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Brain Structure and Function

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26.04.2017 | Original Article

Track-weighted dynamic functional connectivity (TW-dFC): a new method to study time-resolved functional connectivity

verfasst von: Fernando Calamante, Robert E. Smith, Xiaoyun Liang, Andrew Zalesky, Alan Connelly

Erschienen in: Brain Structure and Function | Ausgabe 8/2017

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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.

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While the correlation between pairs of voxels is used for the current study, the correlation between pairs of regions could also have been considered—see “Discussion” Section for further details.

While a sliding window approach was used for the work shown here, the TW-dFC formalism can be directly generalised to other methods to compute dynamic functional connectivity based on BOLD time-series data (Chang and Glover 2010; Cribben et al. 2012; Lindquist et al. 2014).

A relatively large z value (z > 3) was selected to be conservative in the identification of suitable clusters. The threshold for cluster size (160/80 mm² for the individual/group analysis) was empirically chosen based on an initial exploration of the data (see “Discussion” section for automating this choice).

It should be noted that the component dimensionality of the subject-level and group level ICA are not expected to be the same, given that the group-level has 8 times more data (i.e. 1600 time-points vs. 200 time-points).

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Titel: Track-weighted dynamic functional connectivity (TW-dFC): a new method to study time-resolved functional connectivity
verfasst von: Fernando Calamante
Robert E. Smith
Xiaoyun Liang
Andrew Zalesky
Alan Connelly
Publikationsdatum: 26.04.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Brain Structure and Function / Ausgabe 8/2017
Print ISSN: 1863-2653
Elektronische ISSN: 1863-2661
DOI: https://doi.org/10.1007/s00429-017-1431-1

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