Unbiased validation of connectivity-based cohesive brain parcellation

Motivation: Cohesive parcellation provides optimal connectivity-based parcels for downstream brain modeling, but at the cost of high parcel count, making comparisons to traditional parcellations difficult. Goal(s): We aim to fairly compare parcellations across a wide array of parcel sizes and counts. Approach: rsfMRI of 18 healthy subjects were parcellated based on cohesion and evaluated with the distance-controlled boundary coefficient (DCBC), an unbiased metric that incorporates spatial features of parcels in addition to connectivity. Results: Cohesive parcellation compared favorably to traditional parcellations based on DCBC of rsfMRI. Impact: Using an unbiased metric (DCBC), we show that the utility of connectivity-based cohesive parcellation is not simply due to high parcel count.