Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/18138
Title: Track-weighted dynamic functional connectivity (TW-dFC): a new method to study time-resolved functional connectivity.
Authors: Calamante, Fernando;Smith, Robert E;Liang, Xiaoyun;Zalesky, Andrew;Connelly, Alan
Affiliation: The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia
Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia
Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, VIC, Australia
Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC, Australia
Department of Medicine, Northern Health, University of Melbourne, Melbourne, VIC, Australia
Issue Date: Nov-2017
EDate: 2017
Citation: Brain structure & function 2017; 222(8): 3761-3774
Abstract: Interest 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.
URI: http://ahro.austin.org.au/austinjspui/handle/1/18138
DOI: 10.1007/s00429-017-1431-1
ORCID: 0000-0002-7550-3142
0000-0002-1851-3408
PubMed URL: 28447220
Type: Journal Article
Subjects: Fibre-tracking
Functional connectivity
Networks
Parcellation
Sliding window
Structural connectivity
Appears in Collections:Journal articles

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