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Indexed by:期刊论文

Date of Publication:2018-01-01

Journal:Journal of neuroscience methods

Included Journals:PubMed、SCIE

Volume:304

Page Number:24-38

ISSN No.:1872-678X

Key Words:Independent component analysis (ICA); Complex-valued fMRI data; Model order; Component splitting; Phase data; Schizophrenia

Abstract:BACKGROUND: Component splitting at higher model orders is a widely accepted finding for independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. However, our recent study found that intact components occurred with subcomponents at higher model orders.; NEW METHOD: This study investigated model order effects on ICA of resting-state complex-valued fMRI data from 82 subjects, which included 40 healthy controls (HCs) and 42 schizophrenia patients. In addition, we explored underlying causes for distinct component splitting between complex-valued data and magnitude-only data by examining model order effects on ICA of phase fMRI data. A best run selection method was proposed to combine subject averaging and a one-sample t-test. We selected the default mode network (DMN)-, visual-, and sensorimotor-related components from the best run of ICA at varying model orders from 10 to 140.; RESULTS: Results show that component integration occurred in complex-valued and phase analyses, whereas component splitting emerged in magnitude-only analysis with increasing model order. Incorporation of phase data appears to play a complementary role in preserving integrity of brain networks.; COMPARISON WITH EXISTING METHOD(S): When compared with magnitude-only analysis, the intact DMN component obtained in complex-valued analysis at higher model orders exhibited highly significant subject-level differences between HCs and patients with schizophrenia. We detected significantly higher activity and variation in anterior areas for HCs and in posterior areas for patients with schizophrenia.; CONCLUSIONS: These results demonstrate the potential of complex-valued fMRI data to contribute generally and specifically to brain network analysis in identification of schizophrenia-related changes. Copyright © 2018 Elsevier B.V. All rights reserved.