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Resting-state functional connectivity of the dorsal frontal cortex predicts subcortical vascular cognition impairment
Functional magnetic resonance imaging (fMRI) studies have revealed group differences in the frontal area between the subcortical vascular cognition impairment (SVCI) patients and the controls. However, most of the existing research focused on average differences between the two groups, and therefore...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals LLC
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5696245/ https://www.ncbi.nlm.nih.gov/pubmed/29190979 http://dx.doi.org/10.18632/oncotarget.21855 |
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author | Hu, Xiaopeng Zhou, Xia Zhang, Chao Wang, Haibao Yu, Yongqiang Sun, Zhongwu |
author_facet | Hu, Xiaopeng Zhou, Xia Zhang, Chao Wang, Haibao Yu, Yongqiang Sun, Zhongwu |
author_sort | Hu, Xiaopeng |
collection | PubMed |
description | Functional magnetic resonance imaging (fMRI) studies have revealed group differences in the frontal area between the subcortical vascular cognition impairment (SVCI) patients and the controls. However, most of the existing research focused on average differences between the two groups, and therefore had limited clinical applicability. The aim of our study was to investigate whether inter-regions functional connectivity of the dorsal frontal cortex (DFC) can be used to discriminate the SVCI from the controls at the level of the individual. Thirty-two SVCI patients and 32 demographically similar healthy individuals underwent resting-state functional magnetic resonance imaging. The DFC, derived from a prior atlas, was divided into 10 clusters. Features based on DFC were obtained through functional connectivity analysis between pairs of DFC. A nonlinear kernel support vector machine was used for classification and validated using 8-fold cross validation. An excellent classification accuracy was obtained from both the left and the right DFC functional connectivity (accuracy=75.07%, sensitivity=81.57% and specificity=61.71%; accuracy=45.38%, sensitivity=60.74% and specificity=39.48%; P<0.001). These findings shed further light on the pathogenesis of SVCI and showed promising classification performance using machine learning analysis based on DFC fMRI data, which may be useful for the differentiation of SVCI. |
format | Online Article Text |
id | pubmed-5696245 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Impact Journals LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-56962452017-11-29 Resting-state functional connectivity of the dorsal frontal cortex predicts subcortical vascular cognition impairment Hu, Xiaopeng Zhou, Xia Zhang, Chao Wang, Haibao Yu, Yongqiang Sun, Zhongwu Oncotarget Research Paper Functional magnetic resonance imaging (fMRI) studies have revealed group differences in the frontal area between the subcortical vascular cognition impairment (SVCI) patients and the controls. However, most of the existing research focused on average differences between the two groups, and therefore had limited clinical applicability. The aim of our study was to investigate whether inter-regions functional connectivity of the dorsal frontal cortex (DFC) can be used to discriminate the SVCI from the controls at the level of the individual. Thirty-two SVCI patients and 32 demographically similar healthy individuals underwent resting-state functional magnetic resonance imaging. The DFC, derived from a prior atlas, was divided into 10 clusters. Features based on DFC were obtained through functional connectivity analysis between pairs of DFC. A nonlinear kernel support vector machine was used for classification and validated using 8-fold cross validation. An excellent classification accuracy was obtained from both the left and the right DFC functional connectivity (accuracy=75.07%, sensitivity=81.57% and specificity=61.71%; accuracy=45.38%, sensitivity=60.74% and specificity=39.48%; P<0.001). These findings shed further light on the pathogenesis of SVCI and showed promising classification performance using machine learning analysis based on DFC fMRI data, which may be useful for the differentiation of SVCI. Impact Journals LLC 2017-10-16 /pmc/articles/PMC5696245/ /pubmed/29190979 http://dx.doi.org/10.18632/oncotarget.21855 Text en Copyright: © 2017 Hu et al. http://creativecommons.org/licenses/by/3.0/ This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) (CC-BY), which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Research Paper Hu, Xiaopeng Zhou, Xia Zhang, Chao Wang, Haibao Yu, Yongqiang Sun, Zhongwu Resting-state functional connectivity of the dorsal frontal cortex predicts subcortical vascular cognition impairment |
title | Resting-state functional connectivity of the dorsal frontal cortex predicts subcortical vascular cognition impairment |
title_full | Resting-state functional connectivity of the dorsal frontal cortex predicts subcortical vascular cognition impairment |
title_fullStr | Resting-state functional connectivity of the dorsal frontal cortex predicts subcortical vascular cognition impairment |
title_full_unstemmed | Resting-state functional connectivity of the dorsal frontal cortex predicts subcortical vascular cognition impairment |
title_short | Resting-state functional connectivity of the dorsal frontal cortex predicts subcortical vascular cognition impairment |
title_sort | resting-state functional connectivity of the dorsal frontal cortex predicts subcortical vascular cognition impairment |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5696245/ https://www.ncbi.nlm.nih.gov/pubmed/29190979 http://dx.doi.org/10.18632/oncotarget.21855 |
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