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Aberrant dynamic Functional-Structural connectivity coupling of Large-scale brain networks in poststroke motor dysfunction
BACKGROUND AND PURPOSE: Stroke may lead to widespread functional and structural reorganization in the brain. Several studies have reported a potential correlation between functional network changes and structural network changes after stroke. However, it is unclear how functional-structural relation...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10037213/ https://www.ncbi.nlm.nih.gov/pubmed/36708666 http://dx.doi.org/10.1016/j.nicl.2023.103332 |
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author | Liu, Xiaoying Qiu, Shuting Wang, Xiaoyang Chen, Hui Tang, Yuting Qin, Yin |
author_facet | Liu, Xiaoying Qiu, Shuting Wang, Xiaoyang Chen, Hui Tang, Yuting Qin, Yin |
author_sort | Liu, Xiaoying |
collection | PubMed |
description | BACKGROUND AND PURPOSE: Stroke may lead to widespread functional and structural reorganization in the brain. Several studies have reported a potential correlation between functional network changes and structural network changes after stroke. However, it is unclear how functional-structural relationships change dynamically over the course of one resting-state fMRI scan in patients following a stroke; furthermore, we know little about their relationships with the severity of motor dysfunction. Therefore, this study aimed to investigate dynamic functional and structural connectivity (FC-SC) coupling and its relationship with motor function in subcortical stroke from the perspective of network dynamics. METHODS: Resting-state functional magnetic resonance imaging and diffusion tensor imaging were obtained from 39 S patients (19 severe and 20 moderate) and 22 healthy controls (HCs). Brain structural networks were constructed by tracking fiber tracts in diffusion tensor imaging, and structural network topology metrics were calculated using a graph-theoretic approach. Independent component analysis, the sliding window method, and k-means clustering were used to calculate dynamic functional connectivity and to estimate different dynamic connectivity states. The temporal patterns and intergroup differences of FC-SC coupling were analyzed within each state. We also calculated dynamic FC-SC coupling and its relationship with functional network efficiency. In addition, the correlation between FC-SC coupling and the Fugl-Meyer assessment scale was analyzed. RESULTS: For SC, stroke patients showed lower global efficiency than HCs (all P < 0.05), and severely affected patients had a higher characteristic path length (P = 0.003). For FC and FC-SC coupling, stroke patients predominantly showed lower local efficiency and reduced FC-SC coupling than HCs in state 2 (all P < 0.05). Furthermore, severely affected patients also showed lower local efficiency (P = 0.031) and reduced FC-SC coupling (P = 0.043) in state 3, which was markedly linked to the severity of motor dysfunction after stroke. In addition, FC-SC coupling was correlated with functional network efficiency in state 2 in moderately affected patients (r = 0.631, P = 0.004) but not significantly in severely affected patients. CONCLUSIONS: Stroke patients show abnormal dynamic FC-SC coupling characteristics, especially in individuals with severe injuries. These findings may contribute to a better understanding of the anatomical functional interactions underlying motor deficits in stroke patients and provide useful information for personalized rehabilitation strategies. |
format | Online Article Text |
id | pubmed-10037213 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-100372132023-03-25 Aberrant dynamic Functional-Structural connectivity coupling of Large-scale brain networks in poststroke motor dysfunction Liu, Xiaoying Qiu, Shuting Wang, Xiaoyang Chen, Hui Tang, Yuting Qin, Yin Neuroimage Clin Regular Article BACKGROUND AND PURPOSE: Stroke may lead to widespread functional and structural reorganization in the brain. Several studies have reported a potential correlation between functional network changes and structural network changes after stroke. However, it is unclear how functional-structural relationships change dynamically over the course of one resting-state fMRI scan in patients following a stroke; furthermore, we know little about their relationships with the severity of motor dysfunction. Therefore, this study aimed to investigate dynamic functional and structural connectivity (FC-SC) coupling and its relationship with motor function in subcortical stroke from the perspective of network dynamics. METHODS: Resting-state functional magnetic resonance imaging and diffusion tensor imaging were obtained from 39 S patients (19 severe and 20 moderate) and 22 healthy controls (HCs). Brain structural networks were constructed by tracking fiber tracts in diffusion tensor imaging, and structural network topology metrics were calculated using a graph-theoretic approach. Independent component analysis, the sliding window method, and k-means clustering were used to calculate dynamic functional connectivity and to estimate different dynamic connectivity states. The temporal patterns and intergroup differences of FC-SC coupling were analyzed within each state. We also calculated dynamic FC-SC coupling and its relationship with functional network efficiency. In addition, the correlation between FC-SC coupling and the Fugl-Meyer assessment scale was analyzed. RESULTS: For SC, stroke patients showed lower global efficiency than HCs (all P < 0.05), and severely affected patients had a higher characteristic path length (P = 0.003). For FC and FC-SC coupling, stroke patients predominantly showed lower local efficiency and reduced FC-SC coupling than HCs in state 2 (all P < 0.05). Furthermore, severely affected patients also showed lower local efficiency (P = 0.031) and reduced FC-SC coupling (P = 0.043) in state 3, which was markedly linked to the severity of motor dysfunction after stroke. In addition, FC-SC coupling was correlated with functional network efficiency in state 2 in moderately affected patients (r = 0.631, P = 0.004) but not significantly in severely affected patients. CONCLUSIONS: Stroke patients show abnormal dynamic FC-SC coupling characteristics, especially in individuals with severe injuries. These findings may contribute to a better understanding of the anatomical functional interactions underlying motor deficits in stroke patients and provide useful information for personalized rehabilitation strategies. Elsevier 2023-01-23 /pmc/articles/PMC10037213/ /pubmed/36708666 http://dx.doi.org/10.1016/j.nicl.2023.103332 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Regular Article Liu, Xiaoying Qiu, Shuting Wang, Xiaoyang Chen, Hui Tang, Yuting Qin, Yin Aberrant dynamic Functional-Structural connectivity coupling of Large-scale brain networks in poststroke motor dysfunction |
title | Aberrant dynamic Functional-Structural connectivity coupling of Large-scale brain networks in poststroke motor dysfunction |
title_full | Aberrant dynamic Functional-Structural connectivity coupling of Large-scale brain networks in poststroke motor dysfunction |
title_fullStr | Aberrant dynamic Functional-Structural connectivity coupling of Large-scale brain networks in poststroke motor dysfunction |
title_full_unstemmed | Aberrant dynamic Functional-Structural connectivity coupling of Large-scale brain networks in poststroke motor dysfunction |
title_short | Aberrant dynamic Functional-Structural connectivity coupling of Large-scale brain networks in poststroke motor dysfunction |
title_sort | aberrant dynamic functional-structural connectivity coupling of large-scale brain networks in poststroke motor dysfunction |
topic | Regular Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10037213/ https://www.ncbi.nlm.nih.gov/pubmed/36708666 http://dx.doi.org/10.1016/j.nicl.2023.103332 |
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