UNIVERSITY OF CALGARY. Predicting Cognitive Decline in Patients with TIA and Minor Stroke. Muhammad Amlish Munir A THESIS

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1 UNIVERSITY OF CALGARY Predicting Cognitive Decline in Patients with TIA and Minor Stroke by Muhammad Amlish Munir A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE GRADUATE PROGRAM IN NEUROSCIENCE CALGARY, ALBERTA MARCH, 2016 Muhammad Amlish Munir 2016

2 Abstract Dementia is an incurable neurocognitive disorder and recognizing early pathological biomarkers can help to predict future dementia. Transient Ischemic Attack (TIA) and minor stroke patients are at risk of dementia. We hypothesized that TIA and minor stroke patients experience higher brain atrophy rates and that baseline brain and hippocampal volumes may predict cognitive decline at 3 years. Our results suggest that TIA and minor stroke patients experienced a higher percent brain atrophy rate over 3 years compared to controls. Cognitive decline was observed at 3 years for tests assessing processing speed, and short and long delay free recall. Age was a predictor of decline in processing speed and time was a predictor for short and long delay free recall as well. Higher whole-brain atrophy and cognitive decline at 3 years suggests that TIA/minor stroke patients are a high-risk population for dementia. ii

3 Acknowledgements I would like to thank my supervisor Dr. Philip Barber for supporting me and investing the time and energy towards my education. You provided me with encouragement and support for which I grew more confident. You answered endless questions and demonstrated patience that was crucial for my growth as a MSc. Student. I would like to my committee members Dr. Eric Smith and Dr. Richard Frayne: you were consistently available for helping me with my education and provided guidance throughout my journey. Thank you for all your support. I would like to thank Dr. Giovanna Zamboni and Dr. Ludovica Griffanti. The opportunity to visit Oxford and learn directly is one of the greatest highlights of my journey. You instilled into me the knowledge and skills that were served as the foundation for completing my thesis. You taught me how to think the challenges in research and gave me a privilege very few students are lucky to ever receive. I would like to thank everyone at the Seaman Family MR Centre for your support throughout this journey. iii

4 Table of Contents Abstract... ii Acknowledgements... iii Table of Contents... iv List of Tables... vi List of Figures... vii List of Abbreviations... viii CHAPTER ONE: INTRODUCTION DEMENTIA COMMON CAUSES OF DEMENTIA Alzheimer s Disease Cerebrovascular Disease Mixed Dementia PREVENTION OF DEMENTIA THE RELATIONSHIP OF VASCULAR RISK FACTORS WITH LATE-LIFE COGNITIVE DECLINE IDENTIFYING POPULATIONS TO EVALUATE HIGH-RISK FOR DEMENTIA Extended-CATCH Alzheimer s Disease Neuroimaging Initiative (ADNI) ADNI Background ADNI Healthy Controls MRI Protocol and Quality Control DETECTING PRECLINICAL DISEASE PROCESSES: MAGNETIC RESONANCE IMAGING T1-weighted Imaging Diffusion-Weighted Imaging (DWI) MRI-ASSESSED ATROPHY Whole-Brain Atrophy Hippocampal Atrophy NEUROIMAGING ANALYSIS NEUROPSYCHOLOGICAL PREDICTORS OF COGNITIVE DECLINE AND DEMENTIA LINEAR MIXED-EFFECTS REGRESSION THESIS RATIONAL AND EXPERIMENTAL APPROACH...19 CHAPTER TWO: TRANSIENT ISCHEMIC ATTACK AND MINOR STROKE PATIENTS EXPERIENCE HIGHER BRAIN ATROPHY RATES COMPARED TO HEALTHY CONTROLS INTRODUCTION METHODS Study population Image Preparation and Pre-processing Image analysis Statistical Tests...27 iv

5 2.2.5 Inclusion Process RESULTS Demographics Whole-Brain Atrophy Rates Linear Mixed-Effects Regression DISCUSSION CONCLUSION...42 CHAPTER THREE: BRAIN AND HIPPOCAMPAL ATROPHY STATES IN TIA AND MINOR STROKE PATIENTS INTRODUCTION METHODS Study population Neuropsychological Battery Image preparation and pre-processing Image analysis using FIRST (FMRIB s Integrated Registration & Segmentation Tool) and SIENAX Inclusion Process Statistical Tests RESULTS Demographics Neuroimaging Whole-Brain Tissue Hippocampus Linear Mixed-Effects Regression DISCUSSION CONCLUSION...63 CHAPTER FOUR: THESIS CONCLUSION OVERALL CONCLUSIONS IMPLICATIONS AND FUTURE DIRECTIONS...65 REFERENCES:...70 v

6 List of Tables Table 2-1: Major Inclusion and Exclusion Criteria for Extended-CATCH and ADNI Table 2-2: Demographic data and vascular risk factors for Extended-CATCH and ADNI. Significance values was obtained using the Chi-square test for gender, hypertension and diabetes, and a unpaired t-test for age, systolic BP, diastolic BP, BMI and whole-brain atrophy rate Table 2-3: Whole-brain atrophy rates in Extended-CATCH and ADNI. Significance values were obtained using an unpaired t-test whole-brain atrophy rate Table 2-4: Linear Mixed-Effects Regression Analysis for Brain Atrophy. Analysis was performed to identify the independent predictors of the whole brain atrophy in Extended-CATCH and ADNI. Imaging Time Interval corresponds to baseline, 18-month and 3 years for Extended-CATCH and baseline, 12-month and 3 years for ADNI Table 3-1: Inclusion and Exclusion Criteria for Extended-CATCH Table 3-2: Neuropsychological Assessment for Extended-CATCH Table 3-3: Demographic data and vascular risk factors for Extended-CATCH Table 3-4: Linear Mixed-Effects Regression Analysis for Neuropsychological tests with Ratio of left Hippocampus Volume to Intracranial Volume Table 3-5: Linear Mixed-Effects Regression Analysis for Neuropsychological tests with Ratio of right Hippocampus Volume to Intracranial Volume Table 3-6: Linear Mixed-Effects Regression Analysis for Processing Speed and Memory with Ratio of Brain Volume to Intracranial Volume (UBV/ICV) vi

7 List of Figures Figure 2-1: Selection criteria and MR scan allocation following quality control assessment for Extended-CATCH Figure 2-2: Selection criteria and MR scan allocation following quality control assessment for the healthy controls identified from the ADNI repository ( 31 Figure 2-3: Percent Brain volume Change (PBVC) of Extended-CATCH patients in G1 (baseline-18 months) and G2 (18-months to 3-years). Error bars represent standard deviation (SD). For statistical tests, a paired t-test was used determine the differences between G1 and G Figure 3-1: Visual Segmentation Results of Whole-Brain Volume (red) Acquired Using SIENAX Figure 3-2: Visual Segmentation (yellow) Results of Left Hippocampus (top) and Right Hippocampus (bottom) Acquired Using FIRST Figure 3-3: the inclusion process following the quality control assessment for Extended- CATCH Figure 3-4: Baseline Whole-Brain Volume (ml) In Extended-CATCH Figure 3-5: Baseline Hippocampal volume in Extended-CATCH vii

8 List of Abbreviations Symbol AD ADNI BET BMI CATCH CMA COWAT CSF CVD FAST FIRST FLIRT FSL MCI MD MMSE MNI NIHSS PBVC SIENA SIENAX TE TI TIA TR VaD WAIS-IV WHO WMH Definition Alzheimer Disease Alzheimer s Disease Neuroimaging Initiative Brain Extraction Tool Body Mass Index Cognitive Impairment After TIA and MRI measurements of Cerebral and Hippocampal Atrophy Controlled Oral Word Association Test Cerebrospinal Fluid Cerebrovascular Disease FMRIB s Automated Segmentation Tool FMRIB s Integrated Registration & Segmentation Tool FMBRIB s Linear Registration Tool FMRIB s Software Library Mild Cognitive Impairment Mixed Dementia Mini-Mental State Exam Montreal Neurological Institute National Institute of Health Stroke Scale Percent Brain Volume Change Structural Image Evaluation using Normalization of Atrophy Structural Image Evaluation using Normalization of Atrophy Cross-sectional Echo Time Inversion Time Transient Ischemic Attack Repetition Time Vascular Dementia Wechsler Adult Intelligence Scale, Fourth Edition World Health Organization White Matter Hyperintensities viii

9 Chapter One: INTRODUCTION 1.1 DEMENTIA Dementia is an incurable neurocognitive disorder characterized by unhealthy aging processes. It is an acquired, progressive decline in cognition that acts over a long-term period and impairs a person s ability to perform daily tasks. Based on current trajectories, cognition declines as we age but in patients with dementia, the cognitive decline is dramatic enough to cause disability. The progression of dementia likely occurs over year or decades, transitioning from normal cognitive profile to Mild Cognitive Impairment (MCI) followed by dementia 1. MCI is a syndrome characterized by the presence of cognitive symptoms in functionally independent patients. Consequently, both dementia and MCI place a huge burden on a patient s immediate family and the social infrastructure, as they grow increasingly dependent for support. Dementia is a huge public health concern. Currently, there are 47.5 million people living with dementia worldwide 2 and the prevalence is expected to rise up to million people by When considering the economic impact, the cost of dementia for Canadians is $33 billion (2011) and is estimated to rise up to $293 billion (2040) 2. Globally, the economic cost of dementia was $604 billion. Therefore, the impact of dementia remains one of the key focuses of the WHO and the medical community to help address this epidemic 3, COMMON CAUSES OF DEMENTIA Dementia involves a complex interaction of two distinct disease processes, which contribute to the loss of brain cells and brain atrophy before symptoms are detectable years 5-9. These distinct disease processes include: Alzheimer s disease and Cerebrovascular Disease. 1

10 1.2.1 Alzheimer s Disease Alzheimer Disease (AD) is a progressive neurodegenerative disease 10, 11. Traditionally, AD is the most common cause of dementia and can be categorized into early-onset (before the age of 65) and late-onset. The early-onset has highlighted the role of genetics on the pathophysiology of AD, where the genes for Amyloid Precursor Protein (APP), Preselin 1 (PSEN1) and Preselin 2 (PSEN2) are mutated and contribute to the toxic amyloid deposition. The prevailing viewpoint for the cause of AD is the amyloid hypothesis, which suggests that the improper cleavage of the amyloid precursor protein leads to the formation of Aβ 42. The misfolded Aβ 42 deposit and accumulate in the brain parenchyma, followed by a cascade that results in neuronal death. Overtime, the brain is burdened by these extracellular amyloid plaques 11, which are the first histopathological hallmark of AD. The second histopathological characteristic involves, but is not exclusive to AD, the neurofibrillary tangles. The hyperphosphorylation of intracellular tau proteins causes them to disintegrate, which subsequently leads to neuronal death 11. Overtime, these deposits form the intracellular neurofibrillary tangles in the brain. The small-scale neuronal death accumulates over a long period of time, and begins to result into large-scale structural changes within the brain. AD was associated with deficits in verbal and non-verbal memory compared to VaD while patients with VaD demonstrated poor performance on executive function Furthermore, Reed et al., demonstrated that AD patients experienced a significant decline in episodic memory in comparison to executive functions; deterioration of executive function has been proposed to be more characteristic of VaD 16. However, deficits in attention processing speed have been shown to be a characteristic of VaD

11 Cognitively, AD can affect multiple domains such as executive function and verbal and non-verbal memory However, memory is the hallmark domain that is strongly characteristic of AD 16. Given that the pathogenesis of AD rests on the selective toxicity of amyloid protein on neurons in the hippocampus and entorhinal cortex that spreads to the neocortex 20, the cognitive symptoms are associated with the progression. The hippocampus is involved with declarative episodic memory and studies have found that declarative memory is strongly affected in AD. As the disease progresses, functions of the hippocampus become increasingly impaired, resulting in AD patients who have advanced progression performing worst on episodic memory Although no cure for AD exists, acetylcholinesterase inhibitors are given to patients to improve their symptoms 21. During the disease progression, acetylcholine levels decline in the hippocampus and the neocortex 11. By administering acetylcholinesterase inhibitors, this prevents the breakdown of acetylcholine in the synapses, leading to an increase in acetylcholine levels in the brain and helping restore the function that was impaired during the progression of AD Cerebrovascular Disease Cerebrovascular disease is a vascular disease characterized by abnormal blood flow. Overtime, the abnormal blood flow leads to a series of strokes causing brain tissue damage, which manifests as cognitive symptoms that can lead to a condition called Vascular Dementia (VaD). VaD is commonly cited as the second most common cause of dementia after AD. VaD can be classified into strategic infarct dementia or multi-infarct dementia. Strategic infarct dementia is a single stroke that interrupts brain circuits critical for memory and cognition; multiinfarct dementia results from multiple strokes throughout the brain that lead to impaired cognition 22, 23. Cognitively multi-infarct dementia follows an unpredictable course depending on 3

12 the size, localization and number of ischemic insults 22. Patients with VaD demonstrated poor performance on executive function Additionally, deficits in attention processing speed deterioration of executive function has been observed to be a stronger characteristic of VaD compared to AD 16. Treatments for VaD focus on lifestyle management and modifying risk factors such as diet, lifestyle and vascular risk factors (such as hypertension and diabetes) Mixed Dementia AD and VaD don t always exist on their own. In spite of the vast and contributing literature on the dichotomy between AD and VaD, they share cardiovascular risk factors 24-26, including hypertension and diabetes 29, In autopsy studies, AD and VaD co-exist more frequently than anticipated 11, and population-based epidemiological studies indicate that patients with AD with cerebrovascular disease together are more prevalent either AD or cerebrovascular disease alone 39, 40. High blood pressure has long been known to cause stroke 41, and hypertension in mid-life ranks as an important risk factor for late cognitive decline 42, mild cognitive impairment 43, 44, and dementia 28, 45. Furthermore, studies have shown that vascular risk factors contribute to the pathogenesis of AD, improving our understanding of the relationship between AD and vascular disease 43, The relationship between AD and cerebrovascular disease is further supported by the Nun Study, which demonstrated that AD with infarcts reduces the threshold for manifesting dementia and increased the risk of developing dementia by 20-fold compared to those without infarcts 51. As the role of cerebrovascular disease began to be recognized in the manifestation of AD, the distinction between AD and cerebrovascular disease began to narrow. A relationship has been proposed where cerebrovascular disease and AD exist at the poles and that the majority of causes for dementia exist along this spectrum 49, 52, 53. The 4

13 two pathologies are increasingly being recognized as related diseases where both AD and cerebrovascular disease co-exist and interact with each other. Additionally, AD and cerebrovascular disease both contribute to an unhealthy aging process characterized by loss of brain cells/atrophy over a long period of time before symptoms appear and this period is called the preclinical stage. Today, Mixed Dementia is dementia caused by the co-existence of two diseases, commonly AD and VaD. 1.3 PREVENTION OF DEMENTIA Currently, a cure for dementia does not exist and prevention has been proposed by the World Health Organization (WHO) and G8 Dementia Summit (2013) in order to counteract the dementia epidemic 3, 4. The prevention strategies would have to occur during the preclinical stages of dementia, which can extend over years of decades 1. Engaging preventative strategies during the preclinical stages is advantageous because the disease has not advanced extensively, patients are cognitively normal and treatments are more likely to be effective. Therefore, the early identification of high-risk patients for dementia is important for public health cost savings through prevention or postponement of dementia Although the link between vascular risk and stroke and late-life cognitive impairment is well established, clinical trials supporting the efficacy of antihypertensive treatments 57-61, diabetes management and cholesterol lowering therapy 62 on slowing cognitive decline have been inconclusive. These studies overlooked the fundamental challenges evaluating the efficacy of vascular reduction treatments on cognitive decline; their treatment population were not at risk of dementia because of vascular risk factors. Therefore, special consideration needs to be given to 5

14 identifying patients at highest risk of dementia, those most likely to actually benefit from vascular risk reduction, and the timing and type of intervention for optimal effectiveness. For example, the Finnish Geriatric Intervention to Prevent Cognitive Impairment and Disability (FINGER) study is a recent randomized control trial that used a multi-domain interventions involving diet, exercise, cognitive training, and vascular risk monitoring to improve or maintain cognitive function in at risk elderly people from the general population 63. However, experimental therapies for AD have failed and many important lessons can be learned and applied to future clinical trials. First, drugs were administered too late It is important to remember that the preclinical stages of AD can extend decades before symptoms appear 1. Therefore, administrating treatments to patients with clinical symptoms indicates that substantial and irreversible synaptic and neuronal loss already has occurred in critical brain regions 67, 68. This limits the ability to effectively evaluate treatments for their ability to delay or stop the progression of AD pathology. As a result, it is important to study high-risk patients who can help us characterize the preclinical stages where the preclinical disease is present. This information can serve as a stepping-stone to identify whether anything will be predictive of future cognitive decline and dementia. Additionally, these high-risk patients can help evaluate treatments effects more accurately. 1.4 THE RELATIONSHIP OF VASCULAR RISK FACTORS WITH LATE-LIFE COGNITIVE DECLINE Transient Ischemic Attack (TIA) is the sudden onset of neurological dysfunction due to disrupted blood flow where the symptoms resolve within 24 hours 69, 70. TIA or minor (non- 6

15 disabling) strokes are also symptomatic of cerebrovascular disease. When a clot is formed, the arteries are blocked. Oxygen and nutrient deprivation occurs (ischemia), leading to sodium ion accumulation inside the cell, which causes the influx of water into the cell. This cascade can to cell death if proper blood flow is not restored quickly. If the disruption in blood flow is temporary and blood flow is restored quickly, the symptoms of stroke may only be temporary. TIA are often characterizes as warning strokes: studies have found that up 15% to 30% of strokes are preceded by a TIA or minor stroke 71. Currently, TIA and minor stroke patients are treated to prevent short-term recurrent stroke. However, stroke is known to contribute to dementia 72, 73 and post-stroke patients had a 6-times higher risk of developing dementia 74. Therefore, it might seem surprising that TIA is associated with impaired cognitive function, and a 4-fold increased risk of dementia This is because TIA and minor stroke patients have been considered for the short-term stroke risk but they were not considered for the long-term cognitive outcome that accompanies a high-risk population for dementia. Studies have found that within 3 months of a TIA, more than a third of TIA patients experience impairment in one or more cognitive domain that is not entirely explained by silent brain infarcts 75. Another study of stroke and TIA patients detected progressive decline in verbal memory detected at 3 years post-stroke in patients without clinical or radiological evidence of recurrent stroke 80. The same study also showed that decline in composite neuropsychological scores was associated with smaller hippocampi, and brain atrophy at 3 years (as a cross-sectional measure) 80. However, the disease processes (AD or vascular) contributing to cognitive dysfunction is unknown. Few studies have comprehensively studied TIA and minor stroke patients for future cognitive decline 81-83, dementia 84, 85 or the cause of dementia. 7

16 1.5 IDENTIFYING POPULATIONS TO EVALUATE HIGH-RISK FOR DEMENTIA The fact that TIA/minor stroke patients are a high-risk group for dementia, the first step is to characterize the preclinical stage to identify the presence of preclinical disease processes while the patient is cognitive normal. Secondly, we can measure the underlying disease progression, and use those biomarkers to see if any information will be useful for predicting dementia so that high-risk patients can be identified early. This is important because if the disease can be modified and that modification is valuable, this will help us approach treatments in a strategic way to reduce the incidence of dementia. Lastly, the predictability of these biomarkers will be able to help redesign clinical trials so that early identification of high-risk patients can improve the treatment window and help us determine how effective a treatments is by its ability to slow or reduce the progression of atrophy and reduce the risk of dementia Extended-CATCH The Extended-CATCH is a prospective, longitudinal study conducted that recruited TIA and minor stroke patients at the Calgary Stroke Prevention Clinic from March 2009 and December TIA was defined by speech and motor symptoms that lasted longer than 5 minutes but less than 24 hours; minor stroke was defined by an NIHSS (National Institute of Health Stroke Scale) score of less than 4. All patients were fluent in English between the ages of 60 to 80, non-demented, and did not experience other neurological diseases or psychiatric illnesses. The Extended-CATCH study acquired clinical data including demographics and vascular risk factors such as systolic and diastolic blood pressure, body mass index (BMI), history of hypertension and diabetes. It also conducted neuroimaging, physical and neuropsychological tests that evaluated a patient within 5 days of symptom onset. A follow-up 8

17 for neuropsychological evaluation occurred at 90 days and years 1, 2 and 3; neuroimaging was conducted at baseline and follow-up occurred at 18-months and 3 years time points Alzheimer s Disease Neuroimaging Initiative (ADNI) ADNI Background Our study utilized controls from the Alzheimer Disease Neuroimaging (ADNI) study to evaluate whether the preclinical disease in Extended-CATCH is different from healthy controls. ADNI is an ongoing longitudinal, multi-centered study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of AD. The data for ADNI1, the first phase of ADNI, was collected over 3 years, providing insight into how biomarker profiles change. The use of ADNI data to provide data for comparison has been established by other studies that compared specific disease groups with the ADNI subjects The complete procedure and MR protocol is summarized elsewhere ( ADNI Healthy Controls The ADNI population consists of 3 types of participants: controls, patients with mild cognitive impairment and dementia due to AD. In ADNI1, the control population comprised of 200 subjects and had an average age of 75 years because the majority of patients affected with cognitive concerns fall into this age group ( The ADNI controls have been selected to minimize confounding illnesses and are reflective of a healthy population in this age group as well as those enrolled in clinical trails 93. As a result, utilizing ADNI controls for comparing group differences has consistently been conducted in numerous studies 93, 94. In our study, we utilized ADNI control population based on low vascular burden as well as the absence 9

18 of stroke. Therefore, ADNI controls are a useful resource to compare the changes in preclinical disease in a high-risk population and identifying whether those changes are significantly different compared to a healthy control population MRI Protocol and Quality Control The quality control for ADNI scans includes the phantom scan and a volunteer scan to quality the site for participation ( After each visit, the MRI scan included an MP-RAGE (volumetric 3D T1-weighted) scan and a repeat MP-RAGE scan immediately after. In ADNI, the MRI was collected using a 1.5T Siemens, GE or Philips scanner. The image parameters specific for each scanner and can be found at During the longitudinal study, each patient was scanned using the same scanner during each visit. An on-site radiologist reviewed the scans to ensure the absence of visible pathologies and forwarded the scans to the investigators at the Mayo Clinic. At the Mayo Clinic, the scans were re-assessed to ensure that the correct protocol was followed and that any illnesses were not visually present, after which the scans proceeded to the ADNI database and are readily available to use. 1.6 DETECTING PRECLINICAL DISEASE PROCESSES: MAGNETIC RESONANCE IMAGING In order to identify high-risk patients for dementia, it is imperative to employ biomarkers that serve as surrogates for the progression of dementia. These biomarkers can help characterize the preclinical stage for the presence of preclinical disease. During the progression of preclinical 10

19 disease, structural changes precede memory and clinical symptoms 95 and so the changes in biomarker profile can help describe the disease progression. Magnetic Resonance Imaging (MRI) is a type of imaging that uses Nuclear Magnetic Resonance (NMR) to model the brain anatomy. In summary, the body contains billions of protons randomly spin on an axis (precession), and this produces a magnetic field. When placed in an external magnetic field of the scanner, the protons align either parallel or anti-parallel to the direction of the external magnetic field. The parallel orientation characterizes a low-energy state compared to the anti-parallel state and this phenomenon is called the Zeeman effect, which results in more protons oriented in the parallel direction. The Zeeman effect results in a net magnetization field of a person placed in the scanner towards the parallel direction and the protons rotate at a specific frequency dictated by the strength of the external magnetic field. A stronger external magnetic field strength corresponds with a faster precession frequency. When a radiofrequency pulse with the same frequency is applied, the protons absorb the energy. This radiofrequency pulse causes the net magnetization field to rotate perpendicularly into the transverse plane and this causes a decreased net magnetization field in the longitudinal plane and an increased transversal magnetization plane. Additionally, protons become in-phase and point in the same direction as they undergo precession. Once the radiofrequency pulse is turned off, the transverse magnetization plane decreases and the longitudinal magnetization plane recovers overtime. In the processes, when the protons begin to release energy, radio waves will be released. The time taken to recover the magnetization in the longitudinal plane is known as longitudinal relaxation time, or simply T1. On the other hand, the time taken for the transverse magnetization decay and for the protons to become out-of-phase is known as the transverse relaxation time, or simply T2. As the radio waves are released by the relaxations and the net 11

20 magnetization decays, this produces an electrical signal within the coils, which is a signal detected. Gradient coils are used in scanner to vary the magnetic field across the scanner so that spatial information about a signal is acquired. The T1 and T2 relaxation times are used to acquire information on the types of tissues that are imaged because different tissue have different T1 and T2. By varying the magnetization field at a specific location, applying a unique precession frequency causes a signal to be released within that region and this provides information on the location and the type of tissue. Over the long preclinical stages preceding dementia, neuronal death accumulates and present as large-scale brain volume changes. MRI has been used to visualize the preclinical brain volume changes that characterize the preclinical stages of dementia. Studies have found that brain volume changes and brain atrophy can serve as a reliable biomarker that helps characterize preclinical forms of dementia and identify high-risk patients for dementia 96, T1-weighted Imaging T1-weighted imaging is a type of imaging that can be used to visualize the brain. T1- weighted images measure the hydrogen atoms in water to acquire an MR signal. Specifically, it measures the longitudinal relaxation times, or T1, for different tissues after a radiofrequency pulse has been applied. The difference in relaxation times for different tissues characterizes the difference in signal intensities. For example, when a radiofrequency pulse is applied and the longitudinal magnetization decreases, fatty tissues realign with the external magnetic field quickly and this releases energy that presents as a bright signal. Alternatively, cerebrospinal fluid realigns with the external magnetic field much more slowly and this releases fewer signals and appears dark on an image. This concept can be further extended to varying the time between 12

21 successive radiofrequency pulses and the time when the signal is measured. Repetition time (TR) is a measure of the time between when one radiofrequency pulse is given before another. Echo Time (TE) is a measure of time between the excitation of a radiofrequency pulse and the time when the signal is detected. The TR and TE time are important because varying them can produce types of MRI due to variability of tissue T1 and T2. Short TR and TE characterize T1- weighted imaging for optimizing bright fatty tissue signal and dark cerebrospinal fluid on an image. As a result, T1-weighted imaging provides visual anatomical data for acquiring information on brain volume and brain atrophy rates. Studies have found that T1-weighted MR images are the most reliable tool to monitor the progression of dementia 97. The advantages of MRI is that it helps identify high-risk patients for dementia by measuring tissue loss, helps characterize the progression of atrophy and helps assess the effectiveness of therapeutics 97. Therefore, structural MRI helps visualize the brain structure and is recognized as a reliable, accurate and sensitive modality for describing brain changes over time Diffusion-Weighted Imaging (DWI) Diffusion-weighted Imaging (DWI) is a type of MR imaging that tracks the movement of water molecules in tissues. DWI imaging has been crucial for measuring the stroke volume, or the volume of tissue death subsequence to a stroke episode. In the context of a stroke, oxygen and nutrient deprivation occurs (ischemia), leading to sodium ion accumulation inside the cell, which causes the influx of water into the cell. The swelling of the cell causes a reduction in the rate of diffusion, and this presents as bright signal on a DWI scan whose volume can be measured. Since TIA/minor stroke patients are classified by clinical symptoms, they can experience tissue infarction. Studies have found that 25% of TIA patients present with a lesion 13

22 that can be visualized with DWI 98 and this provides insight into the cell death caused by the stroke episode. 1.7 MRI-ASSESSED ATROPHY Whole-Brain Atrophy Whole brain atrophy rate is a reflection of the rate of cell death and has been proposed as a sensitive, reliable and accurate biomarker for the progression of dementia 97, Neuronal loss reflected by global and regional cerebral atrophy can be measured accurately from serially acquired MRI scans before cognitive impairment, and has many advantages over clinical outcomes alone, which are influenced by baseline performance, treatments and confounders of cognitive test including ceiling and floor effects 102, Brain atrophy measurements have lower inter-individual variability than clinical measures 95, can accurate monitor the progression of dementia 97, can be used as outcome measures to increase clinical trial power , and providing support for disease modifying effects Since structural changes in MR images precede clinical symptoms 95, 97, neuroimaging biomarkers can help characterize the structural changes. Studies have demonstrated that whole brain atrophy is associated with progressive cognitive impairment and can reliably characterize the progression of AD 96, 117, 118. Whole and regional brain atrophy are highly correlated with cognitive performance 117, Studies have found that rates of atrophy can be used to differentiate persons with dementia from those without dementia in all age groups 122,123. Regional variations of brain atrophy may vary between subjects but the overall change in brain volume will be reflected in the global assessment of atrophy. However, it is unknown whether 14

23 such MRI measures are also sensitive measures of early brain changes prior to clinical cognitive decline in TIA patients 124, Hippocampal Atrophy Hippocampal volume is another sensitive biomarker of atrophy and neurodegenerative diseases 126. The hippocampus is involved in memory function and is affected early in the degenerative process 20. MRI-assessed hippocampal atrophy has been crucial for characterizing preclinical changes 127. Previous studies have shown that hippocampal atrophy is known to be present in AD and that PSD patients with hippocampal atrophy were found to develop dementia more frequently 130. However, hippocampal atrophy is not limited to AD and frequently exists in patients with VaD Vascular risk factors play an important role in hippocampal atrophy 133. Therefore, hippocampal atrophy in TIA/minor stroke patients can characterize the presence of preclinical disease as well as help characterize the risk of progressing to dementia. 1.8 NEUROIMAGING ANALYSIS In our study, we evaluated preclinical biomarkers of brain atrophy using FSL (FMBRIB Software Library). Within FSL, there are a series of software tools that are designed to aid many research practices including the evaluation of the structural brain volume, rate of brain volume change, segmentation of tissues and registration of MR scans. This helps researchers with the flexibility for employing a variety of software in order answer their research-specific objectives. FSL is widely available software that has been consistently used in research practices related to dementia and using FSL standardizes the methodology in order to remain consistent and ensure 15

24 that differences in brain volume measurements aren t primarily due to the difference in software packages. In our study, we used FSL to measure whole-brain atrophy rates and the whole-brain and hippocampal volumes. FSL is advantageous because studies have found that that FSL can accurately identify preclinical disease characterized by atrophy 100 and differentiate healthy controls from those with dementia due to AD by its ability to reliably differentiate atrophy rates 100. The error rate for identifying the whole-brain atrophy rate is 0.2%, which is similar to those found in more advanced image analysis software such as Boundary Shift Integral (BSI) 100, 134. Although cross-sectional measurements are less sensitive markers, identifying the wholebrain volume using FSL has shown to correlate well with longitudinal measurements of wholebrain atrophy rates 100. This can provide important insight into the disease progression overtime by evaluating the brain volume as a cross-sectional measure. Another advantage of FSL is that a bias field correction is carried out at various steps. When an image acquisition occurs, an intensity inhomogeneity characterizes an inconsistency in voxel intensities across an image with a certain type of tissue (such as white matter). It is caused by imperfect acquisition of MR signal and this leads to errors in tissue-segmentations that can be corrected using FSL. In our study, we will be utilizing ADNI healthy control MR data that will be acquired from across multiple study centers and it is important that MR images can be compared and that measurements can be consistent despite differences in locations. Therefore, using FSL for acquiring whole-brain atrophy rates and brain volume using scans from different centers have been shown to be consistent with each other

25 1.9 NEUROPSYCHOLOGICAL PREDICTORS OF COGNITIVE DECLINE AND DEMENTIA Cognitive function is an important predictor of morbidity and mortality in the elderly. However, cognitive function is not frequently screened for in clinical practice as part of global cardiovascular risk and target organ damage assessments 136, 137. When MCI patients are screened during the early stages of their disease using neuropsychological tests, patterns emerge in cognitive performance 17. AD is characterized predominately by deficits in episodic memory while VaD is characterized by deficits in executive function and attention-processing speed However, neuropsychological profiles are not exclusive to one disease i.e., AD can experience deterioration in executive function 16 and the variability exists that makes it challenging to differentiate AD from VaD 16. In patients with cerebral vascular disease, the frequency of cognitive complaints is underestimated and so it is important that neuropsychological evaluation is emphasized in these patients. Cognitive tests used in dementia studies are designed to measure change over time, and have been selected to minimize floor and ceiling effects, and can distinguish normal aging from prodromal dementia 138. Though challenges exist, cognitive performance has been used to measure the progression to dementia 139 and patients with deficits multiple cognitive domains, including memory, were more likely to progress to dementia LINEAR MIXED-EFFECTS REGRESSION Linear regression is a statistical model used to evaluate the association between an outcome variable, such as brain atrophy or cognitive performance, and the predicting variables 17

26 such as TIA/minor stroke. The model also allows us to adjust for the influence of other confounders such as demographics of age, gender and vascular risk factors. In our study, the linear regression model was used for several reasons. In our study, we anticipate a linear relationship between the independent and dependent variables. This relationship is characterized by the linear coefficient, which highlights the trend of the response variable with the predictor. Linear regression helps model the outcome and response variables while still incorporating patients who may vary in their independent variable. For example, the model can incorporate missing values at certain time points in the longitudinal study such as missing neuropsychological tests at year 2. For our linear regression model, we used the mixed-effects model, which is a statistical model that considers the effects of both the within-subject and between-subject variation. For example, a TIA/minor stroke patient can respond differently over a longitudinal study with respect to brain volume (within-subject) and the variation in response of each individual patient may be different from another patient (between-subject). Essentially, incorporating the mixedeffects model helps us understand whether the conclusions we draw using the Extended-CATCH population can be applied to the TIA/minor stroke population and how accurately the Extended- CATCH population reflect the TIA/minor stroke patient population. This is because the model incorporates both fixed effects and random effects. Fixed effects include age, gender, time, systolic and diastolic blood pressure, diabetes, hypertension and body mass index. Random effects refer to the patient chosen as part of the study because patients can vary if the study was replicated in the future. 18

27 1.11 THESIS RATIONAL AND EXPERIMENTAL APPROACH Given that dementia is a huge public health concern and prevention is a key measure to reduce the prevalance in the futurc, characterizing the preclinical stage using biomarkers is an important step for identifying high-risk patients. TIA and minor stroke patients patients are under-investigated as a group at risk of dementia. TIA and minor stroke patients will be able to physically and cognitively comply with clinical, biochemical, and cognitive assessments, and serial cognitve and MR imaging will be less affected by attrition. Therefore, TIA/minor stroke patients are a important population to characterize the preclinical changes in order to characterize the preclinical disease progression. To compare our findings with healthy controls, we have used healthy control from ADNI ( The pathological disease progression occurs over a long period of time and the clinical features represent the end-stage of the disease progression. However, biomarkers indicative of disease pathology are detectable much earlier than cognitive complaints. Although the underlying cause of dementia is unknown (AD vs. cerebrovascular disease), characterizing the preclinical stages can help predict the risk of dementia. Furthermore, it allows high-risk TIA and minor stroke patients to be triaged for therapeutics and preventative treatments that prevent, delay or slow the progression of dementia. Therefore, this thesis will consider two specific aims: 1. To determine if TIA or minor stroke patients experience whole-brain atrophy rates over 3 years that are greater than ADNI (Alzheimer Disease Neuroimaging Initiative) controls. 2. To determine if cross-sectional baseline whole-brain and regional brain atrophy state (hippocmpal and brain volume) can predict the future risk of cognitive decline. 19

28 For our aim 1, we modeled whole-brain atrophy rates as the outcome variable and aimed to determine if the presence of TIA/minor stroke predicted that response. In our model we adjusted for the effect of confounders such as age, time, sex and vascular risk factors (hypertension, diabetes, body mass index, and systolic and diastolic blood pressure). Essentially, adjusting for these confounders considers the effects of these confounders in explaining the whole-brain atrophy rates observed. For our aim 2, we also modeled cognitive performance over 3 years as the outcome variable and sought to determine if whole-brain and hippocampal volumes predicted this cognitive decline. Additionally, we adjusted for the effect of confounders such as time, age and sex can predict, while maintaining the patient as the random effects in the model. This thesis aims to characterize the preclinical stages in TIA and minor stroke patients by using brain atrophy to assess the risk for dementia. By detecting incipient cerebrovascular or neurodegenerative disease early, this provides the best opportunity for preventing or delaying the onset of dementia. Clinical trials of dementia-prevention strategies or novel therapies are more likely to succeed if they are tested in individuals who are at highest risk and/or have an early predementia stage of dementia. This is because the treatments will target the cause of dementia for which a patient is at risk for dementia oppose to other confounding characteristics. The availability of robust biologically relevant markers of disease progression such as brain atrophy will provide a timely opportunity to identify those at greatest risk of late life cognitive impairment early in the course of disease, supporting future strategies to optimize the attempts for reducing the risk of cognitive decline on an individual basis for the promotion of healthy aging. 20

29 Chapter Two: TRANSIENT ISCHEMIC ATTACK AND MINOR STROKE PATIENTS EXPERIENCE HIGHER BRAIN ATROPHY RATES COMPARED TO HEALTHY CONTROLS 2.1 INTRODUCTION With 35.6 million people living with dementia and the prevalence expected to rise up to 100 million people 140, dementia is a major neurocognitive disorder characterized by progressive cognitive decline and the inability to function independently. Dementia is caused by a progressive neuronal loss related to two dominant disease entities, Alzheimer s Disease (AD) and small vessel cerebrovascular disease, producing brain atrophy years before symptoms are detected 39, 40. Although a cure for dementia does not currently exist, the WHO and G8 Dementia Summit (2013) emphasized prevention as a key element to counteract the dementia epidemic 3, 4. Studies have found that half of late-life cognitive impairment cases are attributed to risk factors at mid-life that are actually modifiable such as lifestyle and vascular risk factors 141. Therefore, The early identification of high-risk patients for dementia provides an opportunity to evaluate treatments that prevent dementia. Transient Ischemic Attack (TIA), a warning of cerebrovascular disease, is caused by transient reduction of blood flow to the brain and is associated with a 4-fold risk of dementia To characterize the preclinical disease processes and identify high-risk patients for dementia, whole-brain atrophy rates has been used as a surrogate of neurodegenerative processes and dementia risk, and is correlate with cognitive decline 97. To date, no long-term investigation has been conducted with the aim of deterring dementia risk by first measuring atrophy rates in TIA/minor stroke patients. Although the underlying cause (AD vs. vascular burden) is unknown, identifying and modifying the neuronal injury that precede cognitive symptoms may provide early evidence that reducing modifiable vascular-risk factor can prevent or delay late life 21

30 cognitive decline 141. The aim of this study was to identify the long-term risk of dementia during the preclinical stages using whole-brain atrophy rates as a surrogate for dementia risk. We hypothesized that, compared to healthy controls, TIA and minor stroke patients will experience an increased brain atrophy rate measured using serial T1-MRI over 3 years. We anticipate this data will support the first critical step to determine the risk of dementia in patients with TIA/minor stroke by first identifying evidence of increased rates of whole brain atrophy that may be related to incipient neurodegeneration and/or cerebrovascular disease before cognitive decline. The early detection and precise measurement of disease progression will inform us about future clinical dementia prevention trial design and sample size calculations that have traditionally relied upon conventional cognitive outcome measures. 2.2 METHODS Study population Patients with TIA and minor stroke were admitted to the Foothills Medical Centre between March 2009 and December 2012 and recruited to the Extended CATCH study following the completion of formal written consent. The University of Calgary Ethics Board formally approved the study. TIA was measured by disruptions in motor and speech symptoms that last longer than 5 minutes but recede within 24 hours. Minor stroke was characterized by a NIHSS score of less than 4. Information about baseline demographics (including vascular risk factors), the number of years of education, maximum education level and medication were acquired. Vascular risk factors were recorded and include: hypertension, diabetes, systolic and diastolic blood pressure, and Body Mass Index (BMI). Within 48 hours of symptoms, a baseline MRI was 22

31 completed and follow-up scans were completed at 18-month and year 3. The MRI protocol at each time point included a high-resolution T1-weighted sequence. Detailed inclusion and exclusion criteria for Extended-CATCH are summarized in Table 2.1. Table 2-1: Major Inclusion and Exclusion Criteria for Extended-CATCH and ADNI Extended-CATCH Inclusion Criteria: Minor stroke (NIHSS <4) or highrisk TIA (speech and motor symptoms greater than 5 minutes) Established vascular risk factors Not demented Acquired serial brain MRI ADNI Healthy Controls Inclusion Criteria: MMSE scores between Clinical Dementia Rating of 0 Non-depressed, non-mci, non-demented Cognitively normal Modified Hachinski score of 4 Geriatric Depression scale < 6 Normal memory function Exclusion Criteria Baseline dementia Comorbid illness with a life expectancy of less than 3 months English as a second language Inability to complete neuropsychological testing Exclusion Criteria Significant neurological disease MRI evidence of infection, infarction and lesions. MRI exclusions include medical devices in the body such as pacemakers and ear implants Psychiatric disorders History of alcohol or substance abuse Any significant illness or medical instability Use of psychoactive medication Well-characterized healthy control data was obtained from the Alzheimer Disease Neuroimaging Initiative study ( made permissible through approval by the ADNI collaborators (see Approval ). ADNI is an ongoing longitudinal, multi-centered 23

32 study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of AD. This aim has been achieved by the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The use of ADNI data for scientific investigations has been 93, 129, established by other studies that compare specific disease groups with the ADNI subjects Longitudinal clinical MRI was downloaded from the ADNI website at specific time points including baseline, and years 1 and 3. The ADNI protocol acquired an original as well as a repeat high-resolution T1-weighted MRI Image Preparation and Pre-processing To evaluate brain atrophy rates, 75 patients underwent a T1 acquisition on a GE and SIEMENS-3T scanner to acquire a high-resolution image at baseline and follow-up at 18 months and 3 years. Different scanners were used based on the availability of scanners in clinical practice. Clinical scans were conducted on the primary Siemens scanner within Foothills Hospital while the research/follow-up scans were conducted on the secondary GE scanner. T1- weighted MRI were acquired for baseline (TE/TR= 2.73ms/6.81ms or 2.87ms/6.63ms, flip angle = 8, TI=650ms, acquisition matrix = 256 x 256) and follow-up (TE/TR= 3.04ms/7.90ms, flip angle = 8, TI=650ms, acquisition matrix = 256 x 256). Based on the repetition time of 6.81ms/6.63ms and the T1 relaxation between 600ms to 1200ms for grey and white matter, the Ernst angle was acquired by utilizing the repetition time and determined to be between 6 and 10 for the acquisition of optimal signal to noise ratio. Prior to image analysis, MRI scans were checked to ensure that all slices were correctly downloaded and present prior to the nifti format conversion. The T1 images were anonymized in 24

33 order to remove any patient information that may link their CATCH ID to patient ID. Nifti format T1-weighted images were used to retain an accurate 3D voxel coordinates and orientation Image analysis A detailed outline of the procedure is provided elsewhere 134, 145. Image analysis using SIENA (Structural Image Evaluation using Normalization of Atrophy) consists of five main steps: brain extraction, registration, mask-fusion, segmentation and change analysis. First, the Brain Extraction Tool (BET) separates the brain from non-brain tissue while producing a binary mask, and estimates the outer skull surface. The MR images underwent the removal of residual neck voxels and a bias field correction to correct for any intensity inhomogeneity that may interfere with intensity histogram generation and segmentation steps. Furthermore, these images also underwent the removal of residual neck voxels because the inclusion of non-brain tissue that can interfere with image analysis. The processed images proceeded to image analysis. A pre-determined fractional intensity threshold is used to determine the skull radius and center-of-gravity (COG). A tessellated surface modeling is used to identify the brain tissue and is segmented from non-brain tissue. A fractional intensity of 0.3 was the optimal fractional intensity threshold that maximized brain tissue inclusion and non-brain tissue. Fractional intensity threshold is a measure of the brain and non-brain boundary, which varies from a value of 0.0 to 1.0. In T1-weighted images, the brain (bright) is separated from the nonbrain (dark) tissue. Lower fractional intensity values correspond to a larger estimate of the brain while higher fractional intensity values correspond to a smaller estimate of the brain. Therefore, varying the intensity threshold can help acquire the most accurate estimation of the brain 25

34 extraction and removing the non-brain tissue. Our study used a value of 0.3 because the inclusion of dura mater and non-brain tissue can skew partial volume estimation or segmentation of tissue count. Previous studies have demonstrated that the optimal BET options include a brain field correction (-B) and a fractional intensity of Also, standard brain masking option (-m) was utilized. When the analysis utilizes the standard masking option, the brain image is transformed onto the MNI-152 space and an MNI-152 average brain image is used to calibrate the brain edge boundaries. Studies indicate that employing a standard-space masking improved brain extraction results, minimized the inclusion of non-brain tissue and improved brain atrophy measurements 147. FLIRT (FMBRIB s Linear Registration Tool) carries out linear registration, using 6 degrees of freedom. FLIRT registers both images utilizing the outer skull surfaces because skull changes do not accompany brain tissue changes. A halfway registration is carried out to both the baseline and follow-up images. This is because a halfway registration ensures that both baseline and follow-up images have undergone equal transformation and that one image has not undergone transformation only. This results in both images being transformed into the middle of the two original images. Once transformation matrices have been acquired, the brains from baseline and follow-up are also registered into the halfway space. Using the brain masks from BET, mask fusion consists of combining the two individual masks from the baseline and follow-up in a binary-fashion. If a voxel in either baseline or follow-up masks is considered to include brain tissue, the combined mask will include brain tissue at that voxel. The combined mask is then processed through the two halfway images to minimize variations of different processing steps. Through this step, a normalized, registered brain proceeds to the change analysis. 26

35 FAST (FMRIB s Automated Segmentation Tool) was used to conduct a tissue-type segmentation based on voxel intensities and produces the three types of brain tissuescerebrospinal fluid, gray matter and white matter. Also, the boundary for brain and non-brain tissue is acquired The Percent Brain Volume Change (PBVC) is acquired by averaging the total displacement of the changes across all brain edge movements between baseline and follow-up images Statistical Tests Statistical analysis included descriptive statistics to compare the mean, medians, standard deviations, frequency distribution and estimated proportions of all variables. Mean differences in atrophy rates between follow-up periods were conducted for both cohorts. Linear Mixed-effects regression analysis was employed to model atrophy rates between Extended-CATCH with ADNI. The model incorporated independent covariates that may independently modify whole brain atrophy rates such as age (years), sex (female), time (years) and vascular risk factors including systolic blood pressure (mmhg), diastolic blood pressure (mmhg), history of hypertension, diabetes and Body Mass Index (BMI) Inclusion Process A total of 98 patients were recruited to the Extended-CATCH study and those patients with 2 consecutive MRI scans by March 2014 were included into the study, resulting in 90 patients. Over the course of 3 years, 2 patients were deceased, 6 withdrew from the study, resulting in 82 patients (10% attrition). 27

36 In the middle of our study, a software upgrade to a Discovery 750 scanner took place. After conducting image analysis using scans from the pre- and post-upgrade MRI, we observed that the image analysis involved poor signal and contrast to noise ratio on the pre-upgrade scans and this led to improper registration and unreliable results. Given the incompatibility of preupgrade scans with the post-upgrade scans, we included MRI from only post-upgrade scans into the analysis. Therefore, our study is comprised of 42 patients with scans from baseline to 18 months (G1, Figure 2-1) and 40 patients with scans from 18 months to 3 years (G2, Figure 2-1). After imaging inspection, 4 patients in the baseline to 18-month interval and 3 patients from in the 18-month to 3 years interval did not fulfill the imaging quality control criteria. The visual imaging inspection exclusion criteria include neck movement, registration problems and intensity inhomogeneity. For neck movement, patient movement resulted in noise and distortion of patient position that were inconsistent with follow-up images, causing the registration processes to become more challenging and less likely to be conducted successful. Registration problems were visualized by warping/distortion of brain anatomy in an attempt to superimpose the two images onto one another, causing the brain imaging to appear atrophied or grown, which causes the image analysis to incorrectly measure the atrophy. Lastly, intensity inhomogeneities are inconsistencies and variations in voxel intensities across an image caused by imperfect acquisition. This variation in intensity leads to a particular intensity to be associated with many different types of tissue, causing the tissue-segmentation steps to be inaccurate. SIENA was repeated for these patients using the same scans but the results did not pass the quality control in the second attempt and were excluded from the study. Of the original 42 patients in the baseline to 18-month interval, 38 patients were included into the analysis; of the original 40 patients in 28

37 the 18-month to 3 years interval, 37 patients were included into the analysis. In total, 75 Extended-CATCH patients were included into the analysis. Of the 417 healthy controls collected by ADNI, we selected for patients who had undergone an MRI at baseline, and follow up scans at years 1 and 3 ± 6 months. The resulting 183 patients were arranged in the ascending order by their research ID and a random number generator was used to select a control. For example, when a random number generator provided a value of 80, the 80 th ADNI control in the ascending order was chosen to be included. When the random number generator provided a value twice, the same person was not included twice into the study and another unique random value was acquired. To ensure that the analysis could be completed in a timely fashion given the available resources, the process was repeated until the ADNI cohort relatively matched the size of Extended-CATCH. Therefore, 72 ADNI subjects were identified and included in the study. After downloading the imaging data from ADNI, T1- weighted MRI scans were available for 51 patients at all 3 time points. After visual imaging inspection, 16 patients from baseline to 1 year (G1, Figure 2-2) and 1 year to 3 years (G2, Figure 2-2) did not fulfill the visual imaging criteria, which include image warp, neck movement, registration problems and intensity inhomogeneity. SIENA was repeated by utilizing the available repeat scans that were acquired as part of the ADNI protocol. Repeat scans were not available for 2 patients in G1 and 1 patient from G2 and these time points were excluded. Of the 14 patients in G1 and 15 patients in G2, repeat quality control was conducted on the repeat scans and 1 patient from G1 fulfilled the quality control. Consequently, this led to the exclusion of the 13 time points in G1 and 15 time points in G2. As a result, there were 46 unique healthy controls. 29

38 Extended-CATCH Inclusion Process Availability of baseline and 18-month scans G1 Extended-CATCH n= 82 Availability of 18-month and 3 year scans G2 Extended-CATCH n= 42 Extended-CATCH n= 40 Extended-CATCH n= 4 Primary exclusion: SIENA warp, neck movement, registration problems, noise and intensity inhomogeneity Extended-CATCH n= 3 Extended-CATCH n= 38 Reanalysis using same scans Extended-CATCH n= 37 Extended-CATCH re-analysis n= 0 Extended-CATCH re-analysis n= 0 Extended-CATCH n= 37 Extended-CATCH n= 38 Unique Extended-CATCH patients n= 75 Figure 2-1: Selection criteria and MR scan allocation following quality control assessment for Extended-CATCH 30

39 ADNI Control Inclusion Process ADNI Controls n= 417 Availability of baseline, 12m and 3y scans ADNI Controls n= 183 Ordered by Research Identification Number Random Number Generator ADNI Controls n= 72 Availability of T1-weighted scans G1 ADNI Controls n= 51 G2 ADNI Controls n= 51 ADNI Controls n= 51 ADNI Controls n= 16 Primary exclusion: SIENA warp, neck movement, registration problems, noise and intensity inhomogeneity ADNI Controls n= 16 ADNI Controls n= 35 Reanalysis using repeat scans/availability ADNI Controls n= 36 ADNI Repeats n= 14 ADNI Repeats n= 15 Reanalysis using repeat scans/availability ADNI Repeats n= 1 ADNI Repeats n= 0 ADNI Controls n= 36 ADNI Controls n= 36 Unique ADNI Controls n= 46 Figure 2-2: Selection criteria and MR scan allocation following quality control assessment for the healthy controls identified from the ADNI repository ( 31

40 2.3 RESULTS Demographics The Extended-CATCH cohort consisted of 75 patients, with an average age of 64.8 years ± 12.2 and a gender distribution of 59 males and 26 females. A diffusion-weighted lesion was present in 43 patients and the average lesion size for the group was 1.37 ml ± For ADNI controls in our study, there were 46 participants, with 20 males and 26 females with the average of 73.6 years ± 7.1. The demographic data and vascular risk factors for Extended-CATCH and ADNI are summarized below in Table 2. Table 2-2: Demographic data and vascular risk factors for Extended-CATCH and ADNI. Significance values was obtained using the Chi-square test for gender, hypertension and diabetes, and a unpaired t-test for age, systolic BP, diastolic BP, BMI and whole-brain atrophy rate. Characteristics Extended-CATCH ADNI P value Number of subjects, n Age, years 64.8 ± ± Gender, males (%) 59 (79) 20 (43) < Systolic BP, mmhg 156 ± ± 13.9 < Diastolic BP, mmhg 87.8 ± ± 10.6 < Hypertension, n (%) 38 (50) 25 (54) Diabetes, n (%) 12 (16) 0 (0) BMI (kg/m 2 ) 27.1 ± ±

41 Table 2-3: Whole-brain atrophy rates in Extended-CATCH and ADNI. Significance values were obtained using an unpaired t-test whole-brain atrophy rate. Whole-Brain Atrophy Rate (%± SD) Extended-CATCH ADNI P value Overall (baseline to year 3) 0.81% ± % ± G1 0.64% ± % ± G2 0.99% ± % ± Whole-Brain Atrophy Rates When the two groups (baseline to 18-months, and 18-months to 3-years) were taken into consideration the whole brain atrophy rates for Extended-CATCH were 0.64% ± 0.55 in the baseline to 18 months group (G1) and 0.99% ± 0.68 in the 18 months to 3 years group (G2) (Figure 2-3). In ADNI, the whole brain atrophy rates were 0.53% ± 0.46 from baseline to 12 months and 0.57% ± 0.34 from 12 months to 3 years. Overall, in Extended-CATCH the annualized whole-brain atrophy rate was 0.81% ± 0.64 while in ADNI the annualized wholebrain atrophy was 0.56% ±0.39 (Figure 2-4). 33

42 Annualized Whole-Brain Atrophy Rate In Extended-CATCH Annualized Percent Brain Volume Change (%) T1 p =0.025 * T2 Cohort Figure 2-3: Percent Brain volume Change (PBVC) of Extended-CATCH patients in G1 (baseline-18 months) and G2 (18-months to 3-years). Error bars represent standard deviation (SD). For statistical tests, a paired t-test was used determine the differences between G1 and G2. 34

43 Annualized Percent Brain Volume Change (%) Annualized Whole-Brain Atrophy Rate Over 3 years p= * Extended-CATCH Cohort ADNI Figure 2-4: Annualized Percent Brain volume Change (PBVC) between Extended-CATCH and ADNI. Error bars represent standard deviation (SD). For statistical tests, an unpaired t-test was used determine the differences between the two cohorts. 35

44 2.3.3 Linear Mixed-Effects Regression From our linear mixed-effects regression model, the independent covariates that predicted increased rates of brain atrophy include TIA/minor stroke, time, age, hypertension and diabetes (Table 2.4). Table 2-4: Linear Mixed-Effects Regression Analysis for Brain Atrophy. Analysis was performed to identify the independent predictors of the whole brain atrophy in Extended- CATCH and ADNI. Imaging Time Interval corresponds to baseline, 18-month and 3 years for Extended-CATCH and baseline, 12-month and 3 years for ADNI Covariates Coefficients Standard Deviation p-value Group (CATCH) Imaging Time Interval Sex (female) Age Systolic BP Diastolic BP Hypertension Diabetes BMI

45 2.4 DISCUSSION In this study, TIA and minor stroke patients experienced whole-brain atrophy rates that were higher over 3 years compared to healthy ADNI controls. These results support the primary hypothesis that patients with TIA/minor stroke have higher rates of whole brain atrophy. In our model, in addition to TIA/minor stroke, independent covariates of time (P = 0.012) and advancing age (P = 0.007) were predictor of whole-brain atrophy rates, with rates of whole-brain atrophy higher in G2 compared to G1. The rate of whole-brain volume loss was larger than the volume of diffusion-weighted lesions, indicating that the stroke infarct volume alone did not explain the atrophy measured. In fact, the progressive brain volume loss suggests that an alterative underlying cause ay explain the higher whole-brain atrophy rates. History of hypertension (P = 0.003) and diabetes (P = 0.003) independently explained higher whole brain. However, we did not find a relationship for whole-brain atrophy rate with systolic and diastolic blood pressure, gender, and BMI. Our data supports epidemiological studies that TIA and minor stroke are at risk for high whole-brain atrophy and increased risk of dementia 94. This data expands on previous findings by demonstrating that higher whole-brain atrophy rates early in the disease progression during the preclinical stages, before symptoms or signs of dementia. In our study, we observed that this understudied group of patients with TIA/minor stroke patients experienced higher whole-brain atrophy rates and this may help identify patients who are at high-risk for dementia. Given that the higher brain atrophy rates precedes cognitive decline, this data supports that TIA/minor stroke patients experience whole-brain atrophy rates similar to those documented with prodromal dementia 94. Additionally, it demonstrates that the early detection of incipient disease offers a treatment window for delaying or preventing the onset of symptoms. We observed that the 37

46 whole-brain atrophy rate exceeded the size of diffusion-weighted lesions at baseline, suggesting that the infarct volume does not completely explain the observed atrophy. Furthermore, the lesion volume provides evidence of disease progression since whole-brain atrophy rates were higher in time period G2 in comparison to G1. Our study demonstrates that advancing age was an independent predictor of whole-brain atrophy rates, with older patients experiencing greater whole-brain atrophy rates. Currently we do not know whether increased rates of brain atrophy can be detected in mid-life in those with modifiable vascular risk 48. Additionally, it is unknown how early incipient disease can be detected in patients at higher risk of stroke and dementia i.e., for instance, can advanced changes in brain volume be detected in subjects with midlife hypertension? Epidemiological data supports the presence of vascular risk factors, particularly hypertension and smoking in mid-life substantially increases the risk of late life dementia 48, 148. The importance of treating and controlling hypertension and diabetes are supported by this data, as our data shows that history of hypertension and diabetes independently contribute to increased whole-brain atrophy. This is consistent with previous literature highlighting the link between increased atrophy rates with diabetes 149. Although the exact cause of increased whole-brain atrophy is not known, vascular risk factors (particularly hypertension, diabetes, smoking and obesity) are linked with increased risk of cerebrovascular disease and Alzheimer s disease 31, 40, 48, and are likely contributing to increased rates of brain atrophy we describe here. Overall, our study is consistent with previous literature demonstrating that higher brain atrophy rate can be measured in established AD and prodromal dementia 117, 120, 121, 127, 150, 151. With no current treatments for dementia, control of modifiable vascular risk factors has been proposed as an important dementia-preventing alternative 152. Clinical trials investigating 38

47 the efficacy of vascular risk factor-reduction treatments for preventing cognitive deterioration or secondary stroke have utilized cognition as a primary outcome measure 153. Although the link between attributable vascular risk and stroke and late life cognitive impairment is well established, clinical trials supporting the efficacy of antihypertensive treatments 57-61, diabetes management and cholesterol lowering therapy 62 on slowing cognitive decline has been inconclusive. In the SCOPE (Cognition and Prognosis in the Elderly), the PROGRESS (Perindopril Protection Against Recurrent Stroke Study) and the HYVET-COG (Hypertension in the Very Elderly Trial Cognition) study, the results were inconclusive regarding the benefit of antihypertensive treatments and preventing cognitive decline and dementia Other studies have found a weak relationship between these treatments and cognitive decline 154. These studies suggest that uncertainty remains about the benefit of vascular risk factor-reduction and reducing dementia risk. Special consideration might need to be given to identifying patients at highest risk of dementia i.e., those potentially most likely to benefit from vascular risk factor reduction, and the timing and type of intervention for optimal effectiveness. Recently, in the Finnish Geriatric Intervention to Prevent Cognitive Impairment and Disability (FINGER), a different approach has been tested in a randomized control trial suggesting that multi-domain intervention involving diet, exercise, cognitive training and vascular risk factor monitoring could improve or maintain the cognitive function in at risk elderly people from the general population 155. The reliance on cognitive outcome in clinical trials might be problematic because cognition as an outcome variable is limited by confounders such as baseline performance, treatments and confounders of cognitive test that include ceiling and floor effects 102, 103. Second, cognitive decline is the last stage of the disease progression. Therefore, by the time patients experience cognitive symptoms, the disease has progressed to such an advanced state that it 39

48 reduces the ability of treatments to be effective. Third, investigations on older populations may be too late if the underlying disease has progressed to an advanced state and may be too late for interventions to be successful 48. Lastly, the vascular risk factor-reduction strategies and preventing cognitive deterioration have not specifically targeted those at highest risk from dementia, and therefore may not have fully benefitted from these treatments. This may have contributed to underestimating the efficacy of vascular risk factor-reduction on populations with one or more vascular risk without evidence of end-organ damage involved in the brain, heart or arteries. To overcome these limitations, our model supports the use of whole-brain atrophy as a biomarker for measuring the efficacy of antihypertensive treatments on preclinical incipient disease that may contributes to dementia. TIA and minor stroke patients would be the optimal population to benefit from vascular risk factor-reduction treatments to evaluate the efficacy of preventative treatments. A future step might be the design of clinical trials to evaluate the efficacy of preventative treatments such as vascular risk factor reduction on whole-brain atrophy rate as an adjunct to cognitive outcomes. Our data supports that rates of whole brain atrophy could be used as marker of early detection of disease that precedes cognitive complaints. The use of rates of brain atrophy may have some advantages as a surrogate marker of preclinical disease progression. First, rates of brain atrophy have been shown to correlate with cognitive decline 117, 120, ; second, brain atrophy rate is more sensitive at predicting cognitive decline and can be measured more precisely than neuropsychological outcomes 106, 110, , which are subject to several potential confounders such as the subject s baseline cognitive performance, co-morbid factors and treatments (especially depression and sedating medication 102, 103 ), and confounders of cognitive tests such as ceiling and floor effects, and learning responses; third, rates of brain atrophy 40

49 potentially may provide evidence of early treatment modification of disease before detectable clinical outcome ; and finally, rates of brain atrophy correlate with the preclinical progression of vascular disease and AD 68, 122, In the middle of our study, a scanner software upgrade took place, which caused our Extended-CATCH cohort to be divided into two equal halves. The incompatibility of scans from pre-upgrade with post-upgrade for atrophy measurements caused loss of scans that could have been included in the study. However, SIENA was able to accurately acquire whole-brain atrophy rates on both scans that were from post-upgraded MRI. We measured changes in brain volume over time but our study does not identify the cause. Identifying the disease that contributes to the change in brain volume will be important because the response to preventative treatments may depend on the stage of disease and its pathology. Future studies can investigate biochemical, molecular, genetic and imaging biomarkers that accompany cerebrovascular disease or AD in order to identify the exact cause of atrophy. Currently available biomarkers include cerebrospinal fluid AD biomarkers of tau and Aβ 1-42 that precede cognitive decline 1 and correlate with atrophy 95, 165. Additionally, biomarkers of cerebrovascular disease include white-matter lesions that correlate with atrophy 166 and cognitive decline/dementia 167. Lastly, our study did not assess regional changes that accompany neurodegeneration. Whole brain atrophy is a global, nonspecific indicator/marker of diffuse neurodegenerative processes involved in disease such as AD but does not highlight the regional changes that are known to exist through different stages of disease progression in AD patients and cerebrovascular disease. 41

50 2.5 CONCLUSION TIA and minor stroke experienced whole-brain atrophy rates that were higher compared to healthy ADNI controls over 3 years. Age, time and vascular risk factors such as hypertension and diabetes independently predict whole-brain atrophy rates. If neurodegenerative changes continue, and precede cognitive symptoms, this suggests that atrophy rates could be used to assess the efficacy of vascular risk factor-reduction treatments and offering a therapeutic window for slowing changes in the whole brain volume that precede cognitive decline in patients at a high risk of dementia. This study has contributed to our knowledge by demonstrating higher atrophy rates in TIA/minor stroke, supporting prior observations that TIA/minor stroke patients are at high-risk for dementia. 42

51 Chapter Three: BRAIN AND HIPPOCAMPAL ATROPHY STATES IN TIA AND MINOR STROKE PATIENTS 3.1 INTRODUCTION The early identification of high-risk patients for dementia is potentially the most important approach for prevention or postponement of dementia and for public health cost savings Current estimates suggest that there are 35.6 million people living with dementia and the prevalence is estimated to rise up to 100 million people 140. However, a cure for dementia does not currently exist and so early detection and prevention are crucial to address this public health problem. The most common cause of dementia are Alzheimer Disease (AD) and Cerebrovascular Disease (CVD) that co-exist more frequently than previously considered 39, 40. Alzheimer s Disease and CVD share common modifiable vascular risk factors most commonly hypertension, smoking, diabetes, atrial fibrillation, ischemic heart disease and lifestyle factors such as obesity, diet and exercise. This is important for prevention because fifty percent of late cognitive impairment are attributed to vascular and lifestyle related risk factors in mid-life 168. Therefore, if incipient disease can be identified early, this may provide an opportunity for effectively preventing or delaying the onset of dementia. A TIA is a clinical entity that occurs following a transient episode of ischemia with a complete reversibility of symptoms within 24 hours 69. TIA and minor stroke patients represent a unique patient population to study because they manifest acute cerebrovascular disease and generally have established vascular risk factors. Studies have found that TIA is associated with impaired cognitive function and cognitive decline, and is associated with a 4-fold risk factor for dementia 79, 169, 170. Additionally, more than a third of patients with TIA have impairment of one or more cognitive domain within 3 months after their TIA that is not entirely explained by silent 43

52 brain infarcts. 75 Given that TIA/minor stroke patients are at high-risk for dementia, the underlying disease processes (AD vs. CVD) is unknown. It is also unknown whether modifying these disease processes by vascular risk factor reduction and if that modification can be measured by using biomarkers that characterize disease progression. Evaluating treatment effects that modify the pathogenesis of dementia can benefit by first recognizing pathological biomarkers that characterize the early stages of dementia and are able to predict it later in life. Additionally, identifying the early stages expands the treatment window where preventative treatments are likely to be most effective at slowing or halting the pathogenesis of dementia. Whole-brain atrophy is a biomarker that has been used as a surrogate of neurodegeneration and has consistently been proposed as a biomarker of dementia 97, Atrophy has shown to be correlated with cognitive performance 96, 117, 118 and is associated with cognitive decline 97. Additionally, hippocampal volume is another surrogate of neurodegeneration dementia 126 and is affected early in the progression 20. Hippocampal changes have been observed in both AD and VaD and is influenced by vascular risk factors 133. Cross-sectional analysis of brain structure may provide insight into progressive preclinical disease processes and might be useful in identifying high-risk patients for of late-life dementia. Such cross-sectional measurements of whole-brain and hippocampal volume, corrected for intracranial volume, may provide an estimate of the future risk of progression to dementia before cognitive decline can be detected by detailed neuropsychological test. In this Chapter, we aim to assess in patients with TIA and minor stroke, whether baseline characteristics of whole-brain and hippocampal volumes corrected for intracranial volume can predict cognitive performance measured at 3 years. We hypothesize that baseline brain and 44

53 hippocampal volumes will predict decline in specific cognitive domains in patients with TIA and minor stroke at 3 years. 3.2 METHODS Study population TIA (speech and motor symptoms lasting longer than 5 minutes) and minor ischemic stroke (NIHSS <4) patients were recruited from the Calgary Stroke Prevention clinic. Although the Extended-CATCH (Cognitive Impairment After TIA and MRI measurements of Cerebral and Hippocampal atrophy) study is ongoing, this chapter only included subjects who completed the 3-year cognitive test as of September The cohort consisted of 37 males and 13 females. All 50 patients have undergone a T1-weighted MR acquisition at baseline within 48 hours of symptoms and a complete neuropsychological battery at 90 days and years 1, 2 and 3 years. Information collected at baseline included patient demographics, years of education, maximum education level and medication. Vascular risk factors were recorded including hypertension, diabetes, body mass index (BMI), and systolic and diastolic blood pressure. A detailed summary of the inclusion and exclusion criteria is included in Table 3. Table 3-1: Inclusion and Exclusion Criteria for Extended-CATCH Inclusion Criteria: Minor stroke (NIHSS <4) or highrisk TIA (speech and motor symptoms greater than 5 minutes) Established vascular risk factors Not demented Acquired serial brain MRI Exclusion Criteria Baseline dementia Comorbid illness with a life expectancy of less than 3 months English as a second language Inability to complete neuropsychological testing 45

54 3.2.2 Neuropsychological Battery Patients underwent a standardized battery of neuropsychological tests completed at specific intervals (90 days, 1, 2 and 3 years) based upon the Hachinski recommended test list for the assessment of vascular cognitive impairment 171. Cognitive testing focused on the domains most affected by stroke, including attention and executive function abilities 9, 10 using a brief battery of standardized neuropsychological tests as well as computerized test of attention and working memory. These neuropsychological tests assessing multiple functions are summarized below: Table 3-2: Neuropsychological Assessment for Extended-CATCH Executive Function Controlled Oral World Association test (COWAT-FAS) Trail Making Test B CLOX-1 Digital Span Backward (WAIS-IV) Executive Function & Processing Speed Digital Symbol Coding (WAIS-IV) Attention Trail Making Test A Digital Span Forward (WAIS-IV) Language Boston Naming Complex Ideational Material- Short Form Verbal Learning and Memory California Verbal Learning Test II Visuospatial Construction and Memory Rey Complex Figure Test Image preparation and pre-processing To evaluate cross-sectional hippocampal and brain volumes, 50 patients underwent a T1- weighted acquisition at baseline on either a GE and SIEMENS-3T scanners. The reason for the usage of two scanners was that clinical scans was conducted on the Siemens scanner while the research/follow-up scans were conducted on the GE scanner. T1-weighted MRI was acquired for baseline using the MP-RAGE and MP-BRAVO sequences (TE/TR= 2.73ms/6.81ms or 2.87ms/6.63ms, flip angle = 8, TI=650ms, acquisition matrix = 256 x 256). ). The repetition 46

55 time of 6.81ms/6.63ms and the T1 relaxation between 600ms to 1200ms for grey and white matter was used to determine the Ernst angle and found to be 6 and 10 for the acquisition of optimal signal to noise ratio. A quality control assessment was conducted on the MRI scans, which involved ensuring the completion of scan and that the scan slices were downloaded correctly from the patient database. The T1 images were anonymized in order to remove any patient information that may link their CATCH ID to patient ID. The nifti format for T1- weighted images were used in order to retain an accurate 3D voxel coordinates and orientation Image analysis using FIRST (FMRIB s Integrated Registration & Segmentation Tool) and SIENAX FIRST estimates the anatomy subcortical structures. A detailed outline of the procedure is provided elsewhere 172. Image analysis using FIRST consists of five main steps: registration to MNI152 standard space, mesh modeling, boundary corrections using randomise and volumetric and vertex analysis. FIRST employs FLIRT (FMRIB s Linear Registration Tool) and registers the T1-input images to the study-specific template through a two stage process. FLIRT registers the T1-inputs to the MNI152 standard space, which is an average image of 152 structural images, in order to remove voxels that are not associated with the subcortical regions. After, the MNI-subcortical masks are utilized to redo the linear registration using 12 degrees of freedom in order to improve alignment. Second, mesh modeling estimates the surface shape of the anatomical structure by utilizing the pre-determined anatomical information on shape and intensity. This process was iterated to maintain consistency regarding the vertex across subjects. Using the vertex locations, the shape mesh modeling acquires the average shape of the subcortical structure. The intensity 47

56 mesh modeling samples the intensities along the surface of the subcortical structure. The mesh model was fit onto surface of the hippocampus within our subject image while still utilizing neighbouring structure for reference. Together, this was carried out to acquire the best shape using intensity. Boundary correction was conducted using FAST (FMRIB s Automated Segmentation Tool) to separate voxels that are part of the anatomical structure form those that aren t (Figure 3-1). This prevents structural overlap. Finally, a vertex analysis was conducted at each vertex and the differences in locations between the pre-determined Center for Morphometric Analysis (CMA) controls and Extended-CATCH are acquired. These distances are utilized to acquire the average shape using randomise, a statistical software step as part of FIRST. Quantification of hippocampus measurement was conducted by fslstats, which is a FSL tool for acquiring values for an image. SIENAX (Structural Imaging Evaluation Using Normalization of Atrophy, Crosssectional) estimates the normalized brain tissue, normalized for head size and the detailed procedures is provided elsewhere 134, 145. Image analysis using SIENAX consists of 4 main steps: brain extraction, registration to MNI152 standard template, standard-space masking, tissue-type segmentation and calculation of total brain volume. The brain extraction begins by employing the bet2 to estimate the outer skull and acquire brain tissue and separate it from brain. A range of fractional intensity values was employed (0.1, 0.15, 0.2, 0.25, 0.3). Based on the outcome results, 0.15 was the optimal fractional intensity threshold that maximized brain tissue inclusion and non-brain tissue exclusion. Second, registration of T1 input images were registered using FLIRT. T1-weighted inputs were underwent linear registration onto the MNI152 standard space using the skull the normalizing agent. MNI152 is a standard space that helps normalize MR inputs using skulls as the 48

57 normalizing agent. SIENAX employs standard-space masking in order to remove the residual eyes/optic nerve that can skew the result. SIENAX also employs FAST and segments the T1 input image (Figure 3-2). FAST helps acquire partial-volume tissue segmented values (white matter, gray matter and ventricular CSF). Lastly, the output values from FAST are summed in order to acquire an intracranial volume using the summation of the quantitative values for white and gray mater. Results are reported in mm 3 were divided by a 10 3 in order to acquire values in milliliter (ml) for comparative analysis. Each MRI underwent an imaging quality control, which included ensuring proper brain extraction results that did not include non-brain tissue, proper registration to MNI152 template and accurate segmentation of the whole-brain and hippocampus (head, body and tail). Baseline cross-sectional brain and hippocampal volume was calculated in milliliters. A cross-sectional volume was acquired using non-normalized data. Quantitative summation of white matter, grey matter, and CSF was used to acquire the total intracranial volume. A ratio of un-normalized brain volume to total intracranial volume was used to normalize the brain volume to that specific patient s intracranial volume. A similar ratio was produced using the left- and right hippocampal volume and the total intracranial volume. Figure 3-1: Visual Segmentation Results of Whole-Brain Volume (red) Acquired Using SIENAX. 49

58 Figure 3-2: Visual Segmentation (yellow) Results of Left Hippocampus (top) and Right Hippocampus (bottom) Acquired Using FIRST. 50

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