GAIT VARIABILITY CORRELATES WITH RESTING-STATE BRAIN NETWORK CONNECTIVITY IN AGING AND DISEASE. (11th November 2018)

• Record Type: Journal Article
• Authors: Lo, O; Halko, M; Zhou, J; Cheong, W; Harrison, R; Wayne, P; Lipsitz, L; Manor, B
• Is Part Of: Innovation in aging. Volume 2(2018)Supplement 1
• Page Start: 518
• Page End: 518
• Publication Date: 2018-11-11
• Subjects: Aging -- Periodicals; Gerontology -- Periodicals; 612.67
• DOI: 10.1093/geroni/igy023.1919
• Languages: English
• ISSNs: 2399-5300

Abstract

Abstract: Gait variability (i.e. stride time-to-stride time fluctuation) is a sensitive predictor of future falls, mental and functional declines in older adults. While gait variability reflects subtle changes due to aging or disease, its neural mechanism remains unclear. One hypothesized mechanism derives from an anti-correlated brain activity between the dorsal attention network (i.e. external-focus network) and default network (i.e. internal-focus network) with known involvement in sustained attention (i.e. moment-to-moment fluctuation). Here, we aimed to establish relationships between gait variability and functional connectivity of large-scale brain networks measured by resting-state functional MRI. We examined data from three separate cohorts: healthy older adults (H-O, N=13, 75.5+-8.0 y/o), functionally-limited older adults (FL-O, N=12, 76.2+-9.5 y/o), and individuals with Parkinson's disease (PD, N=15, 63.2+-6.2 y/o). Gait variability (%, coefficient of variation of stride time) under preferred walking speed was measured and correlated with the degree of functional connectivity within and between seven well-known large-scale functional brain networks. Our results revealed that in all three cohorts, those with less gait variability exhibited greater anti-correlation in resting-state brain activity within the dorsal attention and default networks: H-O, b=1.64, p=.03; FL-O: b=4.38, p=.03; PD: b=1.65, p=.005, with age and sex controlled in the regression model. As this anti-correlation has been linked to performance within tasks of sustained attention, our results suggest that gait variability may depend upon the capacity to dissociate the neural activity of these two networks.