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Mapping individual voxel-wise morphological connectivity using wavelet transform of voxel-based morphology

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  • Xun-Heng Wang
  • Yun Jiao
  • Lihua Li

Abstract

Mapping individual brain networks has drawn significant research interest in recent years. Most individual brain networks developed to date have been based on fMRI or diffusion MRI. Given recent concerns regarding confounding artifacts, various preprocessing steps are generally included in functional or structural brain networks. Notably, voxel-based morphometry (VBM) derived from anatomical MRI exhibits high signal-to-noise ratios and has been applied to individual interregional morphological networks. To the best of our knowledge, individual voxel-wise morphological networks remain unexplored. The goal of this research is twofold: to build novel metrics for individual voxel-wise morphological networks and to test the reliability of the proposed morphological connectivity. To this end, anatomical scans of a cohort of healthy subjects were obtained from a public database. The anatomical datasets were preprocessed and normalized to the standard brain space. For each individual, wavelet-transform was applied on the VBM measures to obtain voxel-wise hierarchical features. The voxel-wise morphological connectivity was computed based on the wavelet features. Reliable brain hubs were detected by the z-scores of degree centrality. High reliability was discovered by test-retest analysis. No effects of wavelet scale, network threshold or network type were found on hubs of group-level degree centrality. However, significant effects of wavelet scale, network threshold and network type were found on individual-level degree centrality. Significant effects of network threshold and network type were found on reliability of degree centrality. The results suggested that the voxel-wise morphological connectivity was reliable and exhibited a hub structure. Moreover, the voxel-wise morphological connectivity could reflect individual differences. In summary, individual voxel-wise wavelet-based features can probe morphological connectivity and may be beneficial for investigating the brain morphological connectomes.

Suggested Citation

  • Xun-Heng Wang & Yun Jiao & Lihua Li, 2018. "Mapping individual voxel-wise morphological connectivity using wavelet transform of voxel-based morphology," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-16, July.
    • Handle: RePEc:plo:pone00:0201243
    DOI: 10.1371/journal.pone.0201243
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    References listed on IDEAS

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    1. Gabriele Lohmann & Daniel S Margulies & Annette Horstmann & Burkhard Pleger & Joeran Lepsien & Dirk Goldhahn & Haiko Schloegl & Michael Stumvoll & Arno Villringer & Robert Turner, 2010. "Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain," PLOS ONE, Public Library of Science, vol. 5(4), pages 1-8, April.
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    Cited by:

    1. Xiaolei Zhang & Weijun Pan, 2019. "Exon prediction based on multiscale products of a genomic-inspired multiscale bilateral filtering," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-15, March.

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