Iterative cross-correlation analysis of resting state functional magnetic resonance imaging data.

Seed-based cross-correlation analysis (sCCA) and independent component analysis have been widely employed to extract functional networks from the resting state functional magnetic resonance imaging data. However, the results of sCCA, in terms of both connectivity strength and network topology, can b...

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Autores principales:	Liqin Yang, Fuchun Lin, Yan Zhou, Jianrong Xu, Chunshui Yu, Wen-Ju Pan, Hao Lei
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/511162f0a7b943d3967741dc7e21f9bb
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spelling	oai:doaj.org-article:511162f0a7b943d3967741dc7e21f9bb2021-11-18T07:53:10ZIterative cross-correlation analysis of resting state functional magnetic resonance imaging data.1932-620310.1371/journal.pone.0058653https://doaj.org/article/511162f0a7b943d3967741dc7e21f9bb2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23527002/?tool=EBIhttps://doaj.org/toc/1932-6203Seed-based cross-correlation analysis (sCCA) and independent component analysis have been widely employed to extract functional networks from the resting state functional magnetic resonance imaging data. However, the results of sCCA, in terms of both connectivity strength and network topology, can be sensitive to seed selection variations. ICA avoids the potential problems due to seed selection, but choosing which component(s) to represent the network of interest could be subjective and problematic. In this study, we proposed a seed-based iterative cross-correlation analysis (siCCA) method for resting state brain network analysis. The method was applied to extract default mode network (DMN) and stable task control network (STCN) in two independent datasets acquired from normal adults. Compared with the networks obtained by traditional sCCA and ICA, the resting state networks produced by siCCA were found to be highly stable and independent on seed selection. siCCA was used to analyze DMN in first-episode major depressive disorder (MDD) patients. It was found that, in the MDD patients, the volume of DMN negatively correlated with the patients' social disability screening schedule scores.Liqin YangFuchun LinYan ZhouJianrong XuChunshui YuWen-Ju PanHao LeiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 3, p e58653 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Liqin Yang Fuchun Lin Yan Zhou Jianrong Xu Chunshui Yu Wen-Ju Pan Hao Lei Iterative cross-correlation analysis of resting state functional magnetic resonance imaging data.
description	Seed-based cross-correlation analysis (sCCA) and independent component analysis have been widely employed to extract functional networks from the resting state functional magnetic resonance imaging data. However, the results of sCCA, in terms of both connectivity strength and network topology, can be sensitive to seed selection variations. ICA avoids the potential problems due to seed selection, but choosing which component(s) to represent the network of interest could be subjective and problematic. In this study, we proposed a seed-based iterative cross-correlation analysis (siCCA) method for resting state brain network analysis. The method was applied to extract default mode network (DMN) and stable task control network (STCN) in two independent datasets acquired from normal adults. Compared with the networks obtained by traditional sCCA and ICA, the resting state networks produced by siCCA were found to be highly stable and independent on seed selection. siCCA was used to analyze DMN in first-episode major depressive disorder (MDD) patients. It was found that, in the MDD patients, the volume of DMN negatively correlated with the patients' social disability screening schedule scores.
format	article
author	Liqin Yang Fuchun Lin Yan Zhou Jianrong Xu Chunshui Yu Wen-Ju Pan Hao Lei
author_facet	Liqin Yang Fuchun Lin Yan Zhou Jianrong Xu Chunshui Yu Wen-Ju Pan Hao Lei
author_sort	Liqin Yang
title	Iterative cross-correlation analysis of resting state functional magnetic resonance imaging data.
title_short	Iterative cross-correlation analysis of resting state functional magnetic resonance imaging data.
title_full	Iterative cross-correlation analysis of resting state functional magnetic resonance imaging data.
title_fullStr	Iterative cross-correlation analysis of resting state functional magnetic resonance imaging data.
title_full_unstemmed	Iterative cross-correlation analysis of resting state functional magnetic resonance imaging data.
title_sort	iterative cross-correlation analysis of resting state functional magnetic resonance imaging data.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/511162f0a7b943d3967741dc7e21f9bb
work_keys_str_mv	AT liqinyang iterativecrosscorrelationanalysisofrestingstatefunctionalmagneticresonanceimagingdata AT fuchunlin iterativecrosscorrelationanalysisofrestingstatefunctionalmagneticresonanceimagingdata AT yanzhou iterativecrosscorrelationanalysisofrestingstatefunctionalmagneticresonanceimagingdata AT jianrongxu iterativecrosscorrelationanalysisofrestingstatefunctionalmagneticresonanceimagingdata AT chunshuiyu iterativecrosscorrelationanalysisofrestingstatefunctionalmagneticresonanceimagingdata AT wenjupan iterativecrosscorrelationanalysisofrestingstatefunctionalmagneticresonanceimagingdata AT haolei iterativecrosscorrelationanalysisofrestingstatefunctionalmagneticresonanceimagingdata
_version_	1718422812374335488

Iterative cross-correlation analysis of resting state functional magnetic resonance imaging data.

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