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:	Xiaoyun Liang, Chia-Lin Koh, Chun-Hung Yeh, Peter Goodin, Gemma Lamp, Alan Connelly, Leeanne M. Carey
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	functional connectivity machine learning regression predictive modelling stroke somatosensory function Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/b1a5b0cd7708409fa0a42defa0e6ff2b
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spelling	oai:doaj.org-article:b1a5b0cd7708409fa0a42defa0e6ff2b2021-11-25T16:56:20ZPredicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study10.3390/brainsci111113882076-3425https://doaj.org/article/b1a5b0cd7708409fa0a42defa0e6ff2b2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3425/11/11/1388https://doaj.org/toc/2076-3425Accumulating 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 <i>r</i> = 0.54 (<i>p</i> = 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.Xiaoyun LiangChia-Lin KohChun-Hung YehPeter GoodinGemma LampAlan ConnellyLeeanne M. CareyMDPI AGarticlefunctional connectivitymachine learningregressionpredictive modellingstrokesomatosensory functionNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENBrain Sciences, Vol 11, Iss 1388, p 1388 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	functional connectivity machine learning regression predictive modelling stroke somatosensory function Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	functional connectivity machine learning regression predictive modelling stroke somatosensory function Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Xiaoyun Liang Chia-Lin Koh Chun-Hung Yeh Peter Goodin Gemma Lamp Alan Connelly Leeanne M. Carey Predicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study
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 <i>r</i> = 0.54 (<i>p</i> = 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.
format	article
author	Xiaoyun Liang Chia-Lin Koh Chun-Hung Yeh Peter Goodin Gemma Lamp Alan Connelly Leeanne M. Carey
author_facet	Xiaoyun Liang Chia-Lin Koh Chun-Hung Yeh Peter Goodin Gemma Lamp Alan Connelly Leeanne M. Carey
author_sort	Xiaoyun Liang
title	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_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_sort	predicting post-stroke somatosensory function from resting-state functional connectivity: a feasibility study
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b1a5b0cd7708409fa0a42defa0e6ff2b
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Predicting Post-Stroke Somatosensory Function from Resting-State Functional Connectivity: A Feasibility Study

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