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Predicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study

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posted on 31.03.2022, 05:21 by Xiaoyun Liang, Chia-Lin Koh, Chun-Hung Yeh, Peter Goodin, Gemma LampGemma Lamp, Alan Connelly, Leeanne CareyLeeanne Carey
Accumulating evidence shows that brain functional deficits may be impacted by damage to remote brain regions. Recent advances in neuroimaging suggest that stroke impairment can be better predicted based on disruption to brain networks rather than from lesion locations or volumes only. Our aim was to explore the feasibility of predicting post-stroke somatosensory function from brain functional connectivity through the application of machine learning techniques. Somatosensory impairment was measured using the Tactile Discrimination Test. Functional connectivity was employed to model the global brain function. Behavioral measures and MRI were collected at the same timepoint. Two machine learning models (linear regression and support vector regression) were chosen to predict somatosensory impairment from disrupted networks. Along with two feature pools (i.e., low-order and high-order functional connectivity, or low-order functional connectivity only) engineered, four predictive models were built and evaluated in the present study. Forty-three chronic stroke survivors participated this study. Results showed that the regression model employing both low-order and high-order functional connectivity can predict outcomes based on correlation coefficient of r = 0.54 (p = 0.0002). A machine learning predictive approach, involving high-and low-order modelling, is feasible for the prediction of residual somatosensory function in stroke patients using functional brain networks.

Funding

This research was funded by the National Health and Medical Research Council of Australia, grant numbers 307902, 1022694, 1077898, 1113352, 1134495, and 2004443.

History

Publication Date

22/10/2021

Journal

Brain Sciences

Volume

11

Issue

11

Article Number

1388

Pagination

14p. (p. 1-14)

Publisher

Multidisciplinary Digital Publishing Institute (MDPI)

ISSN

2076-3425

Rights Statement

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).