We describe the construction of a digital brain atlas composed of data from manually
delineated MRI data. A total of 56 structures were labeled in MRI of 40 healthy, normal
volunteers. This labeling was performed according to a set of protocols developed
for this project. Pairs of raters were assigned to each structure and trained on the
protocol for that structure. Each rater pair was tested for concordance on 6 of the
40 brains; once they had achieved reliability standards, they divided the task of
delineating the remaining 34 brains. The data were then spatially normalized to well-known
templates using 3 popular algorithms: AIR5.2.5's nonlinear warp (Woods et al., 1998)
paired with the ICBM452 Warp 5 atlas (Rex et al., 2003), FSL's FLIRT (Smith et al.,
2004) was paired with its own template, a skull-stripped version of the ICBM152 T1
average; and SPM5's unified segmentation method (Ashburner and Friston, 2005) was
paired with its canonical brain, the whole head ICBM152 T1 average. We thus produced
3 variants of our atlas, where each was constructed from 40 representative samples
of a data processing stream that one might use for analysis. For each normalization
algorithm, the individual structure delineations were then resampled according to
the computed transformations. We next computed averages at each voxel location to
estimate the probability of that voxel belonging to each of the 56 structures. Each
version of the atlas contains, for every voxel, probability densities for each region,
thus providing a resource for automated probabilistic labeling of external data types
registered into standard spaces; we also computed average intensity images and tissue
density maps based on the three methods and target spaces. These atlases will serve
as a resource for diverse applications including meta-analysis of functional and structural
imaging data and other bioinformatics applications where display of arbitrary labels
in probabilistically defined anatomic space will facilitate both knowledge-based development
and visualization of findings from multiple disciplines.