The corticolimbic structural covariance network as an early predictive biosignature for cognitive impairment in Parkinson's disease

The corticolimbic structural covariance network as an early predictive biosignature for cognitive impairment in Parkinson's disease

Abstract Structural covariance assesses similarities in gray matter between brain regions and can be applied to study networks of the brain. In this study, we explored correlations between structural covariance networks (SCNs) and cognitive impairment in Parkinson’s disease patients. 101 PD patients...

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Autores principales: Yueh-Sheng Chen, Hsiu-Ling Chen, Cheng-Hsien Lu, Chih-Ying Lee, Kun-Hsien Chou, Meng-Hsiang Chen, Chiun-Chieh Yu, Yun-Ru Lai, Pi-Ling Chiang, Wei-Che Lin
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/7d17494e599f42f5998bafd8b4d87ca9
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spelling oai:doaj.org-article:7d17494e599f42f5998bafd8b4d87ca92021-12-02T14:01:37ZThe corticolimbic structural covariance network as an early predictive biosignature for cognitive impairment in Parkinson's disease10.1038/s41598-020-79403-x2045-2322https://doaj.org/article/7d17494e599f42f5998bafd8b4d87ca92021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79403-xhttps://doaj.org/toc/2045-2322Abstract Structural covariance assesses similarities in gray matter between brain regions and can be applied to study networks of the brain. In this study, we explored correlations between structural covariance networks (SCNs) and cognitive impairment in Parkinson’s disease patients. 101 PD patients and 58 age- and sex-matched healthy controls were enrolled in the study. For each participant, comprehensive neuropsychological testing using the Wechsler Adult Intelligence Scale-III and Cognitive Ability Screening Instrument were conducted. Structural brain MR images were acquired using a 3.0T whole body GE Signa MRI system. T1 structural images were preprocessed and analyzed using Statistical Parametric Mapping software (SPM12) running on Matlab R2016a for voxel-based morphometric analysis and SCN analysis. PD patients with normal cognition received follow-up neuropsychological testing at 1-year interval. Cognitive impairment in PD is associated with degeneration of the amygdala/hippocampus SCN. PD patients with dementia exhibited increased covariance over the prefrontal cortex compared to PD patients with normal cognition (PDN). PDN patients who had developed cognitive impairment at follow-up exhibited decreased gray matter volume of the amygdala/hippocampus SCN in the initial MRI. Our results support a neural network-based mechanism for cognitive impairment in PD patients. SCN analysis may reveal vulnerable networks that can be used to early predict cognitive decline in PD patients.Yueh-Sheng ChenHsiu-Ling ChenCheng-Hsien LuChih-Ying LeeKun-Hsien ChouMeng-Hsiang ChenChiun-Chieh YuYun-Ru LaiPi-Ling ChiangWei-Che LinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Yueh-Sheng Chen
Hsiu-Ling Chen
Cheng-Hsien Lu
Chih-Ying Lee
Kun-Hsien Chou
Meng-Hsiang Chen
Chiun-Chieh Yu
Yun-Ru Lai
Pi-Ling Chiang
Wei-Che Lin
The corticolimbic structural covariance network as an early predictive biosignature for cognitive impairment in Parkinson's disease
description Abstract Structural covariance assesses similarities in gray matter between brain regions and can be applied to study networks of the brain. In this study, we explored correlations between structural covariance networks (SCNs) and cognitive impairment in Parkinson’s disease patients. 101 PD patients and 58 age- and sex-matched healthy controls were enrolled in the study. For each participant, comprehensive neuropsychological testing using the Wechsler Adult Intelligence Scale-III and Cognitive Ability Screening Instrument were conducted. Structural brain MR images were acquired using a 3.0T whole body GE Signa MRI system. T1 structural images were preprocessed and analyzed using Statistical Parametric Mapping software (SPM12) running on Matlab R2016a for voxel-based morphometric analysis and SCN analysis. PD patients with normal cognition received follow-up neuropsychological testing at 1-year interval. Cognitive impairment in PD is associated with degeneration of the amygdala/hippocampus SCN. PD patients with dementia exhibited increased covariance over the prefrontal cortex compared to PD patients with normal cognition (PDN). PDN patients who had developed cognitive impairment at follow-up exhibited decreased gray matter volume of the amygdala/hippocampus SCN in the initial MRI. Our results support a neural network-based mechanism for cognitive impairment in PD patients. SCN analysis may reveal vulnerable networks that can be used to early predict cognitive decline in PD patients.
format article
author Yueh-Sheng Chen
Hsiu-Ling Chen
Cheng-Hsien Lu
Chih-Ying Lee
Kun-Hsien Chou
Meng-Hsiang Chen
Chiun-Chieh Yu
Yun-Ru Lai
Pi-Ling Chiang
Wei-Che Lin
author_facet Yueh-Sheng Chen
Hsiu-Ling Chen
Cheng-Hsien Lu
Chih-Ying Lee
Kun-Hsien Chou
Meng-Hsiang Chen
Chiun-Chieh Yu
Yun-Ru Lai
Pi-Ling Chiang
Wei-Che Lin
author_sort Yueh-Sheng Chen
title The corticolimbic structural covariance network as an early predictive biosignature for cognitive impairment in Parkinson's disease
title_short The corticolimbic structural covariance network as an early predictive biosignature for cognitive impairment in Parkinson's disease
title_full The corticolimbic structural covariance network as an early predictive biosignature for cognitive impairment in Parkinson's disease
title_fullStr The corticolimbic structural covariance network as an early predictive biosignature for cognitive impairment in Parkinson's disease
title_full_unstemmed The corticolimbic structural covariance network as an early predictive biosignature for cognitive impairment in Parkinson's disease
title_sort corticolimbic structural covariance network as an early predictive biosignature for cognitive impairment in parkinson's disease
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/7d17494e599f42f5998bafd8b4d87ca9
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