Evaluating brain parcellations using the distance controlled boundary coefficient
An important goal of human brain mapping is to define a set of distinct regions that can be linked to unique functions. Numerous brain parcellations have been proposed, using cytoarchitectonic data, structural or functional Magnetic Resonance Imaging (fMRI). The intrinsic smoothness of the brain data, however, poses a problem for current methods seeking to compare different parcellations to each other. For example, criteria that simply compare within-parcel to between-parcel similarity provide even random parcellations with a high value. Furthermore, the evaluation is biased by the spatial scale of the parcellation. To address this problem, we propose the Distance Controlled Boundary Coefficient (DCBC), an unbiased criterion to evaluate discrete parcellations. We employ this new criterion to evaluate whether existing parcellations of the human neocortex can predict functional boundaries on a rich multi-domain task battery. We find that common anatomical parcellations do not perform better than chance, suggesting that task-based functional boundaries do not align well with sulcal landmarks. Parcellations based on resting-state fMRI data perform well; in some cases, as well as a parcellation defined on the evaluation data itself. Finally, multi-modal parcellations that combine functional and anatomical criteria perform substantially worse than those based on functional data alone, indicating that functionally homogeneous regions often span major anatomical landmarks. Overall, the DCBC advances the field of functional brain mapping by providing an unbiased metric that compares the predictive ability of different brain parcellations to define functionally and anatomically homogeneous brain regions.