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

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 fusio...