BrainParcel: A brain parcellation algorithm for cognitive state classification

Abstract

In this study, we propose a novel brain parcellation algorithm, called BrainParcel. BrainParcel defines a set of supervoxels by partitioning a voxel level brain graph into a number of subgraphs, which are assumed to represent “homogeneous” brain regions with respect to a predefined criteria. Aforementioned brain graph is constructed by a set of local meshes, called mesh networks. Then, the supervoxels are obtained using a graph partitioning algorithm. The supervoxels form partitions of brain as an alternative to anatomical regions (AAL). Compared to AAL, supervoxels gather functionally and spatially close voxels. This study shows that BrainParcel can achieve higher accuracies in cognitive state classification compared to AAL. It has a better representation power compared to similar brain segmentation methods, reported the literature.