Determination of vascular dementia brain in distinct frequency bands with whole brain functional connectivity patterns.

Recent studies have shown that multivariate pattern analysis (MVPA) can be useful for distinguishing brain disorders into categories. Such analyses can substantially enrich and facilitate clinical diagnoses. Using MPVA methods, whole brain functional networks, especially those derived using differen...

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Autores principales:	Delong Zhang, Bo Liu, Jun Chen, Xiaoling Peng, Xian Liu, Yuanyuan Fan, Ming Liu, Ruiwang Huang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c0ef2013568f482d99507f88a1017cda
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spelling	oai:doaj.org-article:c0ef2013568f482d99507f88a1017cda2021-11-18T08:00:09ZDetermination of vascular dementia brain in distinct frequency bands with whole brain functional connectivity patterns.1932-620310.1371/journal.pone.0054512https://doaj.org/article/c0ef2013568f482d99507f88a1017cda2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23359801/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Recent studies have shown that multivariate pattern analysis (MVPA) can be useful for distinguishing brain disorders into categories. Such analyses can substantially enrich and facilitate clinical diagnoses. Using MPVA methods, whole brain functional networks, especially those derived using different frequency windows, can be applied to detect brain states. We constructed whole brain functional networks for groups of vascular dementia (VaD) patients and controls using resting state BOLD-fMRI (rsfMRI) data from three frequency bands - slow-5 (0.01 ≈ 0.027 Hz), slow-4 (0.027∼0.073 Hz), and whole-band (0.01 ≈ 0.073 Hz). Then we used the support vector machine (SVM), a type of MVPA classifier, to determine the patterns of functional connectivity. Our results showed that the brain functional networks derived from rsfMRI data (19 VaD patients and 20 controls) in these three frequency bands appear to reflect neurobiological changes in VaD patients. Such differences could be used to differentiate the brain states of VaD patients from those of healthy individuals. We also found that the functional connectivity patterns of the human brain in the three frequency bands differed, as did their ability to differentiate brain states. Specifically, the ability of the functional connectivity pattern to differentiate VaD brains from healthy ones was more efficient in the slow-5 (0.01 ≈ 0.027 Hz) band than in the other two frequency bands. Our findings suggest that the MVPA approach could be used to detect abnormalities in the functional connectivity of VaD patients in distinct frequency bands. Identifying such abnormalities may contribute to our understanding of the pathogenesis of VaD.Delong ZhangBo LiuJun ChenXiaoling PengXian LiuYuanyuan FanMing LiuRuiwang HuangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 1, p e54512 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Delong Zhang Bo Liu Jun Chen Xiaoling Peng Xian Liu Yuanyuan Fan Ming Liu Ruiwang Huang Determination of vascular dementia brain in distinct frequency bands with whole brain functional connectivity patterns.
description	Recent studies have shown that multivariate pattern analysis (MVPA) can be useful for distinguishing brain disorders into categories. Such analyses can substantially enrich and facilitate clinical diagnoses. Using MPVA methods, whole brain functional networks, especially those derived using different frequency windows, can be applied to detect brain states. We constructed whole brain functional networks for groups of vascular dementia (VaD) patients and controls using resting state BOLD-fMRI (rsfMRI) data from three frequency bands - slow-5 (0.01 ≈ 0.027 Hz), slow-4 (0.027∼0.073 Hz), and whole-band (0.01 ≈ 0.073 Hz). Then we used the support vector machine (SVM), a type of MVPA classifier, to determine the patterns of functional connectivity. Our results showed that the brain functional networks derived from rsfMRI data (19 VaD patients and 20 controls) in these three frequency bands appear to reflect neurobiological changes in VaD patients. Such differences could be used to differentiate the brain states of VaD patients from those of healthy individuals. We also found that the functional connectivity patterns of the human brain in the three frequency bands differed, as did their ability to differentiate brain states. Specifically, the ability of the functional connectivity pattern to differentiate VaD brains from healthy ones was more efficient in the slow-5 (0.01 ≈ 0.027 Hz) band than in the other two frequency bands. Our findings suggest that the MVPA approach could be used to detect abnormalities in the functional connectivity of VaD patients in distinct frequency bands. Identifying such abnormalities may contribute to our understanding of the pathogenesis of VaD.
format	article
author	Delong Zhang Bo Liu Jun Chen Xiaoling Peng Xian Liu Yuanyuan Fan Ming Liu Ruiwang Huang
author_facet	Delong Zhang Bo Liu Jun Chen Xiaoling Peng Xian Liu Yuanyuan Fan Ming Liu Ruiwang Huang
author_sort	Delong Zhang
title	Determination of vascular dementia brain in distinct frequency bands with whole brain functional connectivity patterns.
title_short	Determination of vascular dementia brain in distinct frequency bands with whole brain functional connectivity patterns.
title_full	Determination of vascular dementia brain in distinct frequency bands with whole brain functional connectivity patterns.
title_fullStr	Determination of vascular dementia brain in distinct frequency bands with whole brain functional connectivity patterns.
title_full_unstemmed	Determination of vascular dementia brain in distinct frequency bands with whole brain functional connectivity patterns.
title_sort	determination of vascular dementia brain in distinct frequency bands with whole brain functional connectivity patterns.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/c0ef2013568f482d99507f88a1017cda
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Determination of vascular dementia brain in distinct frequency bands with whole brain functional connectivity patterns.

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