4 236

Cited 30 times in

Are brain networks stable during a 24-hour period?

DC FieldValueLanguage
dc.contributor.author이종두-
dc.contributor.author김중일-
dc.contributor.author박범희-
dc.contributor.author박해정-
dc.contributor.author이동하-
dc.date.accessioned2014-12-19T17:30:13Z-
dc.date.available2014-12-19T17:30:13Z-
dc.date.issued2012-
dc.identifier.issn1053-8119-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/91530-
dc.description.abstractDespite the widespread view of the brain as a large complex network, the dynamicity of the brain network over the course of a day has yet to be explored. To investigate whether the spontaneous human brain network maintains long-term stability throughout a day, we evaluated the intra-class correlation coefficient (ICC) of results from an independent component analysis (ICA), seed correlation analysis, and graph-theoretical analysis of resting state functional MRI, acquired from 12 young adults at three-hour intervals over 24 consecutive hours. According to the ICC of the usage strength of the independent network component defined by the root mean square of the temporal weights of the network components, the default mode network centered at the posterior cingulate cortex and precuneus, the superior parietal, and secondary motor networks showed a high temporal stability throughout the day (ICC>0.5). However, high intra-individual dynamicity was observed in the default mode network, including the anterior cingulate cortex and medial prefrontal cortex or posterior-anterior cingulate cortex, the hippocampal network, and the parietal and temporal networks. Seed correlation analysis showed a highly stable (ICC>0.5) extent of functionally connected regions from the posterior cingulate cortex, but poor stability from the hippocampus throughout the day. Graph-theoretical analysis using local and global network efficiency suggested that local brain networks are temporally stable but that long-range integration behaves dynamically in the course of a day. These results imply that dynamic network properties are a nature of the resting state brain network, which remains to be further researched.-
dc.description.statementOfResponsibilityopen-
dc.relation.isPartOfNEUROIMAGE-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.titleAre brain networks stable during a 24-hour period?-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Life Science (의생명과학부)-
dc.contributor.googleauthorBumhee Park-
dc.contributor.googleauthorJoong Il Kim-
dc.contributor.googleauthorDongha Lee-
dc.contributor.googleauthorSeok-Oh Jeong-
dc.contributor.googleauthorJong Doo Lee-
dc.contributor.googleauthorHae-Jeong Park-
dc.identifier.doi10.1016/j.neuroimage.2011.07.049-
dc.admin.authorfalse-
dc.admin.mappingfalse-
dc.contributor.localIdA03138-
dc.contributor.localIdA00962-
dc.contributor.localIdA01472-
dc.contributor.localIdA01730-
dc.contributor.localIdA02736-
dc.relation.journalcodeJ02332-
dc.identifier.eissn1095-9572-
dc.identifier.pmidFunctional MRI ; Resting-state functional connectivity ; Network dynamicity ; Intra-class correlation ; Small-world network-
dc.identifier.urlhttp://www.sciencedirect.com/science/article/pii/S1053811911008263-
dc.subject.keywordFunctional MRI-
dc.subject.keywordResting-state functional connectivity-
dc.subject.keywordNetwork dynamicity-
dc.subject.keywordIntra-class correlation-
dc.subject.keywordSmall-world network-
dc.contributor.alternativeNameLee, Jong Doo-
dc.contributor.alternativeNameKim, Joong Il-
dc.contributor.alternativeNamePark, Bum Hee-
dc.contributor.alternativeNamePark, Hae Jeong-
dc.contributor.alternativeNameLee, Dong Ha-
dc.contributor.affiliatedAuthorLee, Jong Doo-
dc.contributor.affiliatedAuthorKim, Joong Il-
dc.contributor.affiliatedAuthorPark, Bum Hee-
dc.contributor.affiliatedAuthorPark, Hae Jeong-
dc.contributor.affiliatedAuthorLee, Dong Ha-
dc.citation.volume59-
dc.citation.number1-
dc.citation.startPage456-
dc.citation.endPage466-
dc.identifier.bibliographicCitationNEUROIMAGE, Vol.59(1) : 456-466, 2012-
Appears in Collections:
1. College of Medicine (의과대학) > BioMedical Science Institute (의생명과학부) > 1. Journal Papers
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Nuclear Medicine (핵의학교실) > 1. Journal Papers

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.