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Abstract #3885

Investigating the feasibility of predicting somatosensory function of stroke patients from resting-state functional connectivity

Xiaoyun Liang1, Chia-Lin Koh1, Chun-Hung Yeh1, Alan Connelly1,2, and Leeanne Carey1,3

1Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia, 2Florey Department of Neuroscience and Mental Health, The University of Melbourne, Heidelberg, Australia, 3College of Science, Health and Engineering, La Trobe University, Melbourne, Australia

Accumulating evidence from human imaging data supports association between connectivity and outcome after stroke. However, whole-brain functional-connectivity (FC) involves high-dimensional data, which essentially calls for multivariate analysis. Regardless, univariate analysis has been dominantly employed for such studies. More insights into stroke recovery by employing machine learning techniques can be offered due to their multivariate capabilities. In this study, we investigated if residual sensory function (Tactile discrimination threshold, TDT score) of stroke patients can be predicted from resting-state FC. Our results show that TDT scores can be predicted more accurately by combining both low-order and high-order FC than low-order functional connectivity alone.

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