Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography
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DATA ORIGINAL ARTICLE
Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography Colin B Hansen 1 & Qi Yang 1 & Ilwoo Lyu 1,2 & Francois Rheault 3 & Cailey Kerley 2 & Bramsh Qamar Chandio 4 & Shreyas Fadnavis 4 & Owen Williams 5 & Andrea T. Shafer 5 & Susan M. Resnick 5 & David H. Zald 6 & Laurie E Cutting 7 & Warren D Taylor 7 & Brian Boyd 7 & Eleftherios Garyfallidis 4,8 & Adam W Anderson 9,10 & Maxime Descoteaux 3 & Bennett A Landman 1,2 & Kurt G Schilling 9,11 Accepted: 2 November 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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 Additional limitations include small sample sizes, few white matter pathways, and the use of outdated diffusion models and techniques.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. Keywords White matter . Atlas . Tractography
Background & Summary The creation and application of medical image-based brain atlases is widespread in neuroanatomy and neuroscience research. Atlases have proven to be a valuable tool to enable studies on individual subjects and facilitate inferences and
comparisons of different populations, leading to insights into development, cognition, and disease (Cabezas et al. 2011; Toga 1999; Gee et al. 1993; Lawrence et al. 2020). Through the process of spatial normalization, images can be aligned with atlases to facilitate comparisons of brains across subjects, time, or experimental conditions. Additionally, atlases can be
Colin B Hansen and Qi Yang contributed equally to this work. * Kurt G Schilling [email protected] 1
Department of Computer Science, Vanderbilt University, Nashville, TN, USA
2
Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
3
Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Sherbrooke, Canada
4
Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
5
Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
6
Center for Advanced Human Brain Imaging Research, Rutger
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