Connectivity-based whole brain dual parcellation by group ICA reveals tract structures and decreased connectivity in schizophrenia.

ABSTRACT: Mapping brain connectivity based on neuroimaging data is a promising new tool for understanding brain structure and function. In this methods paper, we demonstrate that group independent component analysis (GICA) can be used to perform a dual parcellation of the brain based on its connectivity matrix (cmICA). This dual parcellation consists of a set of spatially independent source maps, and a corresponding set of paired dual maps that define the connectivity of each source map to the brain. These dual maps are called the connectivity profiles of the source maps. Traditional analysis of connectivity matrices has been used previously for brain parcellation, but the present method provides additional information on the connectivity of these segmented regions. In this paper, the whole brain st

SUBMITTER: Wu L 

PROVIDER: S-EPMC4619141 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature