Independent vector analysis for common subspace analysis: Application to multi-subject fMRI data yields meaningful subgroups of schizophrenia

• Published in: NeuroImage (Orlando, Fla.) Vol. 216; p. 116872
• Main Authors: Long, Qunfang, Bhinge, Suchita, Calhoun, Vince D., Adali, Tülay
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.08.2020; Elsevier Limited; Elsevier
• Subjects: Adult; Brain - diagnostic imaging; Brain - physiopathology; Brain mapping; Computer Simulation; Connectome - methods; Connectome - standards; Data analysis; Datasets; Female; Functional magnetic resonance imaging; Heterogeneity of schizophrenia; Humans; Identification; Independent vector analysis; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - standards; Male; Medical imaging; Mental disorders; Middle Aged; Multi-subject medical imaging data; Nerve Net - diagnostic imaging; Nerve Net - physiopathology; Neuroimaging; Normal distribution; Schizophrenia; Schizophrenia - diagnostic imaging; Schizophrenia - physiopathology; Statistical analysis; Subspace analysis; Variables; Functional magnetic resonance imaging; Multi-subject medical imaging data; Independent vector analysis; Subspace analysis; Heterogeneity of schizophrenia
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.116872

The extraction of common and distinct biomedical signatures among different populations allows for a more detailed study of the group-specific as well as distinct information of different populations. A number of subspace analysis algorithms have been developed and successfully applied to data fusion, however they are limited to joint analysis of only a couple of datasets. Since subspace analysis is very promising for analysis of multi-subject medical imaging data as well, we focus on this problem and propose a new method based on independent vector analysis (IVA) for common subspace extraction (IVA-CS) for multi-subject data analysis. IVA-CS leverages the strength of IVA in identification of a complete subspace structure across multiple datasets along with an efficient solution that uses only second-order statistics. We propose a subset analysis approach within IVA-CS to mitigate issues in estimation in IVA due to high dimensionality, both in terms of components estimated and the number of datasets. We introduce a scheme to determine a desirable size for the subset that is high enough to exploit the dependence across datasets and is not affected by the high dimensionality issue. We demonstrate the success of IVA-CS in extracting complex subset structures and apply the method to analysis of functional magnetic resonance imaging data from 179 subjects and show that it successfully identifies shared and complementary brain patterns from patients with schizophrenia (SZ) and healthy controls group. Two components with linked resting-state networks are identified to be unique to the SZ group providing evidence of functional dysconnectivity. IVA-CS also identifies subgroups of SZs that show significant differences in terms of their brain networks and clinical symptoms.