Timothy P Morris1, Agnieszka Burzynska2, Michelle Voss3, Jason Fanning4, Elizabeth A Salerno5, Ruchika Prakash6, Neha P Gothe, Susan Whitfield-Gabrieli, Charles H Hillman, Edward McAuley, Arthur F Kramer. 1. Department of Psychology, Northeastern University, Boston, MA. 2. Department of Human Development and Family Studies, Colorado State University, Fort Collins, CO. 3. Deptartment of Psychology, University of Iowa, Iowa City, IA. 4. Department of Health and Exercise Science, Wake Forest University, Winston-Salem, NC. 5. Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO. 6. Department of Psychology, Ohio State University, Columbus, OH.
Abstract
INTRODUCTION: Individual differences in brain structure and function in older adults are potential proxies of brain reserve or maintenance and may provide mechanistic predictions of adherence to exercise. We hypothesized that multimodal neuroimaging features would predict adherence to a 6-month randomized controlled trial of exercise in 131 older adults (age, 65.79 ± 4.65 yr, 63% female), alone and in combination with psychosocial, cognitive, and health measures. METHODS: Regularized elastic net regression within a nested cross-validation framework was applied to predict adherence to the intervention in three separate models (brain structure and function only; psychosocial, health, and demographic data only; and a multimodal model). RESULTS: Higher cortical thickness in somatosensory and inferior frontal regions and less surface area in primary visual and inferior frontal regions predicted adherence. Higher nodal functional connectivity (degree count) in default, frontoparietal, and attentional networks and less nodal strength in primary visual and temporoparietal networks predicted exercise adherence ( r = 0.24, P = 0.004). Survey and clinical measures of gait and walking self-efficacy, biological sex, and perceived stress also predicted adherence ( r = 0.17, P = 0.056); however, this prediction was not significant when tested against a null test statistic. A combined multimodal model achieved the highest predictive strength ( r = 0.28, P = 0.001). CONCLUSIONS: Our results suggest that there is a substantial utility of using brain-based measures in future research into precision and individualized exercise interventions older adults.
INTRODUCTION: Individual differences in brain structure and function in older adults are potential proxies of brain reserve or maintenance and may provide mechanistic predictions of adherence to exercise. We hypothesized that multimodal neuroimaging features would predict adherence to a 6-month randomized controlled trial of exercise in 131 older adults (age, 65.79 ± 4.65 yr, 63% female), alone and in combination with psychosocial, cognitive, and health measures. METHODS: Regularized elastic net regression within a nested cross-validation framework was applied to predict adherence to the intervention in three separate models (brain structure and function only; psychosocial, health, and demographic data only; and a multimodal model). RESULTS: Higher cortical thickness in somatosensory and inferior frontal regions and less surface area in primary visual and inferior frontal regions predicted adherence. Higher nodal functional connectivity (degree count) in default, frontoparietal, and attentional networks and less nodal strength in primary visual and temporoparietal networks predicted exercise adherence ( r = 0.24, P = 0.004). Survey and clinical measures of gait and walking self-efficacy, biological sex, and perceived stress also predicted adherence ( r = 0.17, P = 0.056); however, this prediction was not significant when tested against a null test statistic. A combined multimodal model achieved the highest predictive strength ( r = 0.28, P = 0.001). CONCLUSIONS: Our results suggest that there is a substantial utility of using brain-based measures in future research into precision and individualized exercise interventions older adults.
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