Classification and Extraction of Resting State Networks Using Healthy and Epilepsy fMRI Data.

ABSTRACT: Functional magnetic resonance imaging studies have significantly expanded the field's understanding of functional brain activity of healthy and patient populations. Resting state (rs-) fMRI, which does not require subjects to perform a task, eliminating confounds of task difficulty, allows examination of neural activity and offers valuable functional mapping information. The purpose of this work was to develop an automatic resting state network (RSN) labeling method which offers value in clinical workflow during rs-fMRI mapping by organizing and quickly labeling spatial maps into functional networks. Here independent component analysis (ICA) and machine learning were applied to rs-fMRI data with the goal of developing a method for the clinically oriented task of extracting and classifying

SUBMITTER: Vergun S 

PROVIDER: S-EPMC5037187 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature