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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Notes
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.
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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Acknowledgements
We are grateful to the National Health and Medical Research Council (NHMRC) of Australia, the Australian Research Council (ARC), and the Victorian Government’s Operational Infrastructure Support Grant for their support. AZ is supported by the NHMRC CDF (GNT1047648).
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This study was funded by the National Health and Medical Research Council (NHMRC) of Australia, the Australian Research Council (ARC), and the Victorian Government (Australia).
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Calamante, F., Smith, R.E., Liang, X. et al. Track-weighted dynamic functional connectivity (TW-dFC): a new method to study time-resolved functional connectivity. Brain Struct Funct 222, 3761–3774 (2017). https://doi.org/10.1007/s00429-017-1431-1
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DOI: https://doi.org/10.1007/s00429-017-1431-1