Construction of a 3D probabilistic atlas of human cortical structures.
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.
Laboratory of Neuro Imaging, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, 635 Charles Young Drive South, NRB1, Suite 225, Los Angeles, CA 90095, USA. shattuck@loni.ucla.edu
David W
DW
Shattuck
Mubeena
M
Mirza
Vitria
V
Adisetiyo
Cornelius
C
Hojatkashani
Georges
G
Salamon
Katherine L
KL
Narr
Russell A
RA
Poldrack
Robert M
RM
Bilder
Arthur W
AW
Toga
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001271:Atlases as Topic; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000033:anatomy & histology* / Q000502:physiology; D017352:Echo-Planar Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016013:Likelihood Functions; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D015588:Observer Variation; D012016:Reference Values