the brain activation patterns associated with CDT completion in both an MCI cohort and a matched healthy control cohort. More specifically, the investigation aimed to identify differences in CDT- related brain activity between the two groups. Overall, this information was gathered to help provide a more comprehensive understanding of the efficacy of the CDT to detect cognitive changes characteristic of patients with MCI.

The experimented formed part of a larger study, which was designed to investigate brain activation patterns underlying driving and different cognitive tests in patients with MCI to determine which test is most effective at assessing driving fitness. Each participant (including patients with MCI and healthy controls) completed two testing sessions, one which required completion of driving scenarios on an MRI-compatible driving simulator during fMRI, and one which involved using the fMRI-compatible tablet to complete different cognitive tests during fMRI. This thesis focuses on the results obtained from the second session (i.e. the tablet session).

During the tablet session, all participants completed (1) cognitive tests on the fMRI-compatible tablet (e.g. CDT, letter cancellation test, trail-making test and maze test) during fMRI; (2) paper and pencil versions of the cognitive tests to validate the tablet versions; (3) cognitive assessments to measure cognitive fitness; and (4) questionnaires to assess handedness and post-experimental feedback. The paper-based cognitive tests, questionnaires and neurocognitive assessments were completed before imaging. Both the fMRI-based cognitive testing protocol and the paper-based cognitive tests and questionnaires took approximately one and a half hours to complete.

3.3.1 Clinical Data Collection

For all of the consenting patients with MCI, relevant clinical data were obtained through the online medical record system at SMH (Sorian Clinicals). This system was also used to access electronic medical records, which were used in screening (see Section 3.2.2). These electronic records provided a comprehensive clinical history on all patients with MCI, including any comorbidities, previous medical incidents and past or current medications.

3.3.2 Pre-Imaging Procedure

Before the fMRI session, all participants went through the consent process to agree to participate in the study. After providing consent, the participants completed a battery of neurocognitive tests and questionnaires (see Section 3.3.3). All participants completed an MRI Screening Form provided by the SMH Imaging Department and reviewed the form with the MRI technologist to ensure that they were safe to undergo the fMRI session. This was followed by a brief presentation from the RA on what was going to happen during the fMRI session, and detailed instructions about how to participate. The presentation included images of the tablet set-up and visual stimuli for the different tasks. Detailed instructions were provided on how to optimally interact with the tablet, such as holding the stylus near the end to avoid touching the tablet surface with the hand. A breakdown of the MRI session and descriptions of each task were provided. Participants were reminded to keep their head as still as possible throughout the imaging procedure. After the presentation, participants were able to use the tablet to complete one practice trial of the CDT to prepare for the fMRI session. The time setting condition used in the practice trial was different from the ones used in the experimental session to prevent a practice effect.

3.3.3 Cognitive Tests and Questionnaires The questionnaires that were administered during the session included the Edinburgh Handedness Inventory (Oldfield, 1971) and a post-experimental questionnaire. A demographic questionnaire was also completed by all participants during their first session (driving simulation) and the data collected from that form is used in this current investigation. Cognitive tests that were administered to all participants included the MoCA and the CDT.

Demographic Questionnaire All participants were administered a demographic questionnaire that collected information on many factors including: age and date of birth, handedness, years of education, highest level of education, occupation (or previous occupation), retirement status, basic medical history, current medications and language fluency.

Edinburgh Handedness Inventory The Edinburgh Handedness Inventory is widely used to measure the preference to use one hand over the other (Oldfield, 1971). The Inventory was developed to use as a simple and brief assessment of handedness using a quantitative scale (Oldfield, 1971). The Inventory is a self- administered questionnaire, which asks respondents to specify their hand preference for ten different everyday activities (e.g. writing, scissors, toothbrush). Respondents are asked to put a “+” in the column that indicates which hand they prefer for that activity, “left” or “right”. If there is a strong preference for a certain hand, such that they “would never try to use the other hand unless absolutely forced to”, the respondents are to write “++” in the corresponding column. In the case that respondents are “really indifferent”, they may put a “+” sign in both columns.

The following procedure is used to quantify handedness. First, an overall score is calculated for each hand. A response of “+” contributes one point to the score, whereas “++” contributes two points. If there is a “+” in both the “left” and “right” columns, then both hands are assigned one point each. The overall score for both the “left” and the “right” hands is then used to calculate a laterality quotient using the following equation:

("Right" 푠푐표푟푒−"Left" 푠푐표푟푒) 퐿푎푡푒푟푎푙푖푡푦 푞푢표푡푖푒푛푡 = x 100 ("Right" 푠푐표푟푒+"Left" 푠푐표푟푒)

A score of +100 indicates complete right hand dependence, whereas a score of -100 indicates pure left hand dependence. A score of 0 is associated with neutral hand dependence. The Edinburgh Handedness Inventory was administered to all participants to ensure that they were right-handed.

Post-Experimental Questionnaire All participants completed a post-experimental questionnaire after their testing session to provide feedback on the experience. The questionnaire collected information on: (1) level of participant fatigue; (2) self-reported performance on tablet-based cognitive tests; (3) self-reported performance on tablet-based cognitive tests compared to the corresponding paper versions; (4) feedback of experimental set-up; and (5) physiological symptoms during the fMRI session (i.e. headache, eyestrain, etc.).

Montreal Cognitive Assessment (MoCA) The MoCA was developed as a simple and comprehensive screening tool to assess cognitive abilities in patients with mild complaints and symptoms (Nasreddine et al., 2005). Research indicated that the popular cognitive battery for dementia screening, the MMSE, did not have adequate sensitivity for identifying patients with MCI (Nasreddine et al., 2005). This led to the development of the MoCA. The MoCA consists of various smaller cognitive tests, which assess eight different cognitive domains including: visuospatial/executive function (score 0-5), naming (score 0-3), attention (score 0-6), language (score 0-3), abstraction (score 0-2), memory delayed recall (score 0-5) and orientation (score 0-6). These individual components summate to a total maximum score of 30 on the MoCA. A score greater than or equal to 26 is indicative of normal cognitive functioning, meanwhile a score below 26 suggests cognitive impairment, which may be associated with MCI, AD or a similar form of dementia. More specifically, the score range between 20-26 has been suggested to be consistent with MCI, while anything below 20 is indicative of established forms of dementia, such as AD (Milani, Marsiske, Cottler, Chen, & Striley, 2018; Nasreddine et al., 2005). The MoCA has been validated in many research investigations as an effective clinical tool with high validity, sensitivity and specificity for many forms of cognitive impairment, including MCI, AD, vascular dementia, vascular cognitive impairment and frontotemporal dementia (Freitas, Prieto, Simões, & Santana, 2014; Goldstein et al., 2014; Kaya et al., 2014; Lam et al., 2013; T. Smith, Gildeh, & Holmes, 2007).

In this thesis, the MoCA was used to screen all healthy control patients for any potential cognitive impairment. Any healthy control that scored below 26 on the MoCA was excluded from further participation and not used for analysis. All control participants completed the MoCA during the first testing session (driving simulator) and that data was used. All patients with MCI completed the MoCA during both testing sessions (driving simulator and tablet). The data from the MoCA completed during the second session (tablet) was used for the patients with MCI because the patients could possibly suffer from accelerated cognitive decline and therefore their test scores could have changed dramatically in a short time period. The MoCA (version 7.2) was administered to all participants at their first session. To reduce practice effects, patients with MCI who had their two sessions within a month of each other completed MoCA (version 7.1) during their second session (tablet).

Paper-Based CDT The CDT is an established screening tool for cognitive impairment associated with dementia (see Section 1.3). However, the paper version of the CDT was not used to measure cognitive impairment in this thesis. Instead, this task was used to validate the tablet-based CDT as an accurate replication of the traditional paper CDT by comparing results on the paper task with those on the tablet task. Both versions of the CDT employed the same method (free drawn circle) and time setting condition (ten minutes after eleven). For the paper-based CDT, participants were provided a blank piece of paper and a pen, and instructed to “draw a large circle, put all the numbers in to make it look like the face of a clock, draw the hands of the clock to show ten minutes after eleven and to stop when completed”. The method of CDT administration was part of the lab protocol that was adapted from the Ministry of Transportation (MTO) version of the CDT. The MTO developed their instructions from a standardized administration practice associated with traditional versions of the CDT (Agrell & Dehlin, 1998). There was no maximum time allotted for the task, and the total time to complete the CDT was recorded.

3.3.4 Tablet-Based CDT

Performance and brain activity characteristic of CDT completion in patients with MCI and healthy controls was investigated using an fMRI-compatible tablet. The tablet is fully equipped with a touch sensitive screen, stylus and real-time visual feedback camera to provide a naturalistic version of the CDT during fMRI (see Section 3.3.5). The tablet-based CDT was presented using E-Prime Version 2.0 (Psychology Software Tools, Inc., Sharpsburg, PA), a commercially available software for behavioural testing. This software was run on a Dell XPS1730 gaming laptop computer (2.4 GHz Intel Core Duo T7700 Processor, 4.0 GB RAM, 512MN NVIDIA GeForce 8700M GT graphics card) and projected onto a screen display in the MRI magnet room.

The tablet-based CDT instructions were identical to the instructions for the paper-based CDT, informing participants to “draw a large circle. Put all the numbers in to make it look like the face of a clock. Draw in the hands of the clock to set the time to the time specified. Stop when completed”. Consistent with the paper-based CDT, the instructions and method of administration for the tablet-based CDT was adapted from the MTO version of the CDT, which used traditional CDT methods. Once participants were presented with a blank screen, they were allotted 90 seconds to comply with the instructions and complete the clock drawing. In addition to the “ten minutes after eleven” trial, participants performed four other trials of the tablet-based CDT with four different time setting conditions. The order of the trials was as follows: three o’clock, twenty minutes to four, ten minutes after eleven, forty-five minutes after ten, and five minutes after six. Multiple trials of the CDT were completed during fMRI to minimize any practice effects while assuring that sufficient data were collected to estimate brain activity with reasonable statistical power. The first trial (3 o’clock) was selected as a simple time setting condition to allow participants to adjust to the task.

The tablet-based CDT protocol was split into two “runs”, with the first run containing the first three CDT trials (three o’clock, twenty minutes to four and ten minutes after eleven) and the second run containing the last two CDT trials (forty-five minutes after ten and five minutes after six). The order of the CDT trials remained fixed across all tablet sessions. In addition to the CDT, each run also consisted of a visual fixation condition. The order of conditions in each CDT run is depicted in Figure 3.1. Participants were presented with a black cross in the center of the

43 display during the visual fixation task and they were required to fix their attention on the cross for 16 seconds. The order of all the conditions (CDT and fixation) as shown in Figure 3.1 was held fixed for all sessions because it allowed for rest periods (visual fixation) to be interleaved easily between the task condition (CDT).

Figure 3.1. The experimental design for each “run” of the tablet-based CDT during fMRI.

Combined, the total duration of both CDT runs was approximately fourteen minutes. As previously mentioned, other cognitive tests were also completed during this tablet session. In order to counterbalance for practice effects, the order of the tests was randomized for each session and the participants alternated between each test such that they did not complete run 1 and run 2 of the same test consecutively. Therefore, there was approximately twelve minutes of other cognitive testing separating the two CDT runs. Between the runs of each cognitive test, participants were offered optional breaks, although none of the participants required a break.

3.3.5 fMRI-Compatible Tablet Technology

The fMRI-compatible tablet is a novel piece of technology developed at Sunnybrook Research Institute (Karimpoor et al., 2015; Tam et al., 2012). The tablet system (Figure 3.2) integrates a touch-sensitive surface, stylus and real-time visual feedback video camera to create an augmented reality environment in the MRI that mimics the conditions of cognitive testing in a clinic. All of the equipment that was inside the magnet room was nonferromagnetic to prevent interaction with the strong magnetic field.

Figure 3.2. Picture of the tablet system used to deliver the CDT during fMRI, including the touch sensitive surface, stylus, visual feedback camera and elevated support system.

An example of the set-up before entering the magnetic bore is shown in Figure 3.3. The tablet is attached to an elevated support platform, which is placed over the waist while the participant lies supine on the patient table. The support platform is adjustable and elbow pads are provided to the participants to support their arms and allow them to interact with the tablet comfortably and accurately. The head coil for receiving the fMRI signals was equipped with a mirror that was angled towards a display screen (visual angle = 15.5o) at the rear of the magnet room. The display screen was illuminated by an MRI-compatible projector (Avotec, Stuart, FL), which portrayed the task instructions and video feedback of tablet interactions. Participants were fitted with MRI-compatible prescription glasses (MediGlasses for fMRI, Cambridge Research Systems, Kent, UK) to help view the screen if necessary.

The visual feedback camera was positioned directly over the tablet surface (see Figure 3.1) to capture real-time video of the hand and stylus movements to help guide the participant to make precise responses. The force of contact detected by the touch sensitive tablet surface was converted into position coordinates that were displayed as drawings on top of the visual stimuli shown on the screen. The video camera recorded the movements of the hand and the stylus, which were isolated from the background of the video and superimposed onto the visual stimulus image.

Figure 3.3. Set-up of the tablet equipment on the patient table before entering the magnet bore.

The combined video of hand/stylus movements and drawings on the visual stimulus background was projected onto the display screen in the MRI room for participants to observe. This technology allowed participants to see both their hand movements and written responses in real- time throughout the task, creating an augmented reality and more naturalistic task setting. An example of the video feedback participants received during CDT completion is shown in Figure 3.4.

Figure 3.4. Video feedback of hand movements and tablet interactions that participants were able to see during CDT completion.

MRI Training Session

In addition to the pre-imaging tablet practice session, all participants completed an MRI Training Session with the tablet set-up in the MRI before starting the experimental procedure. The participants were asked to complete a series of basic tasks using the tablet set-up, such as writing their name, drawing a house, tracing a flower, etc. The purpose of this session was to allow participants to become acquainted with the tablet system and how to interact with it in the MRI environment. The training also provided an opportunity to ensure that the participants were comfortable with the tablet set-up and to address any issues before the imaging commenced.

3.3.6 fMRI Protocol and Data Acquisition The imaging procedures were completed in the SMH Medical Imaging Department using the SMH Research 3.0 Tesla MRI system with a standard 20-channel head coil (Magnetom Skyra, Siemens Healthineers, Erlangen, Germany). A certified MRI technologist acquired all images. The fMRI process took approximately 1.5 hours to complete, with 1 hour dedicated to image acquisition and the remaining time used for instructions and set-up. Anatomical images were acquired using a three-dimensional T1-weighted Magnetization Prepared Rapid Acquisition Gradient Echo protocol (MPRAGE: inversion time (T1)/echo time (TE)/repetition time (TR) = 1090/3.55/2300 ms, flip angle (FA) = 80o, bandwidth (BW) = 200 Hz/pixel, sagittal orientation with field of view (FOV) = 240 mm by 240 mm by 173 mm, 256 by 256 by 192 acquisition matrix, isotropic voxel dimension = 0.9 mm thickness). Functional task-based imaging was acquired during CDT performance using two-dimensional multi-slice T2*-weighted echo planar imaging (EPI: TE/TR = 30/2000 ms, FA = 70o, BW = 2298 Hz/pixel, oblique-axial, slices interleaved ascending, with FOV = 200 by 200 mm, 64 by 64 acquisition matrix, 32 slices with 4.0 mm thickness and 0.5 mm gap, voxels = 3.125 mm by 3.125 mm by 4.0 mm).

3.4 Data Extraction and Analysis

3.4.1 CDT Data Extraction The tablet-based CDT data were extracted from outputs that were automatically generated by E- Prime and saved at the end of each CDT run. To measure performance on the CDT, two different metrics were used: (1) CDT score, and (2) time to complete the task. Two outputs from E-Prime were used for data extraction for these metrics: (1) images of the final clock drawing and (2) log files recording metrics detected by the tablet. E-Prime produced images of each trial the CDT, which were subsequently used to score the results. The log files recorded: (1) the time since the start of the session; (2) the time since the start of the trial; (3) the x-coordinate of the stylus on the tablet surface; and (4) the y-coordinate of the stylus on the tablet surface. Using these files, videos were reconstructed for each CDT trial in Matlab (The MathWorks, Inc., Natick, MA). These videos provided information on the completion time of the entire clock drawing as well as each of the individual components (e.g. clock face drawing, clock number drawing and clock hand drawing).

3.4.2 CDT Scoring

Four different scoring methods were employed to measure performance on the CDT. Two of the scoring methods used were strictly quantitative (Shulman and Sunderland methods), whereas the other two scoring methods were semi-qualitative (Rouleau and Cahn methods) (Cahn et al., 1996; Rouleau et al., 1992; K I Shulman, Gold, Cohen, & Zucchero, 1993; Sunderland et al., 1989). These scoring methods were selected because of their popularity in research and clinical practice in older aged populations and populations with dementia. In particular, the scoring systems of Rouleau, Sunderland and Shulman are the most commonly used in research studies related to MCI (Ehreke et al., 2010). Various studies in the literature have suggested that using more detailed scoring systems would be more effective for detecting MCI, therefore the Cahn and Rouleau scoring systems were selected as they provide more detailed information on CDT error types (Ehreke et al., 2011; Parsey & Schmitter-Edgecombe, 2011; Rubínová et al., 2014). Each trial of the CDT was scored separately by two independent individuals to measure reliability of the scoring method. If there was a discrepancy in the score between the two raters then the average of both scores was calculated and used for further analysis.

Using the criteria proposed by Shulman et al. to guide judgement, a score (maximum of 6) is given to numerically classify the CDT errors (K I Shulman et al., 1993). In this scoring system, a higher score indicates more errors and worse performance on the task. A breakdown of the Shulman scoring criteria is provided in Table 3.1.

Table 3.1. Description of the scoring system proposed by Shulman et al. (1993)

Score Classification Examples of Errors

1 ‘Perfect’ n/a

2 Minor visuospatial errors Mildly impaired spacing of times, draws times outside circle, turns page while writing numbers so that some numbers appear upside down, draws in spokes to orient spacing

3 Inaccurate representation of 10 after 11 Minute hand points to 10, writes ’10 when visuospatial organization is perfect after 11’, unable to make any or shows only minor deviations denotation of time

4 Moderate visuospatial disorganization of Moderately poor spacing, omits times such that accurate denotation of 10 numbers, perseveration, right-left after 11 is impossible reversal (numbers drawn counter clockwise), dysgraphia

5 Severe level of disorganization as n/a described in 4

6 No reasonable representation of a clock. No attempt at all, no semblance of a Exclude severe depression or other clock at all, writes a word or name psychotic states

The scoring system proposed by Sunderland et al. (1989), provides criteria for CDT scores up to a maximum of 10 points (Sunderland et al., 1989). Using this system, higher scores indicate better performance and less errors. The scoring criteria are detailed in Table 3.2.

Table 3.2. Description of the scoring system proposed by Sunderland et al. (1989)

Score Description

10-6 Drawing of clock face with circle and numbers is generally intact

10 Hands are in correct position

9 Slight errors in placement of hands

8 More noticeable errors in placement of hour and minutes hands

7 Placement of hands is significantly off course

6 Inappropriate use of clock hands (i.e. use of digital display or circling of numbers despite repeated instructions)

5-1 Drawing of a clock face with circle and numbers is not intact

5 Crowding of numbers at one end of the clock or reversal of numbers. Hands may still be present in some fashion

4 Further distortion of number sequence. Integrity of clock face is now gone (i.e. numbers missing or placed at outside or boundaries of clock face)

3 Numbers and clock face no longer obviously connected in drawing. Hands are not present

2 Drawing reveals some evidence of instructions being received but only a vague representation of a clock

1 Either no attempt or an uninterpretable effort is made

The Sunderland and Shulman scoring methods (as outlined above) are quantitative as they determine a numerical representation of the scale of impairment displayed in the clock drawing. However, they do not provide detailed information on how many errors are made, what types of errors are made and what components of the drawing patients are struggling with. The Rouleau

50 and Cahn scoring methods are semi-qualitative and provide more detailed information on the errors behind the score, as described below.

The method proposed by Rouleau et al. (1992) breaks down the CDT score into three sub-scores: R1, R2 and R3 (Rouleau et al., 1992). Each sub-score represents a component of the clock drawing performance: R1 assesses the integrity of the clock face (maximum score = 2), R2 assesses the presence, sequence and arrangement of the clock numbers (maximum score = 4), and R3 assesses the presence and placement of the clock hands (maximum score = 4). The total Rouleau score has a maximum of 10 points. Following these criteria, higher scores indicate better performance and less errors. This scoring system indicates the level of performance on each individual component of the CDT, which reveals what types of errors are being made. The complete Rouleau scoring system is outlined in Table 3.3.

Table 3.3. Description of the scoring system proposed by Rouleau et al. (1992)

R1: Integrity of clock face (2 points)

2 Present without gross distortion

1 Incomplete or some distortion

0 Absent or totally inappropriate

R2: Presence and sequencing of the numbers (4 points)

4 All present in the right order and at most minimal error in the spatial arrangement

3 All present but errors in spatial arrangement

2 Numbers missing or added but not gross distortions of the remaining numbers, numbers place in counter clockwise direction OR numbers all present but gross distortion in spatial layout (i.e. hemineglect, numbers outside the clock)

1 Missing or added numbers and gross spatial distortions

0 Absence or poor representation of numbers

R3: Presence and placement of the hands (4 points)

4 Hands are in correct position and the size difference is respected

3 Slight errors in the placement of the hands OR no representation of size difference between the hands

2 Major errors in the placement of the hands (significantly out of course including 10 to 11)

1 Only one hand or poor representation of two hands

0 No hands or perseveration on hands

The scoring system outlined by Cahn et al. (1996) is more descriptive than the Rouleau system as it assigns a qualitative CDT score as well as the traditional quantitative score (Cahn et al., 1996). The Cahn system provides three scores: a quantitative score (I) (maximum 10 points), a qualitative score (II) (maximum 8 points) and a global score (maximum 10 points). The quantitative score (I) is similar to the Rouleau method as it assesses the correctness of each individual clock component (i.e. clock face, clock numbers and clock hands) to give a total score of 10 points, where higher scores indicate better performance. The qualitative score (II) focuses on eight specific types of errors that are made in the CDT and assigns a point for the presence of each type of error. Therefore, higher qualitative scores indicate worse performance and more errors. The global score is calculated using both the quantitative and qualitative score using the following formula:

퐺푙표푏푎푙 푠푐표푟푒 = 푞푢푎푛푡푖푡푎푡푖푣푒 푠푐표푟푒 (퐼) − 푞푢푎푙푖푡푎푡푖푣푒 푠푐표푟푒 (퐼퐼)

The global score has a maximum of 10 points with higher scores indicating better performance. The global score considers both the correctness of the clock drawing as well as the number of errors made, therefore providing a more comprehensive capture of the level of impairment in the CDT performance. A detailed description of the scoring criteria for both the quantitative and qualitative scores of the Cahn system is provided in Table 3.4.

Table 3.4. Description of the scoring system proposed by Cahn et al. (1996)

Quantitative Score (I) Maximum 10 points

Assesses correctness and presence of the clock face (0-2 points)

Placement of the numbers (0-4 points)

Placement of the hands (0-4 points)

Qualitative Score (II) Maximum 8 points, a point is allotted for each of the error types listed below

Error Type Description

Stimulus-bound response Tendency of the drawing to be dominated or guided by a single stimulus

Conceptual deficit Reflects a loss or deficit in accessing knowledge of the attributes, features and meaning of a clock

Perseveration Continuation or recurrence of an activity without an appropriate stimulus

Neglect of left hemisphere All clock attributes are written on the right side of the clock face

Planning deficit Represented by gaps between 12, 3, 6 or 9

Nonspecific spatial error Deficit in spatial layout of numbers, without any specific pattern of the spatial disorganization

Numbers written outside Numbers are written either around the perimeter of the circle or the clock the circle itself

Numbers written counter Arrangement of the numbers with ‘12’ at the top of the clock clockwise face and then continuing around in a counter clockwise fashion

3.4.3 Cognitive and Demographic Data Extraction The cognitive and demographic data were scored and recorded in Excel spreadsheets after each testing session. The variables of interest were extracted from the Excel spreadsheets for analysis.

3.4.4 Demographic and Behavioral Statistical Analysis All variables of interest (i.e. CDT performance, demographic information and MoCA scores) were separated into two groups: patients with MCI, and healthy controls. For each variable, the mean and standard deviation of each group was calculated in Excel. The data were assessed for normality using the Shapiro-Wilk Normality Test. If the test revealed a significance level that was below 0.05 (threshold for statistical significance), then the null hypothesis (i.e. the sample came from a normal distribution) was rejected and the sample was classified as not normally distributed. If the significance level was above 0.05, the distribution of the data was visualized using histograms and quantile-quantile plots to determine whether or not it had a normal distribution. If the data were classified as normally distributed, an independent sample paired t- test was used to compare the MCI and healthy control groups and Pearson’s correlation was used to measure the statistical relationship between variables with significance identified at a False- Discovery Rate (FDR) of 0.05, to adjust for multiple comparisons. If the data were classified as not normally distributed, a paired Mann-Whitney U test was used to compare the groups with significance identified at a FDR of 0.05, to adjust for multiple comparisons, and Spearman’s rank correlation was used to measure the statistical relationship between variables. Due to the small sample size and skewed distribution of data, most variables were not normally distributed, therefore the majority of the analyses employed non-parametric statistical tests. The normality testing, hypothesis testing and correlation analyses listed above were completed in R (The R Foundation, Vienna, Austria).

To analyze performance on the tablet-based CDT, scores were used for each chosen method (Shulman, Sunderland, Rouleau and Cahn methods), as well as the completion time. The completion time metric was the total time to complete the entire clock drawing and was the sum of the completion times for each individual component (e.g. R1, clock face drawing; R2, clock number drawing; R3, clock hand drawing). For each participant, the behavioral CDT measures (scores and completion time) were averaged across all five CDT trials during fMRI. Therefore,

54 measurements of CDT performance include data from all tablet-based CDT trials (three o’clock, twenty minutes to four, ten minutes after eleven, forty-five minutes after ten, and five minutes after six). The averages of the CDT metrics were calculated in Excel. Unless otherwise specified, the total Rouleau score and global Cahn scores were used for analysis.

Both sensitivity and specificity were calculated for all four scoring methods using multiple different CDT cut-offs, to determine the optimal score to distinguish patients with MCI from cognitively healthy controls. These measures provide more information on the efficacy of established CDT scoring methods at screening for MCI. The sensitivity and specificity were calculated in Excel using the average CDT score across all five tablet-based CDT trials.

Cohen’s Kappa inter-reliability test was used to measure the level of agreement between the two raters who scored the CDT for each CDT trial and each scoring method. To assess convergent validity between the tablet-based and paper-based CDT, the scores for both versions were compared using Spearman’s rank correlation. Only the third trial of the tablet-based CDT (ten minutes past eleven) results were used in this analysis to ensure that the time instructions were matched between both CDT versions. Both statistical tests were completed using R.

3.4.5 fMRI Data Extraction Data collected from the MRI system were provided as Digital Imaging and Communications in Medicine (DICOM) image files. For further analysis, these images were converted to Neuroimaging Informatics Technology Initiative (NIfTI) files using the DCM2NII software utility (NeuroImaging Tools & Resources Collaboratory, Washington, D.C.). The fMRI data were subsequently processed and analyzed using both publicly-available open source software and algorithms developed in the laboratory, reported in the next section.

Before analysis of the fMRI data, the onset and durations for the CDT and each CDT component (i.e. clock face, clock numbers and clock hands) were extracted from the reconstructed videos by research personnel. The timing information was adapted to PRONTO format and included: (1) the unit of measurement for task onset/duration (seconds), (2) time between scan volumes (TR = 2000 ms), (3) type of task paradigm (event-related), (4) name of each condition (e.g. CDT, clock

55 face drawing, clock numbers drawing, clock hands drawing, visual fixation), (5) onset for each event (in seconds), and (6) duration of each event (in seconds).

3.4.6 Pre-Processing of fMRI Data Prior to preprocessing, the RA manually inspected the fMRI data and structural scans to identify any potential visual abnormalities. No visual abnormalities were found allowing fMRI data analysis to proceed with the full dataset of participants. Data preprocessing and analysis were completed using a hybrid pipeline initially developed by Churchill et al. (Churchill, Spring, Afshin-Pour, Dong, & Strother, 2015) combining tools from the Analysis of Functional Neuroimages (AFNI) package (https://afni.nimh.nih.gov) (Cox, 1996), the FMRIB Software Library (FSL) package (https://www.fmrib.ox.acuk/fsl) (S. M. Smith et al., 2004) and customized algorithms developed in the lab.

The first and last two scan volumes of each run were discarded. Subsequent processing steps included rigid-body motion correction (AFNI 3dvolreg), removing outlier scan volumes through the SPIKECOR algorithm (Campbell, Grigg, Saverino, Churchill, & Grady, 2013), slice-timing correction (AFNI 3dTshift), spatial smoothing with a 6mm full width at half max (FWHM) isotropic Gaussian kernel (AFNI 3dmerge) and regression of motion parameter estimates and linear-quadratic trends as nuisance covariates. Two processes were used to control for physiological noise: (1) PHYCAA+ (Churchill & Strother, 2013), a data-driven algorithm that down-weighted voxels that did not contain neuronal tissue and (2) seed-based regression of mean BOLD timeseries for seeds placed in white matter (left corona radiata seed = 1032 mm3 and right corona radiata seed = 1072 mm3) and cerebrospinal fluid (left lateral ventricle seed = 432 mm3 and right lateral ventricle seed = 448 mm3). The seeds were manually traced on one plane using the MNI152 (Montreal Neurological Institute) template and were fit to each individual using the spatial normalization protocol. Spatial normalization was completed for all participants to transform their data to a common neuroanatomical template. This was done using the FSL flirt algorithm, which computed the rigid-body transform of the mean fMRI volume for each individual to their corresponding T1-weighted anatomical image as well as the affine warp of each individual’s T1 image to the MNI152 template (Mazziotta, Toga, Evans, Fox, & Lancaster, 1995). The population of the current study included older age participants, leading to a

56 variability in brain size. To ensure robust anatomical transformations, all preprocessed images were visually inspected, and manual segmentation of the brains was performed if required.

After preprocessing the fMRI data, the first level of analysis was done at the individual participant level. A standard least squares general linear model (GLM) was used to measure the effect of task conditions (CDT, clock face drawing, clock numbers drawing, clock hands drawing and fixation) on the BOLD response at each brain voxel. The current analysis used visual fixation as the baseline condition. Therefore, brain activity during the CDT was compared to brain activity during visual fixation, by contrasting GLM coefficient estimates for task conditions against fixation. Brain activation throughout all task conditions during both runs was used in the fMRI analysis. Using the NPAIRS analysis framework (Strother et al., 2002), regression maps were first calculated separately for runs 1 and 2 and then combined to produce a z-scored reproducible brain map for each subject and contrast of interest. The contrasts of interest in this investigation include: (1) the whole CDT vs fixation, (2) clock face drawing vs fixation, (3) clock numbers drawing vs fixation, and (4) clock hands drawing vs fixation. The fMRI analysis used data from both runs of the tablet-based CDT, therefore brain activity associated with all five CDT trials was considered.

Seed-based connectivity analyses were also conducted at the individual participant level. This was achieved by first measuring the mean BOLD timeseries within a pre-specified seed region of interest (ROI), then measuring pairwise correlations with the BOLD time courses for each brain voxel. To ensure that connectivity measurements were specific to the CDT task, we discarded timepoints acquired during fixation along with the two scan volumes at the start of each CDT task block, prior to the calculation of connectivity values. As with the task-based analysis above, analysis was conducted in the NPAIRS framework, where connectivity maps were calculated separately for runs 1 and 2, then combined to produced z-scored reproducible brain maps. The ROI seeds were made using the drawing tool in MRIcron and the Automated Anatomical Labelling (AAL) atlas to specify exact locations of cortical brain areas. ROI seeds were located in the following bilateral areas: angular gyri, caudate nuclei, inferior frontal gyri, middle frontal gyri, middle temporal gyri, superior frontal gyri and supramarginal gyri. These regions were selected based off of current CDT neuroimaging literature, which suggests the involvement of these areas in task completion. Additionally, these regions are implicated in cognitive functions,

57 which are important for CDT completion, such as visuospatial ability, executive function and semantic memory.

3.4.7 Post-Hoc Group-Level Analyses of fMRI Data The second level of analysis was completed using the set of the z-scored participant brain maps obtained in the procedures outlined in Section 3.4.6. Group-level analysis was performed for each of the task contrasts (whole CDT vs. fixation, clock face (R1) vs. fixation, clock numbers (R2) vs. fixation, clock hands (R3) vs. fixation) using voxel-wise one-sample t-tests to identify brain regions showing significant CDT-related brain activity for both patients with MCI and cognitively normal controls. The brain activity patterns characteristic of the two groups (patients with MCI and healthy controls) were visually compared. In addition, for each of the tasks contrasts, voxel-wise paired two-sample t-tests were used to investigate the differences in CDT- related brain activity between the two groups. Statistical thresholding of group level t-statistic maps was performed by first thresholding a voxel-wise significance level of p < 0.005, followed by cluster-size thresholding to identify significant clusters at an adjusted =0.05 significance level. The cluster-size threshold was determined using the 3dFWHMx to estimate spatial smoothness of activation maps, followed by 3dClustSim to determine the minimum cluster-size threshold using simulations.

As an exploratory post-hoc step, brain activation was further compared to measure the group differences in CDT-related activation in focal ROIs. The ROIs used for this analysis were same ones used in the seed-based analysis (see Section 3.4.6). Group changes in brain activation were investigated for the entire CDT compared to visual fixation. T-statistic values for activation of the specified ROI were generated for each participant using the methods outlined above for group-level analysis. The t-statistic values were compared between the patients with MCI and the matched healthy controls through a paired two-sample t-test, with significant ROIs identified at a FDR of 0.05, to adjust for multiple comparisons. This analysis was completed in R (The R Foundation, Vienna, Austria).

For analyses of functional connectivity, group-level analysis was performed using voxel-wise one-sample bootstrap tests (1000 iterations) to identify areas of significant functional

58 connectivity for the group (patients with MCI and healthy controls) during CDT completion compared to fixation. Two-sample repeated-measures bootstrap tests were also used to compare the brain connectivity patterns between the two groups. This non-parametric testing approach was used for functional connectivity measures to avoid any distributional assumptions regarding the functional connectivity measures. The statistical thresholds were determined using the same methods as described above.

The MRIcron software (NeuroImaging Tools & Resources Collaboratory, Washington, D.C.) was used to generate images and localize areas of brain activity. Cluster size (number of voxels), MNI coordinates (x, y and z) and peak levels of activation were obtained for each statistically significant cluster of activation. The AAL atlas was used to identify areas of activation.

Investigating the Effect of MCI on Behavioral CDT Performance

This chapter addresses behavioural CDT performance. In total, 24 patients with MCI and 25 cognitively healthy controls were recruited for this thesis work. Four patients with MCI and five healthy controls were unable to complete both sessions of the study (driving and tablet) due to various reasons including vision issues, claustrophobia, simulator sickness during the driving session and low MoCA scores (for healthy controls). Therefore, data were analyzed from 20 patients with MCI and 20 healthy control participants.

4.1 Results

4.1.1 Participant Demographics

Twenty (20) patients with MCI (mean age = 70.8; mean years of education 16.3; Male, n = 15) and twenty (20) matched healthy controls (mean age = 70.4; mean years of education 16.1; Male, n = 15) completed this study. As reported in Table 4.1 below, patients with MCI did not significantly differ from healthy controls in terms of mean age or education. The healthy controls were age and sex matched to each patient with MCI.

Table 4.1. Demographic characteristics of patients with MCI and healthy controls

Healthy Controls Patients with MCI p-value

(n = 20) (n = 20)

Age, years 70.4  7.5 (56-84) 70.8  7.8 (53-87) 0.847

Sex (male), n (%) 15 (75%) 15 (75%) -

Education, years 16.1  2.7 (12-23) 16.3  3.6 (12-25) 0.981

Note. The values are reported as mean  standard deviation (range) unless otherwise indicated. P-values are reported for the paired t-test (healthy controls vs patients with MCI) for age and for the paired Mann-Whitney U Test (healthy controls vs patients with MCI) for years of education. One healthy control participant did not provide years of education and was not included in that analysis. n, number of participants; MCI = mild cognitive impairment.

Edinburgh Handedness Inventory

The results of the Edinburgh handedness questionnaire confirmed that all participants were right- handed. The average laterality quotient was 76.9 across all participants (patients with MCI and healthy controls) (standard deviation = 23, range = 7 to 100), which indicates strong right lateralization. All participants had a positive laterality quotient, which indicates right hand dominance.

4.1.2 Cognitive Testing

The MoCA results from the participants are reported in Table 4.2 below. Overall, patients with MCI performed significantly worse on the MoCA compared to healthy controls after correction for multiple comparisons (24.1 vs. 27.9, FDR-adjusted p = 0.003). The groups also significantly differed in scores on the visuospatial/executive function domain (3.8 vs 4.7, p = 0.022), the language domain (2.1 vs 2.9, p = 0.005) and the abstraction domain (1.4 vs 1.9, p = 0.029) of the MoCA. However, after correcting for multiple comparisons, only the language domain revealed significant differences in scores between the groups (2.1 vs 2.9, FDR-adjusted p = 0.022). On average, patients with MCI received worse scores on the remaining MoCA domains, however these findings were not statistically significant.

Table 4.2. Mean scores of patients with MCI and healthy controls on the MoCA and sub-scores of the domains of the MoCA

Healthy Controls Patients with MCI p-value False-discovery rate

(n = 20) (n = 20) adjusted p-value

MoCA Total Score 27.9  1.3 (26-30) 24.1  2.4 (20-27) < 0.001 0.003 (/30)

Memory (/5) 3.8  1.2 (1-5) 2.7  1.8 (0-5) 0.059 0.095

Visuospatial/Executive 4.7  0.6 (3-5) 3.8  1.2 (1-5) 0.022 0.058 Function (/5)

Naming (/3) 2.9  0.2 (2-3) 2.8  0.4 (2-3) 0.129 0.149

Language (/3) 2.9  0.5 (1-3) 2.1  0.9 (0-3) 0.005 0.022

Attention (/6) 5.8  0.5 (4-6) 5.6  0.8 (3-6) 0.359 0.359

Abstraction (/2) 1.9  0.4 (1-2) 1.4  0.7 (0-2) 0.029 0.058

Orientation (/6) 6.0  0.0 (6) 5.8  0.4 (5-6) 0.072 0.096

Note. Values are reported as mean  standard deviation (range) format. P-values are reported for the paired Mann-Whitney U Test (healthy controls vs patients with MCI). Adjusted p-values have been corrected for multiple comparisons using false-discovery rate methods. n = number of participants, MoCA = Montreal Cognitive Assessment.

MoCA cognitive domain subtests: Memory: delayed recall; Visuospatial/executive function: TMT-B, copy cube drawing, clock-drawing test; Naming: name three animals; Language: sentence repetition, verbal fluency; Attention: digit span forward and backward, letter “A” tap, serial sevens; Abstraction: similarities test.

The results of the paper-version of the CDT are reported in Table 4.3. Performance on the task was measured using scores from the Rouleau, Sunderland, Shulman and Cahn methods (Rouleau et al., 1992; Sunderland et al., 1989) as well as time to complete the CDT. One patient with MCI

62 did not complete the paper version of the CDT, therefore that patient and the matched healthy control were excluded from this analysis. Although patients with MCI scored worse on the task compared to healthy controls, none of the scoring methods reported a statistically significant difference between the groups. Patients with MCI took on average slightly longer to complete the CDT compared to healthy controls, but this difference was also not statistically significant.

Table 4.3. Mean performance of patients with MCI and healthy controls on the paper version of the CDT

Healthy Controls Patients with MCI p-value False-discovery rate

(n = 20) (n = 20) adjusted p-value

Rouleau Total Score 9.4  1.0 (6.5-10) 8.8  1.7 (4-10) 0.303 0.445

(/10)

R1 Score (/2) 1.9  0.2 (1.5-2) 2.0  0 (2) 0.346 0.445

R2 Score (/4) 3.6  0.8 (1-4) 3.4  1.1 (1-4) 0.238 0.429

R3 Score (/4) 3.8  0.4 (2.5-4) 3.5  0.9 (0-4) 0.214 0.429

Sunderland Score (/10) 9.3  1.8 (4-10) 8.1  2.7 (3-10) 0.114 0.429

Shulman Score (/6) 1.5  0.8 (1-4) 2.1  1.2 (1-4.5) 0.066 0.429

Cahn Quantitative 9.4  1.0 (6.5-10) 8.8  1.7 (4-10) 0.303 0.445 Score (I) (/10)

Cahn Qualitative Score 0.1  0.3 (0-1) 0.4  0.8 (0-2.5) 0.178 0.429 (II) (/8)

Cahn Global Score 9.2  1.3 (6-10) 8.4  2.5 (1.5-10) 0.482 0.504 (/10)

Time of CDT 28.6  10.6 (16.1- 31.8  11.5 (17.2- 0.541 0.541 Completion (s) 53) 65)

4.1.3 Tablet-Based CDT Performance

CDT Performance of Patients with MCI

The first aim of the current investigation was to compare CDT performance in patients with MCI (n = 20) and healthy matched controls (n = 20) to determine the efficacy of the CDT as a screening tool for MCI.

Table 4.4 outlines the results of performance on the tablet-based CDT in healthy controls and patients with MCI, using the scoring systems proposed by Rouleau, Shulman, Sunderland and Cahn (Cahn et al., 1996; Rouleau et al., 1992; K I Shulman et al., 1993; Sunderland et al., 1989). One patient with MCI only completed three trials of the tablet-based CDT, therefore the results for this patient are an average of the three CDT trials instead of five. On average, patients with MCI scored worse than healthy controls. The Rouleau (7.9 vs. 8.9, p = 0.022), Sunderland (7.2 vs. 8.8, p = 0.033) and Shulman (2.6 vs. 2.0, p = 0.040) total scores were significantly different between the two groups before correction for multiple comparisons. However, after FDR- adjustment, none of the scores were significantly different between the two groups. The Rouleau scoring system broke down the total score into three components (R1, R2 and R3). All three components identified lower scores in patients with MCI), however R1 was the only component that showed a significant difference between the two groups (1.7 vs. 1.9, FDR-adjusted p-value = 0.048). The Cahn quantitative score (I) also revealed lower scores in patients with MCI as it followed the same scoring guidelines as the Rouleau total score, however this difference was no longer significant after correction for multiple comparisons. Although the Cahn qualitative (II) and global scores were lower in patients with MCI, there was no statistically significant difference between the groups. Additionally, across all four scoring systems, there was more

64 observed within-group variability in the MCI group compared to the control group (i.e. higher standard deviation values and larger ranges).

The other key metric of CDT performance was completion time of the task. The tablet-based CDT allowed collection of completion time data for all three clock components (R1, R2 and R3) as well as the total clock. The results of the completion time analysis are reported in Table 4.4. Although patients with MCI took longer than healthy controls to complete clock components and the total clock, there was no statistically significant difference in completion times between the two groups.

Table 4.4. Mean performance of patients with MCI and healthy controls on the tablet version of the CDT

Healthy Controls Patients with MCI p-value False-discovery

(n = 20) (n = 20) rate adjusted p- value

Rouleau Total Score (/10) 8.9  0.7 (7-9.9) 7.9  1.3 (5.1-9.6) 0.022 0.110

R1 Score (/2) 1.9  0.1 (1.6-2) 1.7  0.3 (1-2) 0.004 0.048

R2 Score (/4) 3.6  0.5 (2-4) 3.1  0.9 (0.9-4) 0.055 0.110

R3 Score (/4) 3.4  0.4 (2.7-4) 3.2  0.6 (2.2-4) 0.122 0.162

Sunderland Score (/10) 8.8  1.3 (4-10) 7.2  2.4 (3-10) 0.033 0.110

Shulman Score (/6) 2.0  0.5 (1-2.9) 2.6  1.0 (1.1-4.2) 0.040 0.110

Cahn Quantitative Score (I) 8.9  0.7 (7-9.9) 7.9  1.3 (5.1-9.6) 0.022 0.110 (/10)

Cahn Qualitative Score (II) 0.2  0.3 (0-1) 0.5  0.6 (0-2.3) 0.055 0.110 (/8)

Cahn Global Score (/10) 8.6  1.0 (5.6-9.8) 7.5  1.8 (3.1-9.6) 0.070 0.120

Total CDT Completion 45.2  11.7 (28.4- 50.5  11.9 (22.0- 0.114 0.622 Time (s) 70.4) 71.7)

R1 Completion Time 5.1  2.0 (2.4- 5.3  1.4 (3.6-9.7) 0.622 0.259 10.3)

R2 Completion Time 21.1  5.2 (11.1- 22.3  7.1 (7.5- 0.216 0.569 33.0) 37.4)

R3 Completion Time 11.8  5.9 (4.5- 13.2  6.5 (4.3- 0.522 0.162 26.1) 26.9)

Sensitivity and Specificity of the CDT

Although three scoring systems identified a statistically significant difference in tablet-based performance between patients with MCI and healthy controls, further analysis is necessary to justify the CDT as a screening tool for MCI. Sensitivity and specificity are well-established measures that are used to assess the efficacy of screening tools at correctly identifying diseased individuals. Table 4.5 reports the results of sensitivity and specificity analysis using CDT performance from all four scoring systems. For each scoring system, multiple cut-off points were used to measure sensitivity and specificity. An effective screening tool has a minimum sensitivity of 80% and a minimum specificity of 60% (Blake et al., 2002). Following this criteria, none of the scoring systems and cut-offs used in this study have adequate sensitivity and specificity. However, the Rouleau and Sunderland systems with a cut-off of < 9 have the closest values at 75% sensitivity and 55% specificity, and 70% sensitivity and 60% specificity respectively.

Table 4.5. Sensitivity and Specificity of the tablet-based CDT

Scoring Method Cut-Off Value Sensitivity Specificity

Rouleau ≤ 7 30 90

Rouleau < 8 45 90

Rouleau < 8.5 55 85

Rouleau < 9 75 55

Sunderland ≤ 7 35 95

Sunderland < 8 60 90

Sunderland < 9 70 60

Shulman  2 65 45

Shulman > 2 65 55

Shulman > 1.5 85 20

Cahn ≤ 7 45 90

Cahn ≤ 8 50 80

Cahn < 8 50 85

Cahn < 9 85 45

Note. Sensitivity and specificity values are reported as percentages (%). Sensitivity and specificity were calculated in Excel using the average CDT scores for all five tablet-based CDT trials. CDT = clock-drawing test.

Interrater Reliability of the Scoring Methods

To measure interrater reliability for the scoring methods, two independent individuals scored each CDT trial (e.g. five trials of tablet-based CDT and one trial of paper-based CDT) and a Cohen’s Kappa interrater reliability test was used to compare the scores from the two

67 individuals. The Cahn scoring method had the highest Cohen’s Kappa value at 0.77, which is classified as substantial agreement (Cohen, 1960). The Rouleau, Sunderland and Shulman methods had moderate levels of agreement with Cohen’s Kappa values of 0.48, 0.53 and 0.48 respectively (Cohen, 1960).

Correlation between CDT and MoCA Performance

The MoCA is an established and validated tool for MCI detection. To better assess the efficacy of the CDT as a screening tool, correlation between performance on the tablet-based CDT and MoCA was measured. The results of the correlation test are reported in Table 4.6. Outcomes from all four scoring systems were significantly correlated with the total MoCA score with moderate strength. The Rouleau, Sunderland and Cahn method were positively correlated with MoCA scores (rho = 0.41, p < 0.01; rho = 0.35, p = 0.03; rho = 0.43, p < 0.01 respectively), whereas the Shulman method was negatively correlated with MoCA score (rho = -0.36, p = 0.02).

Table 4.6. Correlation between performance on the tablet-based CDT and the MoCA

Scoring Method rho p-value 99% CI

Rouleau 0.41 < 0.01 0.01

0.70

Sunderland 0.35 0.03 -0.06

0.66

Shulman -0.36 0.02 -0.67

0.04

Cahn 0.43 < 0.01 0.03

0.71

Note. Rho and p-values are reported for a Spearman’s correlation test. Rho = correlation coefficient, CI = confidence interval, CDT = clock-drawing test, MoCA = Montreal Cognitive Assessment.

Correlation between CDT Performance and Demographics

A common disadvantage of cognitive assessments is that they are sensitive to demographic effects, such as age or educational background. To determine if the CDT was affected by these variables, the correlation between tablet-based CDT performance and both age and years of education was calculated. The results of this analysis are reported in Table 4.7. There were no statistically significant correlations between CDT performance and both age and years of education for any scoring method. The correlation coefficients for both relationships were small, indicating extremely weak correlations.

Table 4.7 Correlation between performance on the tablet-based CDT and demographic variables (age and years of education)

rho p-value 99% CI

Age and CDT Performance

Rouleau -0.08 0.64 -0.46

0.33

Sunderland -0.11 0.50 -0.49

0.30

Shulman 0.11 0.52 -0.31

0.48

Cahn -0.03 0.86 -0.42

0.37

Years of Education and CDT Performance

Rouleau 0.02 0.89 -0.38

0.42

Sunderland -0.06 0.70 -0.45

0.35

Shulman 0.04 0.83 -0.37

0.43

Cahn 0.02 0.92 -0.39

0.41

Note. Rho and p-values are reported for a Spearman’s correlation test. Rho = correlation coefficient, CI = confidence interval, CDT = clock-drawing test.

Convergent Validity of the Tablet-Based CDT

To determine the validity of the tablet-based CDT, the results were compared to a standard paper version of the CDT. The paper-based CDT required participants to set the clock to “10 minutes past 11”, therefore only the third trial of the tablet-based CDT (“10 minutes past 11”) was used in this analysis. One patient with MCI did not complete the paper version of the CDT and was excluded. The Rouleau, Sunderland and Cahn scoring systems had statistically significant correlations between the tablet-based and paper-based CDT with moderate strength correlations (rho = 0.32, p = 0.04; rho = 0.53, p < 0.001; rho = 0.46, p < 0.01 respectively). However, the Shulman scoring system had a weak correlation between the tablet-based and paper-based CDT, which was not statistically significant (rho = 0.20, p = 0.21).

Post-Experimental Questionnaire

One patient with MCI and five healthy controls did not complete the post-experimental questionnaire, therefore they were excluded from this analysis. Of the remaining participants (n = 34), the majority self-rated their tablet-based CDT performance as good or fair. When compared to the paper version of the task, although a large proportion of the participants rated their tablet-based CDT performance as the same or better, the majority indicated that their tablet- based performance was slightly worse. Participants reported comfort with all aspects of fMRI set-up (e.g. ease of hearing instructions, quality of visual display, comfort with position of the

70 tablet and comfort with using the stylus). One patient with MCI reported difficulty visually focusing and another reported dizziness during the tablet session. One patient with MCI and one healthy control reported severe fatigue after completing the tablet session. No other experiences of adverse physiological symptoms were reported.

4.2 Discussion

The objective of this chapter of the thesis was to determine the effect of MCI on behavioural CDT performance, with the CDT administered in the normal paper format and also using an fMRI-compatible tablet to deliver five unique CDT trials to patients with MCI and matched healthy controls. Two key behavioural metrics were measured, score and time of task completion. Multiple popular CDT scoring systems (Cahn et al., 1996; Rouleau et al., 1992; K I Shulman et al., 1993; Sunderland et al., 1989) were also evaluated in terms of ability to detect MCI-related impairments. The behavioural metrics were averaged for each participant across all five trials.

The results of the investigation are in agreement with current literature on the topic, which show worse CDT performance in patients with MCI compared to healthy controls (Babins et al., 2008; Beinhoff et al., 2005; Donnelly et al., 2008; Ehreke et al., 2011; K. S. Lee et al., 2008; Mazancova et al., 2017; Nunes et al., 2008; Parsey & Schmitter-Edgecombe, 2011; Powlishta et al., 2002; Rubínová et al., 2014; Sager et al., 2006; Thomann et al., 2008). Worse performance was observed in patients with MCI. Specifically, patients with MCI scored lower on the Rouleau R1 (clock face drawing) score, Rouleau total score, Sunderland score, Shulman score and Cahn quantitative (I) score. However, after correction for multiple comparisons, only the R1 (clock face drawing) score was significantly different between the healthy and MCI group. Despite longer completion times in patients with MCI, there was no statistically significant difference between the two groups. Sensitivity and specificity of each scoring system were also computed to determine effectiveness at differentiating between the healthy controls and patients with MCI. However, similar to the results published in current literature (Beinhoff et al., 2005; Donnelly et al., 2008; Ehreke et al., 2010; Nunes et al., 2008; Ravaglia et al., 2013; Sager et al., 2006; Yamamoto et al., 2004), no scoring system had adequate values of sensitivity and specificity for

71 detecting MCI. The Rouleau and Sunderland systems had the closest values at 75% sensitivity and 55% specificity, and 70% sensitivity and 60% specificity respectively.

A discussion, which specifically addresses each hypothesis outlined in Chapter 2: Specific Research Questions and Hypotheses, is detailed below.

4.2.1 Hypothesis 1: Difference in CDT Performance between the Groups

The current results showed that patients with MCI performed worse on the tablet-based CDT compared to healthy controls, however group differences in performance metrics (e.g. CDT score and time of task completion) were not significant after correction for multiple comparisons. These trends are congruent with the first hypothesis, which predicted that patients with MCI would portray impaired CDT performance through worse scores and longer completion times. The differences between the two groups in the tablet-based CDT scores according to the Rouleau system (R1 and Total Score), Shulman system, Sunderland system and Cahn (I) quantitative score were lower in patients with MCI, but the difference in R1 score was the only one that reached statistical significance after correction for multiple comparisons. These results are congruent with the original hypothesis, which predicted that the effect of MCI on CDT performance would not be statistically significant. A large proportion of published studies did not observe significant differences in CDT scores between patients with MCI and healthy controls (Beinhoff et al., 2005; Nunes et al., 2008; Parsey & Schmitter-Edgecombe, 2011; Powlishta et al., 2002; Rubínová et al., 2014; Sager et al., 2006), which guided the formation of this hypothesis.

4.2.2 Hypothesis 2: Effect of MCI on Specific CDT Components

Given the subtle cognitive deficits associated with MCI, it was hypothesized that patients with MCI would demonstrate increased difficulty on the more complex components (clock numbers and clock hands drawing) compared to the simple component (clock face drawing). However, the results of the current study did not support the above hypothesis. Although patients with MCI performed worse on all three components of the tablet-based CDT, only the clock face (R1) score was significantly different between the two groups. Multiple research studies have found the opposite effect, where the clock face (R1) score did not significantly differ between the

72 groups, meanwhile the numbers (R2) and/or hands (R3) scores did (Babins et al., 2008; Ehreke et al., 2011; K. S. Lee et al., 2008).

The MCI cohort had a range of cognitive impairment and associated deficits, resulting in a heterogenous presentation of MCI. Therefore, certain patients within the MCI cohort may have demonstrated significant deficits on the complex components of the tablet-based CDT. Conversely, other patients with MCI may have not exhibited impairments or very slight impairments that were not detected by the scoring system. Approximately 40% of the patients with MCI scored poorly on the numbers (R2) and hands (R3) components, meanwhile the remaining patients with MCI exhibited mild or no impairment on these components. There was more variability in behavioural performance in patients with MCI compared to healthy controls as shown by higher standard deviation values and ranges for the tablet-based CDT measures. This variability may have contributed to the lack of significant differences between the groups for the numbers (R2) and hands (R3) components. Additionally, the lack of significance may be a result of the heterogeneity of the MCI group as the current study included patients with all sub- types of MCI. The different sub-types of MCI have various symptoms and effects on cognitive function. It is important to conduct a study investigating the effect of individual MCI subtypes on CDT performance to confirm the current results. This is further discussed in in Section 6.3.2.

However, whereas the average score on the individual components indicated that the clock face (R1) score was the most impaired in patients with MCI, more impairment on the numbers (R2) and hands (R3) components was shown when the common error types were evaluated. Patients with MCI exhibited less than a perfect score on numbers (R2) and hands (R3) more often than on clock face (R1). In terms of specific error types in the current study, patients with MCI most commonly drew numbers outside the clock followed by conceptual deficit errors. There is evidence from previous studies that indicates conceptual deficits occur more often among patients with MCI than healthy controls, supporting the results of the current study (Chiu et al., 2008; K. S. Lee et al., 2008; Parsey & Schmitter-Edgecombe, 2011; Yamamoto et al., 2004). Conceptual deficits reflect a loss in semantic memory, through difficulty accessing knowledge on the attributes and meaning of a clock. When committing this error, the majority of the MCI cohort misrepresented the time due to an inappropriate use of the hands or absence of the hands, which reflects difficulty with the hands component of the task. These results, in conjunction with current literature, suggest that the numbers (R2) and hands (R3) are more affected by MCI-

73 associated impairment. Therefore, adapting scoring systems to increase focus on those two components may better differentiate between MCI and healthy controls.

4.2.3 Hypothesis 3: Efficacy of CDT Scoring Systems to Detect MCI

Multiple scoring systems have been developed for the CDT to detect levels of impairment characteristic of dementia. Because these systems were originally designed to detect more severe impairment (e.g. established dementia) and patients with MCI experience very mild cognitive deficits, it was predicted that the current scoring methods would not be effective at screening for MCI, although the semi-qualitative systems (Cahn and Rouleau) would be better at detecting MCI than the quantitative systems (Sunderland and Shulman). Sensitivity and specificity were used to measure the efficacy of each screening method, following established guidelines of a sensitivity of at least 80% and specificity of at least 60% classifying an effective screening tool (Blake et al., 2002). The results of the current analysis indicated that, in agreement with the original hypothesis, none of the current scoring methods had an adequate sensitivity and specificity to be classified as an effective screening tool. It was expected that the two semi- qualitative systems (Rouleau and Cahn) would be better at detecting MCI. However, the Rouleau and Sunderland scoring methods were the closest to the established sensitivity and specificity values, suggesting that they were the best of the four systems at detecting MCI. Similarly, previous studies have found inadequate specificity and sensitivity for detecting MCI with these CDT scoring systems (Beinhoff et al., 2005; Chiu et al., 2008; Connor et al., 2005; Duro et al., 2018; Ehreke et al., 2011; Esteban-Santillan, Praditsuwan, Veda, & Geldmacher, 1998; K. S. Lee et al., 2008; Nunes et al., 2008; Powlishta et al., 2002; Ravaglia et al., 2013; Sager et al., 2006). Only one published study, conducted by Yamamoto et al., used the CDT to diagnose MCI with satisfactory sensitivity and specificity and it employed the Cahn scoring system (Yamamoto et al., 2004).

Despite the significant difference in CDT behavioural performance between the two cohorts, current scoring methods are not sensitive enough to differentiate between the CDT impairment characteristics of patients with MCI and cognitively healthy older adults. These results are encouraging, however additional research is necessary to determine if different scoring schemes could improve the diagnostic utility of the CDT. Future investigations should develop an updated

74 scoring method that combines current scales, but is customized to detect common mistakes committed by patients with MCI. Qualitative analysis shows potential to differentiate patients with MCI from healthy controls, therefore focusing more on qualitative components may result in a higher screening efficacy. Increasing the maximum achievable score can allow integration of more qualitative components and will increase sensitivity as small differences in performance will be more easily detected. Additionally, creating a weighted scale, which gives more weight to error-types more commonly committed by patients with MCI (e.g. conceptual errors, errors on numbers (R2) and hands (R3) components) and less weight to common general errors may result in a better screening tool.

4.2.4 Validity of the CDT as a Cognitive Assessment Tool

As with many other cognitive assessment tools it is important to understand if the CDT outcome is heavily biased by education, age or other demographic factors. The Spearman’s correlation between CDT score and both age and years of education revealed no significant correlations with very small correlation coefficients (rho < 0.11), suggesting that neither age nor education had a significant effect on CDT performance. However, current literature states that both age and level of education can impact CDT outcome (Borson, Scanlan, Brush, Vitaliano, & Dokmak, 2000; Brodaty & Moore, 1997; Caffarra et al., 2011). In a population of fifty-six patients with AD and healthy controls, both age and years of education significantly correlated with CDT score with weak strength (Brodaty & Moore, 1997). With a sample size of approximately 250 older adults, Caffarra et al. found age to have a significant effect on CDT performance, meanwhile Borson et al. identified education as a confound for assessing CDT performance (Borson et al., 2000; Caffarra et al., 2011). The demographics of the cohort in this study were not comprehensive, only including older aged adults (53 – 87 years old), the majority of which were between the ages 65 and 79 (62.5%). Only 17.5% of the cohort was over the age of 80 and 7.5% were under the age of 60. Additionally, there were no participants with low education (years of education < 12 years). Most of the sample population had 13-16 years of education (51.3%), suggesting they completed basic post-secondary education and 35.9% completed higher levels of education (17+ years of education), which indicates that this was a well-educated cohort. The skewed nature of

75 the demographics data prevents any clear conclusions from being made as this may have heavily affected the correlation analysis results.

To validate the CDT as a cognitive assessment tool, CDT performance was correlated with MoCA scores. There were moderate significant correlations between CDT score and total MoCA score, which indicates reasonable convergent validity between the two assessments. Most previous studies have used the MMSE to assess convergent validity, however Duro et al. also measured the relationship between CDT scores and MoCA scores and found significant correlations similar to the current investigation (Duro et al., 2018).

4.2.5 Validity of the Tablet-Based CDT

To measure the validity of the tablet-based CDT, post-experimental questionnaires were distributed to the participants for feedback on the experience. In addition, the results of the tablet-based CDT were directly compared to the paper version of the identical task. The post- experimental questionnaire indicated that the majority of participants self-rated their tablet-based CDT performance as good or fair, however slightly worse than the paper version. The use of the tablet may have introduced a slight complexity to the CDT as participants were provided with a smaller space for the drawing (tablet surface vs blank page).

The Spearman’s correlation between the CDT scores on the tablet and paper versions of the task were statistically significant with moderate strength correlations for all of the scoring systems except the Shulman system. This indicates that there was reasonable convergent validity between the two versions of the CDT, however the Shulman scoring system may be sensitive to errors that commonly occur in the tablet environment and should not be used to assess the tablet-based CDT. Previous studies using the fMRI-compatible tablet have similarly found reasonable convergent validity between tablet and paper versions of cognitive tests (Karimpoor et al., 2017; Talwar et al., 2019).

4.3 Chapter Summary

In a sample of twenty patients with MCI and twenty matched cognitively healthy controls, the effect of MCI on behavioural CDT performance was investigated using four scoring methods and completion time as metrics. Through this analysis, patients with MCI were revealed to display impaired performance on the task as reflected by lower scores on the clock face (R1) component, the total Rouleau score, the Shulman score, the Sunderland score and the Cahn (I) Quantitative score. After correction for multiple comparisons, only the clock face (R1) score was significantly different between the two groups. None of the scoring systems had adequate sensitivity and specificity as a screening tool for MCI. This suggests that although patients with MCI display impairment on the task, current scoring methods are not sensitive enough to the mild cognitive deficits characteristic of MCI.

Furthermore, patients with MCI displayed more behavioural variability on CDT performance than healthy controls, which may have contributed to surprising results (i.e. only clock face (R1) component being significantly different between the groups). This is likely a consequence of the heterogeneity of deficits exhibited by patients with MCI and points towards the challenge of developing a single diagnostic test and/or scoring criterion for this population. Further studies are necessary to investigate the difference in CDT performance between sub-types of MCI and potentially develop specific scoring methods for each sub-type.

These results effectively demonstrate that patients with MCI displayed impaired performance on the tablet-based CDT, however current methods for measuring CDT performance are not adequate for screening for MCI. It is unclear which components of the CDT are the most impaired in patients with MCI as the behavioural results from the current study did not completely agree with existing literature. Furthermore, it is unknown what underlying neuropathology is triggering these identified CDT performance impairments observed in patients with MCI. Chapter 5 investigates the effect of MCI on CDT-related brain activity to identify changes in mental processing that may be causing impaired CDT performance.

Investigating the Effect of MCI on Brain Activation Patterns associated with CDT Completion

This chapter addresses the neural activity underlying CDT performance. A total of 20 patients with MCI and 20 healthy control participants completed the tablet-based CDT during fMRI. Brain activity was averaged across all five CDT trials for thirty-nine participants. Brain activity for one participant was only averaged across three CDT trials, as two of the trials could not be completed. Brain activity was analyzed using one sample t-tests to determine neural activation patterns characteristic of each group (patients with MCI and cognitively healthy controls) while completing the whole CDT and each of the individual components (R1, R2 and R3). Activation during CDT completion was compared to activation during visual fixation, such that any patterns of positive activity indicate increased brain activity during the task compared to fixation, and any patterns of negative activity indicate decreased brain activity during the task compared to fixation. Results displayed extensive, reliable activation patterns across many cortical and subcortical brain regions in both groups, suggesting that this seemingly simple task (drawing a clock) in reality engages numerous different brain regions that sub-serve different roles in cognition.

5.1 Results

5.1.1 Brain Activity Patterns of the CDT

One-sample group analysis revealed extensive CDT-related brain activation across multiple brain regions in both healthy controls and patients with MCI. For both groups, patterns of CDT-related activations, both positive (i.e. increased activity relative to fixation) and negative (i.e. reduced activity relative to fixation) are presented in Figure 5.1 and specific clusters of activation are outlined in Table 5.1. Both groups showed significant positive activation during CDT completion anteriorly in regions of the frontal lobes and insula cortices. Specifically, greater frontal activation was observed bilaterally across the middle and inferior frontal gyri; middle frontal lobes; precentral gyri; and supplementary motor areas. Posteriorly, both groups recruited

78 bilateral areas of the cerebellum, occipital, temporal and parietal lobes. Temporal activity was confined to areas of the inferior temporal lobes and the fusiform gyri. Meanwhile, activation of the occipital lobes encompassed inferior, middle and superior regions, including areas of the lingual gyri, calcarine sulci and cuneus. CDT-related positive activity was observed bilaterally in the inferior and superior parietal lobes. Activation of the supramarginal and angular gyri was lateralized to the right hemisphere and activation of the postcentral gyri was lateralized to the left hemisphere. Significant clusters of positive activation were also found in areas of the cingulate cortex, bilaterally in the middle cingulate gyri and in the right anterior cingulate gyrus.

Completion of the CDT in comparison to fixation was also associated with reliable patterns of negative brain activity largely in posterior regions of the brain, including the bilateral temporal lobes, posterior cingulate gyri, occipital lobes, hippocampus and cerebellum. Reduced temporal activation was observed in inferior, middle and superior regions of the temporal lobe, including areas of the fusiform gyri, the temporal poles and Heschl’s gyrus in the right hemisphere.

Overall, the two groups exhibited similar patterns of activation. However, visually comparing the brain activity between the two groups revealed that patients with MCI displayed less activity across certain cortical brain regions in the frontal, parietal and temporal lobes compared to the healthy controls. In the frontal lobe, patients with MCI exhibited less positive activation of the bilateral inferior frontal gyri (pars orbitalis, pars triangularis and pars operculis), right Rolandic operculum and left postcentral gyrus. Similarly, the supramarginal gyrus of the left parietal lobe was noticeably less positively activated in patients with MCI. Patients with MCI also showed reduced negative CDT-related brain activity, mainly in regions of the left temporal lobe and bilateral posterior cingulate gyrus. Patients with MCI also exhibited a few sparse clusters of increased negative activity compared to the control group. These clusters were localized to cortical regions bilaterally in the frontal lobes and localized to the left hemisphere in the parietal, occipital and temporal lobes. These regions include the bilateral Rolandic operculum and post central gyri (frontal lobe) and the left Heschl’s gyrus (temporal lobe), angular gyrus (parietal lobe) and middle occipital gyrus (occipital lobe).

Despite noticeable differences in brain activity patterns between the two group maps, a paired 2- sample voxel-wise t-test comparing activation in the two groups did not identify any significant differences between MCI and control groups after adjusting for multiple comparisons.

Figure 5.1. One-sample t-test activation maps of cognitively healthy matched controls (top row) and patients with MCI (bottom row) during CDT completion compared to visual fixation. On average, healthy controls demonstrated more extensive brain activation compared to patients with MCI. Specifically, patients with MCI exhibited less positive activation in the frontal lobe (bilateral inferior frontal gyri, right Rolandic operculum, left postcentral gyrus) and parietal lobe (left supramarginal gyrus) and less negative activation in the left temporal lobe and bilateral posterior cingulate gyrus. Conversely, patients with MCI displayed more CDT-related negative activity in a few cortical regions of the bilateral frontal lobe (postcentral gyrus, Rolandic operculum) and the left temporal (Heschl’s gyrus), parietal (angular gyrus) and occipital (middle occipital gyrus) lobe. Images are displayed as axial slices (z = -36, -24, -12, 12, 26, 36, 46 in MNI coordinates). Positive activation (i.e. increased activity during CDT compared to visual fixation) is represented in a yellow scale (t-statistic: 3.0 to 5.0) and negative activity (i.e. decreased activity during CDT compared to visual fixation) is represented in a blue scale (t- statistic: -3.0 to -5.0). CDT = clock-drawing test, MCI = mild cognitive impairment, MNI = Montreal Neurological Institute, L = left hemisphere, R = right hemisphere.

Cluster Cluster Size Coordinates of Peak Anatomical Region Number (voxels) Peak Value Value (t-

(x,y,z) statistic)

CDT Performance vs. Fixation: Healthy Controls (Figure 5.1A)

1 10,713 33, -87, 9 11.04 Right middle occipital gyrus

2 4,539 12, -36, -12 -8.81 Left lobule IV, V of cerebellar hemisphere

3 110 57, -54, 18 -5.40 Right middle temporal gyrus

4 85 48, -18, 54 -5.13 Right postcentral gyrus

CDT Performance vs. Fixation: Patients with MCI (Figure 5.1B)

1 9,774 -36, -84, -9 10.53 Left inferior occipital gyrus

2 1,467 39, 3, -39 -8.85 Right inferior temporal gyrus

3 730 6, -78, 30 -6.59 Right cuneus

4 585 -15, -30, -39 -8.36 Left lobule X of cerebellar hemisphere

5 393 -36, 33, 12 5.71 Left inferior frontal gyrus, pars triangularis

6 224 60, 12, 24 5.58 Right inferior frontal gyrus, pars opercularis

7 131 54, -63, 33 -4.82 Right angular gyrus

8 114 54, -57, 24 -4.68 Right superior temporal gyrus

9 94 21, -54, -9 -4.35 Right lingual gyrus

10 85 3, -51, -51 -5.73 Right lobule IX of cerebellar hemisphere

11 83 -36, -15, 3 -4.87 Left insula

Table 5.1 Active clusters of brain areas identified for the clock-drawing test vs fixation contrast for both cohorts. Spatial locations of the center of the cluster reported in Montreal Neurological Institute (MNI) coordinates.

5.1.2 Brain Activity Patterns of the CDT Components

Both groups showed consistent, but distinct activation patterns across each individual CDT component. Results of this analysis provide further insight into what specific areas of the task best highlight MCI-related impairment.

Brain Activation during Clock Face Drawing (R1)

Analyzing each group with a one-sample t-test produced activation maps with significant clusters across various brain regions for both patients with MCI and cognitively healthy controls. The significant clusters identified in each group are presented in Figure 5.2 and further described in Table 5.2. Both groups showed similar positive activation of posterior brain regions, including the cerebellum, occipital, temporal and parietal lobes. Consistent recruitment of the occipital lobe encompassed bilateral inferior, middle and superior areas, including the left calcarine sulcus and right cuneus. Meanwhile, positive activation of the temporal lobe only included the bilateral inferior temporal gyrus and the right fusiform gyrus. Recruitment of the parietal lobes included both the inferior and superior parietal regions, involving the bilateral supramarginal gyri and postcentral gyri. Anteriorly, the positive activation patterns differed more noticeably between the two groups, however there were similarities bilaterally in the insula, middle and anterior cingulate gyrus and regions of the frontal lobe as well as the left putamen and amygdala. In the frontal lobe, positive task-related activity was observed bilaterally in the supplementary motor area and the Rolandic operculum, but the remaining activity in the inferior frontal, middle frontal and precentral gyri were only consistent in the right hemisphere.

Only a few clusters of negative activity were shared between the two groups, all of which were in posterior brain regions. Both groups exhibited bilateral negative activation of the posterior and middle cingulate gyri, the calcarine sulci on the occipital lobe and the precuneus on the parietal lobe. The remaining activation was lateralized to the right hemisphere and localized in the right middle temporal lobe and the right angular gyrus on the parietal lobe.

The brain activity associated with completing the clock face (R1) component presented many distinct differences between the groups, which were apparent when visually comparing the two sets of activation maps. Generally, there was more positive activity in the MCI group and more negative activity in the healthy control group. The most noticeable difference in activation was localized to the frontal lobe, where patients with MCI exhibited more positive activity in the left superior, middle and inferior frontal gyri as well as the precentral gyrus. Patients with MCI also displayed more positive activity in other areas in the left hemisphere, including the cerebellum, the inferior occipital gyrus, the fusiform gyrus and the insular cortex. Bilaterally, the caudate nuclei were more positively activated in the MCI group compared to the healthy controls.

Conversely, healthy controls presented with greater positive activation than patients with MCI in a few cortical regions. Most of these areas were lateralized to the left hemisphere, including the thalamus, the anterior cingulate gyrus, the supramarginal gyrus and the cuneus. There was a small cluster of positive activation of the right middle frontal gyrus only in the healthy control group. Healthy controls also showed more activation of the bilateral postcentral gyri.

In terms of negative activity, there were more negative clusters in the healthy control group compared to patients with MCI. Similar to the positive activity, these dissimilarities were mainly localized to anterior regions of the brain. Across various regions of the bilateral frontal lobe encompassing the inferior frontal gyri, middle frontal gyri and precentral gyri, healthy controls exhibited patterns of negative activation not observed in the MCI group. Beyond the frontal lobe, the bilateral pallidum and caudate nuclei were also only negatively activated in the control group. Posteriorly, there were a few clusters of greater negative activity in the healthy control group and they were observed bilaterally in the cerebellum, temporal, occipital and parietal lobes. More specifically, healthy controls displayed more negative activity in the left middle temporal gyrus, right superior temporal gyrus, right Heschl’s gyrus and bilateral fusiform gyri. The difference in occipital activation was localized to the bilateral lingual gyri. In addition, negative activity across the left inferior parietal lobe, left angular gyrus and bilateral precuneus was observed only in healthy controls.

Similar to the CDT analysis, although the group activation maps displayed clear differences in activity, the paired 2-sample t-test voxel-wise analysis for the clock face (R1) component did not yield any statistically significant differences between MCI and control groups.

Figure 5.2. One-sample t-test activation maps of cognitively healthy matched controls (top row) and patients with MCI (bottom row) during completion of R1 (clock face component) compared to visual fixation. On average, more positive activation was observed in patients with MCI, mainly evident in the left hemisphere in regions of the frontal lobe (superior, middle and inferior frontal gyri, precentral gyrus), the cerebellum, the insular cortex, the inferior occipital gyrus and the fusiform gyrus. A few cortical regions lateralized to the left hemisphere were more positively activated in healthy controls, including the thalamus, supramarginal gyrus, cuneus and anterior cingulate gyrus. In contrast, many clusters of negative activation were observed in healthy controls and not patients with MCI. These clusters were localized bilaterally to anterior regions in the frontal lobes (inferior frontal gyri, middle frontal gyri, precentral gyri), pallidum, caudate nuclei and to posterior regions in the cerebellum, temporal lobes (left middle temporal gyrus, right superior temporal gyrus, right Heschl’s gyrus, bilateral fusiform gyri), occipital lobes (bilateral lingual gyri) and parietal lobes (left inferior parietal lobe, left angular gyrus, bilateral precuneus) Images are displayed as axial slices (z = -36, -24, -4, 16, 26, 34, 46 in MNI coordinates). Positive activation (i.e. increased activity during R1 compared to visual fixation) is

84 represented in a yellow scale (t-statistic: 3.0 to 5.0) and negative activity (i.e. decreased activity during R1 compared to visual fixation) is represented in a blue scale (t-statistic: -3.0 to -5.0). CDT = clock-drawing test, R1 = clock face component, MCI = mild cognitive impairment, MNI = Montreal Neurological Institute, L = left hemisphere, R = right hemisphere.

Cluster Cluster Size Coordinates of Peak Anatomical Region Number (voxels) Peak Value Value (t- (x,y,z) statistic)

Clock face (R1) Performance vs. Fixation: Healthy Controls (Figure 5.1A)

1 5,832 -36, -30, 48 9.36 Left postcentral gyrus

2 1,137 21, -57, -6 -6.05 Right lingual gyrus

3 402 -36, 18, 51 -5.74 Left middle frontal gyrus

4 364 48, -57, 30 -6.70 Right angular gyrus

5 267 -54, -60, 15 -5.50 Left middle temporal gyrus

6 260 -27, -12, -3 6.67 Left pallidum

7 247 -42, -66, -3 6.40 Left inferior occipital gyrus

8 199 -24, -45, -57 6.97 Left lobule VIII of cerebellar hemisphere

9 197 60, 6, 21 6.33 Right precentral gyrus

10 178 63, -15, -18 -6.19 Right middle temporal gyrus

11 155 -15, 15, 3 -6.85 Left putamen

12 148 -66, -33, 6 -4.92 Left middle temporal gyrus

13 120 42, 15, 27 -4.82 Right inferior frontal gyrus, pars opercularis

14 105 42, 42, 30 4.73 Right middle frontal gyrus

15 96 18, -81, -36 -5.57 Right crus II of cerebellar hemisphere

16 83 -27, -57, -24 5.30 Left lobule VI of cerebellar hemisphere

Clock face (R1) Performance vs. Fixation: Patients with MCI (Figure 5.1B)

1 7,145 -42, -75, -3 10.38 Left inferior occipital gyrus

2 939 24, 30, -6 7.19 Right inferior frontal gyrus, pars orbitalis

3 451 45, -69, -6 8.27 Right inferior temporal gyrus

4 397 6, -51, 30 -5.01 Right precuneus

5 334 54, 6, 21 7.84 Right precentral gyrus

6 269 -45, 3, 0 6.79 Left insula

7 155 48, -33, 0 -6.55 Right middle temporal gyrus

8 150 54, -60, 21 -6.90 Right middle temporal gyrus

Table 5.2 Active clusters of brain areas identified for the clock face (R1) component vs fixation contrast for both cohorts. Spatial locations of the center of the cluster reported in Montreal Neurological Institute (MNI) coordinates.

Brain Activation during Clock Numbers Drawing (R2)

The clock numbers (R2) component of the CDT recruited cortical regions mainly localized to the bilateral frontal and occipital lobes in both groups. Statistically-thresholded activation maps are displayed in Figure 5.3 and individual clusters are detailed in Table 5.3. Regions of positive activity in the frontal lobes observed in both groups included the right superior frontal gyrus, the left precentral gyrus, the bilateral middle frontal gyri and bilateral supplementary motor area.

Both groups consistently exhibited positive activity in the bilateral cerebellum, fusiform gyri, occipital and parietal lobes. Occipital activation was extensive, bilaterally encompassing regions in the inferior, middle and superior occipital lobe as well as the lingual gyri and calcarine sulci. Positive activity of the parietal lobe was mainly observed bilaterally in the superior parietal regions and precuneus, as well as the left postcentral gyrus. Additionally, the left middle cingulate gyrus and right anterior cingulate gyrus were recruited in both groups.

Similar patterns of negative activation were exhibited in both participant groups, mainly observed posteriorly. Specifically, bilateral regions of the temporal lobes, parahippocampal gyri, hippocampus, occipital lobes and precuneus showed negative task-related activity. Temporally, both groups showed negative activation of the bilateral fusiform gyrus, inferior temporal gyrus and the right Heschl’s gyrus. In the occipital lobe, both the bilateral calcarine sulci and lingual gyri displayed clusters of negative activation. In the right hemisphere, consistent negative activity was observed in the amygdala, insular cortex and Rolandic operculum. Only the posterior cingulate gyrus had negative activity localized to the left hemisphere.

When visually comparing activation maps of the two groups, there was a difference in the positive activity located in the frontal region of the brain. Healthy controls exhibited more left- sided frontal activation in lower slices of the brain, specifically in regions of the superior and middle frontal gyrus, pars orbitalis. Alternatively, patients with MCI displayed more frontal activation, including areas of the bilateral middle frontal gyrus, right Rolandic operculum and right precentral gyrus. Similarly, in the parietal lobe, the left hemisphere was more positively activated in controls, while the right hemisphere was more positively activated in patients with MCI. Specifically, the left postcentral gyrus showed increased activity in healthy controls and the right inferior parietal lobe, including the supramarginal gyrus, displayed increased activity in patients with MCI. In addition, patients with MCI exhibited more recruitment of the right caudate nucleus and the bilateral middle cingulate gyrus.

The patterns of negative activation were very similar between the two groups, however there were several noteworthy differences. Patients with MCI showed more extensive activity of the bilateral fusiform gyri and inferior temporal gyri than healthy controls. Additionally, the MCI group had significant clusters of negative activation not present in the control group, located in the bilateral inferior parietal lobes, comprising the bilateral angular gyri and the left

87 supramarginal gyrus. On the other hand, healthy controls displayed increased negative activation in regions of the left superior temporal lobe as well as the bilateral middle and posterior cingulate gyri.

Although visual comparison of the group activation maps identified clear differences between the two groups, results of an associated two-sample voxel-wise t-test did not identify any significant differences between MCI and control groups.

Figure 5.3. One-sample t-test activation maps of cognitively healthy matched controls (top row) and patients with MCI (bottom row) during completion of R2 (clock numbers component) compared to visual fixation. Although both groups exhibited similar patterns of brain activation, there were differences between the group in frontal activation. On average, patients with MCI displayed less positive activity in the left superior and middle frontal gyrus, pars orbitalis, and healthy controls displayed less positive activity in the bilateral middle frontal gyrus, right Rolandic operculum and right precentral gyrus. Additionally, patients with MCI exhibited more positive activation of the right parietal lobe (supramarginal gyrus), the right caudate nucleus and

88 the bilateral middle cingulate gyrus. In contrast, increased positive activity was observed in healthy controls in the left postcentral gyrus. More extensive negative activation was shown in patients with MCI across the bilateral temporal lobes (fusiform gyri, inferior temporal gyri) and bilateral parietal lobes (angular gyri, left supramarginal gyrus). Differences were also observed in regions of the temporal lobe (left superior gyrus) and the bilateral middle and posterior cingulate gyri, with increased negative activity apparent in healthy controls. Images are displayed as axial slices (z = -36, -24, -12, 6, 26, 36, 46 in MNI coordinates). Positive activation (i.e. increased activity during R2 compared to visual fixation) is represented in a yellow scale (t- statistic: 3.0 to 5.0) and negative activity (i.e. decreased activity during R2 compared to visual fixation) is represented in a blue scale (t-statistic: -3.0 to -5.0). CDT = clock-drawing test, R2 = clock numbers component, MCI = mild cognitive impairment, MNI = Montreal Neurological Institute, L = left hemisphere, R = right hemisphere.

Cluster Cluster Size Coordinates of Peak Value Anatomical Region Number (voxels) Peak Value (t-statistic)

(x,y,z)

Clock numbers (R2) Performance vs. Fixation: Healthy Controls (Figure 5.1A)

1 1,608 -24, -96, 3 7.86 Left middle occipital gyrus

2 1,517 18, -36, -12 -6.84 Right parahippocampal gyrus

3 862 0, -87, 33 -5.74 Left cuneus

4 760 -33, -6, 48 7.55 Left precentral gyrus

5 730 30, -93, 6 7.46 Right middle occipital gyrus

6 356 27, -60, 48 6.77 Right angular gyrus

7 222 21, 39, 3 6.48 Right anterior cingulate gyrus

8 151 -24, 39, -6 6.56 Left inferior frontal gyrus, pars orbitalis

9 118 24, 3, 51 6.04 Right middle frontal gyrus

10 77 39, -18, 6 -4.72 Right insula

11 74 -3, 3, 54 5.74 Left supplementary motor area

Clock numbers (R2) Performance vs. Fixation: Patients with MCI (Figure 5.1B)

1 2,633 -30, -87, -3 8.49 Left inferior occipital gyrus

2 1,632 27, -66, 33 7.93 Right superior occipital gyrus

3 747 30, -6, -24 -9.42 Right hippocampus

4 641 -27, -27, -21 -5.65 Left parahippocampal gyrus

5 493 3, -84, 30 -6.42 Left cuneus

6 298 -45, -72, 39 -5.26 Left middle occipital gyrus

7 247 21, 27, 3 5.61 Right caudate nucleus

8 192 39, -12, 24 -8.14 Right Rolandic operculum

9 159 15, -33, -36 -5.01 -

10 157 24, -57, -6 -5.44 Right lingual gyrus

11 107 54, -60, 24 -4.83 Right superior temporal gyrus

12 82 -9, -42, 33 -4.98 Left middle cingulate gyrus

13 76 63, 6, 18 5.04 Right precentral gyrus

14 75 -33, 30, 18 4.47 Left inferior frontal gyrus, pars triangularis

Table 5.3 Active clusters of brain areas identified for the clock numbers (R2) component vs fixation contrast for both cohorts. Spatial locations of the center of the cluster reported in Montreal Neurological Institute (MNI) coordinates.

Brain Activation during Clock Hands Drawing (R3)

Analyzing the brain activity associated with completion of the clock hands (R3) component revealed significant patterns of activation in both patients with MCI and healthy controls. These patterns are displayed in Figure 5.4 and the individual clusters of activity are detailed in Table 5.4. Both groups exhibited significant clusters of positive activity posteriorly in the bilateral cerebellum, temporal, occipital and parietal lobes. The temporal activation was mainly in the inferior temporal areas and fusiform gyri. Occipital activation extended to the bilateral inferior, middle and superior regions, including the bilateral lingual gyri and calcarine sulci. Regions of both the inferior and superior parietal lobe were positively activated encompassing the right supramarginal and angular gyrus. Anteriorly, consistent positive activation was evident in the left insula, right anterior cingulate gyrus, bilateral middle cingulate gyrus and regions of the bilateral frontal lobe. Positive frontal activation spanned multiple different gyri bilaterally, including the inferior, middle and superior frontal gyri and the precentral gyri, as well as the bilateral supplementary motor area.

Completion of the clock hands (R3) component was associated with clusters of negative activity in both anterior and posterior brain regions. These clusters were observed in the bilateral frontal lobe, the inferior, medial and superior frontal gyri, pars orbitalis, and Rolandic operculum, the rectus, caudate nuclei as well as in the left insula. Posteriorly, both groups exhibited consistent negative activation in the bilateral posterior and middle cingulate gyri, precuneus, temporal lobes, and occipital lobes. Significant clusters were found in many areas of the temporal lobes, including the inferior, middle and superior areas, the middle and superior temporal poles, the fusiform gyri and the right Heschl’s gyrus. Bilateral activation was observed in both the calcarine sulci and cuneus of the occipital lobe.

Visually contrasting the activation maps of the two groups revealed differences in the patterns of activity. In terms of positive activity, patients with MCI exhibited less extensive activation than healthy controls, specifically in areas of the bilateral cerebellum, frontal and parietal lobes as well as the anterior cingulate gyrus. Areas of the bilateral inferior frontal gyri, bilateral precentral gyri and right Rolandic operculum were more positively active in healthy controls. Differences in recruitment of the parietal lobes was localized to the bilateral postcentral gyri as well as the left supramarginal gyrus.

Similarly, the patterns of negative activation were more extensive in the healthy controls than the patients with MCI. The healthy control group had more significant clusters across bilateral regions of the cerebellum, hippocampus, insular cortex, temporal, occipital and parietal lobes. Differences in negative activation were observed in the bilateral middle and superior temporal regions as well as the bilateral fusiform gyri. Both the bilateral lingual gyri and left calcarine sulcus displayed less negative activity in patients with MCI. The distinction in negative activation of the parietal lobe was lateralized to the left hemisphere for regions of the precuneus and angular gyrus, but lateralized to the right hemisphere for the postcentral gyrus.

Similar to the previous contrasts, despite noticeable qualitative differences in the activation patterns of patients with MCI and healthy controls, results of an associated two-sample voxel- wise t-test did not identify any significant clusters of brain activity.

Figure 5.4. One-sample t-test activation maps of cognitively healthy matched controls (top row) and patients with MCI (bottom row) during completion of R3 (clock hands component)

92 compared to visual fixation. On average, patients with MCI exhibited less task-related brain activity, both negative and positive. Differences in positive activation were observed in regions of the bilateral cerebellum, frontal lobes (inferior frontal gyri, precentral gyri, right Rolandic operculum) and parietal lobes (postcentral gyri, left supramarginal gyrus). Increased negative activity was observed in healthy controls in bilateral areas of the cerebellum, hippocampus, insular cortex, temporal lobes (middle and superior temporal gyri, fusiform gyri), occipital lobes (lingual gyri, left calcarine sulcus) and parietal lobes (left precuneus, left angular gyrus, right postcentral gyrus). Images are displayed as axial slices (z = -36, -24, -4, 6, 30, 36, 46 in MNI coordinates). Positive activation (i.e. increased activity during R3 compared to visual fixation) is represented in a yellow scale (t-statistic: 3.0 to 5.0) and negative activity (i.e. decreased activity during R3 compared to visual fixation) is represented in a blue scale (t-statistic: -3.0 to -5.0). CDT = clock-drawing test, R3 = clock hands component, MCI = mild cognitive impairment, MNI = Montreal Neurological Institute, L = left hemisphere, R = right hemisphere.

Cluster Cluster Size Coordinates of Peak Anatomical Region Number (voxels) Peak Value Value (t- (x,y,z) statistic)

Clock hands (R3) Performance vs. Fixation: Healthy Controls (Figure 5.1A)

1 3,964 51, -36, 39 8.63 Right supramarginal gyrus

2 1,858 -51, -3, -33 -6.54 Left inferior temporal gyrus

3 1,216 6, -51, 21 -7.54 Right precuneus

4 1,084 51, 9, 21 6.24 Right inferior frontal gyrus, pars operculis

5 833 45, -15, 36 -6.27 Right postcentral gyrus

6 180 -51, 9, 30 5.07 Left inferior frontal gyrus, pars operculis

7 154 -42, -66, 27 -6.02 Left angular gyrus

8 134 -24, -6, 39 6.10 -

9 74 12, -72, -45 6.30 Right lobule VIII of cerebellar hemisphere

10 68 -33, 21, -12 4.46 Left inferior frontal gyrus, pars orbitalis

Clock hands (R3) Performance vs. Fixation: Patients with MCI (Figure 5.1B)

1 3,006 -27, -51, 45 7.02 Left inferior parietal lobule

2 828 -6, 36, -15 -9.34 Left rectus

3 660 27, 3, 63 6.62 Right superior frontal gyrus

4 588 -6, -45, 30 -6.15 Left posterior cingulate gyrus

5 510 -39, -12, -27 -6.20 Left inferior temporal gyrus

6 342 -30, 0, 63 5.19 Left precentral gyrus

7 312 3, 24, 48 4.89 Right supplementary motor area

8 214 42, 12, -36 -7.32 Right middle temporal pole

9 206 -3, -48, -48 -6.15 Left lobule IX of cerebellar hemisphere

10 126 39, -15, 12 -6.75 Right insula

11 64 -33, 24, -9 4.82 Left inferior frontal gyrus, pars orbitalis

Table 5.4 Active clusters of brain areas identified for the clock hands (R3) component vs fixation contrast for both cohorts. Spatial locations of the center of the cluster reported in Montreal Neurological Institute (MNI) coordinates.

5.1.3 Differences in Brain Activity in Specific Regions of Interest (ROIs)

The ROI-based analysis was an exploratory step to investigate deeper into the effect of MCI on CDT-related activity. These results provided valuable information on group-related differences in focal brain activity. CDT-related brain activation was compared between the two groups in the specified ROIs (ROIs: bilateral angular gyri, caudate nuclei, inferior frontal gyri, middle frontal gyri, middle temporal gyri, superior frontal gyri and supramarginal gyri). These ROIs were chosen based off of current neuroimaging literature, which identifies their involvement in CDT completion (Cahn-Weiner et al., 1999; Eknoyan et al., 2012; Ino et al., 2003; Matsuoka et al., 2013, 2010; Samton et al., 2005; Thomann et al., 2008; Tranel et al., 2008; Ueda et al., 2002). The mean t-statistics for each group were calculated and compared between the groups (Table 5.5). Although patients with MCI exhibited changes in mean activation compared to healthy controls, a two-sample t-test of the t-statistic values only revealed significant differences in activation of the left caudate nucleus, left middle temporal gyrus and right middle temporal gyrus. However, after correcting for multiple comparisons, none of the ROIs revealed significant differences in activation between the groups.

Mean t-statistic Region of Interest p-value False-discovery rate (ROI) Patients with Healthy Controls adjusted p-value MCI (n = 20) (n = 20)

Left angular gyrus -1.55 -0.77 0.216 0.378

Right angular gyrus -1.58 -1.36 0.671 0.736

Left caudate nucleus 0.55 -0.63 0.024 0.139

Right caudate nucleus 0.21 -0.49 0.213 0.378

Left inferior frontal gyrus 1.57 0.73 0.140 0.378

Right inferior frontal 0.81 1.33 0.187 0.378 gyrus

Left middle frontal gyrus 1.31 0.39 0.082 0.288

Right middle frontal 1.23 1.75 0.462 0.647 gyrus

Left middle temporal -0.07 -0.78 0.013 0.139 gyrus

Right middle temporal -0.20 -1.39 0.030 0.139 gyrus

Left superior frontal -0.13 -0.33 0.716 0.736 gyrus

Right superior frontal 0.99 0.64 0.344 0.536 gyrus

Left supramarginal gyrus 0.27 0.57 0.646 0.736

Right supramarginal 0.25 0.04 0.736 0.73 gyrus

Table 5.5 Statistical reports of brain activation in specified regions of interests (ROIs) during the whole CDT compared to visual fixation. Mean t-statistic values are provided for activation of each ROI in each group (healthy controls and patients with MCI). P-values are reported for the paired 2-sample t-test (healthy controls vs patients with MCI). Adjusted p-values have been corrected for multiple comparisons using false-discovery rate methods. n = number of participants, CDT = Clock-drawing test, ROI = region of interest.

5.1.4 Differences in Functional Connectivity Patterns during the CDT

During completion of the CDT, the combined MCI and control groups displayed extensive patterns of functional connectivity with the identified ROIs (listed in Section 5.1.3). For the whole group analysis, positive values indicate significant correlations with the seed ROI during CDT and negative values indicate significant anti-correlations with the seed ROI during CDT. In the 2-sample bootstrap comparison, connectivity values of patients with MCI were compared to healthy controls and positive values indicate more connectivity in patients with MCI whereas negative values indicated less connectivity in patients with MCI.

Patients with MCI consistently exhibited significantly reduced connectivity compared to the healthy controls during CDT performance. A 2-sample comparison of the groups identified individual clusters with significantly different connectivity for all ROIs except the left angular gyrus. These clusters are detailed in Table 5.6 for each ROI.

Cluster Number Cluster Size Coordinates of Peak Value Anatomical Region (voxels) Peak Value (bootstrap (x,y,z) ratio)

Right Angular Gyrus ROI

1 20,061 0, -24, 54 -6.35 Left paracentral lobule

2 11,313 -12, -87, 42 -4.57 Left superior occipital gyrus

3 10,044 -57, -69, 9 -6.61 Left middle temporal gyrus

4 4,023 54, -57, 6 -5.24 Right middle temporal gyrus

5 2,052 12, -66, -12 -3.54 Right calcarine sulcus

Left Caudate Nucleus ROI

1 41,553 9, -24, 63 -6.19 Right paracentral lobule

2 14,202 -6, -60, -18 -4.80 Left lobule IV and V of cerebellar hemisphere

3 6,885 27, -36, -24 -7.65 Right lobule IV and V of cerebellar hemisphere

4 3,348 -33, 9, 3 -6.79 Left insula

5 2,916 39, 9, 6 -5.00 Right insula

Right Caudate Nucleus ROI

1 88,722 -9, -57, 24 -6.56 Left precuneus

2 16,416 60, -45, 21 -6.13 Right superior temporal gyrus

3 4,563 51, -6, 39 -4.85 Right postcentral gyrus

4 2,349 -21, -93, -12 -4.66 Left lingual gyrus

Left Inferior Frontal Gyrus ROI

1 2,889 -15, -60, -39 4.48 Left lobule VIII of cerebellar hemisphere

2 2,376 -18, -12, 36 -5.33 -

3 2,268 21, -6, 39 -4.48 -

Right Inferior Frontal Gyrus ROI

1 13,743 -57, -69, 12 -6.08 Left middle temporal gyrus

2 4,374 -60, -33, 18 -5.26 Left superior temporal gyrus

3 3,780 -3, -81, -3 -4.13 Left calcarine sulcus

4 2,916 57, -57, 6 -4.35 Right middle temporal gyrus

5 2,835 6, -45, -18 -5.21 Vermis

6 2,322 -39, -81, 30 -3.88 Left middle occipital gyrus

Left Middle Frontal Gyrus ROI

1 10,854 0, -21, 51 -5.05 Left middle cingulate gyrus

2 3,726 54, 27, 15 -4.33 Right inferior frontal gyrus, pars triangularis

3 3,672 9, 51, 39 -4.57 Right medial frontal gyrus

4 2,403 42, 0, 33 -4.23 Right precentral gyrus

Right Middle Frontal Gyrus ROI

1 20,358 -24, 12, 42 -6.11 -

2 18,360 -27, -69, -36 -5.97 Left cerebellum crus

3 9,585 45, 9, 48 -5.31 Right precentral gyrus

4 4,482 -12, -12, 9 -4.87 Left thalamus

5 4,320 21, -72, -27 -4.30 Right cerebellum crus

6 2,430 -51, -33, -15 -5.26 Left middle temporal gyrus

Left Middle Temporal Gyrus ROI

1 17,955 -15, -12, 66 -5.57 Left supplementary motor area

2 7,965 -57, -69, 9 -7.41 Left middle temporal gyrus

3 4,536 -48, -36, 21 -5.19 Left superior temporal gyrus

Right Middle Temporal Gyrus ROI

1 29,646 -45, -15, 51 -5.30 Left postcentral gyrus

2 10,179 -42, -60, 0 -5.20 Left middle temporal gyrus

3 2,403 54, -27, 27 -4.44 Right supramarginal gyrus

Left Superior Frontal Gyrus ROI

1 50,868 -33, -33, 6 -6.06 -

2 9,882 30, -21, 51 -8.25 -

3 2,565 36, -87, -6 -4.52 Right inferior occipital gyrus

4 2,160 36, -63, -36 -5.08 Right cerebellum crus

Right Superior Frontal Gyrus ROI

1 6,966 15, -18, 36 -5.09 Right middle cingulate gyrus

2 6,561 57, -6, 48 -5.49 Right precentral gyrus

Left Supramarginal Gyrus ROI

1 5,049 -33, -21, 33 -6.38 -

Right Supramarginal Gyrus ROI

1 6,831 6, 54, -12 -4.95 Right medial orbitofrontal cortex

2 2,079 -27, 24, 21 -4.59 -

Table 5.6 Clusters of brain areas with significantly different functional connectivity between the two groups. Spatial locations of the center of the cluster reported in Montreal Neurological Institute (MNI) coordinates.

The current thesis will elaborate more extensively on the connectivity results from the following ROIs: the bilateral middle frontal gyri, middle temporal gyri and supramarginal gyri. Current neuroimaging literature has identified the frontal, temporal and parietal lobes as important cortical regions for CDT completion (Ino et al., 2003; Matsuoka et al., 2013, 2010; Nagahama et al., 2005; Takahashi et al., 2008; Thomann et al., 2008; Ueda et al., 2002). More specifically, previous studies have shown significant associations between CDT performance and the middle temporal lobe, the middle frontal gyrus and the supramarginal gyrus (Matsuoka et al., 2013; Thomann et al., 2008; Tranel et al., 2008). Furthermore, results from Section 5.1.1-5.1.3 exhibited no significant differences in brain activation in these areas between patients with MCI and healthy controls, suggesting that MCI may be affecting the connectivity between these brain regions instead.

The middle frontal gyrus connectivity results for the whole group and the 2-sample comparisons are displayed in Figure 5.5. Patients with MCI exhibited decreased functional connectivity from the middle frontal gyrus compared to controls. Interestingly, the group differences were observed mainly in regions of the bilateral frontal lobes, including the inferior, middle and superior frontal gyri as well as the precentral gyri. The whole group connectivity map shows positive connectivity in these frontal regions, suggesting that patients with MCI may have reduced positive connections from the middle frontal gyrus to other frontal areas during the CDT.

Compared to controls, patients with MCI had reduced connectivity from the left middle frontal gyrus to regions of the bilateral anterior and middle cingulate gyri. Meanwhile, changes in connectivity from the right middle frontal gyrus to regions of the bilateral cerebellum and left inferior temporal gyrus, caudate nucleus, putamen and thalamus were observed in patients with

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MCI. These regions showed positive connectivity in the whole group results, similarly suggesting less positive connectivity from the middle frontal gyrus in patients with MCI.

Figure 5.5. One-sample bootstrap test functional connectivity maps of the whole group (top row) and 2-sample comparison of the two groups (bottom row) for connections with the middle frontal gyrus during completion of the CDT. Patients with MCI exhibited less functional connectivity than healthy controls. Differences in connectivity were observed in the bilateral frontal lobes (inferior, middle and superior frontal gyri, precentral gyri) for both the left and right middle

101 frontal gyrus. Changes in connectivity from the right middle frontal gyrus were shown in the bilateral cerebellum and left inferior temporal gyrus as well as some subcortical areas (caudate nucleus, putamen, thalamus). Meanwhile, changes in connectivity from the left middle frontal gyrus were seen in the bilateral anterior and middle cingulate gyri. The one-sample connectivity maps were used purely to understand directionality of connectivity (i.e. positive or negative connections), therefore the bootstrap ratio was thresholded at a nominal value of 1.95 (0.05 2- tailed, for this normally-distributed statistic). Images are displayed as axial slices (z = -14, 6, 16, 26, 38, 50, 58 in MNI coordinates). Positive connectivity for the one-sample map (i.e. increased connectivity during the CDT compared to visual fixation) is represented in a yellow scale (bootstrap ratio: 1.95 to 4.0). Negative connectivity for the two-sample map (i.e. decreased connectivity in patients with MCI compared to healthy controls) is represented in a blue scale (t- statistic: -3.0 to -5.0). CDT = clock-drawing test, MCI = mild cognitive impairment, MNI = Montreal Neurological Institute, L = left hemisphere, R = right hemisphere.

Multiple significant clusters were identified when comparing functional connectivity from the middle temporal gyrus between the two groups. These results along with the whole group connectivity are displayed in Figure 5.6. Less connectivity from the bilateral middle temporal gyri was observed across regions of the temporal, occipital and parietal lobes as well as motor areas, including the precentral gyri and supplementary motor area. The changes in connectivity to the temporal lobe were largely lateralized to the left hemisphere in regions of the inferior, middle and superior temporal gyrus as well as the fusiform gyrus. Similarly, significant clusters were identified in the left occipital lobe in the middle and inferior occipital gyrus as well as the cuneus. The left middle temporal gyrus displayed less connectivity to the left parietal lobe, whereas the right middle temporal gyrus showed a bilateral change in connectivity to the parietal lobes. These changes in connectivity were localized to areas on the supramarginal gyrus, postcentral gyrus, precuneus, superior parietal lobe and inferior parietal lobe.

Similar areas in the occipital, temporal and parietal lobes were positively connected with the right middle temporal gyrus in the whole group connectivity results. This suggests that there may be less positive connectivity from the right middle temporal gyrus to the identified regions in patients with MCI. The brain regions showing group differences did not show significant seed

102 connectivity in the 1-sample maps. Therefore, it is unclear if patients with MCI show decreased positive connectivity or increased negativity connectivity from the left middle temporal gyrus.

Figure 5.6. One-sample bootstrap test functional connectivity maps of the whole group (top row) and 2-sample comparison of the two groups (bottom row) for connections with the middle temporal gyrus during completion of the CDT. Patients with MCI exhibited less functional connectivity than healthy controls. Differences in connectivity were observed in the temporal (inferior, middle and superior temporal gyrus, fusiform gyrus), occipital (middle and inferior

103 occipital gyrus, cuneus) and parietal lobes (supramarginal gyrus, postcentral gyrus, precuneus, superior parietal lobe and inferior parietal lobe) as well as the motor areas (supplementary motor area and precentral gyri). Most of these clusters were lateralized to the left hemisphere, however changes in connectivity from the right middle temporal gyrus were observed in the bilateral parietal lobes. The one-sample connectivity maps were used purely to understand directionality of connectivity (i.e. positive or negative connections), therefore the bootstrap ratio was thresholded at a nominal value of 1.95 (0.05 2-tailed, for this normally-distributed statistic). Images are displayed as axial slices (z = -14, 6, 16, 26, 38, 50, 58 in MNI coordinates). Positive connectivity for the one-sample map (i.e. increased connectivity during the CDT compared to visual fixation) is represented in a yellow scale (bootstrap ratio: 1.95 to 4.0). Negative connectivity for the two-sample map (i.e. decreased connectivity in patients with MCI compared to healthy controls) is represented in a blue scale (t-statistic: -3.0 to -5.0). CDT = clock-drawing test, MCI = mild cognitive impairment, MNI = Montreal Neurological Institute, L = left hemisphere, R = right hemisphere.

Although patients with MCI displayed significantly less connectivity from the bilateral supramarginal gyri compared to controls, the differences from the left supramarginal gyrus were in non-cortical areas. Therefore, the focus will be on the right supramarginal gyrus. The whole group connectivity and 2-sample comparison results for the right supramarginal gyrus are shown in Figure 5.7.

Similar to the middle frontal gyrus, there was less connectivity from the right supramarginal gyrus to regions of the frontal lobes in patients with MCI. Specifically, patients with MCI displayed differences in connectivity in areas of the bilateral medial orbitofrontal cortices, the right middle and superior frontal gyri and the left inferior frontal gyri. The results from the whole group do not show significant connectivity to these frontal regions. Therefore, it is unclear whether patients with MCI display reduced positive connectivity or increased negative connectivity from the right supramarginal gyrus to the frontal lobe.

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Figure 5.7. One-sample bootstrap test functional connectivity maps of the whole group (top row) and 2-sample comparison of the two groups (bottom row) for connections with the right supramarginal gyrus during completion of the CDT. Patients with MCI exhibited less functional connectivity than healthy controls. Differences in connectivity were observed in the bilateral frontal lobes (medial orbitofrontal cortices, inferior, middle and superior frontal gyri). The one- sample connectivity maps were used purely to understand directionality of connectivity (i.e. positive or negative connections), therefore the bootstrap ratio was thresholded at a nominal value of 1.95 (0.05 2-tailed, for this normally-distributed statistic). Images are displayed as axial slices (z = -14, 6, 16, 26, 38, 50, 58 in MNI coordinates). Positive connectivity for the one- sample map (i.e. increased connectivity during the CDT compared to visual fixation) is represented in a yellow scale (bootstrap ratio: 1.95 to 4.0). Negative connectivity for the two- sample map (i.e. decreased connectivity in patients with MCI compared to healthy controls) is represented in a blue scale (t-statistic: -3.0 to -5.0). CDT = clock-drawing test, MCI = mild cognitive impairment, MNI = Montreal Neurological Institute, L = left hemisphere, R = right hemisphere.

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5.2 Discussion

This is the first study to identify brain activation patterns underlying CDT completion in patients with MCI. This investigation explored changes in brain activity associated with completion of the entire CDT as well as each of the individual CDT components (R1, R2 and R3). Although there were no significant differences in brain activity between the groups, visually comparing the results revealed that patients with MCI exhibit noticeable changes in brain activation patterns compared to cognitively healthy controls across each of the CDT components. Differences between the two groups was confirmed in the functional connectivity analysis, where patients with MCI exhibited significantly less functional connectivity compared to healthy controls. A discussion, which specifically addresses each of the hypotheses outlined in Chapter 2 of the current thesis is detailed below.

5.2.1 Hypothesis 1: Brain Activity associated with CDT Completion

It was hypothesized that both patients with MCI and healthy matched controls would demonstrate reliable brain activity across multiple regions previously identified as important for completion of the CDT (e.g. parietal lobe, frontal lobe, temporal lobe, cerebellum, motor areas) (Cahn-Weiner et al., 1999; Formisano et al., 2002; Ino et al., 2003; Leyhe et al., 2009; Matsuoka et al., 2013, 2010; Nagahama et al., 2005; Shoyama et al., 2011; Takahashi et al., 2008; Thomann et al., 2008; Tranel et al., 2008; Trojano et al., 2000; Ueda et al., 2002). The results obtained in the current investigation confirmed this hypothesis, as both groups exhibited reliable patterns of positive brain activation during CDT completion across bilateral parietal, frontal, temporal, cerebellar and motor areas as well as regions of the occipital lobes and cingulate gyri. Significant regions of negative CDT-related activity were also identified, suggesting that completion of the CDT requires suppression of certain brain functions. These areas were involved in the default mode network (e.g. posterior cingulate cortex, hippocampus, temporal lobes) (Fransson & Marrelec, 2008; M. D. Greicius, Supekar, Menon, & Dougherty, 2009; Michael D Greicius, Krasnow, Reiss, & Menon, 2003). Activation of the default mode network is associated with times of wakeful rest, such as while daydreaming or mind wandering, and consequently would not be activated during completion of a task (e.g. CDT) (Mason et al., 2007). Although drawing a clock is thought of by the lay public as a simple task, the fMRI

106 results imply that it is cognitively complex. Extensive recruitment of various regions of the brain suggests that the CDT requires integration of many cognitive domains, including visuospatial function (occipital and parietal lobes), attention (cingulum and frontal lobes), motor control (motor areas and cerebellum) and executive function (frontal lobes).

The overall patterns of CDT-related brain activity observed in both groups are congruent with results in the literature. Functional MRI studies using indirect methods (e.g. visualization of the clock) to study CDT-related brain activity demonstrated significant increased frontal and parietal recruitment during task completion (Formisano et al., 2002; Ino et al., 2003; Leyhe et al., 2009; Trojano et al., 2000), which is consistent with the current findings. More areas of CDT-related activation were identified in the current results, which may be due to the increased complexity of the task compared to simplified versions used in previous studies. Using fNIRS, Shoyama et al. identified increased hemoglobin levels in regions of the prefrontal cortex and temporal lobes during completion of the CDT (Shoyama et al., 2011) identifying similar CDT-related brain areas as this study. The results of the current investigation correspond with previous work, which identified neural activation patterns associated with CDT completion in cognitively healthy older adults (aged 52 to 85) (Talwar et al., 2019). Both studies identify similar areas of CDT-related activity, however the brain activity displayed in the older adult study (Talwar et al., 2019) was more extensive than the groups in the current study. This is more noticeable in the MCI group in particular, where the patients with MCI appear to demonstrate reduced recruitment of regions in the frontal, parietal and temporal lobes compared to the older adult group (Talwar et al., 2019).

Despite similarities in patterns of brain activation between the two groups, it was further hypothesized that upon comparison, patients with MCI would exhibit reduced brain activity in regions of the right parietal lobe and left temporal lobe compared to healthy controls. Although there was no significant difference in activation between the two groups, visual comparison revealed less extensive positive activity across regions of the bilateral frontal and left parietal lobes. This is further supported by the ROI analysis, which showed lower mean activation values in patients with MCI in regions localized to the right frontal lobes and left parietal lobe. However, activation of the ROIs was not significantly different between the two groups. These results do not support the original hypothesis as there was no significant difference in brain activity between the groups. However, the results do indicate that on a group level there were slight deviations in neural recruitment, specifically in key frontal and parietal regions.

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Two previous CDT imaging studies correlated regional grey matter volume with CDT score to identify brain regions that are associated with impaired performance in patients with MCI and AD (Matsuoka et al., 2010; Thomann et al., 2008). Similar to the current study, these investigations identified impaired CDT performance to be associated with the temporal and parietal lobes. Both studies revealed a significant relationship between reduced grey matter density in the bilateral temporal lobes and worse CDT performance, indicating that neuropathology in the temporal lobes contributes to MCI-related impairment on the CDT (Matsuoka et al., 2010; Thomann et al., 2008). Matsuoka et al. also identified a positive correlation between grey matter volume and the parietal lobe, but unlike the current study, they found the effect to be significant only in the right hemisphere (Matsuoka et al., 2010). Beyond these two studies, previous investigations utilized similar methodology to identify brain regions associated with impaired performance, but only in patients with AD. Key findings included significant positive associations between impaired performance and regional CBF measured using SPECT in the bilateral posterior temporal lobes, parietal lobes, hippocampus and right middle frontal gyrus, indicating that patients with less regional CBF in the identified areas performed worse on the task (Matsuoka et al., 2013; Nagahama et al., 2005; Takahashi et al., 2008; Ueda et al., 2002). These results in conjunction with the current results support the idea that in patients with MCI/AD, worse performance on the CDT may be due to decreased functionality of regions in the frontal, temporal and parietal lobes.

The CDT requires function and coordination of multiple cognitive domains and their associated brain regions. Patients with MCI exhibited less extensive positive activity in regions of the bilateral inferior frontal gyri as well as the left supramarginal. Both the frontal and parietal lobes are responsible for important cognitive functions that are involved in the CDT. The frontal lobe and more specifically the prefrontal cortex, are crucial for planning and effectively completing the task through executive functioning (Alvarez & Emory, 2006; Roberts, Robbins, & Weiskrantz, 1998; Donald R Royall et al., 1999; Shoyama et al., 2011; Talwar et al., 2019). Executive function encompasses a set of higher-level cognitive processes, which control and coordinate cognitive abilities to complete complex goals (Alvarez & Emory, 2006; Roberts et al., 1998; D R Royall et al., 1998). This includes functions such as planning, selective attention and problem solving, all of which are important for successful CDT completion. The CDT has both visual and spatial perception demands, which have been linked to regions of the parietal lobe,

108 such as the supramarginal gyrus (Talwar et al., 2019; Tranel et al., 2008). Given the important cognitive functions of frontal and parietal lobes, the current results suggest that less extensive activity in these brain areas may result in MCI-related impairment of behavioural performance on the CDT.

Investigating brain activation during the CDT in specific ROIs revealed group differences in mean activation across multiple brain areas. Compared to healthy controls, patients with MCI exhibited less activity in the bilateral middle temporal gyri and the left caudate nucleus. However, this finding was not statistically significant after correcting for multiple comparisons. The temporal lobe is an area of clinical significance for patients with AD/MCI due to its involvement in memory encoding and storage. Researchers have identified patterns of temporal lobe atrophy in all stages of AD, including early phases such as MCI (Braak & Braak, 1991; Chan et al., 2001; Jack et al., 1998). In the context of the CDT, the temporal lobes are important for semantic memory, which allows participants to recall how a clock looks (Budson, 2009; Eknoyan et al., 2012). As previously mentioned, neuroimaging studies have associated the temporal lobes with CDT behavioural performance, suggesting these regions are essential for task completion (Matsuoka et al., 2010; Thomann et al., 2008). The caudate nucleus is an important component of the frontal subcortical circuit, used to mediate executive function (Elliott et al., 1997; Funahashi, 2001; Mega & Cummings, 1994). The higher-level cognitive processes involved in executive function are crucial in complex tasks, such as the CDT. Current literature has identified a significant correlation between the caudate nucleus and CDT performance (Heinik, Reider-Groswasser, Solomesh, Segev, & Bleich, 2000; Samton et al., 2005; Talwar et al., 2019). Taken together, the current results suggest that impaired activation of the bilateral middle temporal gyri and the caudate nucleus in patients with MCI may lead to worsened CDT performance.

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5.2.2 Hypothesis 2: Brain Activity associated with Completion of the CDT Components (R1, R2 and R3)

It was hypothesized that due to increased complexity of the number (R2) and hands (R3) components of the CDT, patients with MCI would exhibit reduce brain activity during completion of those specific components. Although there were noticeable differences when visually comparing activation maps between the two groups, there was no statistically significant difference in activation observed in any of the three components (R1, R2, R3). Similar to the hypothesis, less brain activity was observed in patients with MCI during completion of the numbers (R2) and hands (R3) components. Interestingly, the most noticeable differences in activation patterns were observed during the hands (R1) component of the task, where patients with MCI exhibit more pronounced recruitment of the frontal lobes.

To our knowledge, no previous studies have investigated the effect of MCI on performance of the CDT components. One previous study correlated regional CBF with scores on the individual components of the task as described by the Rouleau scoring method (Matsuoka et al., 2013). Matsuoka et al. identified significant positive correlations between R2 (clock numbers) score and the bilateral posterior temporal lobes and between R3 (clock hands) score and the bilateral parietal lobes, right middle frontal gyrus, right temporal and occipital lobes, supporting similar conclusions to the current study. However, they did not find a significant relationship between regional CBF and R1 (clock face score). The current study investigated all areas of brain activation during completion of the components, which is different from Matsuoka et al. who only studied specific regions that showed a significant relationship between blood flow and CDT component score. This may explain why the current study identified more brain regions of interest for each component (R1, R2 and R3).

Analyzing the individual components of the CDT (R1, R2 and R3) provided further insight into the neural regions involved in task completion. Interestingly, the brain activation maps related to the CDT components revealed more visually distinct patterns of activity between the two groups than the maps associated with the entire CDT. The most noticeable difference in positive activity between the two groups was observed in the frontal lobes, where patients with MCI exhibited increased recruitment of multiple frontal regions during R1 (clock face) and R2 (clock numbers). Given that R1 is at the beginning of the task after participants receive the clock-setting time instruction, it is possible that brain activity associated with this component is reflective of more

110 extensive cognitive processes beyond those required to draw the clock face. During this time there may be an increased amount of concentration and planning to coordinate how to complete the task. Although participants are given instructions prior to the task stimulus (e.g. the blank slide), studies have established that not all task preparation can be completed before being presented with the task stimulus (Meiran, 1996; Rogers & Monsell, 1995). However, it is necessary for future studies to replicate this protocol with a pre-drawn circle to determine if the frontal recruitment is associated with the beginning planning stages of the task or drawing the clock face.

As discussed in Section 5.2.1, the frontal lobes are involved in executive functions essential for higher-level cognitive processing, such as planning (Alvarez & Emory, 2006; Roberts et al., 1998; Donald R Royall et al., 1999). Taken together, the current results suggest that increased recruitment of the frontal lobes during R1 may be reflective of increased cognitive effort necessary in patients with MCI to plan task completion. Previous literature has demonstrated increased task-related brain activity in patients with MCI compared to healthy controls as a compensatory mechanism to maintain behavioural performance (Angel et al., 2016; Berger et al., 2015; Clément, Gauthier, & Belleville, 2013; Leyhe et al., 2009; C. Li, Zheng, Wang, Gui, & Li, 2009). In both R1 and R2, patients with MCI exhibited more extensive patterns of positive activity in regions of the frontal lobe. Although patients with MCI performed worse on both components (R1 and R2), their scores were not impaired enough to distinguish them from healthy controls with adequate sensitivity and specificity, suggesting that the neural compensation helped them maintain their performance.

Patients with MCI displayed less recruitment of frontal brain areas during R2 and R3 as well as the left supramarginal gyrus during all three components. This finding is consistent with what was observed during the whole CDT as discussed in Section 5.2.1. Similarly, the results imply that reduced recruitment of these regions may contribute to MCI-related impaired CDT performance since the frontal and parietal lobes are responsible for important cognitive functions involved in the CDT.

Overall, comparing the group activation maps for the CDT components reveals more differences in brain activation than observed in the whole CDT activation map. From these results, it is

111 implied that patients with MCI require increased cognitive exertion to complete the CDT, especially during the beginning of the task.

5.2.3 Differences in Functional Connectivity Patterns during the CDT

To further understand the effect of MCI on brain function during the CDT, functional connectivity analysis was completed. Both groups exhibited extensive functional connectivity from regions on the frontal, temporal and parietal lobes. However, the 2-sample group comparison revealed significantly less connectivity in patients with MCI compared to the healthy controls. Notably, patients with MCI showed reduced connectivity to the bilateral frontal lobes and supramarginal gyri.

To our knowledge, this is the first study to measure functional connectivity during CDT completion in patients with MCI. In a resting-state fMRI study, patients with MCI exhibited reduced functional connectivity from the dorsolateral prefrontal cortex to the inferior parietal lobe, the superior and medial frontal gyrus and multiple subcortical regions, including the thalamus and putamen (Liang, Wang, Yang, Jia, & Li, 2011). Additionally, the dorsolateral prefrontal cortex connectivity to the inferior parietal lobe and the thalamus was significantly associated with CDT performance, suggesting the involvement of that network in CDT completion (Liang et al., 2011). Yang et al. identified a significant negative correlation between CDT performance and reduced causal connectivity of the posterior cingulate cortex to the left medial temporal lobe in patients with MCI (Yang et al., 2017). In a similar study, patients with MCI did not show significant correlations between CDT performance and causal connectivity in four resting-state brain networks: default mode network, hippocampal cortical memory network, dorsal attention network and frontal-parietal control network (Liang et al., 2014). Meanwhile, in a sample of older healthy adults, CDT performance was negatively associated with resting-state connectivity in the left frontal-parietal network, and more specifically the left inferior frontal gyrus connectivity (Y. Chen et al., 2018). The current study investigated all functional connectivity during completion of the CDT, which is different from the studies outlined above that only studied connections with significant correlations to CDT performance. Additionally, this study compared connectivity patterns between patients with MCI and healthy controls,

112 which prior studies did not do. This may explain the discrepancy in results between the current study and those presented in the literature.

Analyzing functional connectivity during the CDT provided more detailed information on the effect of MCI on the CDT. Specifically looking at connections from regions important for CDT completion (e.g. frontal, parietal, temporal) revealed that patients with MCI may have impaired connectivity during task completion. There was a significant difference in the connectivity patterns between patients with MCI and healthy controls for all the ROIs except the left angular gyrus. Patients with MCI exhibited less connectivity to various cortical and subcortical regions. These results are interesting because although patients with MCI did not display significant differences in CDT-related brain activity, there were significant differences in functional connectivity between the groups. This indicates that in both groups, brain areas exhibited similar levels of engagement during CDT completion, however the true difference was in the communication between the areas. The reduced connectivity characteristic of patients with MCI implies that early stages of neurodegeneration affects functional connections more than activation. Furthermore, the results showed many 2-sample differences in functional connectivity localized to brain regions that did not have significant 1-sample connectivity, which suggests that weaker functional connections are more vulnerable to disruption in MCI.

The current study focused specifically on connectivity from the bilateral middle frontal gyri, middle temporal gyri and supramarginal gyri. Connections to the frontal lobe from both the bilateral middle frontal gyri and the right supramarginal gyrus were significantly reduced in patients with MCI compared to healthy controls, suggesting impairment in the frontal-parietal network. Meanwhile in patients with MCI, the middle temporal gyrus connectivity showed a significant decrease to regions of the bilateral inferior and superior parietal lobes, including the supramarginal gyri and postcentral gyri.

As mentioned in Section 5.2.1, previous CDT imaging studies have suggested that worse CDT performance in patients with MCI/AD may be linked to impaired functionality of the frontal, temporal and parietal lobes (Matsuoka et al., 2013, 2010; Nagahama et al., 2005; Takahashi et al., 2008; Thomann et al., 2008; Ueda et al., 2002). The current functional connectivity results further support this hypothesis by identifying reduced connectivity in patients with MCI in these specific regions.

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Similar to previous studies, the current results implicate the involvement of the frontal-parietal network in CDT completion (Y. Chen et al., 2018; Ino et al., 2003; Liang et al., 2011). Furthermore, the findings indicate that patients with MCI exhibit impaired connectivity within this network during CDT completion. As previously highlighted, both the frontal and parietal lobes have important cognitive functions for CDT completion. The prefrontal cortex within the frontal lobe is a fundamental component of higher-level cognitive processing through executive functions, such as selective attention, problem solving and planning (Alvarez & Emory, 2006; Roberts et al., 1998; Donald R Royall et al., 1999; Shoyama et al., 2011; Talwar et al., 2019). The parietal lobe and more specifically the supramarginal gyrus is largely responsible for the visual and spatial perception demands of the CDT (Talwar et al., 2019; Tranel et al., 2008). Therefore, coordination of the frontal and parietal lobes and their respective functions is essential for accurate completion of the task.

The temporal lobe has identified connections to regions of the parietal lobe that are important for information retrieval (Binder & Desai, 2011; Wagner, Shannon, Kahn, & Buckner, 2005; Walhovd et al., 2009). In particular, these cortical regions are involved in semantic memory (Binder & Desai, 2011). Semantic memory includes knowledge of facts that are not acquired through experiences and would include pertinent information for the CDT (e.g. what a clock looks like). Therefore, the function of the temporal-parietal network may be important for CDT completion. Patients with MCI demonstrated less connectivity between the middle temporal gyrus and regions of the bilateral parietal lobes, indicating impaired function of this network.

Given the importance of both the frontal-parietal and temporal-parietal networks in CDT completion, the current results suggest that reduced connectivity in these networks in patients with MCI may lead to impaired CDT behavioural performance.

Comparing the group functional connectivity during CDT completion revealed more information on the effect of MCI on the CDT. There was a significant change in connectivity between the two groups as patients with MCI exhibited reduced functional connectivity in key brain networks for CDT completion. These results highlight possible areas of underlying neuropathological impairment in patients with MCI, which may result in corresponding behavioural impairment.

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5.3 Chapter Summary

In the current sample of 20 patients with MCI and 20 matched healthy controls, we identified brain activation patterns characteristic of each group were identified during completion of the entire CDT and each of the individual components (R1, R2 and R3). Both groups recruited extensive regions of the bilateral frontal lobes, insular cortices, cerebellum, motor areas, occipital, temporal and parietal lobes during completion of the CDT. The CDT components revealed slightly different brain activity networks, engaging similar areas (e.g. occipital, parietal, frontal, cerebellar, motor), but with varied spatial extent and patterns. The current results are in accordance with previous literature investigating the neural correlates of the CDT (Cahn-Weiner et al., 1999; Formisano et al., 2002; Ino et al., 2003; Leyhe et al., 2009; Matsuoka et al., 2013, 2010; Nagahama et al., 2005; Shoyama et al., 2011; Takahashi et al., 2008; Talwar et al., 2019; Thomann et al., 2008; Tranel et al., 2008; Trojano et al., 2000; Ueda et al., 2002).

The secondary objective was to identify changes in CDT-related brain activity characteristic of patients with MCI. Although differences were observed when visually comparing the brain activation maps, it is important to note that there were no statistically significant differences in brain activity between the groups, therefore interpretation of these results is speculative. The lack of significance may be due to heterogeneity of the MCI group since the study sample included all sub-types of MCI, which have various symptoms and effects on cognition. As further discussed in Section 6.3.2, it is necessary to conduct a study looking at the individual subtypes of MCI separately to characterize the effect of different forms of MCI on the CDT and confirm the current results.

Despite the lack of significant results from the 2-sample voxel-wise comparison, visual comparison provides insight into brain areas that may be susceptible to MCI-related neuropathology. During the whole CDT and each individual component (R1, R2 and R3), patients with MCI recruited the frontal and parietal lobes less extensively than healthy controls.

To further characterize the effect of MCI on CDT-related brain function, functional connectivity during CDT completion was compared between the two groups. Interestingly, there were significant differences in functional connectivity between the two groups, which suggests MCI affects communication between brain areas during the task. Similar to the brain activation results, regions of the frontal and parietal lobes exhibited significantly less connectivity in

115 patients with MCI compared to healthy controls, which suggests dysfunction of the frontal- parietal network during task completion. Both brain regions have important cognitive functions (executive function and visuospatial ability respectively) that contribute to CDT completion, suggesting that impaired functioning of those areas contributes to behavioural impairment on the task.

When analyzing the individual components, there was a noticeable increase in frontal brain activity in patients with MCI, especially near the beginning of the task. The current results suggest that patients with MCI recruited more brain regions during CDT completion in attempt to compensate for their impairment and maintain good performance on the task. Additionally, these findings support a neural efficiency hypothesis, such that increased skill level may be associated with less neural recruitment as observed in the healthy control cohort.

The current fMRI results effectively portray the underlying brain activity associated with completion of the CDT in a healthy and MCI cohort. Extensive patterns of CDT-related positive activation indicate that the CDT is a complex task, which requires recruitment and coordination of multiple brain regions. Visually comparing the group brain activation maps suggests that patients with MCI displayed less extensive activity in the frontal and parietal lobes, implying that those areas may play a role in the observed CDT behavioural impairment. Furthermore, significant differences in activation between the groups were found in specific ROIs in the bilateral middle temporal gyri. This finding is supported by the functional connectivity results, which showed significantly less connectivity in the frontal-parietal and temporal-parietal brain networks in patients with MCI compared to healthy controls.

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General Discussion

The results of Chapter 4 demonstrated that patients with MCI performed worse on the CDT compared to cognitively healthy matched controls as scored by the Rouleau, Shulman and Sunderland methods, however this was not significantly different after correction for multiple comparisons. Further analysis revealed that although patients with MCI performed worse on all three CDT components (R1, R2 and R3), only R1 reached statistical significance. Despite a noticeable behavioural impairment in the MCI group, none of the scoring systems demonstrated satisfactory sensitivity and specificity to be used as a screening tool for MCI. This finding is congruent with the conclusions of previous studies, which also indicate that the current CDT is not an adequate screening tool for MCI (Chiu et al., 2008; Connor et al., 2005; Duro et al., 2018; Ehreke et al., 2011, 2009; Esteban-Santillan et al., 1998; K. S. Lee et al., 2008; Powlishta et al., 2002; Ravaglia et al., 2013).

The behavioural results reported in Chapter 4 coincide with the neuroimaging results in Chapter 5. Although there was no statistical significance in the brain activity between the two groups, visual comparison of the group activation maps identifies neural areas that may be associated with MCI-related impairment. Specifically, patients with MCI exhibited less positive activity in regions of the frontal and parietal lobes during the CDT relative to controls. Further supporting these results, significant differences in functional connectivity between the two groups were localized to regions in the frontal-parietal and temporal-parietal networks, displaying less connectivity in these networks in patients with MCI. As patients with MCI performed significantly worse on the task, it is suggested that reduced function of these areas contributes to impaired CDT performance. There was a significant difference in functional connectivity between the two groups, but not brain activity, which implies that disrupted communication between these brain regions may be affecting CDT performance in patients with MCI. This conclusion is further strengthened by the fact that the frontal, parietal and temporal lobes are involved in important cognitive functions for the CDT. In contrast, when analyzing the individual CDT components, patients with MCI displayed increased recruitment of multiple

117 brain regions as reflected by more positive activity. Previous studies have suggested that increased activation of brain areas reflects a compensatory mechanism employed in attempt to overcome any cognitive impairment and maintain task performance (Angel et al., 2016; Berger et al., 2015; Clément et al., 2013; Dickerson & Sperling, 2008; Leyhe et al., 2009; C. Li et al., 2009). None of the CDT scoring methods were able to differentiate between patients with MCI and healthy controls with adequate sensitivity and specificity. Taken together, the current results suggest that increased cognitive effort exerted by patients with MCI does compensate for some of the MCI-related impairment as they are able to maintain task performance at a level that is above common clinical cut-offs for CDT score.

Notably, the current results highlight the importance of paying attention to the components of the CDT. Most evident in the fMRI analysis presented in Chapter 5, analyzing the individual CDT components showed different patterns of brain activation than observed in the whole CDT. Comparison of the group activation maps for the CDT components revealed more noticeable changes in brain activity.

However, this effect was not as pronounced behaviourally, although patients with MCI performed worse on all three CDT components, only the R1 score was significantly different. This suggests that the current scoring methods for the CDT components are not sufficiently reflecting underlying MCI neuropathology. By breaking down the CDT, it is clear that patients with MCI experience cognitive difficulties with the task at different stages throughout the process, which may not be reflected when looking at the whole CDT. Specifically, there was notable increased frontal recruitment during R1 at the beginning of the task, which was no longer observed in R3 at the end of the task and not evident when analyzing the whole CDT. These results imply that to measure MCI-related impairment, it is necessary to identify stages where patients struggle and develop performance metrics that can measure impairment at those stages. Preliminary investigations have delivered drawing tasks to patients with cognitive impairment using digital pens and tablets in order to measure non-conventional metrics, such as pressure, time-in-air and time-on-surface (Faundez-Zanuy et al., 2013; Müller, Preische, Heymann, Elbing, & Laske, 2017a, 2017b; Souillard-Mandar et al., 2016). These subtle measures were sensitive to MCI, which was suggested to reflect impaired executive function, especially involving decision making and planning, in patients with MCI (Faundez-Zanuy et al., 2013; Müller et al., 2017a, 2017b; Souillard-Mandar et al., 2016). Therefore, these metrics may be

118 effective at detecting MCI-related impairment during specific CDT stages, such as at the beginning during the initial planning phase. Section 6.3.3 provides further elaboration on this topic.

6.1 Significance

This is the first study to investigate the neural correlates of the CDT and its components in patients with MCI. The results of this investigation are especially important for providing a more holistic view of the potential to use the CDT as a screening tool for MCI. There is currently no quick, easy to administer cognitive assessment tool for GPs to easily use in clinical visits, which leads to a deficit in routine screening for cognitive impairment. As a result, MCI diagnoses are commonly missed unless older adults seek out further treatment in elder or memory clinics. The CDT has been proposed as a potential simple screening tool to help identify patients with cognitive impairment in general practice. However, studies of behavioural performance on the CDT have resulted in varied conclusions, mainly due to the inconsistency in CDT administration and scoring methods, making it unclear whether or not the CDT is an effective MCI screening tool. The current results provide a preliminary understanding of the underlying changes in brain activity associated with the CDT, which are characteristic of patients with MCI. These findings contribute more information to help inform whether the CDT is an effective screening tool for MCI. This investigation can guide researchers to develop behavioural performance metrics that detect impairment specific to the brain areas highlighted in this study (e.g. frontal, parietal, temporal), which may be more sensitive to MCI. Developing an effective version of the CDT is essential to promote dementia screening in general practice and reduce the number of missed diagnoses.

6.2 Limitations

The current study provides new and important insight regarding the effect of MCI on CDT performance and CDT-related brain activity. These results are the first to identify brain activation patterns associated with CDT completion characteristic of MCI. Although the

119 investigation provides important contributions to the literature and current knowledge of the CDT, there are a few methodological limitations, which must be noted.

Amongst the MCI cohort, there was high within-group variability as demonstrated by larger standard deviation values and larger ranges compared to the control cohort. The variability in the MCI group may have impeded the group comparison analysis as well as the sensitivity and specificity calculations. Two main factors likely contributed to the observed variability. First, there are sub-types of MCI (i.e. amnestic, non-amnestic, single domain, multi-domain), which each have different associated cognitive deficits. However, the patient sample used in the current study had an unbalanced number of MCI sub-types (i.e. one single-domain non-amnestic patient, two multi-domain non-amnestic patients, two single-domain amnestic patients and fifteen multi- domain amnestic patients), making it difficult to analyze the sub-types individually. The MCI patients were grouped and analyzed together instead. Second, even amongst the same sub-type, patients with MCI present heterogenous symptoms and cognitive deficits as each patient has a unique degree of impairment. Patients with the multi-domain subtype of MCI specifically exhibit highly variable levels and areas of cognitive impairment as these patients may experience deficits in different cognitive domains (e.g. language versus attention) and a different number of domains (e.g. impairment in two versus three or more domains). The multi-domain subtype was the most common in the current patient sample (85%), therefore it could be a large contributor to the observed variability in the results from the MCI cohort. Variability is a critical factor, which has influenced the result of the current study and needs to be controlled for in future investigations by studying the sub-types of MCI individually.

The demographic background of the sample was another limitation as the majority of the group was male (75%) and well-educated (100% had 12+ years of education, 51.3% had 13-16 years of education). The age range (53-87 years old) was broad for a cohort with MCI, however it was largely composed of older adults between the ages of 65-79 (62.5%). Older age (80 years+), female gender and low levels of education have all been identified to increase the risk of developing dementia (Launer et al., 1999; Ott, Breteler, Van Harskamp, Stijnen, & Hofman, 1998), but they were not represented in the current MCI sample. Forms of dementia in the oldest old adults (80+ years old) are commonly more severe, therefore it is likely that patients in the Memory Clinic at SMH in this age group were too impaired to participate in the study. As previously mentioned, this study was conducted as a part of a larger investigation involving

120 driving simulation, and therefore all participants were required to have their driver’s license and be current drivers. In older couples, males are usually the primary drivers and females may stop driving earlier, potentially explaining the low number of female participants. Generally, patients with higher education and socioeconomic status are drawn to sub-speciality clinics, such as the Memory Clinic. It is necessary for future studies to include a wider range of demographic factors as they may affect CDT performance results.

Although the current study detected significant differences in CDT behavioural performance between the two groups, the sample size of the two groups was relatively small (n = 20), diminishing the power of the investigation. Using a power analysis, it was determined that a minimum sample size of 25 patients with MCI and 25 healthy controls is necessary to have adequate statistical power. Additionally, the sensitivity and specificity of the scoring systems computed in this study should be interpreted with caution as a larger population sample is necessary to accurately measure sensitivity and specificity. Based off of the requirements outlined by Bujang and Adnan (2016), the minimum population size for sensitivity and specificity calculation is approximately 1000 (Bujang & Adnan, 2016). Taken together, the small sample size and the MCI group variability indicate that the current behavioural results may not be generalizable to the MCI population and will need to be validated in future studies. The current sample size has more power in fMRI studies, however due to the within-group fMRI variability, these results also need be validated through replication of this study with a larger sample size.

The administration of the CDT instructed participants to “draw a large circle. Put all the numbers in to make it look like the face of a clock. Draw in the hands of the clock to set the time to the time specified. Stop when completed.” Although this administration was adapted from the traditional CDT practices (Agrell & Dehlin, 1998), it may be limited by the specific instruction to “draw in the hands of the clock”. This instruction may have informed participants that they must use hands to indicate the time on the clock-drawing, which may have led to improved behavioural performance on the task. However, this instruction was kept consistent across both the MCI and healthy control cohort. Future studies should not specify “hands of the clock” and only instruct participants to “set the time to the time specified” to prevent any potential assistance with the task.

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Despite the novelty and utility of the fMRI-compatible tablet, it does have several drawbacks, which can make task completion difficult. The majority of participants rated their CDT performance as better on paper than on the tablet, which indicates that there was some difficulty using the tablet and it did not exactly replicate the traditional clinical testing conditions for the participants. The touch-sensitive surface area of the tablet (118.75cm2) is smaller than that of a regular sheet of paper, restricting the drawing area for task completion. In addition, participants were instructed to keep as still as possible and minimize their arm movements during drawing in order to produce useable MRI data, which as a result restricted their mobility and increased demand for very precise movements. In addition, the current study population consisted of older adults (50+ years old), therefore they may not be as experienced or comfortable using technology similar to the tablet. Unfortunately, due to the limitations of the MRI environment, many obstacles of the tablet-based CDT protocol (e.g. needing to stay as still as possible) cannot be fixed. However, future studies should increase the touch-sensitive surface size on the tablet to facilitate task completion.

Functional MRI has many advantages for studying task-related brain activity, including its superior spatial resolution, ability to acquire both structural and functional images, excellent imaging depth and moderate temporal resolution. However, fMRI acquisition is very susceptible to motion and other artifacts. To optimize the fMRI data, procedures were implemented to minimize the effects of motion during both data acquisition (e.g. sponges placed between participant’s head and head coil, instructions to keep head as still as possible, feedback on head motion provided after each scanning sequence) and data analysis (e.g. robust pre-processing algorithms used to corrected for motion and other artifacts (Churchill et al., 2012). In addition, the current study sample consisted of older adults. However, the BOLD signal contrast mechanism can be affected by physiological changes that occur as a result of aging, therefore causing changes in BOLD signal irrespective of true changes in the neural activation. This is a common limitation of fMRI studies that include older age populations (D’Esposito, Zarahn, Arguirre, & Rypma, 1999; Garrett, Lindenberger, Hoge, & Gauthier, 2017). It is necessary for future studies to include the appropriate physiological imaging measures to control for the age effect on BOLD signal and validate the results found in the current investigation.

The results from the current investigation are in accordance with previous literature on the topic, indicating that the methods yielded valid results despite the limitations outlined above.

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6.3 Future Directions

To our knowledge, this was the first study to identify the neural correlates of the CDT in patients with MCI. The current results provide new and valuable information on the efficacy of the CDT as a screening tool for MCI. Next steps are discussed below, which can ultimately help determine whether the CDT is a valuable cognitive assessment in the context of MCI.

6.3.1 Confirming and Validating Areas and Degree of CDT Impairment Characteristic of MCI

Importantly, large scale research will be necessary to confirm the current results regarding the areas and degree of CDT impairment characteristic of patients with MCI. Similar studies have been conducted, which measure the effect of behavioural performance on the CDT (Babins et al., 2008; Beinhoff et al., 2005; Donnelly et al., 2008; Ehreke et al., 2011; Mazancova et al., 2017; Nunes et al., 2008; Parsey & Schmitter-Edgecombe, 2011; Powlishta et al., 2002; Rubínová et al., 2014; Sager et al., 2006; Thomann et al., 2008; Zhou & Jia, 2008). Due to the inconsistency in results between these studies (Ehreke et al., 2010; Pinto & Peters, 2009), it is necessary to conduct a large-scale, comprehensive investigation to ultimately determine whether or not current scoring methods of the CDT are sensitive to MCI-related impairment. This is the first study to identify CDT-related brain activity in patients with MCI across the entire task and the individual components. However, as mentioned in Section 6.2, there is a large amount of variability in the MCI group. Therefore, it is critical for future research to confirm the results found in this investigation with a bigger sample of patients with MCI (i.e. n = 35), effectively validating the generalizability of the current results to a larger MCI population.

6.3.2 Identifying the Effect of the Sub-Types of MCI on CDT Performance and Brain Activity

The CDT is a complex cognitive task that requires the recruitment and proper functioning of multiple different cognitive domains. Patients with MCI have heterogenous levels and areas of cognitive deficits. Taken together, this information suggest that different presentations of MCI are likely associated with different areas and levels of CDT impairment. Due to a lack of

123 substantial sample sizes, the current investigation did not separate the group into the individual sub-types of MCI, which may have led to increased variability amongst the MCI group. High- levels of variability make it difficult to clearly understand the true behaviour of the group and limit the generalizability of the results. Therefore, it is essential for future studies to analyze the effect of MCI sub-types on CDT performance. Specifically, a large-scale study should be conducted which compares performance between patients with single-domain and multi-domain MCI with cases that have impairment in domains that are recruited during the CDT (e.g. visuospatial ability, executive function, semantic memory, etc.). This may reveal a more substantial difference between patients with MCI and healthy controls, and will provide a deeper understanding of the effect of MCI on CDT performance and brain activity.

6.3.3 Developing Better Performance Measures Specific to MCI

Given the mild nature of the cognitive deficits associated with MCI, traditional scoring and performance metrics developed for more severe forms of dementia (i.e. AD) are not sensitive enough to detect MCI. More detailed scoring systems (e.g. Rouleau, Cahn) have been developed to attempt to measure qualitative aspects of the task and provide a deeper understanding of error- types. However, these scoring methods were not effective at detecting MCI in the current study. There is a significant need for further research into developing a scoring system tailored to impairment characteristic of MCI. It has been suggested in previous studies as well as in the current results that patients with MCI commit more conceptual errors, especially involving the numbers (R2) and hand (R3) components of the CDT (Babins et al., 2008; Chiu et al., 2008; Ehreke et al., 2011; K. S. Lee et al., 2008; Parsey & Schmitter-Edgecombe, 2011; Yamamoto et al., 2004). Identifying which errors are common amongst unimpaired older adults may reveal errors that are unique to MCI-related impairment. With that information, specialized scoring systems can be developed that place increased weight on MCI-related errors compared to common age-related errors. This research is critical before the CDT can be used as a screening tool for MCI.

Beyond traditional methods of measuring performance (e.g. scoring), a new area of focus is using tablet related data (e.g. pressure, velocity, etc.) to differentiate between mild impairment and healthy aging. The current study did not collect data on these metrics. It is important for

124 future tablet studies to collect and analyze this information to determine if they are sensitive to MCI-related impairment. Preliminary research has been done using digital pens and tablets to deliver drawing tasks (e.g. CDT, drawing a 3D house) to patients with cognitive impairment (Faundez-Zanuy et al., 2013; Müller et al., 2017a, 2017b; Souillard-Mandar et al., 2016). These studies have focused on metrics such as time-in-air (i.e. time while moving the stylus above the tablet surface from one stroke to the next), time-on-surface (i.e. time that the stylus produced drawings) and pressure and found that these measures are sensitive to impairment characteristic of patients with MCI (Faundez-Zanuy et al., 2013; Müller et al., 2017a, 2017b; Souillard-Mandar et al., 2016). This research is highly important as it identifies the subtle effects of MCI that alter CDT performance. Since MCI is such a mild form of impairment, the final CDT drawing that is scored does not exhibit significant deficits that are distinguishable from healthy controls as concluded in the current study. However, patients with MCI may struggle more during task completion with planning and execution. This highlights the importance of developing metrics that can measure performance during task completion, such as pressure or time-in-air. Continuing research on the digital CDT is crucial to identify useful metrics for identifying MCI- related impaired performance during task completion and integrating those metrics into current scoring systems.

6.3.4 Identifying the Effect of AD on CDT-Related Brain Activity

The rate of conversion to AD is significantly higher in patients with MCI than matched healthy individuals (R C Petersen et al., 2001). However, there are currently no clear signs that indicate which patients with MCI will progress AD versus those who will not. Future studies should investigate the areas of impairment, both behavioural errors and changes in CDT-related brain activity, common in AD. This information will reveal how AD affects brain activity during CDT completion and how that relates to behavioural task impairment. Furthermore, comparing the CDT brain activation patterns in healthy controls, patients with MCI and patients with AD will provide an understanding of how degree of impairment affects performance and underlying brain function. A potential future study can measure CDT performance and brain activity longitudinally in patients with MCI from onset of mild deficits to potential development of AD. This can build upon current knowledge to understand how the CDT captures cognitive decline

125 along the neurodegenerative process. Additionally, identifying the similarities between onset of MCI and onset of AD may provide information on common neuropathology between the two diseases. This research has potential to pinpoint neural biomarkers that are present in both MCI and AD, which as a result, may be able to classify patients with MCI that are likely to convert to AD. Furthermore, the association between CDT performance and known-AD biomarkers, such as amyloid beta plaques, can be further investigated.

Summary and Conclusions

Currently, there is a lack of dementia screening in general practice due to time constraints and a lack of quick, easy to administer cognitive assessments, resulting in missed diagnoses of cognitive impairment. The CDT is used extensively as a screening tool for various forms of dementia, therefore it has been suggested that the CDT has potential to be effective in an MCI population. However, current literature has varied conclusions on the efficacy of the CDT as an MCI screening tool. Identifying the brain activation patterns associated with the CDT in patients with MCI is a fundamental step in determining the effect of MCI on task performance. This is the first study to investigate the neural correlates of the CDT in patients with MCI. The current results indicate that although patients with MCI perform worse on the task, CDT scores did not have adequate sensitivity and specificity when differentiating the MCI and healthy control cohorts. The behavioural results are in accordance with the neuroimaging findings, which showed that as a group, patients with MCI exhibited less extensive brain activity in the frontal and parietal lobes. These results were supported by the functional connectivity analysis, which showed less connectivity in the frontal-parietal and temporal-parietal brain networks. Given that these areas have important cognitive functions for the CDT, the decreased activity may contribute to the impaired behavioural performance.

Conversely, when looking at brain activity during the CDT components, patients with MCI exhibited increased recruitment of multiple brain regions relative to controls, notably in regions of the frontal lobes. Previous studies have suggested increased brain activation acts as a compensatory mechanism in patients with cognitive impairment to help maintain performance (Clément et al., 2013; Dickerson & Sperling, 2008). Given that performance on the CDT in patients with MCI was not sufficiently distinguishable from healthy controls, the increased brain activation may effectively compensate for MCI neuropathology resulting in milder CDT performance decrements that are not detected by current scoring methods.

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In addition, the component analysis revealed that different stages of task completion are associated with varied patterns of brain activity. More importantly, these stages elicit distinctive cognitive responses in patients with MCI, leading to more noticeable changes in brain activation patterns during certain components (e.g. R1). This is important to note because it signifies that focus should be shifted towards stages of the task that reveal the most apparent group differences in brain activity because during those times, patients with MCI are experiencing the greatest levels of difficulty. Specifically, the beginning of the task, which requires higher-level cognitive functioning, such as planning and decision-making, elucidated an increase in frontal recruitment in patients with MCI, suggesting that patients exert additional cognitive effort to effectively plan completion of the task. Thus, it may be beneficial to measure apprehension or lack of confidence in patients during the initial stages of the task through metrics such as pressure or in-air-time as described in Section 6.3.3.

Overall, considering both the behavioural and neuroimaging results from the current sample, the CDT has potential to detect MCI-related impairment, but the current version of the CDT is not sufficient as a screening tool for MCI. As indicated by the behavioural results, the established scoring systems do not have adequate sensitivity and specificity to differentiate patients with MCI from healthy controls. Despite differences in the group brain activation maps through visual comparison, there were no significant differences in brain activity between the two groups. There were significant differences in functional connectivity, with patients with MCI exhibiting less connectivity in the frontal-parietal and temporal-parietal brain networks. The current results provide preliminary evidence that patients with MCI may exhibit altered brain activation patterns during the CDT, which impact function of the frontal, parietal and temporal lobes. However, these findings need to be validated in a larger, less heterogenous MCI sample before any concrete conclusions can be made.

Future research is necessary to: (1) confirm both the brain areas and degree of CDT impairment characteristic of MCI, (2) identify the effect of the sub-types of MCI on CDT performance and brain activity, (3) develop better CDT performance metrics specific to mild cognitive deficits characteristic of MCI, (4) identify the effect of AD on CDT-related brain activity. These research investigations will be important to further understand the effect of MCI on CDT performance and potentially develop a variation of the traditional CDT that is sensitive to MCI, which can be used by GPs routinely as a screening tool.

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Appendices

Appendix 1: List of Medications taken by Participants

Patients with MCI

Participant Number Medications

1 Aspirin

2 Allopurinol, Metformin, Aspirin

3 Hormone replacement therapy

4 Synthroid, Omeprazole, Tamsulosin, Methylphenidate, Amlodipine

5 Simvastatin, Ezetimibe, Flomax, Viagra, Ibuprofen

6 Sertraline, Alvesco, Ventolin

7 Melatonin, Baby Aspirin, Aleve, Lecithin

8 Trazodone, Pantoloc, Omnaris, Ranitidine, Mobicox

9 Timolol, Baby Aspirin

10 Rivaroxaban, Dutasteride, Tamsulosin, Valsartan, Atorvastatin, Zantac, Crestor

11 Diclofenac, Tramadol, Rabeprazole, Crestor, Imitrex

12 Aspirin, Tylenol, Melatonin, hearing medications

13 Crestor, Amilzide, Alendronate

14 Crestor

15 Insulin, Altace, Aspirin, Crestor, Tecta

16 Amlodipine, Losartan, Venlafaxine

17 Rabeprazole, Zyloprim, Candesartan, Metformin, Crestor, Aspirin

18 Sertraline

19 Wellbutrin

155

20 Metformin, Coversyl, Isoniazid, Rifampin, Pyrazinamide

Healthy Controls

1 None

2 None

3 None

4 Hydrochlorothiazide, Amlodipine, low dose Aspirin

5 Blood pressure medications

6 Blood pressure medications

7 Crestor, Actonel, Breo, Singulair, Spiriva

8 None

9 Invokana, Domperidone, Omeprazole, Vyvanse, Metformin, Terazosin, Aspirin, Olmetec Plus, Crestor, Gliclazide

10 Altace, Crestor, Androgel, Aspirin

11 Atenolol, Altace, Hydrochlorothiazide, Coumadin

12 None

13 Aspirin

14 Crestor, Flomax

15 None

16 Statins

17 None

18 Valsartan, Crestor, Omnaris spray

19 Digoxin

20 None