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    Surface-based fMRI-driven diffusion tractography in the presence of significant brain pathology: A study linking structure and function in cerebral palsy

    Access Status
    Open access via publisher
    Authors
    Reid, L.
    Cunnington, R.
    Boyd, Roslyn
    Rose, S.
    Date
    2016
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Reid, L. and Cunnington, R. and Boyd, R. and Rose, S. 2016. Surface-based fMRI-driven diffusion tractography in the presence of significant brain pathology: A study linking structure and function in cerebral palsy. PLoS One. 11 (8).
    Source Title
    PLoS One
    DOI
    10.1371/journal.pone.0159540
    School
    School of Occupational Therapy and Social Work
    URI
    http://hdl.handle.net/20.500.11937/30765
    Collection
    • Curtin Research Publications
    Abstract

    Diffusion MRI (dMRI) tractography analyses are difficult to perform in the presence of brain pathology. Automated methods that rely on cortical parcellation for structural connectivity studies often fail, while manually defining regions is extremely time consuming and can introduce human error. Both methods also make assumptions about structure-function relationships that may not hold after cortical reorganisation. Seeding tractography with functional-MRI (fMRI) activation is an emerging method that reduces these confounds, but inherent smoothing of fMRI signal may result in the inclusion of irrelevant pathways. This paper describes a novel fMRI-seeded dMRI-analysis pipeline based on surface-meshes that reduces these issues and utilises machine-learning to generate task specific white matter pathways, minimising the requirement for manually-drawn ROIs. We directly compared this new strategy to a standard voxelwise fMRI-dMRI approach, by investigating correlations between clinical scores and dMRI metrics of thalamocortical and corticomotor tracts in 31 children with unilateral cerebral palsy. The surface-based approach successfully processed more participants (87%) than the voxel-based approach (65%), and provided significantly more-coherent tractography. Significant correlations between dMRI metrics and five clinical scores of function were found for the more superior regions of these tracts. These significant correlations were stronger and more frequently found with the surface-based method (15/20 investigated were significant; R2 = 0.43-0.73) than the voxelwise analysis (2 sig. correlations; 0.38 & 0.49). More restricted fMRI signal, better-constrained tractography, and the novel track-classification method all appeared to contribute toward these differences.

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