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Mobility, cognitive function, and exercise : neuroimaging studies in older adults Hsu, Chun Liang 2017

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MOBILITY, COGNITIVE FUNCTION, AND EXERCISE: NEUROIMAGING STUDIES IN OLDER ADULTS   by  Chun Liang Hsu  B.Sc. The University of British Columbia, 2006 M.Sc. The University of British Columbia, 2012     A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY   in   The Faculty of Graduate and Postdoctoral Studies   (Rehabilitation Sciences)    THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)   December 2017   © Chun Liang Hsu, 2017   ii  Abstract As the world’s population ages, mobility and cognitive impairments are major health-care priorities. Given the particular relevance of the brain in the manifestation of both conditions, the aims of my dissertation are two-fold: 1) to advance our knowledge of the intrinsic relationship between mobility and cognitive impairments; and 2) to characterize the underlying functional neural mechanisms by which exercise promotes cognitive and mobility outcomes in older adults with mild cognitive impairment. Using magnetic resonance imaging, I focused on characterizing brain structures and brain function – as measured by functional neural activity as well as regional and network connectivity – associated with falls and slow gait. Additionally, I investigated how aerobic exercise may exert influence on mobility and cognitive function via pathways indicated by magnetic resonance imaging derived neural correlates. My research showed that while falls are associated with lower total and regional gray and white matter volume, slow gait, in conjunction with mild cognitive impairment, is reflected by disrupted neural network connectivity. Moreover, my work aligns with emerging concept of neural efficiency by generating evidence that suggests aerobic exercise training may promote mobility and cognitive function by maintaining or improving neural efficiency.  Overall, these results contribute to a better understanding of the neural underpinnings of mobility and cognitive impairments, as well as provide new insight into the neural mechanisms by which exercise promotes mobility and cognitive function.   iii  Lay Summary Impaired mobility and cognitive function have serious health-care consequences that severely impact the well-being of older adults. Thus, understanding the causes and solutions for these age-related impairments are research priorities. The goals of my thesis are to better understand the brain characteristics related to falls and slow walking speed, as well as the functional neural pathways by which exercise may promote mobility and cognitive outcomes in older adults with mild cognitive impairment.  The key findings of my thesis studies are: 1) A history of falls is associated with smaller brain volume; 2) Slower walking speed in older adults with mild cognitive impairment are associated with sub-optimal brain function; and 3) Aerobic exercise training may be able to promote mobility and cognitive function by maintaining or improving neural efficiency. Taken together, these results help us better understand how mobility and cognitive impairments are linked and how exercise impacts the brain to benefit mobility and cognitive function in older adults.   iv  Preface Content from this dissertation was written and compiled (for the published chapters) by Chun Liang Hsu. Dr. Teresa Liu-Ambrose, Dr. Todd Handy and Dr. Michelle Voss reviewed and provided comments that were taken into consideration in generating the final version of the dissertation.  The research studies included in Chapters 2 to 5 was primarily conducted in the Aging, Mobility, and Cognitive Neuroscience Laboratory at the Research Pavilion of the Vancouver General Hospital. All functional magnetic resonance imaging was conducted at the UBC MRI Research Centre. Ethics approval for all studies was approved by University of British Columbia’s Clinical Research Ethics Board. All the research presented in the thesis have been published or submitted for publication. Detail for each publication is provided below. A version of Chapter 2 is published in Experimental Gerontology. HSU CL, Best JR, Chui BK, Voss MW, Handy TC, Bolandzadeh N, Liu-Ambrose T.  Structural neural correlates of falls status and subsequent decline in executive functions: A 12-month prospective study. Exp Geronto. 2016 Apr 11;80:27-35. doi: 10.1016/j.exger.2016.04.001. TLA and I were responsible for study conception and design, data collection, data analysis and interpretation, and manuscript composition. JRB, MWV, TCH and TLA were responsible for manuscript review and edit. BKC was responsible for visual-inspection of structural MR images. NB was responsible for data collection. Ethical approval was provided by the University of British Columbia’s Clinical Research Ethics Board (H07-01160). v  A version of Chapter 3 will be published in Journal of Gerontology: Medical Sciences. HSU CL, Best JR, Voss MW, Handy TC, Beauchet O, Lim C, Liu-Ambrose T. Functional neural correlates of slow gait among older adults with mild cognitive impairment. TLA and I were responsible for study conception and design, data collection, data analysis and interpretation, and manuscript composition. JRB, MWV, TCH, OB and TLA were responsible for manuscript review and edit. CL was responsible for participant recruitment and data collection. Ethical approval was provided by the University of British Columbia’s Clinical Research Ethics Board (H15-02181). A version of Chapter 4 is published in Frontiers in Human Neuroscience. HSU CL, Best JR, Wang S, Voss MW, Hsiung RGY, Munkacsy M, Cheung W, Handy TC, Liu-Ambrose T. The Impact of Aerobic Exercise on Fronto-Parietal Network Connectivity and Its Relation to Mobility: An Exploratory Analysis of a 6-Month Randomized Controlled Trial. Front Hum Neurosci. 2017; 11: 344. doi:  10.3389/fnhum.2017.00344. TLA, RGYH and I were responsible for study conception and design, data collection, data analysis and interpretation, and manuscript composition. JRB, MWV, TCH, and TLA were responsible for manuscript review and edit. MM, WC, SW were responsible for data collection and participant recruitment. SW was responsible for data analysis. Ethical approval was provided by the University of British Columbia’s Clinical Research Ethics Board (H15-00972). A version of Chapter 5 is published in British Journal of Sports Medicine. HSU CL, Best JR, Davis JC, Nagamatsu LS, Wang S, Boyd LA, Hsiung RGY, Voss MW, Eng JJ, Liu-Ambrose T. Aerobic Exercise Promotes Executive Functions and Impacts Functional Neural Activity among Older Adults with Vascular Cognitive Impairment. BJSM. 2017 Apr 21. pii: bjsports-vi  2016-096846. doi: 10.1136/bjsports-2016-096846. TLA, RGYH and I were responsible for study conception and design, data collection, data analysis and interpretation, and manuscript composition. JRB, JCD, LSN, LAB, JJE, MWV, TCH, and TLA were responsible for manuscript review and edit. SW was responsible for data analysis. Ethical approval was provided by the University of British Columbia’s Clinical Research Ethics Board (H15-00972).  vii  Table of Contents Abstract .............................................................................................................................. ii Lay Summary .................................................................................................................... iii Preface ............................................................................................................................... iv List of Tables .................................................................................................................... xii List of Figures.................................................................................................................. xiii Acknowledgements ...........................................................................................................xiv Dedication .......................................................................................................................... xv Chapter 1: Introduction ......................................................................................................1 1.1 Mobility in Aging .......................................................................................................1 1.1.1 Epidemiology of Mobility Impairment in Community-Dwelling Older Adults .......2 1.1.2 Measures of Mobility ............................................................................................3 1.1.2.1 Gait .................................................................................................................3 1.2 Risk Factors for Mobility Impairment......................................................................5 1.2.1 Risk Factors for Slow Gait.....................................................................................6 1.2.2 Risk Factors for Falls.............................................................................................6 1.2.3 Cognitive Function as a Risk Factor ......................................................................8 1.2.3.1 Executive Functions ...................................................................................... 11 1.3 Association between Mobility and Cognitive Impairments .................................... 13 1.3.1 Neural Underpinnings of Concurrent Mobility and Cognitive Impairments.......... 15 1.3.1.1 White Matter Lesions .................................................................................... 15 1.3.1.2 Functional Neural Correlates of Mobility and Cognitive Impairments ........... 17 1.4 Exercise to Promote Mobility and Cognitive Function .......................................... 19 1.4.1 Studies on Exercise, Gait and Falls ...................................................................... 20 1.4.2 Studies on Exercise and Cognitive Function ........................................................ 22 1.4.3 Neuroimaging Outcomes of Exercise ................................................................... 25 1.4.4 Efficiency, Compensation and Cognitive Reserve ................................................ 27 1.5 Overview of Thesis ................................................................................................... 30 1.5.1 Main Research Questions .................................................................................... 30 1.5.2 Methodology ....................................................................................................... 31 viii  1.5.2.1 Structural Magnetic Resonance Imaging ....................................................... 33 1.5.2.2 Functional Magnetic Resonance Imaging ...................................................... 33 1.5.3 Overview of Chapters .......................................................................................... 35 Chapter 2: Structural Neural Correlates of Impaired Mobility and Subsequent Decline in Executive Functions: a 12-Month Prospective Study .................................................. 38 2.1 Introduction ............................................................................................................. 38 2.2 Material and Methods ............................................................................................. 41 2.2.1 Study Design and Participants ............................................................................. 41 2.2.1.1 Specific Inclusion Criterion for Fallers .......................................................... 41 2.2.1.2 Specific Inclusion Criterion for Non-Fallers .................................................. 42 2.2.2 Measurement ....................................................................................................... 42 2.2.2.1 Global Cognition and Current Physical Activity Level .................................. 42 2.2.2.2 Comorbidity and Depression ......................................................................... 43 2.2.2.3 Physiological Falls Risk ................................................................................ 43 2.2.2.4 Mobility and Balance .................................................................................... 43 2.2.2.5 Executive Functions ...................................................................................... 44 2.2.2.6 Structural MRI Acquisition and Analysis ...................................................... 45 2.2.2.7 Statistical Analysis ........................................................................................ 47 2.3 Results ...................................................................................................................... 49 2.3.1 Participants.......................................................................................................... 49 2.3.2. Structural MRI ................................................................................................... 49 2.3.3. Linear Regression Models .................................................................................. 50 2.4 Discussion ................................................................................................................. 60 Chapter 3: Functional Neural Correlates of Slower Gait Among Older Adults with Mild Cognitive Impairment .............................................................................................. 67 3.1 Introduction ............................................................................................................. 67 3.2 Methods and Measures ............................................................................................ 69 3.2.1 Study Design and Participants ............................................................................. 69 3.2.2 Descriptive Variables .......................................................................................... 70 3.2.3 Usual Gait Speed ................................................................................................. 70 3.2.4 General Mobility and Balance ............................................................................. 70 ix  3.2.5 Executive Functions ............................................................................................ 71 3.2.6 Functional MRI Acquisition ................................................................................ 72 3.2.7 Functional MRI Data Analysis ............................................................................ 72 3.2.7.1 Preprocessing ................................................................................................ 72 3.2.7.2 Functional Connectivity Analysis .................................................................. 74 3.2.8 Statistical Analyses.............................................................................................. 76 3.3 Results ...................................................................................................................... 77 3.3.1 Participants.......................................................................................................... 77 3.3.2 Usual Gait Speed, Mobility, and Executive Functions.......................................... 77 3.3.3 Functional Connectivity ...................................................................................... 78 3.3.4 Partial Correlations .............................................................................................. 80 3.4 Discussion ................................................................................................................. 83 3.5 Conclusions .............................................................................................................. 86 Chapter 4: The Impact of Aerobic Exercise on Fronto-Parietal Network Connectivity and Its Relation to Mobility: an Exploratory Analysis of a 6-Month Randomized Controlled Trial ................................................................................................................ 87 4.1 Introduction ............................................................................................................. 87 4.2 Methods .................................................................................................................... 90 4.2.1 Study Design ....................................................................................................... 90 4.2.2 Participants.......................................................................................................... 90 4.2.3 Randomization .................................................................................................... 93 4.2.4 Aerobic Training and Compliance ....................................................................... 93 4.2.5 Usual Care........................................................................................................... 94 4.2.6 Adverse Effects ................................................................................................... 94 4.2.7 Descriptive Variables .......................................................................................... 95 4.2.8 Functional MRI Acquisition ................................................................................ 95 4.2.9 Mobility, Cardiovascular Capacity, and Physical Activity ................................... 97 4.2.10 Data Analysis .................................................................................................... 97 4.2.10.1 Functional MRI preprocessing .................................................................... 97 4.2.10.2 Functional Connectivity Analysis ................................................................ 98 4.2.10.3 Statistical Analyses ................................................................................... 101 x  4.3 Results .................................................................................................................... 102 4.3.1 Participants and Treatment Fidelity ................................................................... 102 4.3.2 AT Compliance and Adverse Effects ................................................................. 105 4.3.3 fMRI Results ..................................................................................................... 105 4.3.4 Correlation Results ............................................................................................ 108 4.4. Discussion .............................................................................................................. 110 Chapter 5: Aerobic Exercise Promotes Executive Functions and Impacts Functional Neural Activity Among Older Adults with Vascular Cognitive Impairment ............... 116 5.1 Introduction ........................................................................................................... 116 5.2 Methods .................................................................................................................. 118 5.2.1 Study Design ..................................................................................................... 118 5.2.2 Participants........................................................................................................ 119 5.2.3 Randomization .................................................................................................. 123 5.2.4 Sample Size ....................................................................................................... 123 5.2.5 Aerobic Training and Compliance ..................................................................... 123 5.2.6 Usual Care......................................................................................................... 124 5.2.7 Adverse Effects ................................................................................................. 124 5.2.7 Behavioural Analysis......................................................................................... 124 5.2.8 Descriptive Variables ........................................................................................ 125 5.2.9 Magnetic Resonance Imaging (MRI) Data Acquisition ...................................... 125 5.2.10 fMRI Processing and Analysis ......................................................................... 127 5.3 Results .................................................................................................................... 130 5.3.1 Participants........................................................................................................ 130 5.3.2 AT Compliance, Adverse Effects, and Changes in Fitness ................................. 132 5.3.3 Behavioural Results ........................................................................................... 132 5.3.4 fMRI Results ..................................................................................................... 134 5.3.5 Partial Correlation Results ................................................................................. 138 5.4 Discussion ............................................................................................................... 145 5.5 Conclusion .............................................................................................................. 149 Chapter 6: General Discussion ....................................................................................... 150 6.1 Summarizing the Studies ....................................................................................... 150 xi  6.2 Revisiting the Main Research Questions............................................................... 152 6.3 Limitations ............................................................................................................. 156 6.3.1 General Limitations ........................................................................................... 156 6.3.2 fMRI Limitations .............................................................................................. 157 6.4 Future Directions ................................................................................................... 158 6.4.1 Neural Correlates of Mobility and Cognitive Impairments ................................. 158 6.4.2 Strategies to Minimize Mobility and Cognitive Impairments ............................. 159 6.5 Final Conclusions ................................................................................................... 160 References........................................................................................................................ 161 Appendices ...................................................................................................................... 180 Appendix A: Additional Analysis Conducted for Chapter 3 ..................................... 180 Appendix B: Rationale for Regression Model Construction in Chapter 2 ................ 182       xii  List of Tables Table 2.1 Study Sample Characteristics at Baseline. .................................................................. 52 Table 2.2 Study Sample Characteristics – Change in Physical and Cognitive Performance. ....... 54 Table 2.3. Regional Gray Matter Volumetric Differences between Fallers and Non-Fallers. ...... 55 Table 2.4. Regional White Matter Volumetric Differences between Fallers and Non-Fallers ..... 56 Table 2.5. Linear Regression Model for Change in Set Shifting Performance. ........................... 57 Table 2.6. Linear Regression Model Summary for Change in Information Processing, Working Memory, and Psychomotor Speed Performance. ................................................. 58 Table 3.1. Regions of Interest and Relative MNI Coordinates.................................................... 75 Table 3.2a Participant Characteristics. ....................................................................................... 79 Table 3.2b Participant Gait Speed by Group and Sex ................................................................. 79 Table 3.3 Mobility and fMRI Outcome Measures  ..................................................................... 81 Table 3.4 Partial Correlations across Study Sample ................................................................... 82 Table 4.1 Frontoparietal Network Regions of Interest and Relative MNI Coordinates ............... 99 Table 4.2 Participant Characteristics at Baseline...................................................................... 103 Table 4.3 AT Group Pedometer Information over 6-Month Intervention Period ...................... 103 Table 4.4 Mobility and Cardiovascular Capacity Measures ..................................................... 104 Table 4.5 Frontoparietal Network Connectivity during Task ................................................... 107 Table 4.6 Changes in Mobility and Changes in FPN Connectivity Correlation ........................ 108 Table 5.1 Baseline Participant Characteristics ......................................................................... 131 Table 5.2 Flanker Task Accuracy ............................................................................................ 133 Table 5.3 Significant Clusters Identified through fMRI Analysis ............................................. 135 Table 5.4 Percent Signal Change of Incongruent - Congruent .................................................. 136 Table 5.5 Partial Correlation between Change in Regional Brain Activity and Flanker Task Performance at Trial Completion   ................................................................................... 139 Table A1 Network Level Linear Regression Models ............................................................... 178 Table A2 Linear Regression Model with Pairwise ROI Connectivity within Neutral SMN-FPN ................................................................................................................................. 179 Table B1 Correlation Analysis for Rationale for Regression Model in Chapter 2 ..................... 183 xiii  List of Figures Figure 1.1 Conceptual Framework for the Relationship between Brain Health, Mobility Impairment and Cognitive Impairment. ............................................................................. 14 Figure 1.2 Simplified Framework of Efficiency, Compensation and Cognitive Reserve............. 30 Figure 1.3 Flanker Task............................................................................................................. 32 Figure 1.4 Overview of the Studies in the Dissertation. ............................................................. 35 Figure 2.1 Change in Cognitive Performance vs. Left Lateral Orbitofrontal Cortex .................. .59  Figure 4.1 Overview of the Flow of Study Participants Through the 6-Month Study. ................ 92 Figure 4.2 Image of the Frontoparietal Network. ..................................................................... 106 Figure 4.3 Correlation Between Change in TUG Performance and Change in FPN Connectivity During Right Finger Tapping Within the AT Group. ................................... 109 Figure 5.1 Overview of the Flow of Study Participants Through the 6-Month Study. .............. 122 Figure 5.2 Partial Correlation Plot between Left Occipital Cortex Percent Signal Change and Change in Congruent Reaction Time Over the 6-Month Intervention.. ............................. 140 Figure 5.3 Partial Correlation Plot between Superior Temporal Gyrus Percent Signal Change and Change in Congruent Reaction Time Over the 6-Month Intervention.. ...................... 141 Figure 5.4 Partial Correlation Plot between Superior Temporal Gyrus Percent Signal Change and Change in Incongruent Reaction Time Over the 6-Month Intervention.. .................... 142 Figure 5.5 Neural Activity of Left Lateral Occipital Cortex Over the 6-Month Intervention .... 143 Figure 5.6 Neural Activity of Superior Temporal Gyrus Over the 6-Month Intervention. ......... 144   xiv  Acknowledgements This thesis dissertation would not be possible without the mentorship and guidance from my supervisor, Dr. Teresa Liu-Ambrose, as well as members of my PhD committee, Dr. Todd C. Handy and Dr. Michelle Voss.  Throughout my time pursuing post-graduate degrees at UBC, Teresa has generously dedicated enormous amount time and effort into guiding my research – an incredible feat to balance between being meticulous on the important details while also giving sufficient freedom for me to explore my research interests. She had been a constant source of inspiration in life and at work – whether it’s striving to conduct scientifically rigorous research in improving health of older adults, or mentoring students/trainees to be the best they can be via tireless encouragements. Where Todd’s extensive knowledge in cognitive neuroscience and ingenuity in developing ideas/concepts had always filled me with admiration; I am extremely privileged to have learned from Michelle, whose technical expertise in neuroimaging analysis is something I continuously marvel at and the product of her mentorship constitutes the core of this thesis. To my supervisor and mentors, I extend my utmost respect and deepest gratitude.  In addition, I would like to thank all the current and past members of the Aging, Mobility, and Cognitive Neuroscience Laboratory: Dr. John Best, Dr. Cindy Barha, Dr. Jennifer Davis, Dr. Glen Landry, Elizabeth Dao, Lisanne ten Brinke, Ryan Falck, Rachel Crockett, Tracy Dignum, Shirley Wang, Michelle Munkacsy, Winnie Cheung, Chris Lim, and Alison Chan.  I would also like to express my appreciation to the funding agencies that financially supported my studies. Lastly, I must thank my friends and family for the support throughout the years.     xv  Dedication This thesis is dedicated to my sisters, and most importantly my parents, whose support had been an indispensable part of this achievement.    1 Chapter 1: Introduction As the world’s aging population rapidly increases, efficient (i.e., effective and cost-effective/cost-saving) strategies to promote healthy aging at a population level becomes a necessary health care priority. Of particular revelance, the maintenance of mobility and cognitive function are of utmost importance as they are vital to healthy aging, functional independence, and quality of life.  Current evidence established that mobility and cognitive impairments are associated and often co-exist among older adults [1]. Nevertheless, the underlying neural mechanisms are still not well understood. As such, the aims of this thesis is to examine the structural and functional neural mechanisms that underlie the association between mobility and cognitive impairments; as well as the intrinsic functional neural correlates by which targeted exercise training promotes mobility and cognitive function in older adults.  This chapter (Chapter 1) provides a review of the existing literature. Section 1.1 provide a brief review of current knowledge in aging related mobility decline; Section 1.2 discusses the risk factors for mobility impairment; Section 1.3 explains the interplay between mobility and cognitive function; Section 1.4 presents evidence on the potential benefits of exercise in improving mobility and cognitive function; lastly, Section 1.5 provides an overview of this thesis dissertation. 1.1 Mobility in Aging Mobility generally comprises of all types of movement that constituted the basic form of ambulation, whereby optimal mobility is understood as the capacity to traverse across any 2  terrain in a reliable and safe fashion [2]. In this regard, optimal mobility is critical to healthy aging.  This section will open with the epidemiology of mobility impairment in community-dwelling older adults with a specific focus on falls and gait speed, and discuss the relevant key risk factors. Next, I will summarize the existing understanding of the structural and functional neural mechanisms that may regulate the association between mobility and cognitive function. Finally, the section concludes with evidence that supports exercise as a strategy to promote both mobility and cognitive function in older adults.  1.1.1 Epidemiology of Mobility Impairment in Community-Dwelling Older Adults Updated information from Statistics Canada indicated that it was the first time in the census history that the total number of older adults over the age of 65 years exceeded the number of individuals under the age of 15 years, in which individuals between the ages of 65-85 years accounted for 30% of the total population. A more alarming problem resides in the fact that in 2016, more than 13% of the population aged 65 or older consisted of people over the age of 85 years, and the number of individuals within this age bracket had increased by 19% over a 5-year span from 2011 to 2016. Thus, promoting healthy aging - defined by the Centers for Disease Control and Prevention as the “development and maintenance of optimal physical, mental and social well-being and function in older adults” [3] - is a major public health priority and challenge. According to Statistics Canada, the prevalence of impaired mobility among older adults over the age of 65 in 2012 was 20.5%. The Center for Disease Control and Prevention also 3  reported approximately 31.7% of older adults over the age of 65 experience difficulties in walking up to three city blocks, and the prevalence of impaired mobility is expected to increase with the rate of aging [4]. As such, there is a strong incentive to better understand the evolution of mobility-related disability as well as strategies that can effectively promote mobility in older adults.   1.1.2 Measures of Mobility  Mobility is a broad, multi-faceted function. Thus, within the scope of this thesis, I will focus on two specific measures of mobility: 1) gait; and 2) falls.  1.1.2.1 Gait Gait is a complex motor behaviour that is described as locomotion achieved through series of postural motion of the lower limbs [5]. It is the most common form of physical activity for older adults, both as a part of daily behavioural function or leisure events. Thus, restricted gait is often linked with poor social and health outcomes [6]. Gait disorders are prevalent among community-dwelling older adults; approximately 36% of non-demented older adults and 80% of the population over 85 years reported to displayed gait disorders [7]. They are considered as a main risk factor for falls as well as have implications in increased mortality and reduced quality of life [8]. Gait can change over the lifespan of an individual [1, 7], arising from non-neurological (e.g., arthritis, peripheral vascular disease) or neurological (e.g., ataxic, parkinsonian) sources as a function of old age; in which aberrant spatial (i.e., shorter length, width) and temporal (i.e., slower speed) gait patterns are often observed [7, 9]. While loss of mobility is common 4  among older adults, age-associated gait impairments can be ameliorated via altering the modifiable risk factors. Gait disorders may be caused by physiological functional decline, such as sensory system limitations in the form of poor vision or visual contrast sensitivity [10], reduced muscle flexibility, lower-body range of motion and strength, or due to adverse effects of chronic conditions [11, 12].  Facets of gait that are commonly assessed include speed, step-length, stride-length, and cadence [13].  Gait speed is defined as the total distance travelled in unit time; step-length is defined by the distance between successive points of heel contact of the opposite feet; stride-length is defined by the distance between successive points of heel contact of the same foot; and cadence is defined as the total number of steps per unit time. Measurements of the various aspects of gait are considered as the primary constituents for the general assessment of gait variability – an important clinical parameter for evaluating mobility and falls risk in the elderly [14]. For the purposes of the thesis, emphasis is placed on gait speed in the discussion of age-related gait changes.  Gait variability – variations in features that characterizes gait [15] – and gait speed are key measures of gait given the  clinical relevance in disability, falls, and as a proxy for fitness [8]. Gait speed is measured by recording the time required to traverse a defined distance under usual pace with an initial and terminal spatial buffer (generally 1 meter to 2.5 meters) to allow for acceleration/deceleration in order to achieve steady-state walking velocity during the measurement [16]. Studies report evaluation of gait speed with a walking distance ranging from 2 meters to 15 meters; however, the majority adopted a distance of ≥ 4 meters 5  ≤10 meters [16], as it is less optimal to determine gait speed with a walkable distance of less than 4 meters [17].  Slowing of gait is an established predictor of poor mobility, poor cognitive function, and steep functional decline (see Figure 1.1 on page 13 for the proposed relationship) [18]. Notably, slow gait serves as a modifiable clinical indicator for morbidity and survival [19]. Slow gait speed is also a key risk factor for falls among older adults [18, 20, 21], and a designated functional vital sign for overall health status [22, 23].  1.1.2.2 Falls Falls are a major health care problem for seniors. Falls are the third leading cause of chronic disability worldwide [24] and about 30% of community-dwelling adults aged 65 years and older experience one or more falls every year [25]. About 5% of falls result in fracture and one-third of those are hip fractures [25]. The estimated expenditures for fall-related injuries exceed 2 billion for Canadians aged 65 years or older [26]. Other negative impact includes reduced quality of life, disability, and death [26]. Therefore, falls prevention is an essential component for healthy aging.  1.2 Risk Factors for Mobility Impairment Currently, the most recognized classical theoretical model suggests mobility deficits arise as a consequence of physiological abnormalities or alterations that occur with old age [27]. This negatively impacts the functions of various biological systems, and thereby, leads to impairments such as poor postural control, reduced muscle strength and power. While lower limb proprioception, visual contrast sensitivity, hand reaction time, quadriceps strength, and 6  balance [25, 28, 29] are also among the most discussed physical attributes associated with falls in older adults, evidence broaden the scope by placing strong emphasis on the brain as a key structure for investigation as it plays a critical role in the maintenance of proper mobility [30-32] and cognitive function [25, 33, 34]. In particular, executive functions appear to be the cognitive domain of upmost importance in the maintenance of optimal mobility [33-35]. 1.2.1 Risk Factors for Slow Gait A recent study identified seven modifiable risk factors of slow gait; as expected, many of these risk factors overlap with those associated with general mobility impairment discussed in the previous section, including low levels of physical activity, impaired cognitive function, poor muscle strength, pain, obesity, vision, and falls (slow gait also contributes to falls in a bidirectional relationship as will be discussed in the next sections; see Figure 1.1 on page 13 for the proposed relationship) [36]. This study suggests approximately 77% of the proportional risk for incident of slow gait was attributed to the combination of these seven risk factors, in which cognitive impairment was one of the greatest contributors [36]. Notably, the reported adjusted relative risk of the risk factors were 1.94 for low levels of physical activity, 1.77 for impaired cognitive function, 1.48 for poor muscle strength, 1.45 for pain, 1.35 for obesity, 1.36 for vision, and 1.32 for falls.  1.2.2 Risk Factors for Falls Physical functions that are important in the assessment of falls risk include: 1) lower limb proprioception, 2) visual contrast sensitivity, 3) hand reaction time, 4) dominant quadriceps strength, 5) balance (i.e., postural sway on a foam surface) and 6) gait speed [20, 21, 37].  7  The Physiological Profile Assessment (PPA) is an assessment tool that generates a standardized z-score that is a valid [28, 29, 38] and reliable [39] estimation of falls risk in older adults. A z-score of 0-1 indicates mild risk, 1-2 indicates moderate risk, 2-3 indicates high risk, and 3 and above indicates marked risk [40]. The PPA utilizes measurement of the key physical functions described earlier (i.e., lower limb proprioception, visual contrast sensitivity, hand reaction time, dominant quadriceps strength, and balance) for the computation of the standardized score. Proprioception is measured with the subject’s eyes closed,  elevate the feet, and match the position of the big toes with a Perspex sheet in between the feet. The differences in the position of the two toes are noted in units of degrees. Visual contrast sensitivity is tested with the standard Melbourne Edge Test, with which the subject was asked to identify the orientation of a line that is drawn through the middle of circular shapes of various contrast. Sensitivity is measured in units of decibel (dB=-10log Contrast). Reaction time is assessed in milliseconds via a simple device with light as the primary stimulus where the subjects respond to the stimulus by press of a button. Quadriceps strength is assessed by the maximum amount of extension of a spring gauge on the subject’s dominant leg. Balance is tested by the amount of body displacement (in millimeters) in 30 seconds on different surfaces (firm, foam rubber) with both eyes open and closed conditions. The combination of these five measures has a 75% predictive accuracy for falls in older people, and can determine the overall stability of an individual [28, 29]. Performance decline of these functions is associated with increase in number of falls [28, 29]. The relative contribution (i.e., relative weighting, or how each measure contributes to the overall falls risk score) for each of the five measures with respect to falls risk is as follows: -0.33 for edge 8  contrast sensitivity, 0.20 for joint position sense, -0.16 for isometric quadriceps strength, 0.47 for hand reaction time, and 0.51 for postural sway on foam rubber mat with eyes open [41].  Slow gait speed is also well-documented risk factor for falls [20, 37]. Upon quantifying various domains of gait and falls among community-dwelling seniors, a 20-month prospective study demonstrated that slower baseline gait speed was associated with greater risk of falls [37]. Similarly, a 24-month prospective study also reported similar findings where older individuals with the slowest gait had significantly higher incidence of subsequent falls, in which gait variability was stated as the most robust predictor of falls (injurious or not) [20]. This was not surprising due to the notion that canonical view suggests gait variability is normally a well-regulated ability that requires minimal cognitive load [42]; however, subtle brain abnormalities and neurodegeneration may disrupt the top-down cognitive regulation of stable gait variability [31, 43], thereby introducing poor balance and falls. These provide strong indication that cognitive function may be an essential constituent of proper mobility, particularly in gait generation and falls. 1.2.3 Cognitive Function as a Risk Factor Cognitive function embodies all processes involved in attention, working memory, problem solving, planning and strategizing, all of which are essential components in maintaining proper functional status in life [44]. In the last decade, there is growing recognition that cognition plays an important role in mobility [25, 28, 29]. For example, in a prospective observational study, Buchman and colleagues [45] followed 836 older adults over a period of 4.5 years and found that among individuals with normal gait at baseline, 51% of these developed slow gait over time. Further, the authors report global cognition (as measured by a 9  composite score of 18 cognitive tests) was associated with the rate of change in gait speed and more specifically, episodic memory, semantic memory, visual spatial ability, perceptual speed and working memory were related to the incidence of slowing gait, in the absence of significant physical changes (e.g., BMI, physical activities, vascular diseases or risk factors). Similarly, Tinetti and colleagues [25] showed cognitive impairment has a negative impact on falls (relative risk==2.3) independent of other fall risk factors. Lord and colleagues [28] found those individuals with cognitive impairment were more prone to falls (relative risk==2.37). In a prospective study, Allan and colleagues [9] reported a prevalence of 65.7% for people with dementia to sustain at least one fall in the duration of 12 months, and the incidence ratio for falls for the subjects with dementia was significantly higher than the people without dementia [46]. A global epidemic, mild cognitive impairment (MCI) is a clinical entity that is characterized by cognitive decline greater than expected given an individual’s age, education level but without significant impairment of daily function [47]. Notably, the estimated prevalence of MCI among older adults between 70-79 years is approximately 16%-22% (men: 19%; women: 14%) [48, 49]. As a well-recognized prodromal stage for dementia, MCI develops into Alzheimer’s disease (AD) at a rate of 10-30% annually [50, 51], while individuals without MCI progress to dementia at a much slower rate of 1% to 2% each year [50]. Because MCI is temporally positioned at the transitional phase prior to fully developed dementia in the progression spectrum, intervention strategies targeting individuals with MCI could potentially be effective in preventing further functional degeneration. 10  Present understanding of MCI dictates that accurate identification of the syndrome is difficult and suggests clinical evaluation should include 1) individual’s own concern regarding change in cognitive function (i.e., subjective complaint); 2) absence of overt impairments or dementia; 3) preserved ability to perform daily living function albeit mild hindrance; and 4) objective assessment of cognitive impairment in multiple cognitive domains (i.e., memory, executive function, etc.) [52]. In particular, Montreal Cognitive Assessment (MoCA) is an established tool used to objectively assess an individual’s overall cognitive function, and a score of ≤ 26 is often indicative of MCI [53]. Current evidence suggests that even mild reductions in cognitive abilities among otherwise healthy community-dwelling older adults is linked with slow gait [54-56] and increased falls risk [25, 34, 57, 58]. In a study consisted of over 3400 community-dwelling older adults, Doi and colleagues [56] showed slow gait was independently associated with poorer cognitive performance in visual spatial ability, processing speed, attention, and executive functions. Moreover, individuals with both slow gait and MCI exhibited significantly worse cognitive functioning as well as elevated risk for falls. In a cross-sectional study, Liu-Ambrose and colleagues [13] demonstrated that performance on the PPA test and postural sway has a significant association with mild cognitive impairment (MCI) [58]. This study showed that higher PPA score (i.e., increased falls risk) with an increased postural sway, poorer set-shifting, working memory, and response inhibition is observed among participants with MCI [57]. An eight-year follow-up study of non-demented community-dwelling older adults over the age of 70 years also provided evidence that increased fall risk is related to mild declines in cognitive function [34]. Specifically, participants were assessed for cognitive function, physical and health conditions, and number of falls in the previous 12 months. Performance on the cognitive tests 11  was associated with the rate of falling within the eight-year follow-up period. Of particular importance, higher score on the MMSE and better executive functions performance reflected a significant reduction in the fall rate [34]. These findings highlight the intricate relationship between cognitive function, gait and falls among older adults regardless of their current cognitive status. In particular, executive functions are area of concern given its central role in the maintenance of daily function and susceptibility to aging [59, 60]. 1.2.3.1 Executive Functions Executive functions are goal-oriented higher order cognitive processes that involve the ability to focus, to selectively attend, and to plan. Executive functions decline substantially with aging [59]. Importantly, even among healthy and cognitively fit seniors, reduced performance in executive functions is prevalent [44, 61]. Royall and colleagues reported in a study population consisting of mostly cognitively fit members (less than 8.3% scored <24/30 on MMSE), 17.6% failed to complete an executive clock drawing task [44]. A nine-year observational study by Carlson and colleagues [22] indicated that for a study population of over 400 community-dwelling older women age 70 to 80 years, 49% of the study participants developed cognitive impairments over the duration of the study, with executive function as the first cognitive domain to decline [60].  Recent and previous evidence highlighted the particular relevance of executive functions in mobility [62-66]. These studies demonstrated that reduced set-shifting [62, 66, 67], information updating [67], and response inhibition [63, 68, 69] were significantly associated with poorer mobility. Briefly, Miyake and colleagues [70] described mental task set-shifting, information processing, and response inhibition as three key domains of executive functions. 12  Set-shifting refers to one’s ability to switch back and forth between various mental tasks, information processing focuses on the replacement of old memory with new information, and response inhibition concerns with how to actively repress the dominant or automatic response [70]. A cross-section evaluation of information processing ability and gait speed within a cohort of over 2000 older adults from the Health Aging and Body Composition Study (Health ABC) suggest that decline in information processing was associated with poorer gait speed [71]. Supporting these findings, Verghese and colleagues [55] quantified and compared gait performance between individuals with different subtypes of MCI and their healthy counterpart. Specifically, community-dwelling older adults above the age of 70 years were recruited and underwent gait and cognitive assessments. Cognitive tests measured verbal memory, executive functions, attention, working memory, and language. Gait was assessed by gait variability and gait speed. Results from the study suggest individuals with amnestic MCI produced a poorer gait rhythm and variability compared with non-amnestic MCI and healthy controls.  Likewise, it is well-documented that individuals with MCI are at greater risk of falling compared with cognitively healthy individuals [57, 72, 73]. For example, a cross-sectional study consisted of 158 community-dwelling older adults aimed to examine the association between cognitive status and falls risk found that compared with healthy older adults, those with MCI had greater falls risk (as determined by PPA) as well as poorer performance in set-shifting, working memory, and response inhibition [57]. Aligning results were obtained by Delbaere and colleagues [73], who showed, through a prospective cohort study, that compared with older adults without MCI, individuals with MCI displayed significantly worse balance performance, as such, they also were subject to significantly greater falls risk.  13  Moreover, neuroimaging studies revealed that the brain may be the central structure that dictates the observed relationship between executive dysfunction, gait speed and falls [74-76]. For instance, a recent study reported larger cerebellar gray-matter volume was significantly correlated with faster gait speed and better information processing among over 200 older adults in a cross-sectional investigation [76]. Another cross-sectional examination of the association between brain structural volume and gait characteristics among older adults showed that shorter gait length was associated with smaller sensorimotor and frontoparietal regional volumes. Nagamatsu and colleagues [75] found the activity of left frontal orbital cortex - brain regions that contribute to the executive process of conflict resolution - was significantly and negatively associated to changes in falls risk over a 12-month period. These findings further consolidate the importance of executive functions in proper gait execution and falls, especially for older adults with MCI in complex environment. 1.3 Association between Mobility and Cognitive Impairments Current evidence suggests that mobility and cognitive impairments are associated (Figure 1.1) and clinically, they are concomitant in older adults [1, 36, 37, 77]. It is estimated that 60% of older adults with cognitive deficits experience one or more falls each year, which is approximately twice as much as those who were cognitively healthy [78]. Figure 1.1 Conceptual Framework for the Relationship between Brain Health, Mobility Impairment and Cognitive Impairment 14   Notably, it is important to recognize that the relationship between mobility and cognitive deficits is not unidirectional (i.e., impaired cognitive function leads to slow gait and falls, as it may appear in Section 1.2), but rather it is bidirectional [71, 79]. Thus, there is growing recognition that clinical gait abnormalities and falls are early biomarkers of cognitive impairment and dementia. For example, gait speed reportedly decreased a decade before the diagnosis of MCI [80]; and slow gait speed independently predicted decline in cognitive function in older adults [71]. In this prospective cohort study, gait speed over 6 meters, attention, and psychomotor speed were assessed in 2776 older adults. Results from the study suggest that over a period of 5 years, cognitive function of the individuals with slower baseline gait speed declined more significantly compared with those with faster baseline gait speed. A 20-year longitudinal cohort study reached similar conclusions [81]. Two-hundred and four healthy older adults were assessed for neuropsychological as well as motor abilities and were followed over 20 years. Researchers found that among individuals who eventually 15  converted to MCI over the period declined significantly in gait speed, and the reduction in gait speed performance occurred 12.1 years prior to the presence of any detectable clinical symptoms of MCI [81]. While the bidirectional relationship between mobility and cognitive impairments is well-established, the exact mechanism by which this occurs remained equivocal; however, emerging neuroimaging data expanded our present understanding in this topic. 1.3.1 Neural Underpinnings of Concurrent Mobility and Cognitive Impairments Under the assumption that mobility and cognitive impairments are both consequence of age-related deteriorations of the brain, it may be postulated that the candidate mechanistic pathway underlying the bi-directional impairments involve alterations in brain structure – as indicated by white matter lesions; or aberrant brain function – as measured by neural activity or functional connectivity. Currently, we have an abundance of evidence on the structural neural correlates while the investigation of functional neural signatures of this relationship is still a burgeoning field of research that this dissertation aims to address.  1.3.1.1 White Matter Lesions White matter lesions are highly prevalent among older adults; it is estimated that 50% to 98% community-dwelling older adults have these covert lesions [82]. Classical knowledge of the pathophysiology of white matter lesion suggests it is a manifestation of partial loss of myelin or oligodendroglial cells or it could be the consequence of small vessel diseases as stated previously [82, 83]. Individuals with white matter lesions often express reduced 16  cognitive function [84, 85] and particularly, impaired executive functions. [86, 87]. In one recent systematic-review, Bolandzadeh and colleagues [87] concluded that periventricular white matter lesions were associated with poorer memory and executive functions, particularly processing speed. Prins and colleagues [86] report extensive white matter lesions is associated with reduced cognitive function. They conducted a five-year prospective study that found increased periventricular white matter lesions negatively correlated with older adults’ ability to perform executive function related tasks, specifically domains involved in response inhibition, processing speed, and verbal fluency. Studies have critically examined the location of white matter lesions and the related functional disruptions [87, 88]. Smith and colleagues [88] recruited participants consisted of age 65 or older seniors with and without MCI (as determined by a Clinical Dementia Rating score of 0.5). These older adults underwent MRI scanning while performing a neuropsychological battery that included general knowledge, episodic memory, spatial skills, and executive function as the cognitive domains of interest. White matter lesions were quantified by the amount of white matter hyperintensity volumes (WMH volume), and through voxel-based general linear models, the regional associations between WMH and neuropsychological test scores were established. The results showed lesions in the right inferior temporal-occipital white matter, left temporal-occipital periventricular white matter, and right parietal periventricular white matter were associated with reduced episodic memory; whereas bilateral inferior frontal and prefrontal white matter lesions were found to be linked to executive functions loss [88]. In addition to having a detrimental impact on cognitive function, white matter lesions negatively affect mobility and balance in older adults [30, 89, 90]. Masdeu and colleagues [89] demonstrated that increased brain white matter hypodensity (derived from computed 17  tomography scans – similar to hyperintensities in MR images – that indicates reduced white matter volume) was associated with slower gait and poorer balance in older adults [89]. They found older adults with a history of falls were more cognitively impaired than their non-falling counterparts due to white matter lesions in the brain; fallers had notably more white matter hypodensity than non-fallers. This relationship was expected given that gait and balance were thought to be maintained and controlled by fibers ascending from the ventrolateral nucleus of the thalamus to the paracentral lobule (medial superior frontal gyrus) as well as descending corticospinal fibers [89]. White matter lesions, often found in the periventricular region, disrupt these circuits in the brain, thereby causing motor impairments [30, 89, 90].  1.3.1.2 Functional Neural Correlates of Mobility and Cognitive Impairments As previously stated, structural neuroimaging evidence is a much more developed area of study compared with functional neural correlates of mobility and cognitive impairments. However, emerging evidence has offered insight into this relationship. For example, research suggests activity in brain regions responsible for response inhibition may be associated with falls risk [75]. A sub-analysis of a 12-month randomized controlled trial of resistance training with 73 community-dwelling older adults was performed [75]. Study participants underwent fMRI using a variation of the Eriksen Flanker Task - a neuropsychological test that targets response inhibition; while physiological falls risk was assessed at baseline and study end-point after the training program using the PPA. Results from the analysis showed that reduced brain activity in the regions from paracingulate gyrus to anterior cingulate 18  cortex, and areas from left frontal-orbital cortex to insular were significantly associated with higher falls risk [75].  My MSc work also generated evidence that demonstrated differentiable neural network connectivity between older adults with and without a history of falls [91]. This prospective study included 44 community-dwelling older adults with and without a history of falls. Participants underwent fMRI measurement at study baseline while physical and cognitive assessments occurred at baseline as well as at study end-point. Physical assessment focused on mobility and balance outcome measures; whereas cognitive assessment included executive functions measures, particularly working memory, set-shifting, and response inhibition. Results from the study revealed that differences in connectivity between older fallers and non-fallers were detected between the default mode network (DMN) and frontoparietal network (FPN) as well as between the sensori-motor network (SMN) and FPN. Importantly, lower connectivity between SMN and FPN was associated with reduced response inhibition performance and mobility over time, suggesting that these inter-network disruptions in connectivity may be associated with greater decline in both mobility and cognitive function over time. Briefly, the SMN is actively involved in major aspects of movement, including motor-planning, initiation, execution, and coordination [92-94]. The FPN is involved in top-down attentional control [95] and allocation of available neural resources to imminent cognitive processes [96-98], as well as motor planning and motor execution [99-101]. Despite that the focus of this thesis is on examining the intra-network and inter-network connectivity of the SMN and FPN, it should be noted that other networks may also exert influence on mobility 19  and cognitive function among older adults. For example, the salience network is anatomically anchored in the anterior insula and the dorsal anterior cingulate cortex that also include three key subcortical structures in the amygdala, ventral striatum, and the substantia nigra [102]. While the salience network primarily contributes to communication, social/emotional behaviours, and self-awareness via integrating sensory, emotional, and cognitive information [103], study also report it shares some anatomical features with the other neural networks, hence it may be involved in cognitive processes vital to inter-network communication [104]. The DMN is highly relevant in the discussion of early course of Alzheimer’s disease development, schizophrenia, as well as mild cognitive decline due to aging [105-108]. The DMN contains multiple brain regions (e.g., middle prefrontal cortex, posterior cingulate) essential for cognitive processes such as memory and executive functions [109, 110]. Given the particular relevance of the SMN and FPN in the maintenance of mobility and cognitive function, this dissertation will place emphasis on the investigation of these two specific neural networks. 1.4 Exercise to Promote Mobility and Cognitive Function The World Health Organization defined exercise as a subtype of physical activity that is designed, structured and repetitive with the purpose of improving or maintaining physical health on an individual [111]. Within the context of gait, falls, and cognitive function among older adults, the most commonly discusses categories of physical exercise intervention are balance/flexibility, aerobic and resistance training [112]. Balance/flexibility training usually involves some forms of stretching, range of motion and balance exercise without additional loading; whereas aerobic and resistance training usually involve progressive intensity 20  protocols via brisk walking (for aerobic training) or free-weights and resistance machines (for resistance training). It is important to distinguish the types of exercise given that each offers benefit through different mechanistic pathways. Aerobic exercise is often utilized to enhance cardiovascular fitness/capacity and has beneficial effects on cognitive function [113]; whereas resistance training is used to improve muscle mass/strength/endurance and also positively affects cognitive function [113]. The following section will summarize key studies and findings on the effect of exercise on gait and falls as well as its impact on the brain. 1.4.1 Studies on Exercise, Gait and Falls The effect of exercise on gait and falls in older adults is an ongoing and rapidly growing field of research [114-119]. Studies report resistance training [114] and walking/balance training [115] significantly reduced the incidence of falls among frail older adults. Notably, the Otago Exercise Program (OEP) is a home-based strength and balance training program designed specifically to reduce falls among older adults [120, 121]. Randomized controlled trials have demonstrated its effectiveness in preventing falls among community-dwelling older adults [120-122], as well as in older fallers [123]. A community-based randomized controlled trial also demonstrated the beneficial effects of resistance and agility training on falls risk reduction. Specifically, compared with a control group that only performed stretching exercises (which also displayed a 20% reduction in falls risk score), after a period of 25 weeks, older women who completed resistance and agility training showed a significant reduction in falls risk score (57% and 48% respectively as 21  determined by the PPA) that was partially attributable to improvements in postural stability [124]. Importantly, the observed effects on falls risk were sustained for 12 months after the intervention stopped [38]. Additionally, significant improvement in the Timed-Up-and-Go test (a test of general gait and mobility, see section 1.5.2 for more detail) was observed after resistance training among both frail and normal older adults [114, 117-119]. Findings generated from the Lifestyle Intervention and Independence for elders (LIFE study) support this notion. Briefly, the LIFE study is a large, multi-centered randomized controlled trial that compared physical activity training (i.e., a combination of walking, resistance training, and flexibility exercises) with health education program among a sample of over 1600 older adults who were followed longitudinally for an average of 2.7 years. Results from the LIFE study suggest that compared with the control condition (i.e., health education) moderate-intensity exercise training significantly reduced mobility-related disability [125].  While the widely accepted belief is that the observable benefits of exercise intervention across these studies may be accredited  to enhanced lower-extremity strength [114, 117, 118], the effectiveness of exercise extends beyond musculoskeletal gains, where much of its impact may be found in cognitive and brain function improvements (see 1.4.2 and 1.4.3). For instance, in a meta-analysis over 1016 community-dwelling older adults between the age of 65-97 from four OEP randomized trials, data showed that the intervention effectively reduced falls and falls-related injuries by 35%; however, postural sway significantly improved by only 9% and there was no significant improvement in knee extension strength [126]. These findings suggest that perhaps in addition to augmenting physical function, 22  exercise may reduce falls via alternative pathways. Emerging evidence collectively demonstrate that improved cognitive function and associated functional plasticity may be an essential mechanism by which exercise reduces falls and improve mobility in older adults [123, 127]. 1.4.2 Studies on Exercise and Cognitive Function  Although the vast majority of research has focused exclusively on aerobic training, current evidence suggests that both aerobic training and resistance training can enhance cognitive and brain outcomes in older adults. Other forms of exercise training (e.g., yoga) are also being explored but will not be discussed in my thesis. Professor Kramer and colleagues [128-130] have conducted seminal work on aerobic exercise. In a sample of 124 cognitively healthy, but low-fit older adults that were randomized to 6-months of either an aerobic exercise intervention (i.e., brisk walking) or to a stretching-and-toning control condition, Kramer and colleagues [130] demonstrated that the aerobic exercise group, compared with control group, significantly improved executive function. In a widely-cited meta-analytic study examining the impact of aerobic training on cognitive function of older adults,  Colcombe and colleagues [128] found exercise had a positive effect on cognitive function [128]. Specifically, Colcombe and colleagues [128] reviewed 18 articles that focused on randomized controlled trial studies with emphasis on aerobic exercise as the intervention strategy and cognitive function as the primary outcome. They found exercise was significantly associated with improvement in cognitive function, especially in executive function [128]. Of interest, results of this meta-analysis suggest that women may benefit from aerobic exercise more than men. Converging evidence is provided 23  by another recent meta-analysis that investigated sex differences in effects of aerobic and resistance training on cognitive function among older adults [131]. The differentiable impact of exercise as a function of sex is beyond the scope of this thesis, however, the work by Barha and colleagues [131] align with current understand that aerobic exercise can beneficially alter global cognitive function among older adults; resistance and multi-modal (aerobic + resistance) training were associated with better executive function post intervention.  Work from Cassilhas and colleagues [132] is one of the first randomized controlled trials to demonstrate that moderate to high intensity resistance training can significantly improve both short-term and long-term memory, as well as verbal reasoning among older adults. Extending the work of Cassilhas and colleagues, Liu-Ambrose and colleagues [133] found that both once- and twice-weekly moderate intensity resistance training significantly improved the executive processes of response inhibition in senior women. Specifically, community-dwelling senior women participated in a 12-month randomized controlled trial that required them to engage in resistance training either one or two days per week. The intensity of the training stimulus was at a work range of six to eight repetitions to fatigue with a 60-second rest between each set; two sets in total. Compared with a balance and tone control group, those in the resistance training groups performed significantly better on the Stroop Color-Word Test at trial completion. Current evidence also suggests the benefits exercise training on cognitive function is retained in older adults who have MCI or with impaired mobility. A 6-month randomized controlled trial that examined the potential differential effect of aerobic and resistance training on cognitive function among older women with MCI found that resistance training improved response inhibition and associative memory while aerobic 24  exercise improved balance, mobility and cardiovascular capacity [134]. Further, in a 6-month randomized controlled trial investigating the effect of a home-based balance and strengthening exercise on falls risk, mobility and executive function among older adults with a history of falls, researchers found the home-based exercise program significantly improved response inhibition and significant reduced falls [123]. Moreover, it was proposed that improved response inhibition may be a mechanism by which the exercise program reduced falls. Findings from the 2003 meta-analytic study by Colcombe and colleagues [128] showed that aerobic training programs combined with resistance training had a greater positive effect on cognition than aerobic training alone. Data from the LIFE pilot study also reported that a long-term, moderate-intensity exercise regimen consisting of a combination of aerobic, resistance and flexibility training among older adults with mobility impairments was associated with better cognitive function via enhancing physical function. Specifically, improvement in the cognitive updating was significantly correlated with better balance and gait speed [135].  Given the relevance of executive function to gait and falls, it is not difficult to envision that exercise may be a potent strategy to enhance mobility and cognitive outcomes among older adults with deficits in both aspects (i.e., individuals with MCI and significant history of falls or slow gait speed) – the areas of emphasis for this thesis. While these evidence offer insight that exercise may beneficial for those with mobility and cognitive impairments, a more comprehensive investigation of the underlying neural pathways is necessary to understand the positive impact of exercise. 25  1.4.3 Neuroimaging Outcomes of Exercise Neuroimaging evidence from RCT suggests that targeted exercise training impacts both brain structure and brain function in older adults. In a seminal paper examining the association between cardiovascular fitness and cortical plasticity, Colcombe and colleagues [136] showed that fitter, as measured by cardiopulmonary fitness testing, healthy older adults performed executive function tasks faster and with greater neural efficiency in resolving conflicting stimulus, as demonstrated by lower task-related activity in the anterior cingulate cortex [136]. In the same paper, Colcombe and colleagues [136] also provided results from a 6-month randomized controlled trial of aerobic training. They found that after the 6-month intervention period, older adults in the aerobic group, compared with nonaerobic control participants, showed a significantly greater level of task-related activity in attentional areas and a significant reduced level of activity in the anterior cingulate cortex. In another 12-month randomized controlled trial of moderate-intensity aerobic training among community-dwelling older adults, Kramer’s group showed that aerobic training enhances neural network connectivity such that the pattern of connectivity of these older adults resembles those from younger adults [137]. Further, Erickson and colleagues [138] illustrated that increased hippocampal volume was observed among healthy community-dwelling older adults after 12 months of thrice-weekly aerobic training, and improvement on spatial memory performance was associated with the increased size of the hippocampus.  There is also emerging evidence to suggest that exercise may be able to slow down the progression of white matter lesion in older adults [127]. Specifically, in a 52-week randomized controlled trial of resistance training among community-dwelling older women, 26  Bolandzadeh and colleagues [127] found that compared with the controls, those who completed resistance training showed significantly reduced white matter lesion progression over a period of 12-month, and reduced progression was significantly associated with the maintenance of gait speed. This work extends previous studies that support the hypothesis that resistance training, like aerobic training, has benefits for brain. Cassilhas and colleagues [132] also demonstrated that moderate to high intensity resistance training can significantly improve both short-term and long-term memory, as well as verbal reasoning among older adults. Extending beyond healthier older adults, exercise induced neural benefits were reported among those with MCI [134, 139]. Critically, 6 months of twice-weekly resistance training among older women with subjective memory complaints was able to effectively alter cortical activation within the lingual, occipital-fusiform gyri and the frontal pole, in which neural activities of these brain regions were significantly correlated with associative memory performance [134]. This same study also examined the effects of twice-weekly aerobic training and found that, compared with controls, those who were aerobically trained increased both left and right hippocampal volumes [139].  Explanation for these observations is described in part by studies using animal models, which have shown fitness training increases vascularization of the cortex, and higher vascularization leads to greater reserve capacity to respond in conditions that requires higher oxygen expenditure. Also, in rodent studies aerobic training has been reported to increase the level of neurotrophin factor, thereby improve cell proliferation, prolong cell survival, and induce neurogenesis in the hippocampus, hence improving relative functions [136]. These 27  advantageous changes in the brain [136, 140-142] – even among individuals with stroke [143, 144] – is regarded as “brain plasticity” or “neuroplasticity”, which is operationally defined as the ability of the brain to alter its function via changes in the physiological level (e.g., dendritic length, synapse formation, etc.), biochemical level (e.g., metabolites, trophic factors, etc.), or at the system level [145, 146]. As a side-note, mechanistically, dendrites are considered as an excellent biomarker reflecting neuroplasticity [145]. Dendrites constitute the majority of receptor surface where neuronal connections are formed; also, dendrites expand and retract as a direct result of variations in neurochemical levels in the brain. In addition, dendrites constitute a major component of neuropil – regions of largest and highest synaptically concentrated areas – which serves as an important indicator for function integrity of neural networks [145]. At the biochemical level, brain-derived neurotropic factor (BDNF) is often contemplated as a key biomarker for neuroplasticity [146], and as stated previously, it is essential in the survival and growth of other neurons [147]; BDNF also serves as a key mediator for synaptic efficacy and activity-dependent plasticity [148]. Other biochemical markers of exercise training include reduced level of serum homocysteine [149] as well as increased level of insulin-like growth factor one (IGF-1) [132, 150]. Specifically, elevated homocysteine level was associated with cognitive impairment, greater white matter lesions, and Alzheimer’s disease [151-153]. Conversely, IGF-1 improves cognitive function by enhancing cell survival and contributes to neuronal differentiation [146].  1.4.4 Efficiency, Compensation and Cognitive Reserve Emerging evidence suggests that neural plasticity secondary to the upregulation of neuronal factors stimulated by exercise may also benefit overall brain reserve [154]. The notion of 28  brain reserve initially stemmed from clinical observations that a group of older individuals who appeared cognitive normal were found to display advanced stage of Azheimer’s disease (AD) pathology in their brains post-mortem [155]. It was speculated that these individuals exhibited larger than average brain volumes, such that greater amount of damage can be sustained before the manifestation of clinical symptoms. Refinement of the concept of reserve now dictates its categorization into passive or active models [156]. The passive reserve model revolves around the central idea that a critical threshold – termed brain reserve capacity, which encompasses total brain volume as well as total synapse count – exists and varies between individuals. After the extent of brain insult exceeded this threshold, observable functional deficit is clinically expressed. The active reserve model – termed cognitive reserve - suggests that the brain actively copes with brain damage via optimizing cognitive processes (i.e., efficiency) or incorporating compensatory strategies.  Epidemiological [157, 158] and imaging [159, 160] evidence have supported this theory, in which studies reported higher education level and engagement in cognitively challenging leisure activities were associated with lower dementia risk as well as more extensive “silent” AD pathology in the brain (i.e., compared with  those with lower education level, individuals with higher education level may exhibit similar amount of AD pathology in the brain without explicit expressions of clinical AD symptoms). Further investigation of cognitive reserve using neuroimaging techniques provided additional validation of the core concepts that underlie the theory of cognitive reserve: neural efficiency and compensation [161-163]. Specifically, in a fMRI study consisted of 40 younger adults aimed to identify activity patterns of brain regions associated with heavy cognitive load, Habeck and colleagues [161] found that individuals with poorer performance on verbal IQ test showed increased level of 29  activations and poorer performance as the difficulty of the task progressed; whereas those who displayed less neural activations under high cognitive load performed better on the task. In a separate study conducted by the same group, 17 younger adults underwent fMRI scanning while performing a visual recognition task to, again, assess the extent of neural activation relative to progressively increased task difficulty. The authors reported that the level of neural activity was inversely correlated with estimated IQ score [162], suggesting those with higher IQ were more efficient in conducting difficult tasks. In contrast, a cross-sectional study that compared brain activation patterns between 37 younger and 15 older adults during a working memory task demonstrated that compared with younger adults, older adults exhibited larger increase in brain activations with poorer task performance [163], an indication of the extent of compensatory neural processes older adults required for task engagement. Joint findings from the evidence augmented the theoretical framework of neural efficiency, compensation, and cognitive reserve (Figure 1.2); however, more insight into the fundamental neural mechanisms is warranted to provide better understanding as to how to effectively expand reserve capacity such that the onset of functional decline may be delayed in the high-risk population.   30  Figure 1.2 Simplified Framework of Efficiency, Compensation and Cognitive Reserve 1.5 Overview of Thesis 1.5.1 Main Research Questions The previous sections provided quality evidence that the brain is key to manifestation of mobility and cognitive impairments among older adults. My thesis aims to extend our current knowledge in this area by characterizing neural correlates of falls and slower gait and examining the functional neural mechanisms by which exercise promotes mobility and cognitive function in older adults. Specifically, my thesis aims to answer the following research questions:  31  1. What are the brain structures implicated in older adults with a significant history of falls? 2. What are the functional neural correlates of slower gait speed in older adults with MCI? 3. What are the functional neural mechanisms by which aerobic exercise promote mobility and cognitive outcomes in older adults with mild vascular MCI? 1.5.2 Methodology Here I outline the main clinical and neuroimaging outcome variables used throughout this dissertation. A brief description of each measure is included and much greater details are included in the relevant studies (i.e., Chapter 2 to 5).  Mobility Measurements:  Timed-Up-and-Go Test. The Timed-Up-and-Go test (TUG) is a commonly used measure of gait and mobility [164]. The test required participants to rise from a standard chair, walk a distance of 3 meters, turn, walk back to the chair and sit down while being timed throughout the examination. It has high intra-rater and inter-rater reliability among the elderly population (0.92-0.99) as well as high construct validity (i.e., r=0.75 with gait speed) [165].  Falls. Non-syncope falls in the previous 12 months were recorded and this history of falls was used to stratify fallers and non-fallers.  Short Physical Performance Battery (SPPB) [166]. The SPPB measures performances on standing balance, walking (four meters), and sit-to-stand. Each component is rated out of four points, for a maximum of 12 points; a score < 9/12 predicts subsequent disability. 32   Gait Speed is computed with a timed 4-meter walk (i.e., 4 meters / time taken).  Structural MRI (see section 1.5.2.1):  Regional structural volume   White-matter hypointensity volume   Functional MRI (see section 1.5.2.2)  Task-based fMRI analysis  Brain activation analysis o Flanker Task [167]. An executive function task that evaluates response inhibition. Participants were visually presented a series of arrows in a row and were required to respond via button pressing to identify whether the central arrow is pointing in the same direction as the flanking arrows (Figure 1.3). Figure 1.3 Flanker Task    33   Functional connectivity analysis o Motor Finger Tapping Task [91]. A finger tapping task administered to elicit neural activity within the motor network. Participants were required to rhythmically finger tap with respective hands according to visual cue.    Task-free functional connectivity analysis o Resting-state. Participants were required to remain stationary with eyes open for the duration of the scan. 1.5.2.1 Structural Magnetic Resonance Imaging Structural magnetic resonance imaging is a neuroimaging technique that permits quantification of brain regional volume, curvature, and surface area. This is often accomplished through performing a semi-automated analysis pipeline on data acquired from a high-resolution T1-weighted image.  1.5.2.2 Functional Magnetic Resonance Imaging Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that detects neuronal activation via changes in the blood-oxygen-level-dependent signals (BOLD). BOLD signals are indicative of the amount of neural activity in the brain; specifically, this information is derived from the concentration of oxy-haemoglobin found in the arterioles of the brain. Stronger signal is acquired from a higher level of oxy-haemoglobin, reflecting an increased neuronal activity. With this information, one may create activation maps that describe the average engagement of brain regions during specific condition (i.e., rest or task) in response to particular stimuli.  34  It is important to distinguish brain activation maps from model-statistic maps as they are often discussed as different analysis techniques (i.e., task-based and task-free resting-state). Brain activation maps display the regions activated (as a consequence of the introduced stimulus) on a high resolution structural image and is used to infer the relationship of evoked activation – the association between different active voxels (or inactive voxels) in the brain. This is generally done within the scope of a task-based type of fMRI analysis.  Model-statistic maps show the magnitude of activation found in different brain regions with respect to cognitive state under study. Through brain activation maps, one can observe the interaction of different regions or networks (i.e., in terms of correlation) and how these systems function in a synchronous fashion to achieve various cognitive processes. This is commonly regarded to as functional connectivity, which is defined by the temporal coherences of different areas in the brain. Resting-state functional connectivity analysis is conducted in this method. Two of the most popular techniques to perform functional connectivity analysis are independent component analysis (ICA) and seed-based analysis (SBA), which is often regarded to also as region of interest analysis. Independent component analysis offers a way to detect multiple resting state networks at once, and the voxel value is reflective of how well the time-series data of a particular voxel is correlated with the mean time-series of a specific resting state network. Seed-based analysis requires a predetermined set of a-priori regions or “seeds” that can be used to correlate with other locations in the brain. The voxel values in SBA reflect the correlation between the voxel and the designated regions of interest. This dissertation focuses on connectivity analysis performed via the region of interest approach.  35  1.5.3 Overview of Chapters To address the research questions, this thesis is comprised of four studies; each presented as a separate chapter and is summarized here (Figure 1.4). Figure 1.4 Overview of the Studies in the Dissertation   Mobility  Cognition  Neural Correlates Brain Structure  Brain Function  Exercise  Chapter 5 Chapter 4 Chapter 2 Chapter 3 36  In Chapter 2, I investigated whether brain structural volumes are different between recurrent fallers (≥ 2 falls in the last 12 months) and non-fallers. Moreover, I examined whether differences in structural volumes correlated with changes in cognitive function over a 12 month observation period. I found that at baseline, compared with non-fallers, fallers showed significantly smaller gray matter, subcortical and left lateral orbitofrontal white matter volumes. Notably, lower baseline left lateral orbitofrontal white matter volume was independently associated with greater decline in executive functions at 12-months. This work extends previous research that showed associations between falls reduced gray matter by demonstrating that white matter volumes also vary as a function of falls history.  In Chapter 3, I evaluated the functional neural correlates of concurrent mobility and cognitive impairments. Cognitive and mobility impairments co-existence in older adults is well established. Moreover, recent evidence showed that among older adults with mild cognitive impairment (MCI), those with gait abnormalities are at greater risk for functional decline than those without gait abnormalities. However, little is known with respect to the underlying neurobiology of impaired gait in older adults with MCI. A better understanding of the underlying neural mechanisms will provide targets for future prevention and interventions strategies. Thus, using resting-state fMRI, I found that older adults with slower gait and MCI displayed more disconnected inter-network connectivity between the SMN and FPN compared with older adults with normal gait speed and MCI. Further, I provided greater depth by examining specific regions within each network that may be responsible for such observation.  37  In Chapter 4, I examined the central mechanisms involved in improved mobility secondary to exercise training. Using finger tapping fMRI data acquired from a 6-month randomized trial of aerobic exercise, I found that aerobic exercise maintained functional connectivity in the fronto-parietal network (FPN) connectivity while the usual care increased connectivity. Notably, increased FPN connectivity was associated with reduced TUG performance.  In Chapter 5, I explored whether moderate-intensity aerobic training can beneficially alter neural activity in older adults with mild vascular cognitive impairment (VCI). Using task-based fMRI data acquired from a 6-month randomized trial of aerobic exercise, I found that aerobic training significantly improved cognitive performance and reduced neural activation in the lateral occipital and superior temporal regions. The reduced neural activity was significantly correlated with improved task performance, suggesting aerobic training may improve neural efficiency in older adults with VCI. Results from Chapters 4 and 5 both support the notion that aerobic exercise may enhance neural efficiency as measured by both cortical activation as well as neural network synchrony. This dissertation concludes by revisiting the proposed research questions with an integrated discussion on the association between the brain, mobility impairment and cognitive impairment as well as how exercise may offer potential remedy for these issues among at-risk older adults; followed by an overview of the overall limitations and future directions.   38  Chapter 2: Structural Neural Correlates of Impaired Mobility and Subsequent Decline in Executive Functions: a 12-Month Prospective Study A version of this chapter is published as HSU CL, Best JR, Chui BK, Voss MW, Handy TC, Bolandzadeh N, Liu-Ambrose T.  Structural neural correlates of falls status and subsequent decline in executive functions: A 12-month prospective study. Experimental Gerontology. 2016 Apr 11;80:27-35. doi: 10.1016/j.exger.2016.04.001 2.1 Introduction Worldwide, the number of individuals aged 60 years or over is expected to more than double, from 841 million people in 2013 to more than 2 billion in 2050 [168]. Consequently, geriatric syndromes such as cognitive impairment and impaired mobility will place increasing demand on the public health system. Both geriatric syndromes are associated with institutionalization, reduced quality of life, disability, and death [26]. Current evidence suggests that cognitive impairment and impaired mobility are associated and often co-exist among older adults [1]. Notably, there is growing recognition that clinical gait abnormalities and falls are early biomarkers of cognitive impairment and dementia [77]. For example, with data from the Health, Aging and Body Composition Study, Inzitari and colleagues [71] showed that slower gait speed at baseline was predictive of subsequent cognitive decline over approximately one decade. Others have shown that gait speed begins to decline more precipitously approximately one decade before the diagnosis of MCI [81]. A recent 12-month prospective study of 125 cognitively normal, community-dwelling older adults demonstrated that higher levels of Pittsburgh compound B retention – a biomarker for greater 39  amyloid deposition - and Alzheimer’s disease (AD) related cerebrospinal fluid biomarkers were associated with a faster time to first fall [169].  Consistent with these links between mobility and cognition, evidence from neuroimaging studies suggests that slower gait speed or a history of falls is associated with subclinical alterations in both brain structure and function [74, 91, 170]. With respect to structure, in a cross-sectional study of 220 community-dwelling older adults, Rosano and colleagues [74] demonstrated that lower gray matter volume in the sensorimotor regions and frontoparietal regions were associated with impaired gait (i.e., shorter steps and longer double support times). In another cross-sectional study of 112 community-dwelling older adults, Ezzati and colleagues [171] reported that lower gray matter and hippocampal volumes were associated with slower gait speed. Hippocampal atrophy over 2.5 years was associated with concurrent gait speed decline among 225 older adults between age of 60 to 86 [172]. These brain-mobility associations found in gray matter and subcortical structures are highly relevant within the context of cognitive aging as studies have consistently demonstrated the contribution of total gray matter and hippocampal volumes to cognitive performance in late life [173-175]. Thus, impaired mobility may be an overt biomarker for covert gray and subcortical neural degeneration that predicts subsequent cognitive decline.   In terms of brain function, our own work demonstrated that compared with their non-faller counterparts, older adults with a history of multiple falls (i.e., ≥ 2 non-syncope falls in the previous 12 months) showed aberrant neural network functional connectivity – a measure of synchronous brain activity [91]. Notably, the aberrant network connectivity demonstrated by fallers was independently associated with greater decline in both executive functions and 40  mobility over a 12-month period after accounting for relevant covariates [91]. Hence, a recent history of multiple falls among older adults without a diagnosis of dementia may indicate sub-clinical changes in brain function and increased risk for subsequent decline.  However, more evidence generated from longitudinal studies is needed to establish falls as a biomarker of covert neural degeneration as well as to evaluate the significance of these changes in relation to subsequent decline in cognitive function among otherwise healthy community-dwelling older adults. Moreover, the contribution of regional cerebral white matter volume in mobility has not been explored extensively to date. Yet, significant age-related reduction in cerebral white matter volume co-occurs with gray matter loss [176, 177], particularly in the frontal and parietal regions [178]. Reduced white matter volume is also associated with cognitive impairment and dementia [179].  Thus, we conducted a 12-month prospective study to determine whether there are differences in brain structure between otherwise healthy community-dwelling older adults with and without a recent history of multiple falls, and to evaluate the significance of these differences in relation to subsequent changes in cognitive function – with a focus on executive function. Given our emphasis on executive functions, our specific aims were to examine whether: 1) community-dwelling older adults with a recent history of multiple falls have lower regional frontal and parietal gray matter, frontal and parietal cerebral white matter, and subcortical volumes compared with their non-falling counterparts; and 2) whether baseline volumetric differences are independently associated with change in cognitive functions over 12 months. Importantly, this research may lead to the early identification of those at risk for cognitive decline, and thereby facilitate the timely implementation of effective prevention strategies.   41  2.2 Material and Methods 2.2.1 Study Design and Participants We conducted a 12-month prospective exploratory study with 66 older adults. Participants were recruited from metropolitan Vancouver via newspaper advertisements. Individuals were eligible if they: 1) were aged 70 to 80 years; 2) scored > 24/30 on the Mini-Mental State Examination (MMSE) [180]; 3) were right hand dominant as measured by the Edinburgh Handedness Inventory [181]; 4) were living independently in their own homes; 5) had visual acuity of at least 20/40, with or without corrective lenses; and 6) provided informed consent. We excluded those who: 1) had a neurodegenerative disease, stroke, dementia (of any type), or psychiatric condition; 2) had clinically significant peripheral neuropathy or severe musculoskeletal or joint disease; 3) were taking psychotropic medication; 4) had a history indicative of carotid sinus sensitivity; 5) were living in a nursing home, extended care facility, or assisted-care facility; or 6) were ineligible for MRI scanning.  Based on their falls history in the 12 months prior to study entry, participants were classified as a faller or non-faller (see 2.1.1 and 2.1.2). Ethics approval was obtained from the Vancouver Coastal Research Health Institute and University of British Columbia’s Clinical Research Ethics Board. All participants provided written consent.  2.2.1.1 Specific Inclusion Criterion for Fallers  An individual must have experienced ≥ 2 minimal displacement non-syncopal falls in the previous 12 months, with one of the falls occurring in the last 6 months [182]. This was 42  determined from two sources: 1) participant recall and 2) participant’s immediate family member or friend recall. Falls were defined as “unintentionally coming to rest on the ground, floor, or lower level” [183]. 2.2.1.2 Specific Inclusion Criterion for Non-Fallers An individual must not have experienced > 1 displacement falls (with or without syncope) in the previous 12 months. This was determined based on two sources: 1) participant recall and 2) participant’s immediate family member or friend recall. We highlight that individuals with one fall (non-injurious) in the previous 12 months resemble the physiological profile of non-fallers [184, 185]. Specifically, a prospective study found that single fallers had similar physical and mental status compared with non-fallers, while multiple fallers showed significant musculoskeletal and neurological deficits [184].  2.2.2 Measurement All measures, with the exception of neuroimaging, were assessed at baseline and 12 months. All assessors were trained and standardized protocols were used. Neuroimaging data were acquired at baseline only. 2.2.2.1 Global Cognition and Current Physical Activity Level Global cognition was assessed using the MMSE [180] and the Montreal Cognitive Assessment (MoCA) [53]. The MoCA is a 30-point test that covers multiple cognitive domains. The MoCA has been found to have good internal consistency and test-retest reliability and was able to correctly identify 90% of a large sample of individuals with MCI 43  from two different clinics with a cut-off scores of < 26/30 [53]. Current level of physical activity (i.e., last 7 days) was determined by the Physical Activities Scale for the Elderly (PASE) self-report questionnaire [186].  2.2.2.2 Comorbidity and Depression Comorbidities were assessed with the Functional Comorbidity Index (FCI) [187], an 18-item questionnaire that calculates the total number of comorbidities associated with physical functioning [187]. We used the 15-item Geriatric Depression Scale (GDS) [188, 189] to indicate the presence of depression; a score ≥ 5 indicates depression [190]. 2.2.2.3 Physiological Falls Risk Physiological falls risk was assessed using the short form of the Physiological Profile Assessment (PPA). The PPA is a valid [28, 29] and reliable [39] measure of falls risk. Based on a participant’s performance in five physiological domains – postural sway, reaction time, strength, proprioception, and vision – the PPA computes a falls risk score (standardized score) that has a 75% predictive accuracy for falls among older people [185, 191]. A PPA Z-score ≥ 0.60 indicates high physiological falls risk [192]. 2.2.2.4 Mobility and Balance  Mobility and balance were assessed using the Short Physical Performance Battery (SPPB) [166], gait speed, and the Timed-Up-and-Go Test (TUG) [164]. For the SPPB, participants were assessed on performances of standing balance, walking (four meters), and sit-to-stand. Each component is rated out of four points, for a maximum of 12 points; a score < 9 predicts 44  subsequent disability [193]. Gait speed was calculated by dividing four meters by the time taken to perform the four-meter walking test in SPPB. The four-meter walking test was performed twice for each individual; therefore gait speed is calculated by dividing four meters by the average time taken from the two walks. For the TUG, participants rose from a standard chair, walked a distance of three meters, turned, walked back to the chair and sat down [164]. We recorded the time (s) to complete the TUG, based on the average of two separate trials. 2.2.2.5 Executive Functions We used: 1) the Stroop Test [194] to assess selective attention and conflict resolution; 2) the Trail Making Tests (Part A & B) to assess set shifting[195] ; 3) the Verbal Digits Forward and  Backward Tests to index working memory [196]; and 4) Digit Symbol Substitution Test (DSST) [197] to assess information processing, working memory, and psychomotor speed. For the Stroop Test [194], participants first read out words printed in black ink (e.g., BLUE). Second, they named the display colour of coloured-X’s (i.e., letter “X” printed in different colours). Finally, they were shown a page with colour-words printed in incongruent coloured inks (e.g., the word “BLUE” printed in red ink). Participants were asked to name the ink colour in which the words were printed (while ignoring the word itself). We recorded the time participants took to read the items in each condition and calculated the time difference between the third condition and the second condition (i.e., Stroop colour-words condition minus Stroop coloured-X’s condition). Smaller time differences indicate better selective attention and conflict resolution performance. 45  For the Trail Making Tests (Part A & B) [195], participants were required to draw lines connecting encircled numbers sequentially (Part A) or alternating between numbers and letters (Part B). A standardized score based on performance on Part B and Part A (i.e., (B-A)/A) was calculated, with smaller scores indicating better set shifting performance. For the Verbal Digits Forward and Backward Tests [196], participants repeated progressively longer random number sequences in the same order as presented (forward) and the reversed order (backward). Successful performance on the verbal digits span backward test represents a measure of central executive function due to the additional requirement of manipulation of information within temporary storage [198]. Thus, we subtracted the verbal digits backward test score from the verbal digits forward test score to provide an index of working memory with smaller difference scores indicating better working memory. For the DSST, participants were first presented with a series of numbers (1 to 9) and their corresponding symbols. They were then asked to draw the correct symbol for any digit - placed randomly in pre-defined series - in 90 seconds [197]. A higher number score indicates better information processing, working memory, and psychomotor speed. 2.2.2.6 Structural MRI Acquisition and Analysis High resolution structural MRI was performed at the UBC MRI Research Center located at the UBC Hospital on a 3.0 Tesla Intera Achieva MRI Scanner (Phillips Medical Systems Canada, Markham, Ontario) using an 8-channel SENSE neurovascular coil. The high resolution T1 images were acquired using the following parameters: 170 slices (1 mm thick), TR of 7.7 ms, TE of 3.6 ms, FA of 8 degrees, FoV of 256 mm, acquisition matrix of 256x200. 46  Cortical reconstruction and brain volumetric segmentation were performed using the FreeSurfer image analysis suite [199] developed at the Martinos Center for Biomedical Imaging by Laboratory for Computational Neuroimaging (http://surfer.nmr.mgh.harvard.edu/). Data processing included skull-stripping [200], motion correction [201], Talairach transformation [202, 203], atlas registration [204] and brain parcellation [205, 206]. White matter hypointensity  labels were determined through a probabilistic process in which total white matter hypointensity volume was calculated per hemisphere and subsequently summed to generate a single white matter hypointensity value for each individual [202]. The accuracy of this method was shown to be comparable to previously validated segmentation process [202]. All scans underwent manual checking following the automated segmentation (probabilistic process) by one of the co-authors (BKC). No changes resulted from the manual checks. These steps provide quantification of cerebral white matter, gray matter, and subcortical volume. It also provided white matter hypointensity volume and estimated total intracranial volume (to account for difference in head size) which we included as covariates in our analyses [207, 208]. Specifically, estimated total intracranial volume is determined through Freesurfer via atlas scaling factor – a determinant of linear transformation used to align individual structural image to an atlas [209]. These results generated from the Freesurfer processing stream included Jacobian white matter correction to provide better estimation of regional volumes. Based on evidence to date [74, 171, 173], we focus specifically on cerebral white matter and gray matter volume within the frontal and parietal regions, as well as subcortical gray matter volume (Table 3). 47  2.2.2.7 Statistical Analysis Data were analyzed using the IBM SPSS Statistic 19 (SPSS Inc., Chicago, IL) and the Glmnet package version 2.0-2 within R version 3.2.1. The first step was to identify brain regions (based on white and/or gray matter volume) that distinguished fallers from non-fallers at baseline. To do so, we used an elastic net regularized logistic regression model via Glmnet. Elastic net regression is a mixture of lasso and ridge regression models and introduces penalties to the estimated coefficients to address the problem of having numerous correlated predictor variables [210], as is the case when using many brain regions as predictor variables. The elastic net penalizes the size of the coefficients for correlated predictors, with some coefficients being shrunk exactly to zero. Glmnet requires the user to set two parameters related to the penalty: alpha was set at 0.5 to represent an exact mixture of lasso and ridge regression and lambda was determined using k-fold cross-validation to minimize misclassification error. Fifty-seven brain regions were included as predictors and 3 additional variables (age, total intracranial volume, and log-transformed white matter hypointensity) were included as covariates, based on biological relevance [207, 208]. These covariates were not subjected to the elastic net penalty. The brain regions that were selected in the elastic net regression model (i.e., whose coefficients were not shrunk to zero) were utilized for the subsequent analysis of covariance (ANCOVA) to statistically test for significant differences in regional brain volume between older fallers and non-fallers. Change scores were calculated by subtracting the baseline score from the 12-month score and this was done for each of the four executive function measures. Thus, a negative change score reflects improved performance in selective attention and conflict resolution, set shifting, 48  and working memory. Conversely, a positive change score reflects improved psychomotor speed.  Finally, to investigate whether fall-related structural alterations were independently associated with subsequent changes in executive functions, we constructed multiple linear regression models using regional brain volume as independent variables, and changes in executive functions as dependent variables. To maximize statistical power, these analyses included all 66 participants and included falls status, age and total white matter hypointensity volume as covariates in the first step. Under the premise that fallers and non-fallers may have different trajectories in cognitive aging across time, and that the start of these different trajectories might precede the baseline assessment, baseline executive functioning was not included as a covariate. The brain regions of interest were entered in the second step to determine their unique contribution to change in executive functions. The overall alpha level was set at p ≤ 0.05; for trend-level effects, the alpha level was set at p ≤ 0.10. To check the assumptions of the linear regression models, we examined histograms of the residual values and scatterplots of the predictor variables versus the residuals. These showed that the residual values were normally distributed, free of outliers, and homoscedastic.  49  2.3 Results 2.3.1 Participants  Table 2.1 provides descriptive characteristics of the study sample categorized by their falls status.  All participants were right-hand dominant as indicated by the Edinburgh Handedness Inventory [181]. At baseline, compared with non-fallers, fallers showed statistically significantly slower gait speed (p=0.02; Table 2.1) and evidence of a trend for higher GDS score (p=0.08; Table 2.1) as well as higher white matter hypointensity volume (p=0.06; Table 2.1). The trend level significance for GDS persisted across the 12-month period with fallers showing a reduced score over time (p=0.07; Table 2.2); however, there were no statistically significant between-group differences in change for all other measures over the 12-month study period (Table 2.2).  2.3.2. Structural MRI The elastic net logistic regression analysis reduced the total number of regional brain volumes from 56 to 14 cortical and subcortical gray matter regions (Table 2.3) and 5 cerebral white matter regions (Table 2.4). Together with the covariates, these regions significantly—though modestly—distinguished fallers from non-fallers, based on an area under the receiver operating curve of 0.70, 95% CI [0.59, 0.80]. Analysis of covariance results showed that compared with non-fallers, fallers had lower volume in gray matter and subcortical structures (Table 2.3), as well as lower volume in regional cerebral white matter at baseline (Table 2.4). Specifically, after adjusting for baseline age, total white matter hypointensity volume, and estimated total intracranial volume, fallers had significantly lower gray matter volume in left 50  lateral orbitofrontal cortex (p=0.03) and right insula (p=0.01). For subcortical structures, fallers had significantly lower total volume in total pallidum (p=0.01) and total hippocampus (p=0.02). Left insula gray matter volume (p=0.05) and right rostral anterior cingulate cortex volume (p=0.06) showed trend level differences between the two groups, with fallers exhibiting lower left insula and higher right rostral anterior cingulate cortex volumes. For cerebral white matter volume, fallers had significantly lower volume in left lateral orbitofrontal cortex (p=0.01), left pars triangularis (p=0.03), and right pars triangularis (p=0.02).  2.3.3. Linear Regression Models We constructed two multiple linear regression models across all 66 participants: one to test the unique contribution of left lateral orbitofrontal cortex white matter volume to changes in set shifting performance (i.e., Trail-Making Test performance; (B-A)/A) and a second to test the unique predictive association between hippocampal volume and changes in information processing, working memory, and psychomotor speed (i.e., DSST performance). Please see Appendix B for more detail on rationale for the construction of the two regression models.  Change in Set Shifting Performance In the final model, left lateral orbitofrontal cortex white matter volume was significantly associated with change in set shifting performance. Falls status, age and white matter hypointensity volume together accounted for 2.00% of the variance. Adding left lateral orbitofronal white matter volume to the model resulted in a R2 change of 9.00% (F Change 5.84, p=0.02) and a total R2 of 11.00% (Table 2.5; Figure 2.1). 51  Change in Information Processing, Working Memory, and Psychomotor Speed Performance In the final model, there was a non-significant association between total hippocampal volume and change in information processing and psychomotor speed performance (p=0.13). Falls status, age and white matter hypointensity volume together accounted for 8.00% of the variance. Adding hippocampal volume to the model resulted in a R2 change of 4.00% (F Change 2.41, p=0.13) and a total R2 of 12.00% (Table 2.6).  52 Table 2.1 Study Sample Characteristics at Baseline  Baseline  Non-Fallers (N=36) SD Fallers (N=30) SD p-value Age (yr) 74.25 2.86 73.83 3.06 0.57 Height (cm) 165.74 8.44 163.12 6.66 0.18 Weight (kg) 72.33 17.03 72.06 12.53 0.94 Sex (M/F) 25/11 - 24/6 - 0.22 BMI (kg/m2) 26.10 4.87 26.83 4.59 0.41 MMSE (30 pts max) 28.31 1.51 28.48 1.53 0.64 MOCA (30 pts max) 24.72 3.24 24.59 3.33 0.87 MCI, n (%)  20 (56%) - 16 (53%) - 0.86 GDS  0.25 0.65 0.80 1.69 0.08 FCI 2.67 2.16 3.28 1.71 0.22 PPA 0.22 0.83 0.50 0.87 0.19 SPPB (12 pts max) 10.64 1.57 10.10 1.74 0.20 Gait Speed (m/s) 1.30 0.23 1.17 0.22 0.02 TUG (s) 7.46 1.63 8.44 3.64 0.15 Stroop CW (s) 108.22 28.58 111.78 35.23 0.65 Stroop C (s) 56.66 12.71 56.28 13.31 0.91 Stroop Cw - Stroop C (s) 51.56 23.52 55.50 26.84 0.53 Trail B (s) 107.29 43.93 128.28 41.52 0.43 Trail A (s) 59.69 18.69 56.86 13.60 0.56 Trail B - A (s) 47.60 40.67 71.42 147.30 0.35 (Trail B – A)/A 0.84 0.67 1.08 1.62 0.43 Digit Forward 8.36 2.39 7.63 2.74 0.21 Digit Backward 4.36 2.07 4.33 2.37 0.92 53   Baseline  Non-Fallers (N=36) SD Fallers (N=30) SD p-value Digit Forward -Backward 4.00 2.29 3.14 2.10 0.12 DSST 29.36 5.51 28.66 6.43 0.64 White matter hypointensity (mm^3) 3713.72 1848.94 6643.76 7315.72 0.06 Statistical comparisons were conducted by performing ANCOVA on SPSS. Note: BMI==Body Mass Index; MCI==Mild Cognitive Impairment (i.e., MOCA < 26); MMSE==Mini-Mental Status Examination; MoCA==Montreal Cognitive Assessment; GDS==Geriatric Depression Scale; FCI==Functional Comorbidity Index; ABC==Activities-specific Balance Confidence scale; PPA==Physiological Profile Assessment; SPPB==Short Physical Performance Battery; TUG==Time-Up-and-Go test; Stroop CW: Stroop Colour-Words condition; Stroop C: Stroop Coloured-X’s condition; DSST==Digit Symbol Substitution Test. For Stroop CW - Stroop C, Trail B - A, and Digit Forward - Backward, smaller difference reflect better performance.    54 Table 2.2 Study Sample Characteristics – Change in Physical and Cognitive Performance. ∆ Over 12-Months╫ Non-Fallers (N=35) SD Fallers (N=29) SD p-value* MMSE (30 pts max) -0.31 1.53 -0.14 1.73 0.75 MOCA (30 pts max) -0.49 3.14 -0.38 2.14 0.98 GDS 0.53 2.19 -0.28 1.00 0.07 FCI 0.32 1.53 0.52 1.55 0.62 PPA -0.03 0.94 -0.06 0.93 0.83 SPPB (12 pts max) 0.40 1.12 0.34 1.61 0.77 Gait Speed (m/s) -0.07 0.23 -0.002 0.18 0.38 TUG (s) 0.57 4.20 -0.45 3.17 0.29 Stroop CW (s) -5.79 18.38 -2.01 28.19 0.58 Stroop C (s) -1.65 8.02 0.02 6.56 0.43 Stroop CW - Stroop C (s) -4.23 16.22 -2.02 25.28 0.76 Trail B (s) -5.83 27.77 -0.93 36.68 0.69 Trail A (s) -1.97 17.70 -2.06 14.31 0.47 Trail B - A (s) -3.72 28.26 1.13 40.16 0.48 (Trail B – A)/A -0.03 0.55 0.13 0.78 0.33 Digit Forward -0.34 2.15 0.24 2.36 0.12 Digit Backward -0.17 1.90 0.14 1.51 0.33 Digit Forward - Backward -0.17 3.17 0.10 2.97 0.55 DSST 1.60 4.83 -0.24 3.53 0.18 Statistical comparisons were conducted by performing ANCOVA on SPSS. Note: BMI==Body Mass Index; MMSE==Mini-Mental Status Examination; MoCA==Montreal Cognitive Assessment; GDS==Geriatric Depression Scale; FCI==Functional Comorbidity Index; ABC==Activities-specific Balance Confidence scale; PPA==Physiological Profile Assessment; SPPB==Short Physical Performance Battery; TUG==Time-Up-and-Go test; Stroop CW: Stroop Colour-Words condition; Stroop C: Stroop Coloured-X’s condition; DSST==Digit Symbol Substitution Test.  Two participants (one non-faller and one faller) did not participate in the 12-month assessments. For Stroop CW - Stroop C, Trail B - A, and Digit Forward - Backward, negative scores reflect better performance over 12-months. *All comparisons were adjusted for baseline age, and total white matter hypointensity volume. ╫ ∆ over 12-Months is calculated as 12-month performance minus baseline performance.  55 Table 2.3 Regional Gray Matter Volumetric Differences between Fallers and Non-Fallers Regions  Non-Fallers (N=36) Fallers (N=30)    Structural Volume (mm^3) SD Structural Volume (mm^3) SD p-value* Frontal Areas Laterality      Rostral anterior cingulate RH 1882.83 302.69 2010.67 432.02 0.06 Caudal anterior cingulate LH 1689.11 386.91 1763.20 524.04 0.22 Rostral middle frontal LH 13073.47 1604.18 12827.17 1466.79 0.82 Caudal middle frontal RH 5148.64 1036.34 5164.20 1075.81 0.47 Medial orbitofrontal LH 4666.78 532.87 4430.53 583.39 0.22 Lateral orbitofrontal LH 6608.39 904.32 6223.83 597.05 0.03 Pars opercularis RH 3356.50 511.58 3368.80 491.18 0.53 Pars triangularis LH 3070.56 405.37 2865.33 451.02 0.12 Pars triangularis RH 3543.22 472.68 3335.60 400.60 0.14 Other Areas       Insula LH 6215.03 628.92 5905.23 541.48 0.05 Insula RH 6363.06 733.20 5881.10 641.23 0.01 Subcortical Areas       Total Amygdala  3010.43 419.57 2849.39 348.07 0.14 Amygdala LH 1476.74 212.52 1391.48 220.38 0.11 Amygdala RH 1533.69 248.16 1457.91 162.17 0.30 Total Pallidum  2801.71 351.94 2523.99 352.38 0.01 Pallidum LH 1435.56 204.89 1264.29 204.10 0.01 Pallidum RH 1366.15 194.06 1259.70 211.19 0.04 Total Hippocampus  7666.96 873.03 7191.10 712.6 0.02 Hippocampus LH 3775.46 446.57 3551.86 363.28 0.04 Hippocampus RH 3891.50 460.03 3639.23 402.26 0.03 Statistical comparisons were conducted by performing ANCOVA on SPSS.                56  Note: LH==Left-hemisphere; RH=Right-hemisphere. *All comparisons were adjusted for baseline age, total white matter hypointensity volume and estimated total intracranial volume.   Table 2.4 Regional White Matter Volumetric Differences between Fallers and Non-Fallers                 Regions  Non-Fallers (N=36) Fallers (N=30)    Structural Volume (mm^3) SD Structural Volume (mm^3) SD p-value* Frontal Areas Laterality      Caudal anterior cingulate RH 2868.55 512.26 2858.64 577.98 0.59 Lateral orbitofrontal LH 6551.07 863.94 6042.02 704.27 0.01 Pars triangularis LH 2928.88 474.45 2625.87 358.52 0.03 Pars triangularis RH 3139.51 454.87 2817.54 412.86 0.02 Parietal Areas       Inferior parietal LH 10238.39 1688.24 9412.48 1482.45 0.07                 Statistical comparisons were conducted by performing ANCOVA on SPSS. Note: LH==Left-hemisphere; RH=Right-hemisphere. *All comparisons were adjusted for baseline age, total white matter hypointensity volume, and estimated total intracranial volume.                   57  Table 2.5 Linear Regression Model for Change in Set Shifting Performance     ∆ Set Shifting╫    Independent Variable R2 R2 Change Unstandardized B (SE) Standardized B p-value Model 1 0.02     Falls status   0.18 (0.17) 0.14 0.31 Age (yr)   0.01 (0.03) 0.04 0.79 White matter hypointensity (mm^3)   -0.08 (0.31) -0.04 0.78 Model 2 0.11 0.09   0.02 Falls status   0.01 (0.18) 0.01 0.95 Age (yr)   -0.02 (0.03) -0.08 0.58 White matter hypointensity (mm^3)   0.20 (0.32) 0.09 0.54 LH-lateral orbitofrontal white matter volume(mm^3)   -0.01 (0.01) -0.35 0.02 Note: LH==Left-hemisphere. ╫Calculated as 12-month (TMTB-TMTA)/TMTA – baseline (TMTB-TMTA)/TMTA                      58  Table 2.6 Linear Regression Model Summary for Change in Information Processing, Working Memory, and Psychomotor Speed Performance    ∆ DSST╫   Independent Variable R2 R2 Change Unstandardized B (SE) Standardized B p-value Model 1 0.08     Falls status   -1.53 (1.10) -0.18 0.17 Age (yr)   0.02 (0.19) 0.02 0.91 White matter hypointensity (mm^3)   -2.89 (1.94) -0.19 0.14 Model 2 0.12 0.04   0.13 Falls status   -1.06 (1.13) -0.12 0.35 Age (yr)   0.11 (0.19) 0.07 0.58 White matter hypointensity (mm^3)   -2.68 (1.93) -0.18 0.17 Hippocampus (mm^3)   <0.01 (<0.01) 0.21 0.13 ╫Calculated as 12-month DSST – baseline DSST        59  Figure 2.1 Change in Cognitive Performance vs. Left Lateral Orbitofrontal Cortex   Note: Values on the graph reflect residuals after adjusting for falls status, age, and white matter hypointensity.    60 2.4 Discussion  Compared with non-fallers, we found that community-dwelling older fallers without dementia demonstrated significantly lower gray matter volume, subcortical volume, and cerebral white matter volume. Specifically, fallers demonstrated lower gray matter in left lateral orbitofrontal cortex, right insula, pallidum, and hippocampus. Fallers also showed lower cerebral white matter volume in left lateral orbitofrontal cortex and bilateral pars triangularis. Moreover, there was a trend for fallers to demonstrate lower gray matter volume in the left insula and right rostral anterior cingulate cortex. These areas significantly contribute to executive functions (i.e., left lateral orbitofrontal cortex, bilateral pars triangularis, and left superior frontal gyrus) [211-214], motor control (i.e., pallidum and hippocampus) [215-217], and memory (i.e., left pars triangularis and hippocampus) [218, 219]. Notably, lower left lateral orbitofrontal cortex white matter volume at baseline was independently associated with greater decline in set shifting performance over the 12-month period. This significant association was observed even after adjusting for baseline age and total white matter hypointensity volume. Thus, a recent history of multiple falls among older adults without a diagnosis of dementia may indicate the presence of subclinical alterations in regional brain volume that are associated with subsequent decline in executive functions.   Our cross-sectional neuroimaging findings concur with previous findings of associations between impaired mobility and lower gray matter and subcortical volume among community-dwelling older adults without a diagnosis of dementia. Among 326 community-dwelling older adults, Rosano and colleagues [30] demonstrated that lower volume in the basal ganglia and superior parietal cortex were significantly associated with impaired balance 61  control. Recently, Rosso and colleagues [170] extended these previous findings by demonstrating that lower integrity (or high mean diffusivity as measured via diffusion tensor imaging) of normal-appearing gray matter, especially of the hippocampus and anterior cingulate gyrus, was associated with higher step length variability (i.e., poor gait control). A number of cross-sectional studies have found that lower hippocampal volume is associated with poor gait control [170, 216, 220, 221]. Specifically, Beauchet and colleagues [216] recently showed that lower hippocampal volume was associated with higher gait variability among older adults with MCI. However, among other cognitively healthy older adults, higher hippocampal volume was associated with higher gait variability. Thus, more research is needed to better understand the role of the hippocampus in mobility. With some exceptions, the majority of the evidence to date suggests that gray matter is more strongly associated with impaired mobility among community-dwelling older adults without a diagnosis of dementia, compared to white matter, [30, 170, 216, 220, 221]. Furthermore, we extend previous studies by demonstrating differences in regional cerebral white matter volume between fallers and non-fallers. To our knowledge, this has not been previously demonstrated. Nevertheless, it does concur with observations that show covert white matter lesions are associated with impaired mobility [222, 223].  Our finding of regional volumetric differences between fallers and non-fallers may have potential implication in the association between impaired executive functions and mobility [224]. Specifically, compared with non-fallers, fallers demonstrated lower volume in the left lateral orbitofrontal cortex, right insula, bilateral pars triangularis, and left superior frontal gyrus. These regions significantly contribute to executive functions. Executive functions are higher order cognitive processes that control, integrate, organize, and maintain other 62  cognitive abilities [225]. Current evidence suggest that reduced executive functions are associated with falls [34, 226-229] and with increased risk of a major fall-related injury [230]. Moreover, Anstey and colleagues [224] demonstrated that compared with non-fallers, fallers performed significantly worse in processing speed and set-shifting tasks. Intriguingly, however, we did not observe statistical differences in overt cognitive performance—at baseline or over time—between fallers and non-fallers in our sample, although fallers consistently had lower scores numerically. We postulate that this observation may be attributed to: 1) a 12-month follow-up may not provide sufficient time for fallers to show significant decline in cognitive function; 2) our study population consisted of older adults with significant falls history but were without a formal diagnosis of cognitive impairment or dementia, which could constrict the variance in executive function performance with the sample. Also, this observation supports those findings of Buracchio and colleagues [81], who demonstrated that impaired mobility precedes clinical evidence of cognitive decline.  The right insula is implicated in mediating the switching between the executive and default-mode networks [98], the latter of which is associated with mind wandering epochs [231, 232]. We previously demonstrated that falls are associated with an increased propensity to mind wander, or having one's thoughts and attention transiently drift away from the on-going task at hand [233]. Thus, our current finding of lower grey matter volume in the right insula in fallers, relative to non-fallers, suggests that fallers may be susceptible to falls because they have reduced ability to re-engage executive functions once in a mind wandering state, such that they are slower in applying executive resources in response to dynamic, external task demands. While this remains speculative at this point, this possibility is an intriguing line of future inquiry. 63  In addition, we found that fallers had significantly lower hippocampal volume compared with non-fallers. There is also growing recognition of an association between impaired memory and mobility [234-236]. Specifically, higher stride time variability was significantly associated with lower episodic memory performance among community-dwelling older adults [236]. We note that while the hippocampus is a key brain structure for memory, there is evidence to suggest that the left pars triangularis is involved in the cognitive control of memory [218, 237, 238]. Moreover, of relevance to our study, the right pars triangularis is involved in the executive process of motor inhibition control [239]. Emerging evidence in the literature also suggests that the hippocampus may play a larger role in falls-relevant cognitive processing than being solely involved in memory function [240], including spatial-processing [241], navigation [242], and attention [243]. Notably, our study also provides novel data that demonstrate the prospective impact of regional cerebral white matter volumetric differences associated with impaired mobility, indexed as a recent history of multiple falls in this study, on cognitive performance among community-dwelling older adults without a diagnosis of dementia. Specifically, lower left lateral orbitofrontal cortex white matter volume at baseline was significantly associated with greater decline in set shifting performance. Previous studies have shown that the lateral orbitofrontal region is involved in set shifting [214, 244, 245] and inhibitory control [211-214].   In line with this, impaired set shifting has been identified as a significant cognitive risk factor for falls [230, 246]. Specifically, in a prospective study with 325 community-dwelling older adults, Nevitt and colleagues [230] demonstrated that slower Trail Making B test time was 64  independently associated with greater risk for injurious falls. Among older adults between the age of 62 to 95 years, Lord and colleagues [246] also found reduced set-shifting ability was associated with reduced performance in choice-stepping task. Moreover, those with a history of falls displayed poorer performance in choice-stepping reaction time compared with non-fallers. Our current prospective findings extends the current understanding of the neural correlates that underpin the observed relationship between reduced inhibitory control, reduced set shifting, and impaired mobility. Specifically, a recent history of falls was associated with less left lateral orbitofrontal cortex white matter volume, which was independently associated with subsequent decline in set shifting performance over a 12-month period. Together with previous research, our current study highlights the concomitant, bi-directional relationship between falls risk and cognitive decline in community-dwelling older adults. We recognize the limitations of our study. The validity of our findings depends on accurate identification of recurrent fallers and non-fallers and previous research has demonstrated that falls recall in older adults is subject to retrospective recall bias [247]. However, we corroborated falls history with immediate family members or close friends. Our study sample consisted exclusively of independent community-dwelling older adults specifically between the age-range of 70-80 who were without significant physical or cognitive impairment. Thus, our results may not generalize beyond this population of older adults and we may have underestimated the association between falls and cortical and subcortical volume. With respect to study inclusion/exclusion, we have only excluded older adults taking psychotropic medication and thus our study sample may include older adults taking medication of other pharmacological classes or under the influence of polypharmacy. In addition, atlas 65  registration procedure performed during the MRI analysis may have reduced inter-subject variability and impacted the accuracy of gray matter volume estimation. Furthermore, we did not perform a comprehensive assessment of memory function within the scope of the study; thus, we cannot eliminate the possibility that our study sample may be consisted of a mix of older adults with and without amnestic MCI. Also, our study may have been underpowered, as indicated by the several trend-level associations we found. Also due to the small sample size and concern over power, we did not adjust the significance level to account for multiple testing, which might lead to false positive findings. Although we observed a prospective relationship between baseline brain volume and changes in cognition, future longitudinal studies—in which both brain volume and cognition are assessed repeatedly over time—would provide more direct evidence that changes in the brain regions identified herein among fallers precede changes in executive function. Lastly, fMRI studies could shed light on the functional importance of the brain differences observed between fallers and non-fallers.  In summary, community-dwelling older adults with a recent history of multiple falls demonstrated lower gray matter volume, subcortical volume, and cerebral white matter volume, compared with non-fallers. Importantly, lower left lateral orbitofrontal cortex white matter volume at baseline was independently associated with greater decline in executive functions over the 12-month period. A better understanding of the neural correlates that underlie the association between impaired mobility and cognitive decline improves our capacity to refine and develop intervention to maintain mobility among older adults. Moreover, in consideration of our findings as well as those of previous studies, health care 66  professionals working with older adults should consider falls history in their assessment to potentially identify those who may be at greater risk for subsequent cognitive decline.   67  Chapter 3: Functional Neural Correlates of Slower Gait Among Older Adults with Mild Cognitive Impairment A version of this chapter is submitted as HSU CL, Best JR, Voss MW, Handy TC, Beauchet O, Lim C, Liu-Ambrose T. Functional neural correlates of slow gait among older adults with MCI. Currently under “Major Revision” in Journal of Gerontology: Medical Sciences. 3.1 Introduction Mild cognitive impairment (MCI) is a clinical entity characterized by cognitive decline greater than that expected for an individual’s age and education level but that does not interfere notably with everyday function [248]. While MCI is currently characterised by cognitive decline and individuals with MCI are at increased risk for dementia [249], there is growing recognition that subtle, but observable, changes in mobility (i.e., slowing of gait) often exist among this population [55]. Notably, these changes are not inconsequential. For example, Doi and colleagues [54] showed that the combination of MCI and slow gait has a higher risk of disability than each condition alone. Also, high dual-task gait cost (i.e., [single-task gait velocity - dual-task gait velocity]/ single-task gait velocity) was associated with incident dementia in MCI [250].  There is a strong interest to understand better the underlying neural correlates of gait slowing in the prodromal stages of the dementia process, such as in MCI [251, 252]. Onen and colleagues [251] showed that periventricular leuoaraiosis was associated with slow gait among older adults with MCI. Moreover, Beauchet and colleagues [252] demonstrated that slower gait speed was associated with larger brain ventricle volume among older adults with 68  MCI. However, whether slowing of gait speed among older adults with MCI is also associated with aberrant functional connectivity of the relevant neural networks remains largely unexplored. A better understanding of the functional neural mechanisms underlying slowing of gait speed among older adults with MCI would complement previous structural findings and provide new insights to future strategies to maintain mobility and functional independence among those with MCI.  Therefore, we aimed to characterize patterns of functional connectivity associated with slower gait speed in older adults with MCI. We specifically focused on two functional networks, the sensorimotor network (SMN) and the frontoparietal network (FPN). The SMN is actively involved in major aspects of movement, including motor-planning, initiation, execution, and coordination [92]. The FPN is involved in top-down attentional control [95] and allocation of available neural resources to important cognitive processes [98], as well as motor planning and motor execution [101]. Of relevance, our previous 12-month prospective study showed that lower inter-network connectivity between the SMN and the FPN was associated with subsequent decline in both executive functions and lower extremity physical performance in community-dwelling older adults [91].  Our objectives are to determine whether among older adults with MCI: 1) slower gait is characterized by lower connectivity between the SMN and FPN; and 2) poorer performance of general mobility and balance, as well as executive functions, are associated with lower connectivity between the SMN and FPN. 69  3.2 Methods and Measures 3.2.1 Study Design and Participants Forty-nine community-dwelling older adults with MCI were included in this cross-sectional study. Mild cognitive impairment was defined as the following: 1) a Montreal Cognitive Assessment (MoCA) score > 22/30 and < 26/30; 2) have subjective memory complaints (SMC); 3) no significant functional impairment; and 4) no dementia.  Participants were recruited from metropolitan Vancouver and interested individuals were telephone screened to confirm general eligibility according to the inclusion and exclusion criteria. We included those who: 1) were aged > 60 years; 2) scored < 26/30 on the MoCA [53]; 3) had SMC, defined as the self-reported feeling of memory worsening with an onset within the last 5 years, as determined by interview [253]. The MoCA is a 30-point test that covers multiple cognitive domains [53]. The MoCA has been found to have good internal consistency and test-retest reliability and was able to correctly identify 90% of a large sample of MCI individuals from two different clinics [53]; 4) preserved general cognition as indicated by a Mini-Mental State Examination (MMSE) [254] score > 24/30; 5) score > 6/8 on the Lawton and Brody [255] Instrumental Activities of Daily Living Scale; 6) were right hand dominant as measured by the Edinburgh Handedness Inventory [181]; 7) were living independently in their own homes; 8) had visual acuity of at least 20/40, with or without corrective lenses; and 9) provided informed consent. We excluded those who: 1) had a formal diagnosis of neurodegenerative disease, stroke, dementia (of any type), or psychiatric condition; 2) had clinically significant peripheral neuropathy or severe musculoskeletal or joint disease; 3) were taking psychotropic medication; 4) had a history indicative of carotid 70  sinus sensitivity; 5) were living in a nursing home, extended care facility, or assisted-care facility; or 6) were ineligible for MRI scanning. All participants provided written consent and ethics approval was acquired from the Vancouver Coastal Research Health Institute and University of British Columbia’s Clinical Research Ethics Board. 3.2.2 Descriptive Variables Age was quantified in years and education level was assessed by self-report. Standing height was measured as stretch stature to the 0.1 cm per standard protocol. Weight was measured twice to the 0.1 kg on a calibrated digital scale.  3.2.3 Usual Gait Speed  Participants walked at their usual pace along a 4-meter path. To avoid acceleration and deceleration effects, participants started walking 1-meter before reaching the 4-meter path and completed their walk one meter beyond it. Usual gait speed (m/s) was calculated from the mean of two trials. The test-retest reliability of usual gait speed in our laboratory is 0.95 (ICC) [256]. Sex-specific group median gait speed was calculated. Participants with usual gait speed > the group median were classified as having normal usual gait speed (NG). Participants with usual gait speed < the group median were classified as having slower usual gait speed (SG). 3.2.4 General Mobility and Balance  Participants’ general mobility and balance was assessed using the Short Physical Performance Battery (SPPB) [166]. The SPPB measures performances on standing balance, 71  walking (four meters), and sit-to-stand. Each component is rated out of four points, for a maximum of 12 points; a score < 9/12 predicts subsequent disability.  3.2.5 Executive Functions Executive functions (i.e., higher order cognitive processes) and underlying neural substrates are particularly important for the maintenance of gait speed [257]. We used: 1) the Stroop Test [194] to assess selective attention and conflict resolution; 2) the Trail Making Tests (Part A & B) to assess set shifting [195] ; and 3) Digit Symbol Substitution Test (DSST) [197] to assess information processing, working memory, and psychomotor speed. For the Stroop Test [194], participants first read out words printed in black ink (e.g., BLUE). Second, they named the display colour of coloured-X’s (i.e., letter “X” printed in different colours). Finally, they were shown a page with colour-words printed in incongruent coloured inks (e.g., the word “BLUE” printed in red ink). Participants were asked to name the ink colour in which the words were printed (while ignoring the word itself). Time to complete each condition was recorded. The Stroop interference score was calculated by taking the time difference between the third condition (Stroop 3) and the second condition (Stroop 2). Less interference (i.e., smaller time difference between Stroop 3 and Stroop 2) indicates better selective attention and response inhibition. For the Trail Making Tests (Part A & B) [195], participants were required to draw lines connecting encircled numbers sequentially (Part A) or alternating between numbers and letters (Part B). A standardized score based on performance on Part B and Part A (i.e., (B-A)/A) was calculated, with smaller scores indicating better set shifting performance. 72  For the DSST, participants were first presented with a series of numbers (1 to 9) and their corresponding symbols. They were then asked to draw the correct symbol for any digit - placed randomly in pre-defined series - in 90 seconds [197]. A higher number score indicates better information processing, working memory, and psychomotor speed. 3.2.6 Functional MRI Acquisition All MRI was conducted at the University of British Columbia (UBC) MRI Research Center located at the UBC Hospital on a 3.0 Tesla Intera Achieva MRI Scanner (Phillips Medical Systems Canada, Markham, Ontario) using an 8-channel SENSE neurovascular coil. The session consisted of a resting-state scan with 360 dynamic images of 36 slices (3 mm thick) with the following parameters: repetition time (TR) of 2000 milliseconds (ms), echo time (TE) of 30 ms, flip angle (FA) of 90 degrees, field of view (FoV) of 240 mm, acquisition matrix 80x80. High resolution anatomical MRI T1 images were acquired using the following parameters: 170 slices (1 mm thick), TR of 7.7 ms, TE of 3.6 ms, FA of 8 degrees, FoV of 256 mm, acquisition matrix of 256x200. During the resting-state scan, participants were instructed to rest with eyes open and refrain from thinking about anything in particular while remaining stationary for a total duration of 731.9 seconds (12 minutes, 11.9 seconds).  3.2.7 Functional MRI Data Analysis 3.2.7.1 Preprocessing 73  Image preprocessing was carried out using tools from FSL (FMRIB’s Software Library), MATLAB (Matrix Laboratory), and toolboxes from SPM (Statistical Parametric Mapping). Excess unwanted structures (i.e., bones, skull, etc.) in high resolution T1 images were removed via optimized Brain Extraction Tool (optiBET) [258]; rigid body motion correction was completed using MCFLIRT (absolute and relative mean displacement were subsequently extracted and included in the statistical analysis as covariates); spatial smoothing was carried out using Gaussian kernel of Full-Width-Half-Maximum (FWHM) 6.0 mm; temporal filtering was applied with high pass frequency cut-off of 120 seconds. Additional image artifacts were identified through Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) and further removed (an average of approximately 20 components were removed per subject). Specifically, the temporal and spatial patterns as well as power spectrum of each component were visually inspected and subsequently designated as either component of interest or noise. In instances where the peak power resides in frequency ranges exceeding the proposed resting-state band (i.e., 0.008-0.08 Hz), the component is considered as noise. Data points corrupted with large amount of motion were determined via FSL Motion Outliers and the effects of these time points on subsequent analyses were removed using a confound matrix. Effects of motion were removed from data through MELODIC denoising instead of performing linear regression of the motion parameters as the influence of motion may vary depending on the spatial location of the voxels, hence unbiased regression of motion parameters is not ideal. Prior to data analysis, an additional low pass temporal filtering was also applied to ensure the fMRI signal fluctuated between 0.008<f<0.080 Hz, the optimal bandwidth to examine functional connectivity. Furthermore, the application of a low pass filter eliminated potentially 74  confounding high frequency signals. Functional data were registered to personal high resolution T1 anatomical images, which were subsequently registered to standardized 152 T1 Montreal Neurological Institute (MNI) space. Noise generated from both physiological and non-physiological sources was removed through regression of the cerebral-spinal fluid (CSF) signal, white matter signal, and global brain signal. Global signal regression has been reported as both valid and useful step in functional connectivity analyses [259] that may potentially improve specificity [260]. The first four volumes of data were discarded to account for delay of the hemodynamic response. 3.2.7.2 Functional Connectivity Analysis Selection of regions of interest (ROI) was guided by previous work examining connectivity in examining connectivity of networks relevant to mobility or gait speed [91, 137]. The respective MNI space coordinates for each region of interest (ROI) are presented in Table 3.1. To better understand the neural substrates of mobility and gait speed, we first examined overall inter-network connectivity of the SMN and FPN, then further examined connections between regions of interest in these networks (i.e., specific ROI-ROI).    75  Table 3.1 Regions of Interest and Relative MNI Coordinates Network ROI X Y Z SMN LPCG -39 -21 55  RPCG 34 -25 53  LCB -24 -66 -19  RCB 25 -71 -23  LPM -16 0 57  RPM 20 -17 61  SMA -5 -1 52 FPN RIPS 25 -62 53  RVV 36 -62 0  LVV -44 -60 -6  RSMG 32 -38 38  RSLOC 26 -64 54  LSLOC -26 -60 52  RFEF 28 -4 58  LFEF -26 -8 54 Note: LPCG=left precentral gyrus; RPCG=right precentral gyrus; LCB=left cerebellum; RCB=right cerebellum; LPM=left premotor; RPM=right premotor; SMA=supplementary motor area RIPS=right inferior parietal sulcus; RVV=right ventral visual; LVV=left ventral visual; RSMG=right supramarginal gyrus; RSLOC=right lateral occipital cortex; LSLOC=left lateral occipital cortex; RFEF==right frontal eye field; LFEF=left frontal eye field.    76  Specifically, the overall inter-network connectivity between the SMN and FPN was first calculated by taking the average of all the pairwise ROI-ROI correlation for each network. Statistical test for difference between groups in the overall inter-network connectivity was conducted prior to examination of the distinct pairwise ROI-ROI connections driving the observed group differences. For each ROI, preprocessed time-series data were extracted with 14mm spherical regions of interest drawn around their respective MNI coordinates in standard space. Regions of interest time-series data were subsequently cross-correlated to establish functional connectivity maps of their associated neural networks, in which pairwise correlation between time-series extracted from ROI listed above was calculated. Group-level between-subject results were generated via ordinary least-squares regression using FSL’s flameo [261]. The statistical map thresholding was set at Z=2.33, with cluster correction of p<0.05. 3.2.8 Statistical Analyses Statistical analysis was conducted using the IBM SPSS Statistic 19 for Windows (SPSS Inc., Chicago, IL). Comparisons of group characteristics were undertaken using a Chi Square test for differences in proportions and Analysis of Covariance (ANCOVA), adjusting for age and MoCA, for differences in means. Alpha was set at p≤0.05 for all analyses.  To achieve our first objective, ANCOVA was performed to statistically test for significant between-group differences in functional inter-network connectivity while adjusting for age and MoCA. Partial correlation analyses (adjusted for age and MoCA) were then performed to 77  determine whether any significant differences in functional inter-network connectivity between older adults with NG and those with SG correlated with usual gait speed.  To achieve our second objective, we conducted additional partial correlation analyses to determine if differences in functional inter-network connectivity were correlated with SPPB and executive functions. These correlational analyses included the entire sample (N=49).  Please note additional statistical analyses were performed in lieu to reviewer suggestions (see Appendix A for detail). 3.3 Results 3.3.1 Participants Table 3.2a provides descriptive characteristics of the study sample categorized by their gait speed status. Briefly, a total of 49 individuals were included in this cross-sectional study; 25 were classified as having NG (51%) and 24 having SG (49%). Compared with the NG group, participants in the SG group were older (p=0.01) and had lower MoCA scores (p=0.01).  3.3.2 Usual Gait Speed, Mobility, and Executive Functions Table 3.2b reports the mean and median gait speed of the entire cohort differentiated by group and sex. Table 3.3 shows that after adjusting for age and MoCA, those in the SG group had slower usual gait speed (p<0.001), poorer SPPB performance (p=0.01), and poorer Stroop Test interference performance (p=0.05).  78  As compared with results from a meta-analysis that congregated findings acquired from 41 studies and reported gait speed of a total of 23,111 adults spanning across different age [262], the mean gait speed of our male participants was similar to average males aged 70-79 years (1.22 m/s vs 1.26 m/s); also, our female participants were similar to average females aged 70-79 years (1.14 m/s vs. 1.13 m/s respectively).  3.3.3 Functional Connectivity Importantly, the amount of relative motion during the fMRI session did not differ between the two groups (0.12 mm and 0.10 mm respectively for SG and NG groups, p=0.15). Compared with the NG group, participants in the SG group demonstrated less overall inter-network connectivity between the SMN and FPN (p<0.01). Distinct pairwise ROI-ROI connections included the supplementary motor area (SMA) and the right supramarginal gyrus (RSMG) (p=0.03), SMA and the bilateral superior lateral occipital cortex (BSLOC) (p<0.01), as well as the SMA and the bilateral frontal eye field (BFEF) (p<0.01; Table 3.3).   79 Table 3.2a Participant Characteristics (N=49)  NG Group, n=25 SG Group, n=24 Overall Group, N=49  p-value1 Variables* Mean (SD) or n Mean (SD) or n Mean (SD) or n  Age (yr) 73.2 (5.8) 77.7 (6.1) 75.4 (6.3) 0.01 Height (cm) 168.0 (10.7) 163.8 (11.8) 166.0 (11.4) 0.20 Weight (kg) 72.5 (13.9) 70.6 (15.8) 71.6 (14.7) 0.66 Sex (M/F) 11/14  8/16 19/30 0.45 MMSE (30 points max) 28.0 (1.2) 27.2 (1.5) 27.6 (1.4) 0.03 MOCA (30 points max) 23.3 (1.7) 21.4 (3.1) 22.3 (2.6) 0.01 *MMSE=Mini-Mental Status Examination; MoCA=Montreal Cognitive Assessment  1For statistical comparison of NG to SG group.  Table 3.2b Participant Gait Speed by Group and Sex (N=49) Variables NG Group, n=25 Mean (SD)  SG Group, n=24 Mean (SD)  Overall Group, N=49 Mean (SD) Male Mean Gait Speed (m/s) 1.34 (0.17) 1.06 (0.10) 1.22 (0.20) Female Mean Gait Speed (m/s) 1.32 (0.19) 0.99 (0.13) 1.14 (0.23) Male Median Gait Speed (m/s) 1.35 1.11 1.15 Female Median Gait Speed (m/s) 1.25 1.00 1.14   80 3.3.4 Partial Correlations After adjusting for age and MoCA, partial correlation analysis across the study sample (N=49, Table 3.4) showed that the overall functional inter-network connectivity between the SMN and FPN was positively associated with usual gait speed (r=0.30, p=0.04), and negatively associated with Stroop Test interference score (r=-0.30, p=0.04). Moreover, usual gait speed was positively associated with connectivity between the SMA and BSLOC (r=0.39, p=0.01) and with connectivity between the SMA and BFEF (r=0.53, p<0.01). A trend-level significance was observed between the connectivity between SMA and RSMG and the Stroop interference score (r=-0.29, p=0.052).   81  Table 3.3 Mobility and fMRI Outcome Measures Variables Mean (SD) Adjusted Mean (SE) p-value*  NG SG NG SG   n=25 n=24 n=25 n=24  Mobility Measures      Usual Gait Speed (m/s) 1.33 (0.2) 1.01 (0.1) 1.33 (0.03) 1.01 (0.03) <0.01 SPPB (max 12 points) 11.4 (0.9) 10.7 (1.2) 11.4 (0.2) 10.7 (0.2) 0.01 Executive Functions      Stroop 1 (seconds) 39.1 (5.5) 43.3 (7.4) 39.4 (1.4) 43.1 (1.4) 0.08 Stroop 3 (seconds) 110.6 (31.9) 142.1 (51.7) 110.6 (8.6) 142.1 (8.7) 0.01 Stroop 2 (seconds) 57.5 (11.1) 66.6 (12.4) 57.5 (2.4) 66.6 (2.4) 0.01 Stroop Interference 53.0 (24.2) 75.5 (49.6) 53.0 (7.8) 75.5 (7.9) 0.05 Trail Making A (seconds) 40.2 (14.3) 47.0 (16.6) 41.9 (3.3) 45.3 (3.3) 0.49 Trail Making B (seconds) 98.8 (36.0) 134.0 (63.8) 112.1 (9.3) 120.0 (9.6) 0.58 Trail Making Score 1.6 (0.8) 1.9 (1.2) 1.7 (0.2) 1.8 (0.2) 0.94 DSST  44.7 (9.3) 38.1 (10.5) 42.4 (1.8) 40.5 (1.8) 0.46 Functional Connectivity      SMN-FPN 0.16 (0.15) 0.05 (0.15) 0.20 (0.03) 0.01 (0.03) <0.01 SMA- RSMG 0.14 (0.25) 0.03 (0.25) 0.18 (0.05) -0.02 (0.05) 0.01 SMA- BSLOC 0.27 (0.25) 0.10 (0.18) 0.29 (0.05) 0.08 (0.05) <0.01 SMA- BFEF 0.40 (0.27) 0.20 (0.27) 0.45 (0.05) 0.14 (0.06) <0.01 *Adjusted for participant age and MoCA  Note: SPPB=Short Physical Performance Battery; Stroop Interference=Stroop3-2; Trail Making Test Score=(B-A)/A; DSST=Digit Symbol Substitution Test; SMN=sensori-motor network; FPN=fronto-parietal network; SMA=supplementary motor area; RSMG=right supramarginal gyrus; BSLOC=bilateral occipital cortex; BFEF=bilateral frontal eye field.     82  Table 3.4 Partial Correlations across Study Sample (N=49)  SPPB Usual Gait Speed Stroop3 Stroop2 Stroop Interference Trail-Making Score DSST SMN-FPN <0.01 0.30* -0.32* -0.15 -0.30* -0.11 -0.22 SMA- RSMG -0.23 0.25 -0.33* -0.21 -0.29╫ 0.02 -0.08 SMA- BSLOC 0.13 0.39* 0.03 0.06 0.02 0.03 -0.11 SMA- BFEF 0.17 0.53* 0.01 -0.12 0.05 -0.12 -0.24 Adjusted for participant age and MoCA; *p<0.05; ╫p==0.052 Note: SPPB=Short Physical Performance Battery; Stroop Interference=Stroop3-2; Trail Making Score=(B-A)/A; DSST=Digit Symbol Substitution Test; SMN=sensori-motor network; FPN=fronto-parietal network; SMA=supplementary motor area; RSMG=right supramarginal gyrus; BSLOC=bilateral occipital cortex; BFEF=bilateral frontal eye field.   83  3.4 Discussion In the present cross-sectional study, we demonstrated that slower usual gait speed among older adults with MCI demonstrate significantly less inter-network connectivity between the SMN and the FPN compared with their counterparts with normal usual gait speed. Moreover, we showed that slower gait and poorer executive functions were associated with less inter-network connectivity between these two functional networks. Thus, we provide preliminary evidence to suggest that preservation of the functional coupling between the SMN and FPN may be critical for the maintenance of usual gait speed and executive functions among older adults with MCI.  Our current exploratory study was, in part, motivated by the concept of motor cognitive risk syndrome (MCR), a new syndrome proposed by Verghese and colleagues [263] to identify individuals at high risk for dementia. MCR is operationally defined by the presence of concomitant cognitive complaints and slow gait, without severe functional impairments or dementia [263]. Studies showed that MCR is highly prevalent among older adults (9.7% from a pooled analysis of 26,802 older adults from 17 countries) [263] with high incidence (65.2/1000 person-years; 95% CI: 53.3-77.1) [264]. Specifically, the Einstein Aging Study showed that the incidence rate of dementia was more than doubled among older adults with subjective cognitive complaint, MCI, and slow gait compared with those without MCR [265]. Based on our existing understanding of MCR, it is reasonable to hypothesize that older adults with MCI who have slower gait may be at greater risk for subsequent decline and progression to dementia than those without slower gait. Our current findings support this hypothesis by demonstrating that older adults identified with MCI and slower usual gait 84  speed have lower overall inter-network functional connectivity between the SMN and the FPN than those with normal usual gait speed. In a previous 12-month prospective study, we demonstrated that lower connectivity between these two functional networks among community-dwelling older adults was significantly associated with greater decline in both executive functions, as measured by the Stroop Test, and general balance and mobility, as measured by the SPPB [91].  Lending additional support to our results, Inman and colleagues [266] also found less connectivity between the SMN and FPN during resting-state in stroke survivors – a population that is also at significant risk for falls and dementia [267]. Thus, the current and previous findings collectively support our original hypothesis that these two functionally, and anatomically (partially), overlapping networks [91] are of specific interest in understanding the neural basis for the co-occurrence of impaired mobility and cognitive function. Less connectivity between these two networks during resting state may suggest reduced motor preparatory inputs, in anticipation of motor performance, from FPN to the SMN. This, in turn, may impair mobility and increase falls risk. These observations extend our past findings by identifying the specific pair-wise ROIs that contributed to the overall inter-network functional disconnectivity. Key hubs within the SMN and FPN contribute to processing and relaying of visual sensory inputs and conveying the information into appropriate motor outputs [268], including movement planning, preparation and execution [269]. Compared with those with MCI and normal usual gait speed, we found older adults with MCI and slower gait speed had significantly lower connectivity between the supplementary motor area and the lateral occipital cortex, as well as lower connectivity 85  between the supplementary motor area and the frontal eye field. The supplementary motor area has strong implications in gait control [270]; whereas both the lateral occipital and frontal eye field regions are key components in conducting visual processing [271]. This indicates that disrupted communication between key regions of the SMN and FPN may have obstructed the conveyance of visual input to motor output, thus interrupting proper gait control and execution. We also found that overall lower inter-network functional connectivity between the SMN and FPN was significantly correlated with poorer Stroop Test performance. As previously stated, a major component of the FPN’s function is attentional control [95] – the central cognitive process engaged by the Stroop Test. Further, evidence in the literature demonstrated that the SMN consists of regions that contain reciprocal neuronal projections to the prefrontal cortex [272], thus, SMN may also contribute to the performance of higher order cognitive processes.  For specific pair-wise ROI connectivity, we observed a trend-level association between the connectivity of SMA and supramarginal gyrus with Stroop Test interference score. Previous studies have demonstrated the involvement of SMA in cognitive tasks that require shifting of attention or task switching [273]. Of particular relevance, in a comprehensive review on the pre- and supplementary motor areas, Nachev and colleagues [273] found that both regions show activation during Stroop Test performance. Similarly, the supramarginal gyrus is activated involved in attentional control during selective attention-related tasks [274].  The key strength of the present study is the focus on investigating inter-network connectivity as opposed to the more commonly researched intra-network connectivity. Regardless, a few 86  limitations should be considered. First, our sample size may not be powered to examine all the pair-wise ROI connectivity between the SMN and FPN; hence our results may be subject to type II error. Moreover, there is much controversy in regards to global signal regression and potential observation of artificial anti-correlations. However, given the context of the networks under investigation are task-positive networks, the effects of induced anti-correlation are less significant. Lastly, the involvement of functional neural correlate may extend beyond that of resting-state functional connectivity, and future study including task-based fMRI paradigm may be needed to provide greater clarity. 3.5 Conclusions Results of this cross-sectional investigation of slower usual gait speed among older adults with MCI highlight the potential importance of the functional coupling between the SMN and FPN. Specifically, lower connectivity between these two functional networks and their specific ROIs were correlated with both slower usual gait speed and poorer performance of selective attention and conflict resolutions. Thus, interventions that maintain or strengthen the connectivity between the SMN and FPN may promote mobility and functional independence among those with MCI.    87  Chapter 4: The Impact of Aerobic Exercise on Fronto-Parietal Network Connectivity and Its Relation to Mobility: an Exploratory Analysis of a 6-Month Randomized Controlled Trial A version of this chapter is published as HSU CL, Best JR, Wang S, Voss MW, Hsiung RGY, Munkacsy M, Cheung W, Handy TC, Liu-Ambrose T. The Impact of Aerobic Exercise on Fronto-Parietal Network Connectivity and Its Relation to Mobility: An Exploratory Analysis of a 6-Month Randomized Controlled Trial. Frontiers in Human Neuroscience. 2017; 11: 344. doi:  10.3389/fnhum.2017.00344 4.1 Introduction  Impaired mobility is a major concern for older adults and is associated with increased risk for disability, institutionalization, and death [275]. The prevalence of impaired mobility is 14% at age 75 years and involves half of the population over 84 years [276]. Falls are a significant consequence of impaired mobility.  Current evidence supports the recommendation of targeted exercise training to improve mobility,  prevent major mobility disability, and reduce the risk of future falls [120, 125]. The widely accepted view is that improved physical function, such as improved balance and increased muscle strength, primarily underlies the effectiveness of the exercise in improving mobility and reducing falls [277]. However, in a meta-analysis of four randomized trials of exercise, falls were significantly reduced by 35% while postural sway significantly improved by only 9% and there was no significant improvement in knee extension strength [278]. Moreover, in a proof-of-concept randomized controlled trial, we demonstrated that a home-88  based exercise signficantly reduced falls by 47% in older adults –  in the absence of significant improvement in physical function (i.e., balance and muscle strength) [123]. Rather, significant improvement in executive functions were observed in the exercise group as compared with the usual care (i.e., control) group. These data suggest that exercise may reduce falls in older adults via several mechanisms, not just via improved physical function. We previously proposed that cognitive and neural plasticity may be an important, yet under-appreciated mechanism by which exercise promotes mobility and reduce falls [279]. This hypothesis stems from the growing evidence that suggest: 1) cognitive impairment and impaired mobility are associated  [1, 79, 80]; 2) reduced executive function, is associated with impaired mobility and increased falls risk [33, 73]; 3) aberrant neural network functional connectivity is associated with impaired mobility [91]; and 4) targeted exercise training, particulary aerobic-based, promotes cognitive and cortical plasticity, including executive function and its neural correlates, in older adults [137, 280].  Despite the growing recognition that targeted exercise training may promote mobility outcomes in older adults via central mechanisms [279], few intervention studies of exercise to date have provided direct evidence for this theory [127]. A better understanding of the neural mechanisms underlying exercise-induced improvements in mobility may facilitate the development and refinement of preventative/intervention strategies, as well as identify the populations for whom these effects apply. Older adults with subcortical ischemic vascular cognitive impairment (SIVCI), the most common form of vascular cognitive impairment (VCI ) [281], are at particular risk for both impaired mobility and dementia secondary to underlying white matter lesions (WMLs) or 89  lacunar infarcts [282, 283]. Vascular cognitive impairment is the second most common cause of dementia after Alzheimer’s disease (AD) [284-287]. The clinical consequences of covert ischemic strokes are substantial [282, 283]. These WMLs and lacunar infarcts typically manifest in brain regions such as caudate, pallidum, thalamus, frontal and prefrontal white-matter [283]. As a result, they may disrupt the integrity of functional neural networks and negatively impact cognitive function, particulary executive functions, and mobility [288, 289].  Among the relevant neural networks, most notably, is the frontoparietal network (FPN). The FPN is involved in top-down attentional control and allocation of available neural resources that contribute to executive processes, such as response anticipation and conflict processing [96-98], as well as motor planning and motor execution [99-101]. Of particular relevance, previous studies have shown that key regions within the FPN were actively recruited during actual as well as imagined completion of the walking while talking (WWT) test [290, 291]. Specifically, neural activity within these FPN regions were positively associated with both task difficulty and cognitive performance of the WWT test [290, 291]. Although previous studies have linked aspects of mobility to FPN connectivity, its potential role understanding exercise-induced effects on mobility is unkown. Thus, we propose FPN connectivity as one of the neural mechanisms by which exercise promotes mobility in older adults with mild SIVCI. Using functional magnetic resonance imaging (fMRI) data from a six-month single-blind randomized controlled trial (clinicaltrials.gov Identifier: NCT01027858), we conducted a planned secondary analysis to assess the impact of moderate-intensity aerobic exercise training on functional connectivity 90  of FPN among older adults with mild SIVCI. We hypothesized that aerobic exercise-induced increases in FPN connectivity would correlate with improved mobility. The primary results from the parent study have been published [292], which provided preliminary evidence that six months of thrice-weekly progressive aerobic training promotes cognitive performance in community-dwelling adults with mild SIVCI, relative to usual care plus education. 4.2 Methods 4.2.1 Study Design This is a secondary analysis of neuroimaging data acquired from a six-month proof-of-concept RCT (NCT01027858) of aerobic exercise in older adults with mild SIVCI [292, 293]. Trained study assessors were blinded to group allocation of participants. Functional MRI (fMRI) data were acquired at baseline prior to randomization and at trial completion (i.e., six months) in a subset of eligible participants. 4.2.2 Participants As the current study was a secondary analysis, we sought to recruit as many eligible and consenting individuals from the parent study as possible. To briefly describe the recruitment process, we recruited from the University of British Columbia Hospital Clinic for Alzheimer’s Disease and Related Disorders, the Vancouver General Hospital Stroke Prevention Clinic, and specialized geriatric clinics in Metro Vancouver, BC. Recruitment occurred between December 2009 and April 2014 with randomization occurring on an ongoing basis. Study participants were clinically diagnosed with mild SIVCI as determined by the presence of cognitive syndrome and small vessel ischaemic disease [294]. Small 91  vessel ischemic disease was defined as evidence of relevant cerebrovascular disease by brain computed tomography or MRI defined as the presence of both: 1) Periventricular and deep WMLs; 2) Absence of cortical and/or cortico-subcortical non-lacunar territorial infarcts and watershed infarcts, hemorrhages indicating large vessel disease, signs of normal pressure hydrocephalus, or other specific causes of WMLs (i.e., multiple sclerosis, leukodystrophies, sarcoidosis, brain irradiation). In addition to the neuroimaging evidence, the presence or a history of neurological signs such as Babinski sign, sensory deficit, gait disorder, or extrapyramidal signs consistent with sub-cortical brain lesion(s) was required and confirmed by study physicians (G-YRH and PL). Cognitive syndrome was defined as a baseline Montreal Cognitive Assessment (MoCA) score less than 26/30. However, participants were free of frank dementia (i.e., clinically diagnosis of dementia) as determined by a Mini-Mental State Examination (MMSE) score ≥ 20 and the absence of diagnosed dementia. Progressive cognitive decline was confirmed through medical records or caregiver/family member interviews. The Consolidated Standards of Reporting Trial flowchart shows the number and distribution of participants included in this secondary analysis (Figure 4.1). Of the 38 participants (54% of parent sample) that completed baseline MRI scanning, 7 (18% of the sample) dropped out from the study and 10 (26% of the sample) failed to correctly perform the motor finger tapping task (e.g., finger tapped during resting blocks). Consequently, 21 participants who completed MRI at baseline and trial completion were included in this secondary analysis (30% of parent sample). Ethical approval was provided by the University of British Columbia’s Clinical Research Ethics Board (H07-01160). All participants provided written informed consent.   92 Figure 4.1 Overview of the flow of study participants through the 6-month studyInitial Screen for Eligible Participants (N=582) Excluded:   Failed to meet inclusion criteria (N=81)  No response (N=61)  No interest (N=318) In Person Screening (N=122) Excluded:  Failed to meet inclusion criteria (N=38)  No interest (N=13) Consented, Completed Baseline Assessment and Randomized (N=70) CON (N=35) 6-Month fMRI (N=9) 6-Month fMRI (N=12) AT (N=35) Baseline fMRI (N=19) Baseline fMRI (N=19) Dropout (N=5) Dropout (N=5) No interest in MRI (N=16) No interest in MRI (N=16) Unable to complete Motor Task due to not attending to the task (N=5)  Unable to complete Motor Task due to not attending to the task (N=2)   93 4.2.3 Randomization The randomization sequence was generated using the web application www.randomization.com with a ratio of 1:1 to aerobic training (AT) or usual care (CON). A research team member not involved with the study held this sequence at a remote location. After the completion of consent and baseline testing, the research coordinator contacted the team member holding the list to determine the next allocation.  4.2.4 Aerobic Training and Compliance For the AT group, aerobic training consisted of supervised thrice-weekly 60-minute classes of walking for the six-month intervention period. All AT group classes were led by instructors certified to instruct seniors and were delivered in a group setting. Each 60-minute class included a 10-minute warm-up, 40-minutes of walking, and a 10-minute cool down. Both the warm-up and cool-down included passive and active stretches, as well as range of motion exercise.  Walking occurred outdoors and followed predetermined routes around local areas. The intensity of the AT program was monitored and progressed using three approaches: 1) heart rate monitoring with an initial intensity of 40% of age specific target heart rate (i.e., heart rate reserve; HRR). HRR was calculated by subtracting resting heart rate from maximum heart rate (using the formula: 206.9 – 0.67 x Age [295]) and recalculation each month. Participants progressed over the first 12 weeks to the range of 60% to 70% of HRR, after which this was sustained for the remainder of the intervention period; 2) subjective monitoring using the Borg’s Rating of Perceived Exertion (RPE) [296] with a target RPE of 94  14 to 15; and 3) the “talk” test [297], starting at a walking pace allowing comfortable conversation and progressing to a walking pace where conversation was difficult. Individual training logs (i.e., target heart rate, heart rate achieved, and rate of perceived exertion) were maintained throughout the intervention period. The AT group was also given a pedometer to serve as both an incentive and monitoring tool. Participants recorded the number of steps each day taken outside the AT classes on standard logs provided by the research team.  4.2.5 Usual Care Participants in the CON group received usual care, in which they were provided with monthly educational materials about VCI and healthy diet. However, no specific information regarding physical activity was provided. In addition, research staff phoned the CON participants on a monthly basis to maintain contact and to acquire research data.  4.2.6 Adverse Effects All participants were instructed to report any adverse effects due to the AT exercises to our research coordinator, such as falls or musculoskeletal pain persisting longer than 48 hours. Participants were also questioned about the presence of any adverse effects, such as musculoskeletal pain or discomfort, at each exercise session. All instructors also monitored participants for symptoms of angina and shortness of breath during the exercise classes. External experts from our safety monitoring committee reviewed all adverse events reported on a monthly basis. 95  4.2.7 Descriptive Variables At baseline, participants underwent a clinical assessment with study physicians (GYRH and PL) to confirm current health status and study eligibility. Age in years and education level were assessed by self-report. Standing height was measured as stretch stature to the 0.1 cm per standard protocol. Weight was measured twice to the 0.1 kg on a calibrated digital scale. Waist-to-hip ratio was determined by measuring the widest part of the hip circumference and the waist just above the navel in centimeters. The Functional Comorbidity Index [298] assessed  the number of comorbid conditions related to physical functioning.  Global cognition was assessed using the MMSE [180] and the MoCA. The MMSE and MoCA are 30-point tests that encompass several cognitive domains. The MoCA has been found to have good internal consistency and test-retest reliability and was able to correctly identify 90% of a large sample of individuals with MCI from two different clinics with a cut-off scores of ≤ 26/30 [53]. 4.2.8 Functional MRI Acquisition All MRI was conducted at the University of British Columbia (UBC) MRI Research Center located at the UBC Hospital on a 3.0 Tesla Intera Achieva MRI Scanner (Phillips Medical Systems Canada, Markham, Ontario) using an 8-channel SENSE neurovascular coil. The fMRI consisted of 2 successive runs with 165 dynamic images of 36 slices (3 mm thick) with the following parameters: repetition time (TR) of 2000 milliseconds (ms), echo time (TE) of 30 ms, flip angle (FA) of 90 degrees, field of view (FoV) of 240 mm, acquisition matrix 80x80, voxel size of 3 mm x 3 mm x 3 mm. High resolution anatomical MRI T1 images were 96  acquired using the following parameters: 170 slices (1 mm thick), TR of 7.7 ms, TE of 3.6 ms, FA of 8 degrees, FoV of 256 mm, acquisition matrix of 256x200.  During each scanning session, the study participants were asked to perform a finger tapping motor task that had been previously administered and described [91]. Briefly, the task consisted of three conditions: right finger tapping, rest, and left finger tapping. The specific instructions given required the participants to finger tap in a particular sequence regardless of condition: start with the index finger and progress towards the little (pinky) finger continuously until a different condition is presented. For the rest condition, participants were asked to rest with their eyes open. The exact order of motor task blocks was not disclosed to the participants and was counter balanced over two runs as follow: Run A: Rest, Left Tap, Rest, Right Tap, Rest, Right Tap, Rest, Left Tap, Rest Run B: Rest, Right Tap, Rest, Left Tap, Rest, Left Tap, Rest, Right Tap, Rest Total WML volume (in mm3) at baseline was quantified with structural MRI data acquired on the same MRI scanner (3T Achieva, Philips Medical Systems, Markham, Ontario) at the UBC MRI Research Centre. A T2-weighted scan and a proton-density-weighted (PD-weighted) scan were acquired for each subject. For the T2-weighted images, the repetition time (TR) was 5,431 ms and the echo time (TE) was 90 ms, and for the PD-weighted images, the TR was 2,000 ms, and the TE was 8 ms. T2- and PD-weighted scans had dimensions of 256 x 256 x 60 voxels and a voxel size of 0.937 x 0.937 x 3.000 mm. Briefly, WMLs were identified and digitally marked (i.e., placing seed points) by a radiologist on T2 and PD weighted images. Marked WMLs were automatically segmented by a customized Parzen 97  windows classifier that estimated the intensity distribution of the lesions – which also included heuristics that optimized the accuracy of the estimated distributions [127, 299, 300]. WML segmentation was reviewed by a trained technician to ensure accuracy. 4.2.9 Mobility, Cardiovascular Capacity, and Physical Activity Mobility was assessed with the Timed-Up-and-Go test (TUG) and the Short Physical Performance Battery (SPPB). The TUG required participants to rise from a standard chair, walk a distance of three meters, turn, walk back to the chair and sit down [164]. We recorded the time (s) to complete the TUG, based on the average of two separate trials.  For the SPPB, participants were assessed on performances of standing balance, walking, and sit-to-stand. Each component is rated out of four points, for a maximum of 12 points; a score < 9/12 predicts subsequent disability [193]. Participant’s cardiovascular capacity was assessed using the 6-Minute Walk Test [301]. The total distance walked (meters) within the span of 6 minutes was recorded. Monthly total physical activity level was determined by the Physical Activities Scale for the Elderly (PASE) self-report questionnaire [186]. 4.2.10 Data Analysis 4.2.10.1 Functional MRI preprocessing  Image preprocessing was carried out using tools from FSL (FMRIB’s Software Library) [78], MATLAB (Matrix Laboratory), and toolboxes from SPM (Statistical Parametric Mapping). 98  Excess unwanted structures (i.e., bones, skull, etc.) in high resolution T1 images were removed via Brain Extraction Tool (BET); rigid body motion correction was completed using MCFLIRT (absolute and relative mean displacement were subsequently extracted and included in the statistical analysis as covariates); spatial smoothing was carried out using Gaussian kernel of Full-Width-Half-Maximum (FWHM) 6.0 mm; temporal filtering was applied with high pass frequency cut-off of 120 seconds. In addition, a low pass temporal filtering was also included to ensure the fMRI signal fluctuated between 0.008<f<0.080 Hz, the ideal bandwidth to examine functional connectivity. Furthermore, the application of a low pass filter eliminated high frequency signals that could be confounds. Participants’ low-resolution functional data were registered to personal high resolution T1 anatomical images, which were subsequently registered to standardized 152 T1 Montreal Neurological Institute (MNI) space. Noise generated from both physiological and non-physiological sources were removed through regression of the cerebral-spinal fluid (CSF) signal, white matter signal, and global brain signal. Global signal regression had been reported as both valid and useful step in functional connectivity analyses [259] that may improve specificity [260]. 4.2.10.2 Functional Connectivity Analysis Previous studies guided our choice of seeds in the whole brain analysis of the FPN [137]. The FPN included the inferior parietal sulcus (IPS), ventral visual cortex (VV), supramarginal gyrus (SMG), superior lateral occipital cortex (SLOC), frontal eye field (FEF) , as well as overlapping areas in the temporal-parietal junction. The respective MNI space coordinates for each region of interest (ROI) are provided in Table 4.1. 99  Table 4.1 Frontoparietal Network Regions of Interest and Relative MNI Coordinates FPN ROI X Y Z  RIPS 25 -62 53  RVV 36 -62 0  LVV -44 -60 -6  RSMG 32 -38 38  RSLOC 26 -64 54  LSLOC -26 -60 52  RFEF 28 -4 58  LFEF -26 -8 54 Note: RIPS==right inferior parietal sulcus; RVV==right ventral visual; LVV==left ventral visual; RSMG==right supramarginal gyrus; RSLOC==right lateral occipital cortex; LSLOC==left lateral occipital cortex; RFEF===right frontal eye field; LFEF==left frontal eye field  100  From each ROI, preprocessed time-series data were extracted with 14mm spherical regions of interest drawn around their respective MNI coordinates in standard space. The different conditions (i.e., left, right, and rest) within each block of the motor task were extracted and compiled together. To concatenate the time-series data, the stimulus onset time for each task condition was acquired from the task program. Each volume of the data was then sorted according to their respective condition. Once the data were properly categorized, the task-specific volumes (e.g., all the “left” volumes) were merged using a script provided in the FSL program. The first three volumes of any condition were discarded to account for delay of the hemodynamic response. Evidence in the literature has demonstrated that functional connectivity derived from temporally spliced/merged resting-state data from blocked fMRI design is not significantly different from connectivity derived from continuous data [302]. Recent study using motor task fMRI also showed that quantifying functional connectivity via similar seed-based approach using concatenated data is comparable to results from continuous data [303]. Region of interest time-series data were subsequently cross-correlated to establish functional connectivity maps of their associated neural networks, in which pairwise correlation between time-series extracted from ROI listed above was calculated. Individual-level within-subject results were generated via ordinary least-squares (OLS) regression using FSL’s flameo [261] in FSL by congregating the voxel-wise functional connectivity maps from each condition. Similarly, for group results, a mixed-level OLS analysis was conducted. The statistical map thresholding was set at Z=2.33, with cluster correction of p<0.05.  101  4.2.10.3 Statistical Analyses Statistical analysis was conducted using the IBM SPSS Statistic 23 for Windows (SPSS Inc., Chicago, IL). Statistical significance was set at p≤0.05 for all analyses. Change in network connectivity strength was computed in SPSS as six-month FPN connectivity minus baseline FPN connectivity. Linear mixed models with random intercepts and time-varying outcome measures were constructed to statistically test for significant between-group differences in change in network connectivity while adjusting for baseline total WML and age. A group by time interaction indicated group differences in changes in FPN connectivity from baseline to post-intervention. Similar analyses were conducted to determine whether there were group differences in changes in TUG, SPPB, 6MWT, and PASE scores. The primary analyses included the 21 participants with valid baseline and post-intervention fMRI data. Secondary analyses followed the intention-to-treat principle by including 9 additional individuals with valid baseline fMRI but were lost to follow-up; maximum likelihood estimation allowed for these individuals to inform the treatment effects, despite having missing follow-up data and to determine whether loss to follow-up might bias the treatment effects estimated with only treatment completers. Bivariate correlation analyses were performed to determine whether any significant changes in intra-network FPN connectivity (during rest, left tap, and right tap) in the AT group (n=12) correlated with change in mobility, as measured by TUG and SPPB, or change in 6MWT across the six-month study duration.   102  4.3 Results 4.3.1 Participants and Treatment Fidelity Among the 70 randomized individuals in the parent study, we observed a significant effect of AT on six-minute walk performance, a well-established tool that accurately evaluates cardiovascular fitness [304] (B===30.34, p=0.02), indicating that AT had a positive effect on cardiovascular capacity [292]. Twenty-one participants who completed fMRI scans at both baseline and 6-months were included in the primary analysis (Figure 4.1). Study demographics are reported in Table 4.2, pedometer information over the intervention period is reported in Table 4.3, mobility and cardiovascular capacity measures are reported in Table 4.4; these measures do not differ between groups at baseline nor differ significantly from the 70 eligible participants enrolled in the parent study [292]. The mean age of all participants included in this this secondary analysis was 71.1 years (SD=8.7 years), which is not significantly different from the mean age of the parent cohort at 74.3 years (SD=8.3 years). Compared to the 9 individuals with valid baseline data only, the study sample did not differ on baseline FPN connectivity (all p>0.19) but did have higher average baseline MoCA scores (23.2 versus 20.4; p=0.02). Neither of the mobility measures nor self-reported physical activity differed significantly between groups across the 6 months. However, we observed a trend-level group difference in the change in 6-Minute Walk Test performance (p=0.08; Table 4.4), in which the AT group showed greater improvement (48.6 meters) compared with the CON group (-0.3 meters).    103  Table 4.2 Participant Characteristics at Baseline (N=21)  CON Group, n=9 AT Group, n=12 Variables Mean (SD) or n Mean (SD) or n Age (yr) 69.9 (9.2) 72.0 (8.6) Height (cm) 165.4 (11.4) 169.0 (15.9) Weight (kg) 73.6 (13.1) 73.6 (14.8) Sex (M/F) 5/4 8/4 MMSE (30 points max) 27.2 (1.9) 26.4 (2.8) MOCA (30 points max) 24.2 (2.3) 22.5 (2.0) FCI 3.0 (1.9) 3.1 (1.7) White Matter Lesion (mm^3) 1389.5 (2023.4) 3492.8 (3882.1) Relative Head Motion (mm) 0.17 (0.09) 0.14 (0.07) Note: MMSE=Mini-Mental Status Examination; MoCA=Montreal Cognitive Assessment; FCI=Functional Comorbidity Index All measures were not statistically significant at p<0.05  Table 4.3 AT Group Pedometer Information over 6-Month Intervention Period  Average Pedometer Count  Mean (SD) Baseline 7002.1 (5496.4) Month 1 9009.2 (6337.9) Month 2 9786.3 (6944.2) Month 3 10713.8 (7209.0) Month 4 10922.1 (7645.7) Month 5 11484.8 (8074.6) Month 6 11552.7 (7411.9)   104 Table 4.4 Mobility and Cardiovascular Capacity Measures (N=21)  Baseline  Change from Baseline to 6-Month╫  (Trial Completion)  Variables Mean (SD) Adjusted Within-Group Change (SE)       Between-Group p-value  CON AT CON AT Timed Up and Go (s) 8.2 (1.3) 7.6 (1.8) 0.16 (0.46) -0.06 (0.41) 0.73 Short Physical Performance Battery (max 12 points) 10.4 (0.9) 11.2 (1.3) 0.48 (0.34) -0.23 (0.31) 0.15 6-Minute Walk Test (meters) 531.2 (56.9) 555.6 (104.3) -0.30 (19.29) 48.64 (16.57) 0.08 PASE Score 132.2 (61.3) 134.6 (94.0) -6.05 (39.62) -4.99 (34.05) 0.99 ╫Adjusted for baseline WML and age; PASE==Physical Activities Scale for the Elderly 105 4.3.2 AT Compliance and Adverse Effects The average compliance observed in the AT group was 76% for the walking classes and 65% for the nutrition education classes; whereas the average compliance observed in the CON group was 74%. Two study-related adverse events were reported in the AT group and one in the CON group. All three were non-syncopal falls. One of the falls in the AT group resulted in a broken tooth and required assessment in the Emergency Department; the remaining two did not result in injury. 4.3.3 fMRI Results Results from the seed-based functional connectivity analysis on the FPN (Figure 4.2) showed there were no significant between group differences in the mean network connectivity strength at baseline, regardless of task conditions (Table 4.5). At trial completion, compared with AT, CON exhibited significantly greater intra-network coupling of the FPN during right finger tapping (p<0.02) after adjusting for baseline WML and age. No AT effects were observed for FPN connectivity during left finger tapping (p=0.26) or during rest (p=0.50). We conducted a secondary, intention-to-treat analysis using all 30 participants with usable baseline data, regardless of loss to follow-up, and observed similar, though weaker, between-group differences in FPN coupling during right finger tapping (p=0.08). As with the primary analyses, CON showed an increase in intra-network coupling of the FPN (mean=0.18, SE=0.09), whereas AT showed no significant change over time (mean=-0.04, SE=0.08).   106  Figure 4.2 Image of the Frontoparietal Network   107 Table 4.5 Frontoparietal Network Connectivity during Task (N=21)  Baseline Change from Baseline to 6-Month ╫ (Trial Completion)   Task Condition Mean (SD) Adjusted Within-Group Change (SE) Between-Group p-value  CON AT CON AT  Right Tapping 0.25 (0.28) 0.34 (0.22) 0.25 (0.09) -0.06 (0.07) 0.02 Rest 0.29 (0.24) 0.26 (0.24) 0.06 (0.09) -0.02 (0.07) 0.50 Left Tapping 0.35 (0.24) 0.27 (0.20) -0.05 (0.08) 0.08 (0.07) 0.26 ╫Adjusted for baseline WML and age.108 4.3.4 Correlation Results Bivariate correlation across the study sample showed that the change in FPN connectivity during right tapping was significantly associated with change in 6-Minute Walk Test performance (r=-0.43, p=0.05; Table 4.6). Within the AT group (N=12), the change in FPN connectivity during right tapping was significantly associated with change in TUG performance (r=0.67, p=0.02; Table 4.6). Specifically, reduced FPN connectivity from baseline to post-intervention correlated with improved TUG performance over the same period of time (Figure 4.3).  Table 4.6 Changes in Mobility and Changes in FPN Connectivity Correlation  ∆Timed Up and Go ∆Short Physical Performance Battery ∆ 6-Minute Walk Test  Group (N=21)    ∆FPN Connectivity During Right Tapping 0.14 0.34 -0.43* AT Group Only (n=12)    ∆FPN Connectivity During Right Tapping 0.67* 0.14 -0.37 Note: ∆=6-Months– Baseline  * p<0.05   109 Figure 4.3 Correlation between Change in TUG Performance and Change in FPN Connectivity During Right Finger Tapping Within the AT Group  -3-2-101234-0.4 -0.2 0 0.2 0.4∆TUG Performance over 6-months     ∆FPN Connectivity over 6-months ∆FPN-∆TUG Correlation AT Group110  4.4. Discussion Contrary to our initial hypothesis, we found that a six-month aerobic training intervention did not significantly increase, but rather maintained FPN connectivity during right finger tapping among older adults with mild SIVCI. The observed effect of aerobic exercise on the FPN during right tapping was significantly associated with improved mobility and cardiovascular capacity. While these results are preliminary, our data suggest aerobic exercise may promote mobility among older adults with mild SIVCI via maintaining the integrity FPN connectivity.  Our findings are in contrast to previous findings that show altered FPN connectivity is associated with aging [110] and with cognitive deficits [305-307]. Specifically, Poppe and colleagues [307] demonstrated that compared with healthy controls, patients with schizophrenia had significantly less connectivity in the FPN during goal-oriented task performance. Compared with controls, task performance was also significantly worse among patients. Similarly, He and colleagues [305] found that compared with age-matched healthy controls, individuals who suffered an acute stroke showed significantly less left-right posterior intraparietal sulcus connectivity while performing a spatial-orientation task. Lower connectivity between the left and right posterior intraparietal sulcus were significantly associated with poorer task accuracy and slower task reaction time. Moreover, current evidence suggests that SIVCI is generally associated with less functional connectivity of neural networks. For example, among those with SIVCI, Yi and colleagues [308] found lower functional connectivity in the medial prefrontal cortex and the middle temporal gyrus. Yu and colleagues [309] demonstrated that compared with healthy controls, individuals with SIVCI had less network efficiency in the fronto-temporal and parietal 111  regions. Nonetheless, aberrant functional connectivity has been repeatedly observed among those with SIVCI [308-311], and the pattern (i.e., increased or decreased connectively) is not consistent across studies. For instance, Ding and colleagues [311] found increased functional connectivity in the left middle temporal lobe, right inferior temporal lobe, and left superior frontal gyrus among patients with SIVCI as compared with healthy controls. However, our results do concur with and extend emerging evidence that show less functional connectivity of large-scale networks may be advantageous [312], especially within the context of mobility. In one cross-sectional study, Rosenberg-Katz and colleagues [313] demonstrated that compared with healthy older adults and individuals  with Parkinson’s disease who were non-fallers, those with Parkinson’s disease who were fallers showed significantly greater connectivity between the posterior parietal lobule and the inferior parietal lobule. This data suggest increased connectivity between parietal regions may be associated with more severe motor impairments and more generally, heightened neural activity (e.g., activation or connectivity) may reflect the inability of networks to actively suppress irrelevant neural events, causing regions to compete unnecessarily for available neural resources. In contrast, diminished connectivity may represent greater efficiency as the networks can effectively allocate resources to areas of immediate importance. Certainly, emerging evidence suggests that lifestyle interventions can improve neural efficiency [314, 315].  Our observation that aerobic training impacted FPN connectivity only during right hand finger tapping concurs with the literature that suggests the FPN connectivity is lateralized [316-318]. Specifically, using independent component analysis, Smith and colleagues [316] 112  revealed that among the neural networks identified, only the FPN exhibit distinct left-right lateralized components. Also, Jancke and colleagues [319] found contra-lateralized FPN activation during right index finger tapping task (without visual cue), including the left dorsal lateral premotor cortex and the left inferior parietal lobule. Moreover, Yuan and colleagues [320] recently demonstrated that gait velocity among cognitively normal older adults was significantly associated with connectivity of the left-FPN. Therefore, given that our study participants were all right-hand dominant, our results are supported by the literature. We also extend the current state of knowledge by using data generated from a randomized controlled trial to demonstrate the potential impact of aerobic exercise on FPN connectivity during right hand finger tapping and the significant association between FPN connectivity and mobility and cardiovascular capacity. An alternative interpretation of these results may be that aerobic exercise may help maintain mobility and cardiovascular capacity among older adults with SIVCI via reducing cognitive load (i.e., less FPN connectivity) required to perform less attention-demanding motor task (i.e., dominant hand finger tapping). Our own previous work supports this latter concept [321]. Critically, in this separate sub-analysis of the same 6-month RCT, we found that after aerobic training, older adults confirmed with SIVCI performed significantly better at the Eriksen flanker task compared with the no-exercise controls. The observed improvement in task performance was associated with overall reduction in activation in the lateral occipital cortex and superior temporal gyrus.  It should be noted that we are aware of only one study in the relevant field in the literature that investigated the association between functional connectivity and cardiorespiratory fitness among older adults [322]. While our findings deviate from evidence presented, in which the authors reported greater connectivity is associated with higher fitness among 113  healthy older adults [322], several distinctions from the current study should be considered. Specifically, the differences were: 1) the fMRI task (visual vs motor); 2) the network examined (default mode network vs. FPN); and 3) study participant (healthy older adults vs. older adults with SIVCI). The combination of these variations could have resulted in disparities in the reported findings. A few limitations should be taken into consideration. First, our study participants are likely healthier and to have superior physical functioning than average older adults with mild SIVCI. This potential sample bias is somewhat unavoidable given the requirement that participants be able to engage in progressive AT safely. However, it also limits the generalizability of our findings to the population of older adults with mild SIVCI as a whole. Secondly, due to the small sample size of the current study, the current dataset may not possess enough power to detect small differences between the two groups. Provided that the study population is generally frail and older, the occurrence of drop-out from potentially strenuous fMRI session is to be expected. Future studies designed with larger sample sizes are necessary to validate the notion of functional network efficiency/inefficiency by providing sufficient power despite the expectation of drop-out. Thirdly, it is possible that subsets of pairwise connectivity between ROI within the FPN may have driven the effects we observed; however, this was not further investigated due to potential issue with type I error with the current sample size. Moreover, there is much controversy in regards to global signal regression and potential observation of artificial anti-correlations. This may be particularly influential when examining functional connectivity between networks deemed anti-correlated in nature (e.g., default mode network and FPN). In assessing within-network connectivity, it may be that the effects of induced anti-correlation are less significant. However, as stated by 114  Murphy and Fox [260] ‘there is not a single “right” way to process resting state data that reveals the “true” nature of the brain.’ They also summarized the several advantages of global signal regression including removal of motion, cardiac and respiratory signals. In addition, despite evidence supporting its use [302, 303], we recognize temporally splicing and concatenating data is not recommended and can potentially lead to increase in signal noise. Nevertheless, studies demonstrated that connectivity derived from concatenation does not differ significantly from those acquired from continuous data [302, 303]. In addition, our data is limited by the fact that only the connectivity during right hand tapping was statistically significant while left hand was not. Differences in social interactions experienced by the experimental groups may present addition confounding factors to our data. Specifically, active attention provided by trainers within the AT group may potentially influence our findings. Lastly, the relationship between connectivity and SIVCI status is equivocal with much of the evidence generated from cross-sectional studies. Thus, the inclusion of fMRI data from a healthy-aged matched cohort might have facilitated interpretation of our results. Nevertheless, we highlight the key strengths of our currents study design – a randomized controlled trial – which are: 1) provides evidence of causation; and 2) increased internal validity. Thus, our study provides preliminary evidence to suggest that aerobic exercise may impact functional connectivity in older adults with SIVCI, and this is associated with the maintenance of mobility. To conclude, our results demonstrate that neural network functional connectivity may contribute to the effects of aerobic exercise on mobility among older adults with SIVCI. We observed that six months of AT maintains motor task-based connectivity within the FPN of older adults with SIVCI, and the degree of decoupling within this region correlates with 115  improvements in mobility. As such, our current findings support emerging results from others that lower functional connectivity within certain neural networks might represent a beneficial change in older adults with mild SIVCI, especially vis-à-vis their mobility. More broadly, these results bring further support to the burgeoning notion that functional neural changes contribute to exercised-induced improvements to mobility among older adults. As extension of these findings, future studies should explore potential interaction between mobility and cognitive outcomes among this population.   116  Chapter 5: Aerobic Exercise Promotes Executive Functions and Impacts Functional Neural Activity Among Older Adults with Vascular Cognitive Impairment A version of this chapter is published as HSU CL, Best JR, Davis JC, Nagamatsu LS, Wang S, Boyd LA, Hsiung RGY, Voss MW, Eng JJ, Liu-Ambrose T. Aerobic Exercise Promotes Executive Functions and Impacts Functional Neural Activity among Older Adults with Vascular Cognitive Impairment. British Journal of Sports Medicine. 2017 Apr 21. pii: bjsports-2016-096846. doi: 10.1136/bjsports-2016-096846  5.1 Introduction  Vascular cognitive ischaemia (VCI) is the second most common type of cognitive dysfunction worldwide [284] and refers to cognitive impairment attributable to cerebrovascular disease [323]. Current evidence suggests small vessel disease may be the primary contributor in the development of VCI, particularly subcortical ischemic vascular cognitive impairment (SIVCI) [324] – the most common form of VCI [281]. It is understood that consequences of small vessel disease include white-matter lesions (WMLs) – incomplete infarcts which appear as hyperintensities on T2-weighted magnetic resonance imaging (MRI). Evidence suggests that WMLs are associated with impaired mobility and cognitive decline [87, 325]. Small-vessel diseases that entail incomplete infarcts result in demyelination or oligodendrocyte atrophy that can be detected through magnetic resonance imaging (MRI) as white-matter lesions [324]. In more severe cases where there is acute ischemia and more extensive tissue damage, small-vessel diseases can lead to lacunar infarcts as well as cortical and subcortical lesions [324]. These small-vessel disease-related lacunar infarcts and white-117  matter lesions are often found in brain regions such as caudate, pallidum, thalamus, frontal and prefrontal white-matter, hence disrupting the integrity of neural networks and affecting cognitive function [288]. In line with these findings, recent evidence found that compared with healthy older adults without diagnosis of SIVCI, individuals with SIVCI but without dementia showed significantly greater brain activation in dorsal anterior cingulate, bilateral middle frontal gyri, bilateral inferior frontal gyri, inferior parietal lobule, insular, and basal ganglia [326]. Individuals with SIVCI often have impaired executive functions, and are at risk for functional decline [282, 283]. Thus, those with SIVCI are in need of intervention strategies to minimize progressive decline. Fortunately, SIVCI may be the most treatable form of cognitive dysfunction in adults because its key risk factors, which include hypertension, diabetes mellitus, and hypercholesterolemia, are modifiable by lifestyle interventions, including exercise training.  Targeted aerobic exercise is a promising approach to delay the progression of SIVCI [327-331] as it effectively modifies key cardiometabolic risk factors, improves vascular function, and alters inflammatory response during ischemia [332, 333]. Moreover, moderate-intensity aerobic exercise benefits executive functions and alters brain activity in older adults [136]. Specifically, Colcombe and colleagues [136], using task-based fMRI, showed that aerobic training improved response inhibition performance while increasing neural activation in prefrontal and parietal cortices and decreasing activation in the anterior cingulate cortex - areas of the brain involved in selective attention and conflict resolution.  118  Despite the demonstrated benefits of targeted aerobic exercise in otherwise healthy community-dwelling older adults [136], it is currently not known whether these cognitive and neural benefits extend to those with SIVCI or how exercise alters patterns of brain activity in this population. Thus, we collected task-based functional magnetic resonance imaging (fMRI) data as part of a 6-month, single-blinded, randomized controlled trial (RCT) [334] to conduct a planned secondary analysis to assess the effect of moderate-intensity aerobic exercise training on response inhibition and associated functional activation of the cortex among older adults with mild SIVCI. Based on previous work [136], we hypothesized that aerobic exercise would improve response inhibition performance via enhanced functional activity as indicated by increased activation in the frontal and parietal cortices – specifically, the middle frontal gyrus, superior frontal gyrus, and superior parietal lobe – and reduced activation in the anterior cingulate cortex. No previous fMRI study has examined the effect of aerobic exercise on functional brain activity associated with response inhibition among those with SIVCI; therefore, a whole-brain analysis was performed to determine whether other regions show altered activation following aerobic training. 5.2 Methods 5.2.1 Study Design This is a planned secondary analysis of neuroimaging data acquired from a 6-month proof-of-concept RCT (NCT01027858) of aerobic exercise in older adults with mild SIVCI [293]. The research team trained assessors, who were blinded to group allocation of participants. Functional MRI data were acquired at baseline prior to randomization and at trial completion (i.e., six months) in a subset of eligible participants. 119  5.2.2 Participants We recruited from the University of British Columbia Hospital Clinic for Alzheimer’s Disease and Related Disorders, the Vancouver General Hospital Stroke Prevention Clinic, and specialized geriatric clinics in Metro Vancouver, BC. Recruitment occurred between December 2009 and April 2014 with randomization occurring on an ongoing basis. Study participants were clinically diagnosed with mild SIVCI as determined by the presence of cognitive syndrome and small vessel ischaemic disease [294]. Small vessel ischemic disease was defined as evidence of relevant cerebrovascular disease by brain computed tomography or MRI defined as the presence of both: 1) Periventricular and deep WMLs; 2) Absence of cortical and/or cortico-subcortical non-lacunar territorial infarcts and watershed infarcts, hemorrhages indicating large vessel disease, signs of normal pressure hydrocephalus, or other specific causes of WMLs (i.e., multiple sclerosis, leukodystrophies, sarcoidosis, brain irradiation). In addition to the neuroimaging evidence, the presence or a history of neurological signs such as Babinski sign, sensory deficit, gait disorder, or extrapyramidal signs consistent with sub-cortical brain lesion(s) was required and confirmed by study physicians (G-YRH and PL). Cognitive syndrome was defined as a baseline Montreal Cognitive Assessment (MoCA)[335] score less than 26/30. However, participants were free of frank dementia (i.e., clinically diagnosis of dementia) as determined by a Mini-Mental State Examination (MMSE) score ≥ 20 and the absence of diagnosed dementia of any type [180]. Progressive cognitive decline was confirmed through medical records or caregiver/family member interviews. Additionally, all individuals met the following inclusion criteria: 1) community-dwelling in Metro Vancouver; 2) had a caregiver, family member, or friend who interacts with him/her on a weekly basis; 3) able to comply with 120  scheduled visits, treatment plan, and other trial procedures; 4) able to read, write, and speak English in which psychometric tests are provided with acceptable visual and auditory acuity; 5) stable on a fixed dose of cognitive medications (e.g., donepezil, galantamine, rivastigmine, memantine, etc.) that was not expected to change during the 12-month study period, or, if they are not on any of these medications, they are not expected to start them during the 12-month study period; 6) able to walk independently; and 7) was in sufficient health to participate in the study’s aerobic-based exercise training program, based on medical history, vital signs, physical examination by study physicians, and written recommendation by family physician. The exclusion criteria were: 1) diagnosis of another type of neurodegenerative (e.g., AD) or neurological condition (e.g., multiple sclerosis, Parkinson’s disease, etc.) that affects cognition and mobility; 2) at high risk for cardiac complications during exercise and/or unable to self-regulate activity or to understand recommended activity level (i.e., Class C of the American Heart Risk Stratification Criteria); 3) clinically significant peripheral neuropathy or severe musculoskeletal or joint disease that impairs mobility; 4) taking medications that may negatively affect cognitive function, such as anticholinergics, including agents with pronounced anticholinergic properties (e.g., amitriptyline), major tranquilizers (typical and atypical antipsychotics), and anticonvulsants (e.g., gabapentin, valproic acid, etc.); 5) participation in a clinical drug trial concurrent to this study; or 6) diagnosed with dementia of any type.  The Consolidated Standards of Reporting Trial flowchart shows the number and distribution of participants included in this secondary analysis (Figure 5.1). Of the 38 participants (54% 121  of parent sample) that completed baseline MRI scanning, 10 (26% of the sample) dropped out from the study and 7 (18% of the sample) failed to complete the full flanker task (i.e., not attentive to the task and missed substantial amount of the test) at trial completion. Consequently, 21 participants who completed MRI at baseline and trial completion (i.e., 6 months) were included in this secondary analysis (30% of parent sample). Ethical approval was provided by the University of British Columbia’s Clinical Research Ethics Board (H07-01160). All participants provided written informed consent.   122 Figure 5.1 Overview of the flow of study participants through the 6-month studyInitial Screen for Eligible Participants (N=582) Excluded:   Failed to meet inclusion criteria (N=81)  No response (N=61)  No interest (N=318) In Person Screening (N=122) Excluded:  Failed to meet inclusion criteria (N=38)  No interest (N=13) Consented, Completed Baseline Assessment and Randomized (N=70) CON (N=35) 6-Month fMRI (N=11) 6-Month fMRI (N=10) AT (N=35) Baseline fMRI (N=19) Baseline fMRI (N=19) Dropout (N=5) Dropout (N=5) No interest in MRI (N=16) No interest in MRI (N=16) Unable to complete Flanker at 6M fMRI session due to not attending to the task (N=3)  Unable to complete Flanker at 6M fMRI session due to not attending to the task (N=4)   123 5.2.3 Randomization The randomization sequence was generated using the web application www.randomization.com with a ratio of 1:1 to aerobic training (AT) or usual care plus education (CON). A research team member not involved with the study held this sequence at a remote location. After the completion of consent and baseline testing, the research coordinator contacted the team member holding the list to determine the next allocation.  5.2.4 Sample Size This secondary analysis was planned a priori as indicated by the inclusion of the fMRI sequences. The sample size for the parent study was determined based on providing adequate power (>0.75) to detect an effect of aerobic training on the Alzheimer’s Disease Assessment Scale cognitive subscale (ADAS-Cog) [293]. Of the 70 participants recruited and randomized in the parent study, potentially eligible participants were approached to participant in the fMRI sub-study. Thirty-eight participants agreed to participate in baseline fMRI scanning. 5.2.5 Aerobic Training and Compliance For the AT group, aerobic training consisted of thrice-weekly 60-minute classes of walking for the 6-month intervention period. Each 60-minute class included a 10-minute warm-up, 40-minutes of walking, and a 10-minute cool down. Participants were monitored using heart rate monitors, rate of perceived exertion, the “talk test”, and pedometers; exercise intensity was progressed to the range of 60% to 70% of heart rate reserve, after which this was sustained for the remainder of the intervention period. Compliance, expressed as the 124  percentage of the total AT classes attended, was calculated from attendance sheets. In addition to exercise training, the AT group also received monthly educational materials about VCI and healthy diet. 5.2.6 Usual Care Participants in the CON group received usual care plus additional monthly educational materials about VCI and healthy diet (i.e., the same materials provided to the AT group). However, no specific information regarding physical activity was provided. In addition, research staff phoned the CON participants on a monthly basis to maintain contact and to acquire research data.  5.2.7 Adverse Effects All participants were instructed to report any adverse effects due to the AT exercises to our research coordinator, such as falls or musculoskeletal pain persisting longer than 48 hours. Participants were also questioned about the presence of any adverse effects, such as musculoskeletal pain or discomfort, at each exercise session. All instructors also monitored participants for symptoms of angina and shortness of breath during the exercise classes. External experts from our safety monitoring committee reviewed all adverse events reported on a monthly basis. 5.2.7 Behavioural Analysis Performance during all 16 trials for the six runs (96 trials total) was examined to assess accuracy for each participant at both time-points. Only correct responses were included in 125  subsequent analyses. Mean reaction time was calculated by averaging reaction time of the accurate trials (i.e., correct responses) in the six runs and comparison of mean reaction time between the experimental groups were conducted with an overall alpha level of p<0.05. 5.2.8 Descriptive Variables At baseline, participants underwent a clinical assessment with study physicians (GYRH and PL) to confirm current health status and study eligibility. Age in years and education level were assessed by self-report. Standing height was measured as stretch stature to the 0.1 cm per standard protocol. Weight was measured twice to the 0.1 kg on a calibrated digital scale. Waist-to-hip ratio was determined by measuring the widest part of the hip circumference and the waist just above the navel in centimeters. The Functional Comorbidity Index[298] assessed  the number of comorbid conditions related to physical functioning.  Global cognition was assessed using the MMSE [180] and the MoCA [53]. The MMSE is a 30-point test that encompasses several cognitive domains. The MoCA is a 30-point test that covers multiple cognitive domains. The MoCA has been found to have good internal consistency and test-retest reliability and was able to correctly identify 90% of a large sample of individuals with MCI from two different clinics with a cut-off scores of < 26/30 [53]. We used the Six-Minute Walk Test (6MWT) [336] to assess general cardiovascular capacity [304]. Total distance walked (in meters) in six minutes was recorded. 5.2.9 Magnetic Resonance Imaging (MRI) Data Acquisition All MRI was conducted at the University of British Columbia (UBC) MRI Research Center located at the UBC Hospital on a 3.0 Tesla Intera Achieva MRI Scanner (Phillips Medical 126  Systems Canada, Markham, Ontario) using an 8-channel SENSE neurovascular coil. The functional MRI (fMRI) consisted of six successive runs with 70 dynamic images of 36 slices (3 mm thick) with the following parameters: repetition time (TR) of 2000 milliseconds (ms), echo time (TE) of 30 ms, flip angle (FA) of 90 degrees, field of view (FoV) of 240 mm, acquisition matrix 80x80. High resolution anatomical MRI T1 images were acquired using the following parameters: 170 slices (1 mm thick), TR of 7.7 ms, TE of 3.6 ms, FA of 8 degrees, FoV of 256 mm, acquisition matrix of 256x200.  During the scanning session, participants performed a modified Eriksen flanker task [167] – an executive task of selective attention and response inhibition. Participants viewed a series of five arrows with a boxed central arrow cue flanked by a pair of arrows on either side. This modification was included to reduce cognitive demand given the study population [312]. In half the trials, the flanking arrows pointed in the same direction as the central arrow cue (e.g., < < < < < <; congruent condition). In the other half of trials, the flanking arrows pointed in the opposite direction (e.g., > > < > >; incongruent condition). For each trial, participants were instructed to signal the direction of the central arrow points via a simple key press. Each trial began with a three-second fixation cross, followed by a one-second pre cue cross to notify the participants of the stimulus onset. The arrows remained on the screen until a button was pressed or for the duration of a predetermined time (4 seconds), after which the next trial would begin. There were a total of 16 trials per run, with a total of six consecutive runs, in which the visual presentation of left and right stimuli was counter-balanced over the six runs.  127  Total WML volume (in mm3) at baseline was quantified with structural MRI data acquired on the same MRI scanner (3T Achieva, Philips Medical Systems, Markham, Ontario) at the UBC MRI Research Centre. A T2-weighted scan and a proton-density-weighted (PD-weighted) scan were acquired for each subject. For the T2-weighted images, the repetition time (TR) was 5,431 ms and the echo time (TE) was 90 ms, and for the PD-weighted images, the TR was 2,000 ms, and the TE was 8 ms. T2- and PD-weighted scans had dimensions of 256 x 256 x 60 voxels and a voxel size of 0.937 x 0.937 x 3.000 mm. Briefly, WMLs were identified and digitally marked (i.e., placing seed points) by a radiologist on T2 and PD weighted images. Marked WMLs were automatically segmented by a customized Parzen windows classifier that estimated the intensity distribution of the lesions – which also included heuristics that optimized the accuracy of the estimated distributions [127, 299, 300]. WML segmentation was reviewed by a trained technician to ensure accuracy. 5.2.10 fMRI Processing and Analysis Data were processed using FEAT (Version 5.0.6), which is part of FSL (FMRIB’s Software Library, Version 4.1.4; FMRIB Analysis Group, Oxford University, UK). Data were motion corrected [337], registered by FLIRT [338], and spatially smoothed with a Gaussian kernel of 6.0 mm full width at half maximum. The event-related onset was then convolved using a double-gamma function. Contrasts between experimental conditions (i.e., incongruent – congruent) were constructed to investigate neural activity associated with conflict resolution [136, 339]. Parameter estimates of these contrasts were carried forward to a secondary individual-level fixed-effects analysis to examine contrast across time (i.e., 6-months – baseline). Parameter estimates resulting from previous two levels of fixed-effect analyses 128  were subsequently entered in a higher-level mixed-effects (FMRIB's Local Analysis of Mixed Effects or FLAME) group analysis to provide an accurate estimation of group differences in neural activity across time. Significantly activated clusters were identified using voxel-wise threshold of z>1.65, cluster correction p<0.05. The anatomical location of significant clusters was labeled using Harvard-Oxford Cortical Structural Atlas in FSL. For each cluster, a 14 mm spherical region of interest (ROI) was drawn using Montreal Neurological Institute (MNI) coordinates surrounding the local maxima; individual-level percent signal change was extracted via Featquery in FSL and exported to IBM SPSS Statistic 19 (SPSS Inc., Chicago, IL) for further statistical analyses.  The primary purpose of extracting percent signal change data was to examine whether the group differences in changes in neural activity identified in the FLAME analyses were maintained after adjusting for baseline variables of interest. Difference in percent signal change was computed in SPSS as 6-month percent signal change minus baseline percent signal change. Analysis of covariance (ANCOVA) was used to statistically test for significant differences in cognitive performance, flanker task performance (i.e., reaction time), and regional percent signal change between CON and AT groups. To reduce the probability of Type I error when comparing percent signal change across time between the two experimental groups, the overall alpha level was set at p≤0.03. Finally, partial correlation analyses were conducted to determine whether changes in neural activity in regions correlated with changes in behavioural performance. We restricted our correlational analyses to regions and behaviour performance that were significantly different 129  between the two experimental groups at trial completion. Baseline MoCA, baseline WML, and baseline flanker task performance were entered as covariates.    130  5.3 Results 5.3.1 Participants A total of 38 participants completed fMRI scanning at baseline and data from 21 participants were included in the final analysis (Figure 5.1). Baseline characteristics of the final 21 participants (who completed both baseline and trial completion fMRI) are reported in Table 5.1; these characteristics do not significantly differ from 70 eligible participants enrolled in the parent study [340]. Their mean age (standard deviation) was 71.5 (8.6) years; this is similar to the mean age of the entire cohort, which was 74.3 (8.3) years.   131  Table 5.1 Baseline Participant Characteristics (N=21)  Baseline Mean (SD) 6-Month Mean (SD) Adjusted 6-Month Change╫ Mean (SE) CON n=11 n=11 n=11 Age (yr) 72.3 (8.8) 72.6 (8.9) - Height (cm) 167.6 (9.3) 167.8 (9.9) - Weight (kg) 77.4 (9.7) 76.4 (9.6) - Sex (M/F) 4/7 4/7 - MMSE (30 point) 27.7 (1.5) 27.7 (1.3) - MOCA (30 point) 24.1 (2.1) 23.6 (3.3) - FCI 2.6 (1.9) 3.3 (1.7) - 6-Minute Walk (m) 515.6 (50.2) 516.6 -9.7 (16.3) Flanker Task – Congruent (ms) 805.2 (254.2) 681.3 (76.2) -40.9 (17.2) Flanker Task – Incongruent (ms) 823.7 (109.7) 764.4 (70.5)  -7.5 (20.7) White Matter Lesion (mm^3) 2106.2 (3671.7) -- - AT n=10 n=10 n=10 Age (yr) 71.7 (8.8) 73.5 (7.9) - Height (cm) 167.7 (12.1) 169.6 (11.8) - Weight (kg) 71.9 (17.4) 71.2 (16.9) - Sex (M/F) 4/6 4/6 - MMSE (30 point) 26.8 (2.3) 26.3 (2.7) - MOCA (30 point) 22.2 (2.4) 22.3 (1.4) - FCI 2.7 (1.4) 2.6 (1.3) - 6-Minute Walk (m) 539.6 (93.4) 570.9 (91.2) 43.0 (17.3) Flanker Task – Congruent (ms) 681.7 (96.8) 636.3 (71.3) -136.7 (18.1) * Flanker Task – Incongruent (ms) 737.9 (119.7) 708.0 (74.4) -86.8 (21.9) * White Matter Lesion (mm^3) 4012.5 (4344.9) -- - Note: MMSE===Mini-Mental Status Examination; MoCA===Montreal Cognitive Assessment; FCI===Functional Comorbidity Index * p<0.05 for between-group difference ╫Adjusted for baseline MoCA, baseline WML and baseline task performance    132  5.3.2 AT Compliance, Adverse Effects, and Changes in Fitness The average compliance observed in the AT group for the parent study was 68%. Two study-related adverse events were reported in the AT group and one in the CON group. All three were non-syncopal falls. One of the falls in the AT group resulted in a broken tooth and required assessment in the Emergency Department; the remaining two did not result in injury. There was tentative evidence for a difference in cardiovascular capacity between the two experimental groups (p===0.057), such that the AT group increased 6MWT performance whereas the CON group showed no significant change after adjusting for baseline 6MWT performance, MoCA score, and WML (Table 5.1). 5.3.3 Behavioural Results  The overall accuracy of flanker task performance during fMRI scanning at study baseline and study completion is reported in Table 5.2. Task performance reported in the main manuscript pertains to trials that were correctly responded as described in the previous section. Compared with the CON group, the AT group significantly improved performance (see adjusted change in mean reaction time across 6-months, Table 5.1) during both congruent (p<0.01) and incongruent trials (p=0.03). No significant group differences were detected for cognitive cost during flanker task interference (i.e., incongruent – congruent).   133  Table 5.2 Flanker Task Accuracy   Baseline Mean (SD) 6-Month Mean (SD)  CON n=11 n=11 Congruent 0.79 (0.37) 0.91 (0.27) Incongruent 0.75 (0.36) 0.83 (0.35) AT n=10 n=10 Congruent 0.78 (0.35) 0.95 (0.10) Incongruent 0.68 (0.39) 0.89(0.22)    134  5.3.4 fMRI Results The fMRI analysis identified three clusters that were significantly activated during conflict resolution (i.e., incongruent – congruent) contrasted across time and group. Table 5.3 lists the specific MNI coordinates, the respective brain regions, and the number of voxels constituting these clusters. Percent signal change at baseline and difference in percent signal change across 6-month for all ROIs in each group is presented in Table 5.4. For difference in percent signal change across the 6-month period, we found that compared with the CON group, the AT group showed reduced activity in the left lateral occipital cortex (p<0.03) and right superior temporal gyrus (p=0.03) after adjusting for baseline MoCA, baseline WML, and baseline percent signal change.    135 Table 5.3 Significant Clusters Identified through fMRI Analysis        MNI Coordinates Cluster Cluster Sub-regions Hemisphere Region Cluster Size (voxels) Z Max X Y Z 3 1 Right Occiptal pole 1066 3.61 4 -90 18  2 Right Cuneal cortex  3.38 4 -78 22  3 Right Supracarine cortex  3.27 16 -64 18  4 Right Lateral occipital cortex  3.26 22 -76 24  5 Right Occiptial pole  3.23 4 -90 30  6 Right Intracalcarine cortex  3.20 8 -78 14 2 1 Left Lateral occipital cortex 638 3.46 -32 -80 14  2 Left Occipital pole  3.17 -16 -88 14  3 Left Lateral occipital cortex  3.16 -32 -76 24  4 Left Lateral occipital cortex  3.07 -20 -84 26  5 Left Lateral occipital cortex  3.00 -38 -76 28 1 1 Right Middle temporal gyrus 579 3.45 40 -40 4  2 Right Superior temporal gyrus  3.15 42 -28 4  3 Right Lateral occipital cortex  3.13 40 -62 4  4 Right Lateral occipital cortex  3.09 48 -60 -2  5 Right Superior temporal gyrus  3.00 46 -26 -4 Threshold at Z=1.65 Note: These regions were identified through the contrasts of incongruent – congruent across time and group 136 Table 5.4 Percent Signal Change of Incongruent - Congruent    Baseline % Signal Change Difference in % Signal Change  (6-Month – Baseline) ╫ CON n=11    Cluster Hemisphere Cluster Sub-regions % Signal Change (SD) % Signal Change (SD) 3 Right Occiptal pole 0.0418 (0.0512) 0.0373 (0.1647)  Right Cuneal cortex 0.0511 (0.0487) 0.0862 (0.1953)  Right Supracarine cortex 0.0301 (0.0482) 0.0970 (0.1571)  Right Lateral occipital  0.0417 (0.0647) 0.0420 (0.0918)  Right Occiptial pole 0.0246 (0.0498) 0.1698 (0.2880)  Right Intracalcarine cortex 0.0405 (0.0446) 0.0947 (0.2435) 2 Left Lateral occipital  0.0403 (0.0502) 0.0637 (0.1072)  Left Occipital pole 0.0313 (0.048) 0.0460 (0.1677)  Left Lateral occipital  0.0363 (0.0457) 0.0536 (0.0967)  Left Lateral occipital  0.0317 (0.044) 0.0634 (0.0895)  Left Lateral occipital  0.0139 (0.0209) 0.0716 (0.1105) 1 Right Middle temporal  0.0156 (0.0246) 0.0641 (0.0870)  Right Superior temporal  0.0149 (0.0309) 0.0957 (0.0979)  Right Lateral occipital  0.0143 (0.0284) 0.0648 (0.0762)  Right Lateral occipital  0.0252 (0.033) 0.0646 (0.1106)  Right Superior temporal  0.0118 (0.0144) 0.0882 (0.0837)      AT n=10    Cluster Hemisphere Cluster Sub-regions % Signal Change (SD) % Signal Change (SD) 3 Right Occiptal pole 0.1100 (0.0755) -0.0175 (0.2468)  Right Cuneal cortex 0.1080 (0.0736) -0.0511 (0.1300)  Right Supracarine cortex 0.0761 (0.0582) -0.0146 (0.0652)  Right Lateral occipital  0.0746 (0.0535) -0.0342 (0.0543)  Right Occiptial pole 0.1382 (0.1392) -0.0719 (0.1041)  Right Intracalcarine cortex 0.1549 (0.2) -0.0556 (0.1575) 2 Left Lateral occipital  0.0645 (0.0471) -0.0394 (0.0885)*  137     Baseline % Signal Change Difference in % Signal Change  (6-Month – Baseline) ╫ AT n=10    2 Left Occipital pole 0.0927 (0.1028) -0.0388 (0.0924)  Left Lateral occipital  0.0956 (0.095) -0.0451 (0.0359)  Left Lateral occipital  0.1030 (0.1242) -0.0753 (0.1103)*  Left Lateral occipital  0.0738 (0.0881) -0.0384 (0.0564) 1 Right Middle temporal  0.0706 (0.0741) -0.0321 (0.1039)  Right Superior temporal  0.0469 (0.0688) -0.0131 (0.0802)*   Right Lateral occipital  0.0455 (0.0422) -0.0142 (0.0435)  Right Lateral occipital  0.0833 (0.0626) -0.0425 (0.0650)  Right Superior temporal  0.0437(0.0414) -0.0092 (0.0597) ╫Adjusted for baseline MoCA, baseline WML, and baseline % signal change * p≤0.03 for between-group differences 138 5.3.5 Partial Correlation Results Partial correlations between change in neural activity across time and flanker task performance during: i) congruent; ii) incongruent condition; and iii) incongruent minus congruent conditions were determined for the entire study sample.  i) Flanker Task - Congruent  After adjusting for baseline MoCA, baseline WML, and baseline congruent trial performance, reduced percent signal change of left lateral occipital cortex (r=0.484, p=0.04) and superior temporal gyrus (r=0.482, p=0.04) were significantly associated with improved (i.e., faster reaction time) flanker task congruent trials performance at trial completion (Table 5.5 and Figures 5.2 and 5.3).   ii) Flanker Task – Incongruent After adjusting for baseline MoCA, baseline WML, and baseline incongruent trial performance, reduced percent signal change of superior temporal gyrus (r=0.471, p=0.05) was significantly associated with improved (i.e., faster reaction time) flanker task incongruent trials performance at trial completion (Table 5.5 and Figure 5.4).  iii) Flanker Task – Incongruent minus Congruent No significant correlations were observed between flanker performance (i.e., incongruent – congruent reaction time) and percent signal change with the ROIs (Table 5.5). Figure 5.5 and Figure 5.6 illustrates the difference in percent signal change of the left lateral occipital cortex and superior temporal gyrus across the 6-month trial for the two experimental groups, after adjusting for baseline percent signal change.  139 Table 5.5 Partial Correlation between Change in Regional Brain Activity and Flanker Task Performance at Trial Completion   N=21  Difference in % Signal Change  (6-Month – Baseline) Flanker Task Congruent Condition Reaction Time (6-Month) Flanker Task Incongruent Condition Reaction Time (6-Month) Flanker Task Incongruent – Congruent Reaction Time Change (6-Month – Baseline) Cluster Hemisphere Cluster Sub-regions Correlation Coefficient╫ Correlation Coefficient╫ Correlation Coefficient╫ 3 Right Occipital pole 0.252 0.312 0.250  Right Cuneal cortex 0.333 0.339 0.142  Right Supracarine cortex 0.204 0.221 0.232  Right Lateral occipital cortex 0.405 0.373 0.044  Right Occipital pole 0.422 0.407 0.082  Right Intracalcarine cortex 0.271 0.237 0.061 2 Left Lateral occipital cortex 0.362 0.248 -0.156  Left Occipital pole 0.255 0.362 0.115  Left Lateral occipital cortex 0.241 0.251 -0.099  Left Lateral occipital cortex 0.484* 0.429 -0.119  Left Lateral occipital cortex 0.155 0.232 0.041 1 Right Middle temporal gyrus 0.272 0.097 0.063  Right Superior temporal gyrus 0.482* 0.471* -0.067  Right Lateral occipital cortex 0.364 0.257 -0.102  Right Lateral occipital cortex 0.540 0.589 0.005  Right Superior temporal gyrus 0.280 0.369 0.191 ╫Adjusted for baseline MoCA, baseline WML, and baseline flanker task performance * p<0.05 for significant correlation; positive correlations indicate increase neural activity from baseline is associated with poorer (i.e., increased reaction time) flanker task performance at trial completion    140  Figure 5.2 Partial Correlation Plot between Left Occipital Cortex Percent Signal Change and Change in Congruent Reaction Time Over the 6-month Intervention (r=0.484, p=0.04).   Note: Individuals’ group assignment is indicated by the color of the dot   141 Figure 5.3 Partial Correlation Plot between Superior Temporal Gyrus Percent Signal Change and Change in Congruent Reaction Time Over the 6-month Intervention (r=0.482, p=0.04).   Note: Individuals’ group assignment is indicated by the color of the dot  142 Figure 5.4 Partial Correlation Plot between Superior Temporal Gyrus Percent Signal Change and Change in Incongruent Reaction Time Over the 6-month Intervention (r=0.471, p=0.05).   Note: Individuals’ group assignment is indicated by the color of the dot   143 Figure 5.5 Neural Activity of Left Lateral Occipital Cortex Over the 6-Month Intervention   144  Figure 5.6 Neural Activity of Superior Temporal Gyrus Over the 6-Month Intervention    145  5.4 Discussion  In community-dwelling older adults with mild SIVCI, relative to usual care, six months of thrice-weekly aerobic training significantly improved behavioral performance on the flanker task and reduced task-related neural activity in the right superior temporal gyrus and left lateral occipital cortex. Moreover, we found that improved performance of executive functions was significantly associated with reduced hemodynamic response during task in the left lateral occipital and right superior temporal gyrus over the 6-month intervention period. Overall, we provide novel evidence that suggest aerobic training has cognitive and neural benefits for older adults with mild SIVCI.  Individuals with SIVCI generally show impaired executive functions, and consequently, are at risk for functional decline [282, 283]. Our results of positive impact of aerobic training on flanker task performance and functional activity of the cortex extend previous observations among otherwise healthy community-dwelling older adults [136]. Colcombe and colleagues [136] demonstrated six months of thrice-weekly aerobic training significantly improved flanker task performance. Moreover, they showed that aerobic training significantly increased task-related activity in regions of the prefrontal and parietal cortices that are involved in flanker task performance as compared with non-aerobic training. In contrast to these findings, our results suggest that thrice-weekly aerobic training results in reduced task-related activity in the right superior temporal gyrus and left lateral occipital cortices relative to usual care. Both of these regions are also implicated in flanker-type tasks [341-343]. The discrepancy in the neuroimaging findings may be due to difference in study sample composition. Our study included older adults with a clinical diagnosis of mild SIVCI while 146  Colcombe and colleagues included sedentary, but otherwise healthy, older adults. Current evidence suggests that WMLs impact the integrity of functional neural networks, including those that subserve executive functions [87, 207]. Of particular relevance to our findings, Li and colleagues [326] showed that individuals with mild SIVCI  have greater neural activity compared with healthy controls while performing the Stroop Test – also an executive task of selective attention and response inhibition; whereas participants with subcortical ischemic vascular dementia (SIVD) exhibited less activation in the same anatomical regions as compared with the controls. However, there were no significant differences in Stroop Test behavioural performance between the SIVCI and SIVD groups. Thus, Li and colleagues suggested that the observed greater neural activity was a compensatory mechanism to maintain cognitive function in the presence of small vessel disease. Chuang and colleagues [312] also demonstrated that older adults with higher cardiovascular risk had greater task-related activation during flanker task performance and that increased activation was associated with poorer performance. In light of this cross-sectional neuroimaging evidence, our results suggest that thrice-weekly aerobic training may maintain, or increase, neural efficiency among older adults with mild SIVCI, hence reducing the need for compensatory neural processes.  The results of our partial correlation analysis support the notion of neural efficiency. We found a significant association between reduced neural activity in the right superior temporal gyrus and faster response times on both conditions of the flanker task. Of particular relevance, in a cross-sectional study also using the flanker task, Casey and colleagues [341] reported decreased task-related activity in inferior parietal and superior temporal regions during the incongruent condition relative to the congruent condition. This effect was opposite 147  to what they observed in the superior parietal and frontal regions, which showed task-related activation increases during incongruent relative to congruent trials. The authors proposed the inverse relation between neural regions may reflect competing attentional and perceptual processes – between focusing attention narrowly versus broadening attention to the periphery – which would be engaged during incongruent and congruent trials, respectively [344]. Evidence suggests that aerobic exercise is an efficacious intervention strategy for improving cognitive function [128]. The cognitive benefits of aerobic exercise may be due to greater grey matter volumes within brain regions involved in executive control [345], greater white matter integrity [346], and up-regulation of growth factors supportive of neurogenesis and neural health, including vascular endothelial factor, insulin-like growth factor, and brain-derived neurotropic factor [347].    We also found a strong association between neural activity within the left lateral occipital cortex and performance of the flanker task during the congruent condition. The lateral occipital cortices are key regions responsive to visual sensory stimuli [348], particularly for a task that requires prolonged visual attention such as the flanker task. In a previous study using the go-no go task as a measure of selective attention and response inhibition, Boehler and colleagues [343] found that healthy younger adults displayed greater activation within the lateral occipital cortices during trials requiring response inhibition (no go trials) as compared with neutral trials (go trials), which suggests that these brain regions are responsive to situations where salient visual stimuli are presented. In the current study, we found that reductions in lateral occipital activation over the course of the intervention was associated with improvements in task performance, but only on trials with minimal demands for selective attention and response inhibition (i.e., congruent trials). This may be evidence 148  that aerobic training may have enhanced neural processing such that the amount of cognitive load required to process visual information during the congruent condition of the flanker task is reduced over time.  Contrary to the findings of Colcombe and colleagues [136], we did not observe significant changes in task-related activity in other key regions involved in flanker task performance including the 1) anterior cingulate cortex, 2) dorsolateral prefrontal cortex, and 3) superior parietal cortex. Our study included older adults with mild SIVCI while Colcombe’s study included healthy older adults. Notably, all of our study participants were recruited from geriatric or memory clinics. Thus, it is very likely that our participants are frailer than those included in Colcombe’s study. Lastly, due to the cognitive status of our study participants, we used a modified flanker task [312] that visually cued participants to the central arrow to lower the demand of the task. In turn, this could minimize recruitment of regions underlying response inhibition, including those found by Colcombe and colleagues.  The conclusions of our proof-of-concept randomized controlled trial are limited by several factors. First, given our inclusion and exclusion criteria (e.g., be able to participate in aerobic training, be able to undergo fMRI scanning), our study sample may represent those who are significantly healthier than the average older adult with mild SIVCI. While this study only included individuals with clinical diagnosis of small vessel disease by specialists, supported by MRI/CT findings [294] and neuropsychological testing, the clinical presentation may include mixed pathology such as concomitant AD [349]. To minimize the inclusion of those with concurrent AD pathology, future studies will need to examine additional biomarkers such as amyloid imaging or cerebrospinal fluid Aβ42 levels. The fMRI analysis was 149  performed with a liberal cluster threshold level (z==1.65), which may have resulted in false-positives. However, we do highlight that a threshold of z==1.65 has been used in many previous studies [339, 350-352]. Also, the intervention group had greater amount of social engagement compared with the control group in our study, which may have influenced the results. However, we also observed tentative evidence for differential cardiovascular fitness effects, suggesting that the cognitive and cortical activation effects observed herein might be attributed to aerobic-training induced physiological changes. Moreover, our study is limited by the small sample size. As such, we lack the necessary statistical power to detect small effects in our data. Future studies with larger sample sizes are required to support our current results.   5.5 Conclusion Individuals with SIVCI represent a population particularly at risk for dementia, hence, timely intervention is vital to ameliorate the symptoms of impairment and slow the progression of cognitive decline. Our 6-month proof-of-concept RCT provides insight into the underlying neural mechanisms by which aerobic training may positively impact cognitive performance among older adults with mild SIVCI. Our findings point to exercise improved neural efficiency as beneficial to cognitive performance and extend growing evidence that physical activity should be considered as a potential recommendation for this clinical population.   150  Chapter 6: General Discussion The preceding 4 chapters presented data acquired from fMRI studies that offered compelling evidence that falls, slow gait, and MCI may be independently as well as synergistically associated with detrimental changes in the brain, thereby impacting the structural and functional integrity. This thesis also demonstrated aerobic exercise as a potent strategy to mitigate functional loss and enhance neural efficiency among individuals with cognitive deficits. The aim of this chapter is to summarize and integrate the information introduced thus far; start with a synopsis of each study included in the dissertation, then revisit the research questions proposed in the first chapter, and lastly, end with a brief comment on the limitations and future direction for this area of research. 6.1 Summarizing the Studies Chapter 2, a 12-month prospective study of community-dwelling older adults, examined the structural neural correlates of falls as well as the association of these neural correlates with change in cognitive function. Results from the study showed that compared with non-fallers, older fallers had significantly lower cortical and subcortical gray matter volume as well as lower left lateral orbitofrontal white matter volume. Moreover, lower left lateral orbitofrontal white matter volume at baseline was associated with greater decline in set-shifting performance over 12 months. These results suggest that falls may an indicator of subclinical changes in the brain that are linked with subsequent decline in executive functions.  Chapter 3, a cross-sectional study of older adults with MCI, aimed to assess the functional neural mechanisms underlying slower gait. Compared with the older adults with normal gait 151  speed, those with slower gait had significantly lower inter-network connectivity between the SMN and the FPN. Moreover, lower connectivity between the SMN and FPN was positively associated with usual gait speed, and negatively associated with Stroop Test interference score. These correlational results suggest that less inter-network functional connectivity between the SMN and FPN may be a functional neural mechanism for the slowing of gait and impaired executive functions in older adults with MCI.  Chapter 4, a secondary analysis of data collected from a six-month proof-of-concept RCT (NCT01027858), assessed the impact of moderate-intensity aerobic exercise training on functional connectivity of FPN among older adults with mild SIVCI. Contrary to my hypothesis, aerobic training did not significantly increase, but rather maintained FPN connectivity during right finger tapping among older adults with mild SIVCI. Moreover, increased FPN connectivity over the six-month RCT was associated with poorer mobility performance, as measured by the Timed-Up-and-Go test. These results suggest that aerobic training may promote mobility via the maintenance of intra-network connectivity within the FPN. Chapter 5, also a secondary analysis of data collected from a six-month proof-of-concept RCT (NCT01027858), examined the effect of moderate-intensity aerobic exercise training on executive functions and functional neural activity among older adults with mild SIVCI. I found aerobic training significantly improved flanker task reaction time. In addition, aerobic training reduced activation in the left lateral occipital cortex and right superior temporal gyrus. Importantly, reduced activity in these brain regions was significantly associated with improved (i.e., faster) flanker task performance at trial completion, suggesting aerobic 152  training can improve executive functions and enhance neural efficiency of relevant brain areas. 6.2 Revisiting the Main Research Questions In Chapter 1, I listed 3 research questions (the third research question broken down into two parts) that are central to this dissertation, here I will revisit them and provide a concluding remark for each. 1. What are the brain structures implicated in older adults with a significant history of falls? Recent research has established several brain structural abnormalities independently associated with slow gait [171] or in conjunction with MCI [252, 353], including smaller volume in total and cortical gray matter [171, 252], total hippocampal [171], presubiculum [171], premotor cortex [252], prefrontal cortex [252], and dorsolateral prefrontal cortex [252]; as well as larger lateral ventricular volume, particularly in the temporal horns and the left lateral ventricle [353]. Chapter 2 of this thesis extends providing additional insight into structural neural correlates of falls, which are significant brain structures that are negatively impacted by another aspect of mobility impairment – falls. As discussed in Chapter 2, my research suggests that, similar to findings from the slow gait research, falls are correlated with smaller gray matter volume of the left lateral orbitofrontal cortex, right insula, pallidum, and hippocampus. Moreover, I found older fallers also exhibited significantly less volume in cerebral white matter of the left lateral orbitofrontal cortex and bilateral pars triangularis. 153  Bridging current understanding on gait dysfunction related brain changes with my findings on neuroimaging data regarding falls among community-dwelling older adults; it can be argued that certain brain structures may be particularly susceptible to mobility impairments secondary to aging, namely the total volume of cortical gray matter, the hippocampus and gray matter of the frontal cortical regions. This observation is not surprising given the relevance of these structures in the maintenance of executive functions and memory - cognitive domains that are often reported to be affected by mobility impairments. Also, given that the orbitofrontal area is functionally involved in execution functions, the observation of falls-related lower gray matter volume in the orbitofrontal region may be an indication that altered fronto-executive network connectivity may be expected within this population. The novel aspect of my research demonstrated that differentiable regional white matter volume may be observed between older fallers and non-fallers. While the finding may be preliminary (no other study to date in this area of research has reported similar results), I postulate that this difference in white matter volume may be attributable to how slow gait and falls manifest with respect to aging. Knowing that slow gait is a precursor to many of the risk factors for falls, it is possible that the deterioration of white matter may materialize at a later stage of the aging process, such that loss of gray matter preceded the loss of white matter. Under this assumption, these falls-related white matter disintegrations may be an early indicator of steeper subsequent structural and functional decline, thereby highlighting falls among older adults as a key biomarker. 2. What are the functional neural correlates of slower gait speed in older adults with MCI?  154  Considering the conceptual framework proposed in Chapter 1 (Figure 1), the basis for brain health is founded in intact structure and function. The effect of slow gait and MCI on brain structure is covered in part in the previous research question; here I address the functional aspects of the brain impacted by the combinatory influence of the two geriatric conditions.  As demonstrated in Chapter 3, older adults with slower usual gait speed and MCI expressed significantly less inter-network functional connectivity between the SMN and the FPN compared with their counterparts with normal usual gait speed and MCI. Moreover, less inter-network connectivity between these two functional networks was associated with slower gait, poorer balance and worse executive functions. Notably, no differences in intra-network functional connectivity were observed, hence the interplay between slow gait and cognitive impairment may reflect the temporal relationship between inter-network and intra-network connectivity disruptions. While it is well-understood that cognitive impairment may independently affect neural function [326, 354, 355], few studies examined the contribution of mobility impairment in variations in brain function [320, 356], with only one – to my knowledge – that examined neural network connectivity with respect to gait speed among cognitive healthy older adults [320]. Of particular relevance, researchers identified intrinsically connected neural networks under usual walking condition include the SMN as well as the FPN [320], suggesting these two networks may be specifically targeted during the manifestation of gait dysfunction. Extending these findings, I found that connections between these two networks were important in the discussion of gait speed among individuals that were cognitive impaired. The synergistic effect of slow gait and MCI may exert its influence on interfering inter-network connectivity prior to disrupting intra-network connections. This aligns with evidence that report aging induces a reduction in global 155  network integration (i.e., loss of communication between neural networks) [357] and suggest that the combination of slow gait and MCI may represent an accelerated rate of disintegration in functional neural network efficiency. 3. What are the functional neural mechanisms by which aerobic exercise promote mobility and cognitive outcomes in older adults with mild vascular MCI? The research that comprises Chapter 4 and Chapter 5 of this dissertation provides evidence that aerobic exercise training may contribute to positive changes in brain function even among older individuals diagnosed with cognitive impairment. To reiterate, in Chapter 4, I found that 6 months of thrice-weekly aerobic training maintained FPN network connectivity, whereas those who did not undergo exercise training showed significant increase in the strength of connectivity in the same network. Similarly, results from Chapter 5 of the thesis suggest that the same type of exercise training regimen alters brain regional activity patterns, such that compared with study participants that were in the control group, those who completed the aerobic exercise training displayed lower levels of neural activation with better executive function performance.  Incorporating results acquired from my work from Chapter 4, Chapter 5 and the current knowledge in favorable brain changes relative to aerobic exercise, it is probable that aerobic training achieves maintenance of functional network integrity and enhances neural activities via improving neurophysiology, increasing brain reserve, and upholding greater neural efficiency. As mentioned throughout sections of the dissertation, aerobic exercise is associated with elevating levels of neurotrophic factors, including BDNF and IGF-1 [358], both of which are relevant in the topic of neural plasticity [146]. The up-regulation of these 156  advantageous neurochemicals and subsequent neural plastic events (for example, changes in dendritic length, synaptic formation, etc.) may ultimately lead to augmented brain reserve. Another facet where aerobic exercise may benefit brain function resides in the concept of neural efficiency and compensation (see section 1.4.4). Promoting neural efficiency reduces the amount of active neural resources required to conduct cognitively demanding tasks, thereby diminishes the amount of compensation necessary for a given cognitive domain and allow the available neural resources to be reallocated to other areas of need in the impaired brain. 6.3 Limitations  The limitations of the studies included in this thesis have been discussed in the respective chapters; here I will provide the overarching impediments that should be considered. 6.3.1 General Limitations Due to the cost of neuroimaging, the sample size for studies from Chapter 4 and Chapter 5 was relatively small and likely consisted of participants that were considerably healthier than their peers given their ability to partake in exercise intervention trials and undergo MRI. The evaluation of gait speed via a 4-meter walk in Chapter 4 may not be an accurate representation of the individuals’ true gait speed, given that it is less ideal to evaluate gait speed with a distance less than 4 meters [17]. 157  6.3.2 fMRI Limitations The field of fMRI research is susceptible to many constraints, due to a lack of standardized protocols (a side-product of the constantly evolving techniques as well as advancement in neuroimaging technology).  For example, cluster-forming threshold adopted in fMRI studies to date for computing significantly activated brain regions varies between a Z=1.66 to Z=3.1. In the instance of the fMRI study in Chapter 5 of this dissertation, the thresholds used was Z=1.66. Secondly, regarding functional connectivity analyses, the types of preprocessing procedure conducted were under serious debate in the recent past. Critically, as fMRI data is highly susceptible to the effects of head motion, methods for motion correction were subject to much scrutiny. The canonical strategy commonly involves extracting motion parameters (usually 6 degrees of motion parameters, reflecting translational and rotational motion) and removing their confounding effects by including these as covariates in subsequent analyses. However, since motion may exert differentiable effect on voxels in different spatial location, unbiased statistically regression of the motion parameters throughout all the voxels in the brain may not be ideal. Therefore, it is currently advised to implementing a step in the analysis pipeline that addresses significant motion by first identifying the frames (temporally on the timeseries data) with excessive movement as outliers and specifically removing the effect of these timepoints from the remaining data through a confound matrix and general linear modelling; followed by a ICA-oriented denoising procedure that removes all motion-related artifacts (as well as other artifacts; e.g., physiological noise) in the data.  158  Further, global signal regression is another topic under considerable amount of controversy. Traditionally, removing spurious signals arising from the brain globally was understood as a necessary step to remove the effect of motion (partially), physiological noises, and enhance signal-to-noise ratio. However, the discovery that zero-centering the global signal may artificially introduce anti-correlations and shift connectivity patterns between brain regions instigated discussion of the validity in global signal regression. However, given that global signal regression is a powerful step in eliminating artefactual variances but may distort connectivity relationships [359], the general consensus regarding this issue is that the investigator should consider the context of research questions at hand and evaluate if the benefit of such nuisance removal step outweighs the potential drawbacks (as done in Chapter 4). Moreover, in cases where there may be a large amount of uncertainty with respect to how the procedure may influence the data, it is recommended to proceed with the analyses both with and without global signal regression to determine if the reported findings are scientifically robust. 6.4 Future Directions This section will attempt to offer guidance for future research aimed to examine neural underpinnings of mobility impairment, cognitive impairment and investigate targeted strategies to amend functional deficits secondary to these impairments. 6.4.1 Neural Correlates of Mobility and Cognitive Impairments As suggested in Chapters 1, 2 and 3, the neural mechanism by which mobility impairment (with or without concomitant cognitive impairment) manifests among older adults remains 159  equivocal. Particularly, outside the scope of this thesis, there is a lack of knowledge on how brain functions are affected by these impairments while emerging evidence begins to offer insight into gait related structural abnormalities [171, 216, 252, 353]. To add to our current understanding in this topic, future studies should consider examining neural correlates of other aspects of mobility impairments in addition to gait and falls, as well as investigate the potential synergistic detrimental effect of mobility and cognitive impairments by studying populations that exhibit various degrees of these impairments. This will aid the identification of those at greater risk for functional decline as well as provide insight on biomarkers that can be targeted for intervention. Other considerations for fMRI applications should include administering longitudinal studies with repeated resting-state and task-based sessions. This will allow us to better understand the temporal relationship within the neural signatures (i.e., how do neural activities change across time) as well as between the neural correlates and mobility/cognitive impairments (i.e., how does the associations between neural correlates and impairment change across time). Additionally, implementation of multi-modal imaging techniques would provide a more comprehensive picture of neural mechanisms underlying aging-associated changes in the brain. For example, a multi-modal imaging protocol that includes EEG and fMRI may be able to resolve the natural limitations of both techniques (i.e., temporal resolution regarding fMRI and spatial resolution regarding EEG), thereby providing a clearer picture of the true underlying neural pathways.  6.4.2 Strategies to Minimize Mobility and Cognitive Impairments As shown in Chapters 4 and 5 of this dissertation, aerobic exercise may be a promising method to alleviate loss of function caused by these geriatric conditions. It will be 160  advantageous for future researcher to consider administering training outside of the field of aerobic exercise and multi-modal intervention programs. For example, cognitive training is an emerging field of study that is gaining traction in promoting brain health [360]. The LIFE study also showed promising results in their adaption of multi-modal physical activity training [125]. Further, Liu-Ambrose and colleagues will soon begin an extensive investigation of the influence of a combination of various exercise regimens (i.e., resistance, aerobic, and cognitive training) on mobility, cognitive and brain outcomes. Exploring new strategies in conjunction with combining previously-established intervention methods will undoubtedly further research in the field of promoting healthy aging. 6.5 Final Conclusions Throughout this dissertation I have provided converging evidence from the literature as well as 4 separate studies as parts of my recent work. These data suggest that while mobility and cognitive deficits negatively impact functional independence and quality of life in older adults, they are modifiable. Through better understanding the underlying causes of these impairments via comprehensive examinations of the integrity of both structure and function of the brain, it is plausible that more effective diagnostic and treatment strategies can be developed.       161  References 1. Montero-Odasso, M., et al., Gait and cognition: a complementary approach to understanding brain function and the risk of falling. J Am Geriatr Soc, 2012. 60(11): p. 2127-36. 2. Satariano, W.A., et al., Mobility and aging: new directions for public health action. Am J Public Health, 2012. 102(8): p. 1508-15. 3. Lang, J.E., et al., The Prevention Research Centers Healthy Aging Research Network. Prev Chronic Dis, 2006. 3(1): p. A17. 4. Prevention, C.f.D.C.a., Prevalence and most common causes of disability among adults—United States, 2005. . MMWR Morb Mortal Wkly Rep., 2009. 58(16): p. 421-426. 5. Lacquaniti, F., R. Grasso, and M. Zago, Motor Patterns in Walking. News Physiol Sci, 1999. 14: p. 168-174. 6. 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Rapp, A quantitative neural network approach to understanding aging phenotypes. Ageing Res Rev, 2014. 15: p. 44-50. 358. Cotman, C.W., N.C. Berchtold, and L.A. Christie, Exercise builds brain health: key roles of growth factor cascades and inflammation. Trends Neurosci, 2007. 30(9): p. 464-72. 359. Power, J.D., et al., Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage, 2014. 84: p. 320-41. 360. Ten Brinke, L.F., et al., Effects of computerized cognitive training on neuroimaging outcomes in older adults: a systematic review. BMC Geriatr, 2017. 17(1): p. 139.     180  Appendices Appendix A: Additional Analysis Conducted for Chapter 3 Additional statistical analyses were performed for data from Chapter 3, specifically to address concerns proposed by the reviewers. The reviewers suggested an alternative investigation the associations reported in Chapter 3 by examining usual gait speed as a continuous variable as opposed to stratifying our study participants in a dichotomous fashion. As such, I generated the following linear regression models (Tables A1 and A2). Results from the regression analyses did not differ significantly from my original ANOVA analyses. Critically, after adjusting for the covariates, I found connectivity of neutral SMN-FPN explained a statistically significant amount of variation in gait speed (p<0.05; Table A1). Models constructed with distinct pairwise ROI-ROI connections within the neutral SMN-FPN showed that statistically significant variability in gait speed can be explained by connectivity between the SMA and bilateral ventral visual cortices (BVV) (p=0.01), SMA and the bilateral superior lateral occipital cortex (BSLOC) (p<0.01), as well as the SMA and the bilateral frontal eye field (BFEF) (p<0.01; Table A2). Table A1. Network Level Linear Regression Models  Gait speed  Independent variables Standardized beta p-value Model 1 Adjusted R2 12.1 0.03 Age -0.17 0.24 MoCA 0.34 0.02 Left SMN-FPN connectivity -0.04 0.76 Model 2 Adjusted R2 0.13 0.02 Age -0.18 0.21 MoCA 0.33 0.02 Right SMN-FPN connectivity 0.12 0.40 Model 3 Adjusted R2 0.20 0.01 Age -0.23 0.10 MoCA 0.36 0.01 Neutral SMN-FPN connectivity 0.28 0.05 Note: MoCA=Montreal Cognitive Assessment; SMN=sensori-motor network; FPN=fronto-parietal network   181  Table A2. Linear Regression Model with Pairwise ROI Connectivity within Neutral SMN-FPN (N=49)  Gait speed  Independent variables Standardized beta p-value Model 1 Adjusted R2 12.1 0.03 Age -0.17 0.22 MoCA 0.35 0.02 SMA-RIPS 0.03 0.81 Model 2 Adjusted R2 0.24 <0.01 Age -0.15 0.26 MoCA 0.35 0.01 SMA-BVV -0.33 0.01 Model 3 Adjusted R2 0.17 <0.01 Age -0.19 0.18 MoCA 0.39 0.01 SMA-RSMG 0.23 0.09 Model 4 Adjusted R2 0.25 <0.01 Age -0.24 0.08 MoCA 0.27 0.05 SMA-BSLOC 0.36 0.01 Model 5 Adjusted R2 0.37 <0.01 Age -0.29 0.02 MoCA 0.33 0.01 SMA-BFEF 0.50 <0.01 Note: MoCA=Montreal Cognitive Assessment; SMA=supplementary motor area; RIPS=right inferior parietal sulcus; BVV=bilateral ventral visual; RSMG=right supramarginal gyrus; BSLOC=bilateral occipital cortex; BFEF=bilateral frontal eye field    182 Appendix B: Rationale for Regression Model Construction in Chapter 2 Pearson’s correlations were computed to assess the association between gray and white matter regions that were significantly or trend-level different between fallers and non-fallers at baseline with changes in executive functions over the 12-month observation period. Change scores were calculated by subtracting the baseline score from the 12-month score and this was done for each of the four executive function measures. Thus, a negative change score reflects improved performance in selective attention and conflict resolution, set shifting, and working memory. Conversely, a positive change score reflects improved psychomotor speed.  Finally, to investigate whether fall-related structural alterations were independently associated with subsequent changes in executive functions across all 66 participants, we constructed multiple linear regression models using brain regions identified in the previous correlation analyses as independent variables, and changes in executive functions as dependent variables.  For each linear regression model, baseline age and total WMH were statistically controlled by forcing these two variables into the regression model first. The brain regions of interest were entered in the second step to determine their unique contribution to change in executive functions. The overall alpha level was set at p ≤ 0.05.   183  Table B1. Correlation Analysis for Rationale for Regression Model in Chapter 2  ∆ Stroop Test ∆ Trail-Making Test ∆ Verbal Digit Forward and Backward Test ∆ DSST  Pearson’s Coefficient p-value Pearson’s Coefficient p-value Pearson’s Coefficient p-value Pearson’s Coefficient p-value Gray Matter Volume         LH-lateral orbitofrontal 0.16 0.22 0.11 0.93 -0.22 0.09 0.01 0.92 RH-insula 0.15 0.25 -0.01 0.92 -0.01 0.93 0.13 0.29 Pallidum  0.02 0.86 0.02 0.90 0.04 0.76 0.03 0.84 Hippocampus  -0.14 0.26 -0.04 0.77 -0.11 0.41 0.25 0.05 White Matter Volume         LH-lateral orbitofrontal 0.08 0.54 -0.28 0.02 -0.09 0.47 0.05 0.72 LH-pars triangularis  -0.01 0.94 <0.01 0.97 0.13 0.31 <-0.01 0.99 RH-pars triangularis  0.11 0.40 -0.11 0.39 0.05 0.71 0.10 0.42 Note: LH=Left-hemisphere; RH=Right-hemisphere; Stroop Test=Stroop Coloured-Words – Stroop Colour-X’s; Trail-Making Test=Trail-Making Test B – Trail-Making Test A; Verbal Digit Forward and Backward Test=Digit Forward Test – Digit Backward Test; DSST=Digit Symbol Substitution Test; ∆ in performance over 12 months is calculated as 12-month performance minus baseline performance; For ∆ Stroop Test, ∆ Trail-Making Test, and ∆ Verbal Digit Forward and Backward Test, negative scores reflect better performance over 12-months.   

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