期刊
NEUROINFORMATICS
卷 19, 期 3, 页码 447-460出版社
HUMANA PRESS INC
DOI: 10.1007/s12021-020-09497-1
关键词
White matter; Atlas; Tractography
资金
- National Institutes of Health [R01EB017230, T32EB001628, R21 MH099218, R01NS075270]
- VICTR [VR3029]
- National Center for Research Resources [UL1 RR024975-01]
- Department of Defense [W81XWH-17-2-055]
- Intramural Research Program, National Institute on Aging, NIH
- Neuroinformatics Research Chair
- Compute Canada
- NSERC
Brain atlases are valuable neuroscience tools for localizing regions of interest and performing statistical inferences, but many existing atlases lack comprehensive information on white matter structures. A new population-based white matter atlas presented in this study provides detailed and accurate representation of white matter bundles derived from different automated tractography techniques and a large sample size, making it a useful resource for parcellation and segmentation.
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 Additional limitations include small sample sizes, few white matter pathways, and the use of outdated diffusion models and techniques.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.
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