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Disrupted morphological grey matter networks in early-stage Parkinson’s disease

While previous structural-covariance studies have an advanced understanding of brain alterations in Parkinson's disease (PD), brain–behavior relationships have not been examined at the individual level. This study investigated the topological organization of grey matter (GM) networks, their rel...

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Autores principales: Suo, Xueling, Lei, Du, Li, Nannan, Li, Wenbin, Kemp, Graham J., Sweeney, John A., Peng, Rong, Gong, Qiyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8096749/
https://www.ncbi.nlm.nih.gov/pubmed/33825053
http://dx.doi.org/10.1007/s00429-020-02200-9
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author Suo, Xueling
Lei, Du
Li, Nannan
Li, Wenbin
Kemp, Graham J.
Sweeney, John A.
Peng, Rong
Gong, Qiyong
author_facet Suo, Xueling
Lei, Du
Li, Nannan
Li, Wenbin
Kemp, Graham J.
Sweeney, John A.
Peng, Rong
Gong, Qiyong
author_sort Suo, Xueling
collection PubMed
description While previous structural-covariance studies have an advanced understanding of brain alterations in Parkinson's disease (PD), brain–behavior relationships have not been examined at the individual level. This study investigated the topological organization of grey matter (GM) networks, their relation to disease severity, and their potential imaging diagnostic value in PD. Fifty-four early-stage PD patients and 54 healthy controls (HC) underwent structural T1-weighted magnetic resonance imaging. GM networks were constructed by estimating interregional similarity in the distributions of regional GM volume using the Kullback–Leibler divergence measure. Results were analyzed using graph theory and network-based statistics (NBS), and the relationship to disease severity was assessed. Exploratory support vector machine analyses were conducted to discriminate PD patients from HC and different motor subtypes. Compared with HC, GM networks in PD showed a higher clustering coefficient (P = 0.014) and local efficiency (P = 0.014). Locally, nodal centralities in PD were lower in postcentral gyrus and temporal-occipital regions, and higher in right superior frontal gyrus and left putamen. NBS analysis revealed decreased morphological connections in the sensorimotor and default mode networks and increased connections in the salience and frontoparietal networks in PD. Connection matrices and graph-based metrics allowed single-subject classification of PD and HC with significant accuracy of 73.1 and 72.7%, respectively, while graph-based metrics allowed single-subject classification of tremor-dominant and akinetic–rigid motor subtypes with significant accuracy of 67.0%. The topological organization of GM networks was disrupted in early-stage PD in a way that suggests greater segregation of information processing. There is potential for application to early imaging diagnosis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00429-020-02200-9.
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spelling pubmed-80967492021-05-05 Disrupted morphological grey matter networks in early-stage Parkinson’s disease Suo, Xueling Lei, Du Li, Nannan Li, Wenbin Kemp, Graham J. Sweeney, John A. Peng, Rong Gong, Qiyong Brain Struct Funct Original Article While previous structural-covariance studies have an advanced understanding of brain alterations in Parkinson's disease (PD), brain–behavior relationships have not been examined at the individual level. This study investigated the topological organization of grey matter (GM) networks, their relation to disease severity, and their potential imaging diagnostic value in PD. Fifty-four early-stage PD patients and 54 healthy controls (HC) underwent structural T1-weighted magnetic resonance imaging. GM networks were constructed by estimating interregional similarity in the distributions of regional GM volume using the Kullback–Leibler divergence measure. Results were analyzed using graph theory and network-based statistics (NBS), and the relationship to disease severity was assessed. Exploratory support vector machine analyses were conducted to discriminate PD patients from HC and different motor subtypes. Compared with HC, GM networks in PD showed a higher clustering coefficient (P = 0.014) and local efficiency (P = 0.014). Locally, nodal centralities in PD were lower in postcentral gyrus and temporal-occipital regions, and higher in right superior frontal gyrus and left putamen. NBS analysis revealed decreased morphological connections in the sensorimotor and default mode networks and increased connections in the salience and frontoparietal networks in PD. Connection matrices and graph-based metrics allowed single-subject classification of PD and HC with significant accuracy of 73.1 and 72.7%, respectively, while graph-based metrics allowed single-subject classification of tremor-dominant and akinetic–rigid motor subtypes with significant accuracy of 67.0%. The topological organization of GM networks was disrupted in early-stage PD in a way that suggests greater segregation of information processing. There is potential for application to early imaging diagnosis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00429-020-02200-9. Springer Berlin Heidelberg 2021-04-07 2021 /pmc/articles/PMC8096749/ /pubmed/33825053 http://dx.doi.org/10.1007/s00429-020-02200-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Suo, Xueling
Lei, Du
Li, Nannan
Li, Wenbin
Kemp, Graham J.
Sweeney, John A.
Peng, Rong
Gong, Qiyong
Disrupted morphological grey matter networks in early-stage Parkinson’s disease
title Disrupted morphological grey matter networks in early-stage Parkinson’s disease
title_full Disrupted morphological grey matter networks in early-stage Parkinson’s disease
title_fullStr Disrupted morphological grey matter networks in early-stage Parkinson’s disease
title_full_unstemmed Disrupted morphological grey matter networks in early-stage Parkinson’s disease
title_short Disrupted morphological grey matter networks in early-stage Parkinson’s disease
title_sort disrupted morphological grey matter networks in early-stage parkinson’s disease
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8096749/
https://www.ncbi.nlm.nih.gov/pubmed/33825053
http://dx.doi.org/10.1007/s00429-020-02200-9
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