The new hybrid-BCI-DNI atlas is a high-resolution MPRAGE, single-subject atlas, constructed using both anatomical
and functional information to guide the parcellation of the cerebral cortex. Anatomical labeling was performed
manually on coronal single-slice images guided by sulcal and gyral landmarks to generate the original (non-hybrid)
BCI-DNI atlas. Functional sub-parcellations of the gyral ROIs were then generated from 40 minimally preprocessed
resting fMRI datasets from the HCP database. Gyral ROIs were transferred from the BCI-DNI atlas to the 40 subjects
using the HCP grayordinate space as a reference. For each subject, each gyral ROI was subdivided using the fMRI
data by applying spectral clustering to a similarity matrix computed from the fMRI time-series correlations between
each vertex pair. The sub-parcellations were then transferred back to the original cortical mesh to create the subparcellated
hBCI-DNI atlas with a total of 67 cortical regions per hemisphere. To assess the stability of the gyral
subdivisons, a separate set of 60 HCP datasets were processed as follows: 1) coregistration of the structural scans to
the hBCI-DNI atlas; 2) coregistration of the anatomical BCI-DNI atlas without functional subdivisions, followed by
sub-parcellation of each subject’s resting fMRI data as described above. We then computed consistency between the
anatomically-driven delineation of each gyral subdivision and that obtained per subject using individual fMRI data.
The gyral sub-parcellations generated by atlas-based registration show variable but generally good overlap of the
confidence intervals with the resting fMRI-based subdivisions. These consistency measures will provide a quantitative
measure of reliability of each subdivision to users of the atlas.
Anand A. Joshi, Soyoung Choi, Gaurav Sonkar, Minqi Chong, Jorge Gonzalez-Martinez, Dileep Nair, David W. Shattuck, Hanna Damasio, and Richard M. Leahy, "A whole brain atlas with sub-parcellation of cortical gyri using resting fMRI," Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330O (Presented at SPIE Medical Imaging: February 13, 2017; Published: 2 March 2017); https://doi.org/10.1117/12.2254681.
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