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Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography

Posted by on Sunday, November 29, 2020 in Big Data, Diffusion Tensor Imaging, Diffusion Weighted MRI, Harmonization, Tractography.

Colin B. HansenQi Yang,  Ilwoo LyuFrancois RheaultCailey KerleyBramsh Qamar ChandioShreyas FadnavisOwen WilliamsAndrea T. ShaferSusan M. ResnickDavid H. ZaldLaurie E. CuttingWarren D. TaylorBrian BoydEleftherios GaryfallidisAdam W. AndersonMaxime DescoteauxBennett A. Landman, Kurt G. Schilling. Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography. Neuroinform (2020).

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Abstract

Brain atlases have proven to be valuable neuroscience tools for localizing regions of interest and performing statistical inferences on populations. Although many human brain atlases exist, most do not contain information about white matter structures, often neglecting them completely or labelling all white matter as a single homogenous substrate. While few white matter atlases do exist based on diffusion MRI fiber tractography, they are often limited to descriptions of white matter as spatially separate “regions” rather than as white matter “bundles” or fascicles, which are well-known to overlap throughout the brain. Additional limitations include small sample sizes, few white matter pathways, and the use of outdated diffusion models and techniques. Here, we present a new population-based collection of white matter atlases represented in both volumetric and surface coordinates in a standard space. These atlases are based on 2443 subjects, and include 216 white matter bundles derived from 6 different automated state-of-the-art tractography techniques. This atlas is freely available and will be a useful resource for parcellation and segmentation.

Experimental workflow and generation of Pandora atlases. Data from three repositories (HCP, BLSA, and VU) were curated. Subject-level processing includes tractography and registration to MNI space. Volumetric atlases for each set of bundle definitions is created by population-averaging in standard space. Point clouds are displayed which allow qualitative visualization of probability densities of a number of fiber pathways. Finally, surface atlases are created by assigning indices to the vertices of the MNI template white matter/gray matter boundary.
Experimental workflow and generation of Pandora atlases. Data from three repositories (HCP, BLSA, and VU) were curated. Subject-level processing includes tractography and registration to MNI space. Volumetric atlases for each set of bundle definitions is created by population-averaging in standard space. Point clouds are displayed which allow qualitative visualization of probability densities of a number of fiber pathways. Finally, surface atlases are created by assigning indices to the vertices of the MNI template white matter/gray matter boundary.