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Structural Covariance Network Disruption and Functional Compensation in Parkinson’s Disease

Purpose: To investigate the structural covariance network disruption in Parkinson’s disease (PD), and explore the functional alterations of disrupted structural covariance network. Methods: A cohort of 100 PD patients and 70 healthy participants underwent structural and functional magnetic resonance...

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Autores principales: Zhou, Cheng, Gao, Ting, Guo, Tao, Wu, Jingjing, Guan, Xiaojun, Zhou, Weiwen, Huang, Peiyu, Xuan, Min, Gu, Quanquan, Xu, Xiaojun, Xia, Shunren, Kong, Dexing, Wu, Jian, Zhang, Minming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351504/
https://www.ncbi.nlm.nih.gov/pubmed/32714179
http://dx.doi.org/10.3389/fnagi.2020.00199
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author Zhou, Cheng
Gao, Ting
Guo, Tao
Wu, Jingjing
Guan, Xiaojun
Zhou, Weiwen
Huang, Peiyu
Xuan, Min
Gu, Quanquan
Xu, Xiaojun
Xia, Shunren
Kong, Dexing
Wu, Jian
Zhang, Minming
author_facet Zhou, Cheng
Gao, Ting
Guo, Tao
Wu, Jingjing
Guan, Xiaojun
Zhou, Weiwen
Huang, Peiyu
Xuan, Min
Gu, Quanquan
Xu, Xiaojun
Xia, Shunren
Kong, Dexing
Wu, Jian
Zhang, Minming
author_sort Zhou, Cheng
collection PubMed
description Purpose: To investigate the structural covariance network disruption in Parkinson’s disease (PD), and explore the functional alterations of disrupted structural covariance network. Methods: A cohort of 100 PD patients and 70 healthy participants underwent structural and functional magnetic resonance scanning. Independent component analysis (ICA) was applied separately to both deformation-based morphometry (DBM) maps and functional maps with the same calculating parameters (both decomposed into 20 independent components (ICs) and computed 20 times the Infomax algorithm in ICASSO). Disrupted structural covariance network in PD patients was identified, and then, we performed goodness of fit analysis to obtain the functional network that showed the highest spatial overlap with it. We investigated the relationship between structural covariance network and functional network alterations. Finally, to further understand the structural and functional alterations over time, we performed a longitudinal subgroup analysis (51 patients were followed up for 2 years) with the same procedures. Results: In a cross-sectional analysis, PD patients showed decreased structural covariance between anterior and posterior cingulate subnetworks. The functional components showed best overlap with anterior and posterior cingulate structural subnetworks were selected as anterior and posterior cingulate functional subnetworks. The functional connectivity between them was significantly increased [assessed by Functional Network Connectivity (FNC) toolbox]; and the increased functional connectivity was negatively correlated with cingulate structural covariance network integrity. Longitudinal subgroup analysis showed cingulate structural covariance network disruption was worse at follow-up, while the functional connectivity between anterior and posterior cingulate network was increased at baseline and decreased at follow-up. Conclusion: This study indicated that the cingulate structural covariance network displayed a high susceptibility in PD patients. This study indicated that the cingulate structural covariance network displayed a high susceptibility in PD patients. Considering that disrupted structural covariance network coexisted with enhanced/remained functional activity during disease development, enhanced functional activity underlying the disrupted cingulate structural covariance network might represent a temporal compensation for maintaining clinical performance.
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spelling pubmed-73515042020-07-25 Structural Covariance Network Disruption and Functional Compensation in Parkinson’s Disease Zhou, Cheng Gao, Ting Guo, Tao Wu, Jingjing Guan, Xiaojun Zhou, Weiwen Huang, Peiyu Xuan, Min Gu, Quanquan Xu, Xiaojun Xia, Shunren Kong, Dexing Wu, Jian Zhang, Minming Front Aging Neurosci Neuroscience Purpose: To investigate the structural covariance network disruption in Parkinson’s disease (PD), and explore the functional alterations of disrupted structural covariance network. Methods: A cohort of 100 PD patients and 70 healthy participants underwent structural and functional magnetic resonance scanning. Independent component analysis (ICA) was applied separately to both deformation-based morphometry (DBM) maps and functional maps with the same calculating parameters (both decomposed into 20 independent components (ICs) and computed 20 times the Infomax algorithm in ICASSO). Disrupted structural covariance network in PD patients was identified, and then, we performed goodness of fit analysis to obtain the functional network that showed the highest spatial overlap with it. We investigated the relationship between structural covariance network and functional network alterations. Finally, to further understand the structural and functional alterations over time, we performed a longitudinal subgroup analysis (51 patients were followed up for 2 years) with the same procedures. Results: In a cross-sectional analysis, PD patients showed decreased structural covariance between anterior and posterior cingulate subnetworks. The functional components showed best overlap with anterior and posterior cingulate structural subnetworks were selected as anterior and posterior cingulate functional subnetworks. The functional connectivity between them was significantly increased [assessed by Functional Network Connectivity (FNC) toolbox]; and the increased functional connectivity was negatively correlated with cingulate structural covariance network integrity. Longitudinal subgroup analysis showed cingulate structural covariance network disruption was worse at follow-up, while the functional connectivity between anterior and posterior cingulate network was increased at baseline and decreased at follow-up. Conclusion: This study indicated that the cingulate structural covariance network displayed a high susceptibility in PD patients. This study indicated that the cingulate structural covariance network displayed a high susceptibility in PD patients. Considering that disrupted structural covariance network coexisted with enhanced/remained functional activity during disease development, enhanced functional activity underlying the disrupted cingulate structural covariance network might represent a temporal compensation for maintaining clinical performance. Frontiers Media S.A. 2020-07-02 /pmc/articles/PMC7351504/ /pubmed/32714179 http://dx.doi.org/10.3389/fnagi.2020.00199 Text en Copyright © 2020 Zhou, Gao, Guo, Wu, Guan, Zhou, Huang, Xuan, Gu, Xu, Xia, Kong, Wu and Zhang. http://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 Neuroscience
Zhou, Cheng
Gao, Ting
Guo, Tao
Wu, Jingjing
Guan, Xiaojun
Zhou, Weiwen
Huang, Peiyu
Xuan, Min
Gu, Quanquan
Xu, Xiaojun
Xia, Shunren
Kong, Dexing
Wu, Jian
Zhang, Minming
Structural Covariance Network Disruption and Functional Compensation in Parkinson’s Disease
title Structural Covariance Network Disruption and Functional Compensation in Parkinson’s Disease
title_full Structural Covariance Network Disruption and Functional Compensation in Parkinson’s Disease
title_fullStr Structural Covariance Network Disruption and Functional Compensation in Parkinson’s Disease
title_full_unstemmed Structural Covariance Network Disruption and Functional Compensation in Parkinson’s Disease
title_short Structural Covariance Network Disruption and Functional Compensation in Parkinson’s Disease
title_sort structural covariance network disruption and functional compensation in parkinson’s disease
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351504/
https://www.ncbi.nlm.nih.gov/pubmed/32714179
http://dx.doi.org/10.3389/fnagi.2020.00199
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