GraMPa: Graph-based multi-modal parcellation of the cortex using fusion moves

Abstract

Parcellating the brain into a set of distinct subregions is an essential step for building and studying brain connectivity networks. Connectivity driven parcellation is a natural approach,but suffers from the lack of reliability of connectivity data. Combining modalities in the parcellation task has the potential to yield more robust parcellations,yet hasn’t been explored much. In this paper,we propose a graphbased multi-modal parcellation method that iteratively computes a set of modality specific parcellations and merges them using the concept of fusion moves. The merged parcellation initialises the next iteration,forcing all modalities to converge towards a set of mutually informed parcellations. Experiments on 50 subjects of the Human Connectome Project database show that the multi-modal setting yields parcels that are more reproducible and more representative of the underlying connectivity.