Individual parcellation of resting fMRI with a group functional connectivity prior

Cortical parcellation based on resting fMRI is an important tool for investigating the functional organization and connectivity of the cerebral cortex. Group parcellation based on co-registration of anatomical images to a common atlas will inevitably result in errors in the locations of the boundaries of functional parcels when they are mapped back from the atlas to the individual. This is because areas of functional specialization vary across individuals in a manner that cannot be fully determined from the sulcal and gyral anatomy that is used for mapping between atlas and individual. We describe a method that avoids this problem by refining an initial group parcellation so that for each subject the parcel boundaries are optimized with respect to that subject's resting fMRI. Initialization with a common parcellation results in automatic correspondence between parcels across subjects. Further, by using a group sparsity constraint to model connectivity, we exploit group similarities in connectivity between parcels while optimizing their boundaries for each individual. We applied this approach with initialization on both high and low density group cortical parcellations and used resting fMRI data to refine across a group of individuals. Cross validation studies show improved homogeneity of resting activity within the refined parcels. Comparisons with task-based localizers show consistent reduction of variance of statistical parametric maps within the refined parcels relative to the group-based initialization indicating improved delineation of regions of functional specialization. This method enables a more accurate estimation of individual subject functional areas, facilitating group analysis of functional connectivity, while maintaining consistency across individuals with a standardized topological atlas. •We describe a method for cortical parcellation that adapts a common functional atlas to produce individualized parcellations consistent with each subjects resting fMRI.•Using a group‐sparsity prior we are able to exploit similarities in connectivity across the population to obtain a consistent set of parcels across subjects while also respecting individual differences.•Quantitative evaluation using HCP data comparing resting fMRI parcellations to functional task localizer data indicates improved consistency between the two relative to use of a common functional atlas.•The approach has potential to improve statistical power in region‐based analysis of eith