Journal
JOURNAL OF NEUROSCIENCE METHODS
Volume 374, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.jneumeth.2022.109566
Keywords
Brain atlas; Anatomical MRI; Functional MRI; Neuroanatomy; Segmentation
Categories
Funding
- Department of Defense, USA [W81XWH-18-1-061]
- National Institutes of Health (NIH) , USA [R01 NS074980, R01 NS089212, F31NS106828, K23 HD099309, T32 MH 111360]
- Human Connectome Project, WU-Minn Consortium [1U54MH091657]
- 16 NIH Institutes and Centers, USA
- NIH Blueprint for Neuroscience Research
- McDonnell Center for Systems Neuroscience at Washington University
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This study presents a new high-quality single-subject brain atlas with fine anatomical details, high SNR, and excellent tissue contrast. The atlas includes both manual labeling based on known features and sub-parcellation guided by functional information. It provides consistent parcellation and labeling, and can be used as a reference template for structural coregistration and labeling of individual brains. The atlas contains 66 cortical regions, 29 noncortical regions, and additional subregions based on connectivity analysis. It also includes a probabilistic map for reliability assessment.
We present a new high-quality, single-subject atlas with sub-millimeter voxel resolution, high SNR, and excellent gray-white tissue contrast to resolve fine anatomical details. The atlas is labeled into two parcellation schemes: 1) the anatomical BCI-DNI atlas, which is manually labeled based on known morphological and anatomical features, and 2) the hybrid USCBrain atlas, which incorporates functional information to guide the sub-parcellation of cerebral cortex. In both cases, we provide consistent volumetric and cortical surface-based parcellation and labeling. The intended use of the atlas is as a reference template for structural coregistration and labeling of individual brains. A single-subject T1-weighted image was acquired five times at a resolution of 0.547 mm x 0.547 mm x 0.800 mm and averaged. Images were processed by an expert neuroanatomist using semi automated methods in BrainSuite to extract the brain, classify tissue-types, and render anatomical surfaces. Sixty-six cortical and 29 noncortical regions were manually labeled to generate the BCI-DNI atlas. The cortical regions were further sub-parcellated into 130 cortical regions based on multi-subject connectivity analysis using resting fMRI (rfMRI) data from the Human Connectome Project (HCP) database to produce the USCBrain atlas. In addition, we provide a delineation between sulcal valleys and gyral crowns, which offer an additional set of 26 sulcal subregions per hemisphere. Lastly, a probabilistic map is provided to give users a quantitative measure of reliability for each gyral subdivision. Utility of the atlas was assessed by computing Adjusted Rand Indices (ARIs) between individual sub-parcellations obtained through structural-only coregistration to the USCBrain atlas and sub-parcellations obtained directly from each subject's resting fMRI data. Both atlas parcellations can be used with the BrainSuite, FreeSurfer, and FSL software packages.
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