A Supervoxel-Based Method for Groupwise Whole Brain Parcellation with Resting-State fMRI Data.

ABSTRACT: Node definition is a very important issue in human brain network analysis and functional connectivity studies. Typically, the atlases generated from meta-analysis, random criteria, and structural criteria are utilized as nodes in related applications. However, these atlases are not originally designed for such purposes and may not be suitable. In this study, we combined normalized cut (Ncut) and a supervoxel method called simple linear iterative clustering (SLIC) to parcellate whole brain resting-state fMRI data in order to generate appropriate brain atlases. Specifically, Ncut was employed to extract features from connectivity matrices, and then SLIC was applied on the extracted features to generate parcellations. To obtain group level parcellations, two approaches named mean SLIC and two

SUBMITTER: Wang J 

PROVIDER: S-EPMC5187473 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature