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

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...

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Autores principales: Liang, Xiaoyun, Koh, Chia-Lin, Yeh, Chun-Hung, Goodin, Peter, Lamp, Gemma, Connelly, Alan, Carey, Leeanne M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615819/
https://www.ncbi.nlm.nih.gov/pubmed/34827387
http://dx.doi.org/10.3390/brainsci11111388
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author Liang, Xiaoyun
Koh, Chia-Lin
Yeh, Chun-Hung
Goodin, Peter
Lamp, Gemma
Connelly, Alan
Carey, Leeanne M.
author_facet Liang, Xiaoyun
Koh, Chia-Lin
Yeh, Chun-Hung
Goodin, Peter
Lamp, Gemma
Connelly, Alan
Carey, Leeanne M.
author_sort Liang, Xiaoyun
collection PubMed
description 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.
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spelling pubmed-86158192021-11-26 Predicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study Liang, Xiaoyun Koh, Chia-Lin Yeh, Chun-Hung Goodin, Peter Lamp, Gemma Connelly, Alan Carey, Leeanne M. Brain Sci Article 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. MDPI 2021-10-22 /pmc/articles/PMC8615819/ /pubmed/34827387 http://dx.doi.org/10.3390/brainsci11111388 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Article
Liang, Xiaoyun
Koh, Chia-Lin
Yeh, Chun-Hung
Goodin, Peter
Lamp, Gemma
Connelly, Alan
Carey, Leeanne M.
Predicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study
title Predicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study
title_full Predicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study
title_fullStr Predicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study
title_full_unstemmed Predicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study
title_short Predicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study
title_sort predicting post-stroke somatosensory function from resting-state functional connectivity: a feasibility study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615819/
https://www.ncbi.nlm.nih.gov/pubmed/34827387
http://dx.doi.org/10.3390/brainsci11111388
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