Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pcbi.1000808
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dc.titleThe organization of local and distant functional connectivity in the human brain
dc.contributor.authorSepulcre J.
dc.contributor.authorLiu H.
dc.contributor.authorTalukdar T.
dc.contributor.authorMartincorena I.
dc.contributor.authorThomas Yeo B.T.
dc.contributor.authorBuckner R.L.
dc.date.accessioned2020-03-13T05:27:19Z
dc.date.available2020-03-13T05:27:19Z
dc.date.issued2010
dc.identifier.citationSepulcre J., Liu H., Talukdar T., Martincorena I., Thomas Yeo B.T., Buckner R.L. (2010). The organization of local and distant functional connectivity in the human brain. PLoS Computational Biology 6 (6) : 1-15. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pcbi.1000808
dc.identifier.issn1553734X
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/165419
dc.description.abstractInformation processing in the human brain arises from both interactions between adjacent areas and from distant projections that form distributed brain systems. Here we map interactions across different spatial scales by estimating the degree of intrinsic functional connectivity for the local (≤14 mm) neighborhood directly surrounding brain regions as contrasted with distant (>14 mm) interactions. The balance between local and distant functional interactions measured at rest forms a map that separates sensorimotor cortices from heteromodal association areas and further identifies regions that possess both high local and distant cortical-cortical interactions. Map estimates of network measures demonstrate that high local connectivity is most often associated with a high clustering coefficient, long path length, and low physical cost. Task performance changed the balance between local and distant functional coupling in a subset of regions, particularly, increasing local functional coupling in regions engaged by the task. The observed properties suggest that the brain has evolved a balance that optimizes information-processing efficiency across different classes of specialized areas as well as mechanisms to modulate coupling in support of dynamically changing processing demands. We discuss the implications of these observations and applications of the present method for exploring normal and atypical brain function. © 2010 Sepulcre et al.
dc.publisherPublic Library of Science
dc.sourceUnpaywall 20200320
dc.subjectadult
dc.subjectarticle
dc.subjectbrain function
dc.subjectbrain mapping
dc.subjectbrain region
dc.subjectcontrolled study
dc.subjectfemale
dc.subjecthuman
dc.subjecthuman experiment
dc.subjectmale
dc.subjectmental performance
dc.subjectnerve cell network
dc.subjectnormal human
dc.subjectsensorimotor cortex
dc.subjectbiological model
dc.subjectbiology
dc.subjectbrain cortex
dc.subjectcluster analysis
dc.subjectmethodology
dc.subjectneocortex
dc.subjectnuclear magnetic resonance imaging
dc.subjectphysiology
dc.subjectAdult
dc.subjectCerebral Cortex
dc.subjectCluster Analysis
dc.subjectComputational Biology
dc.subjectFemale
dc.subjectHumans
dc.subjectMagnetic Resonance Imaging
dc.subjectMale
dc.subjectModels, Neurological
dc.subjectNeocortex
dc.typeArticle
dc.contributor.departmentELECTRICAL AND COMPUTER ENGINEERING
dc.description.doi10.1371/journal.pcbi.1000808
dc.description.sourcetitlePLoS Computational Biology
dc.description.volume6
dc.description.issue6
dc.description.page1-15
dc.published.statePublished
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