The effect of preprocessing pipelines in subject classification and detection of abnormal resting state functional network connectivity using group ICA.

ABSTRACT: Resting state functional network connectivity (rsFNC) derived from functional magnetic resonance (fMRI) imaging is emerging as a possible biomarker to identify several brain disorders. Recently it has been pointed out that methods used to preprocess head motion variance might not fully remove all unwanted effects in the data. Proposed processing pipelines locate the treatment of head motion effects either close to the beginning or as one of the final steps. In this work, we assess several preprocessing pipelines applied in group independent component analysis (gICA) methods to study the rsFNC of the brain. The evaluation method utilizes patient/control classification performance based on linear support vector machines and leave-one-out cross validation. In addition, we explored group tests

SUBMITTER: Vergara VM 

PROVIDER: S-EPMC5035165 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature