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Evidence of neuroplasticity with brain–computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis

BACKGROUND: Brain–computer interface (BCI) has been widely used for functional recovery after stroke. Understanding the brain mechanisms following BCI intervention to optimize BCI strategies is crucial for the benefit of stroke patients. METHODS: Forty-six patients with upper limb motor dysfunction...

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Autores principales: Ma, Zhen-Zhen, Wu, Jia-Jia, Hua, Xu-Yun, Zheng, Mou-Xiong, Xing, Xiang-Xin, Ma, Jie, Shan, Chun-Lei, Xu, Jian-Guang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10281191/
https://www.ncbi.nlm.nih.gov/pubmed/37346164
http://dx.doi.org/10.3389/fneur.2023.1135466
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author Ma, Zhen-Zhen
Wu, Jia-Jia
Hua, Xu-Yun
Zheng, Mou-Xiong
Xing, Xiang-Xin
Ma, Jie
Shan, Chun-Lei
Xu, Jian-Guang
author_facet Ma, Zhen-Zhen
Wu, Jia-Jia
Hua, Xu-Yun
Zheng, Mou-Xiong
Xing, Xiang-Xin
Ma, Jie
Shan, Chun-Lei
Xu, Jian-Guang
author_sort Ma, Zhen-Zhen
collection PubMed
description BACKGROUND: Brain–computer interface (BCI) has been widely used for functional recovery after stroke. Understanding the brain mechanisms following BCI intervention to optimize BCI strategies is crucial for the benefit of stroke patients. METHODS: Forty-six patients with upper limb motor dysfunction after stroke were recruited and randomly divided into the control group or the BCI group. The primary outcome was measured by the assessment of Fugl–Meyer Assessment of Upper Extremity (FMA-UE). Meanwhile, we performed resting-state functional magnetic resonance imaging (rs-fMRI) in all patients, followed by independent component analysis (ICA) to identify functionally connected brain networks. Finally, we assessed the topological efficiency of both groups using graph-theoretic analysis in these brain subnetworks. RESULTS: The FMA-UE score of the BCI group was significantly higher than that of the control group after treatment (p = 0.035). From the network topology analysis, we first identified seven subnetworks from the rs-fMRI data. In the following analysis of subnetwork properties, small-world properties including γ (p = 0.035) and σ (p = 0.031) within the visual network (VN) decreased in the BCI group. For the analysis of the dorsal attention network (DAN), significant differences were found in assortativity (p = 0.045) between the groups. Additionally, the improvement in FMA-UE was positively correlated with the assortativity of the dorsal attention network (R = 0.498, p = 0.011). CONCLUSION: Brain–computer interface can promote the recovery of upper limbs after stroke by regulating VN and DAN. The correlation trend of weak intensity proves that functional recovery in stroke patients is likely to be related to the brain’s visuospatial processing ability, which can be used to optimize BCI strategies. CLINICAL TRIAL REGISTRATION: The trial is registered in the Chinese Clinical Trial Registry, number ChiCTR2000034848. Registered 21 July 2020.
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spelling pubmed-102811912023-06-21 Evidence of neuroplasticity with brain–computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis Ma, Zhen-Zhen Wu, Jia-Jia Hua, Xu-Yun Zheng, Mou-Xiong Xing, Xiang-Xin Ma, Jie Shan, Chun-Lei Xu, Jian-Guang Front Neurol Neurology BACKGROUND: Brain–computer interface (BCI) has been widely used for functional recovery after stroke. Understanding the brain mechanisms following BCI intervention to optimize BCI strategies is crucial for the benefit of stroke patients. METHODS: Forty-six patients with upper limb motor dysfunction after stroke were recruited and randomly divided into the control group or the BCI group. The primary outcome was measured by the assessment of Fugl–Meyer Assessment of Upper Extremity (FMA-UE). Meanwhile, we performed resting-state functional magnetic resonance imaging (rs-fMRI) in all patients, followed by independent component analysis (ICA) to identify functionally connected brain networks. Finally, we assessed the topological efficiency of both groups using graph-theoretic analysis in these brain subnetworks. RESULTS: The FMA-UE score of the BCI group was significantly higher than that of the control group after treatment (p = 0.035). From the network topology analysis, we first identified seven subnetworks from the rs-fMRI data. In the following analysis of subnetwork properties, small-world properties including γ (p = 0.035) and σ (p = 0.031) within the visual network (VN) decreased in the BCI group. For the analysis of the dorsal attention network (DAN), significant differences were found in assortativity (p = 0.045) between the groups. Additionally, the improvement in FMA-UE was positively correlated with the assortativity of the dorsal attention network (R = 0.498, p = 0.011). CONCLUSION: Brain–computer interface can promote the recovery of upper limbs after stroke by regulating VN and DAN. The correlation trend of weak intensity proves that functional recovery in stroke patients is likely to be related to the brain’s visuospatial processing ability, which can be used to optimize BCI strategies. CLINICAL TRIAL REGISTRATION: The trial is registered in the Chinese Clinical Trial Registry, number ChiCTR2000034848. Registered 21 July 2020. Frontiers Media S.A. 2023-06-06 /pmc/articles/PMC10281191/ /pubmed/37346164 http://dx.doi.org/10.3389/fneur.2023.1135466 Text en Copyright © 2023 Ma, Wu, Hua, Zheng, Xing, Ma, Shan and Xu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Ma, Zhen-Zhen
Wu, Jia-Jia
Hua, Xu-Yun
Zheng, Mou-Xiong
Xing, Xiang-Xin
Ma, Jie
Shan, Chun-Lei
Xu, Jian-Guang
Evidence of neuroplasticity with brain–computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis
title Evidence of neuroplasticity with brain–computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis
title_full Evidence of neuroplasticity with brain–computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis
title_fullStr Evidence of neuroplasticity with brain–computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis
title_full_unstemmed Evidence of neuroplasticity with brain–computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis
title_short Evidence of neuroplasticity with brain–computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis
title_sort evidence of neuroplasticity with brain–computer interface in a randomized trial for post-stroke rehabilitation: a graph-theoretic study of subnetwork analysis
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10281191/
https://www.ncbi.nlm.nih.gov/pubmed/37346164
http://dx.doi.org/10.3389/fneur.2023.1135466
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