The associations between grey matter volume covariance patterns and gait variability - The Tasmanian Study of Cognition and Gait
Jayakody, O and Breslin, M and Beare, R and Srikanth, VK and Blumen, HM and Callisaya, ML, The associations between grey matter volume covariance patterns and gait variability - The Tasmanian Study of Cognition and Gait, Brain Topography, 34, (4) pp. 478-488. ISSN 0896-0267 (2021) [Refereed Article]
Greater gait variability predicts dementia. However, little is known about the neural correlates of gait variability. The aims of this study were to determine (1) grey matter volume covariance patterns associated with gait variability and (2) whether these patterns were associated with specific cognitive domains. Participants (n = 351; mean age 71.9 ± 7.1) were randomly selected from the Southern Tasmanian electoral roll. Step time, step length, step width and double support time were measured using an electronic walkway. Gait variability was calculated as the standard deviation of all steps for each gait measure. Voxel-based morphometry and multivariate covariance-based analyses were used to identify grey matter patterns associated with each gait variability measure. The individual expressions of grey matter patterns were correlated with processing speed, memory, executive and visuospatial functions. The grey matter covariance pattern of double support time variability included frontal, medial temporal, anterior cingulate, insula, cerebellar and striatal regions. Greater expression of this pattern was correlated with poorer performance in all cognitive functions (p < 0.001). The covariance pattern of step length variability included frontal, temporal, insula, occipital and cerebellar regions and was correlated with all cognitive functions (p < 0.05), except memory (p = 0.76). The covariance pattern of step width variability was limited to the cerebellum and correlated only with memory (p = 0.047). No significant pattern was identified for step time variability. In conclusion, different grey matter covariance patterns were associated with individual gait variability measures. These patterns were also correlated with specific cognitive functions, suggesting common neural networks may underlie both gait and cognition.