A submodular approach to create individualized parcellations of the human brain

Abstract

Recent studies on functional neuroimaging (e.g. fMRI) attempt to model the brain as a network. A conventional functional connectivity approach for defining nodes in the network is grouping similar voxels together, a method known as functional parcellation. The majority of previous work on human brain parcellation employs a group-level analysis by collapsing data from the entire population. However, these methods ignore the large amount of inter-individual variability and uniqueness in connectivity. This is particularly relevant for patient studies or even developmental studies where a single functional atlas may not be appropriate for all individuals or conditions. To account for the individual differences, we developed an approach to individualized parcellation. The algorithm starts with an initial group-level parcellation and forms the individualized ones using a local exemplar-based submodular clustering method. The utility of individualized parcellations is further demonstrated through improvement in the accuracy of a predictive model that predicts IQ using functional connectome.