Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/18138
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dc.contributor.authorCalamante, Fernando-
dc.contributor.authorSmith, Robert E-
dc.contributor.authorLiang, Xiaoyun-
dc.contributor.authorZalesky, Andrew-
dc.contributor.authorConnelly, Alan-
dc.date2017-
dc.date.accessioned2018-08-07T06:30:38Z-
dc.date.available2018-08-07T06:30:38Z-
dc.date.issued2017-11-
dc.identifier.citationBrain structure & function 2017; 222(8): 3761-3774-
dc.identifier.urihttps://ahro.austin.org.au/austinjspui/handle/1/18138-
dc.description.abstractInterest in the study of brain connectivity is growing, particularly in understanding the dynamics of the structural/functional connectivity relation. Structural and functional connectivity are most often analysed independently of each other. Track-weighted functional connectivity (TW-FC) was recently proposed as a means to combine structural/functional connectivity information into a single image. We extend here TW-FC in two important ways: first, all the functional data are used without having to define a prior functional network (cf. TW-FC generates a map for a pre-specified network); second, we incorporate time-resolved connectivity information, thus allowing dynamic characterisation of functional connectivity. We refer to this technique as track-weighted dynamic functional connectivity (TW-dFC), which fuses structural/functional connectivity data into a four-dimensional image, providing a new approach to investigate dynamic connectivity. The structural connectivity information effectively 'constrains' the extremely large number of possible connections in the functional connectivity data (i.e. each voxel's connection to every other voxel), thus providing a way of reducing the problem's dimensionality while still maintaining key data features. The methodology is demonstrated in data from eight healthy subjects, and independent component analysis was subsequently applied to parcellate the corpus callosum, as an illustration of a possible application. TW-dFC maps demonstrate that different white matter pathways can have very different temporal characteristics, corresponding to correlated fluctuations in the grey matter regions they link. A realistic parcellation of the corpus callosum was generated, which was qualitatively similar to topography previously reported. TW-dFC, therefore, provides a complementary new tool to investigate the dynamic nature of brain connectivity.-
dc.language.isoeng-
dc.subjectFibre-tracking-
dc.subjectFunctional connectivity-
dc.subjectNetworks-
dc.subjectParcellation-
dc.subjectSliding window-
dc.subjectStructural connectivity-
dc.titleTrack-weighted dynamic functional connectivity (TW-dFC): a new method to study time-resolved functional connectivity.-
dc.typeJournal Article-
dc.identifier.journaltitleBrain structure & function-
dc.identifier.affiliationThe Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia-
dc.identifier.affiliationFlorey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia-
dc.identifier.affiliationDepartment of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia-
dc.identifier.affiliationMelbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia-
dc.identifier.affiliationDepartment of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Victoria, Australia-
dc.identifier.affiliationDepartment of Medicine, Northern Health, University of Melbourne, Melbourne, Victoria, Australia-
dc.identifier.doi10.1007/s00429-017-1431-1-
dc.identifier.orcid0000-0002-7550-3142-
dc.identifier.orcid0000-0002-1851-3408-
dc.identifier.pubmedid28447220-
dc.type.austinJournal Article-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeJournal Article-
item.grantfulltextnone-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.languageiso639-1en-
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