Group ICA for identifying biomarkers in schizophrenia: ‘Adaptive’ networks via spatially constrained ICA show more sensitivity to group differences than spatio-temporal regression

• Published in: NeuroImage clinical Vol. 22; p. 101747
• Main Authors: Salman, Mustafa S., Du, Yuhui, Lin, Dongdong, Fu, Zening, Fedorov, Alex, Damaraju, Eswar, Sui, Jing, Chen, Jiayu, Mayer, Andrew R., Posse, Stefan, Mathalon, Daniel H., Ford, Judith M., Van Erp, Theodorus, Calhoun, Vince D.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.01.2019; Elsevier
• Subjects: Adult; Brain - diagnostic imaging; Classification; Databases, Factual - classification; Female; fMRI; Functional network; GIG-ICA; Humans; ICA; Magnetic Resonance Imaging - methods; Male; Nerve Net - diagnostic imaging; Principal Component Analysis - classification; Radiology; Regular; Schizophrenia; Schizophrenia - classification; Schizophrenia - diagnostic imaging; Spatio-temporal regression; fMRI; Spatio-temporal regression; GIG-ICA; ICA; Classification; Schizophrenia; Functional network
• ISSN: 2213-1582; 2213-1582
• DOI: 10.1016/j.nicl.2019.101747

Brain functional networks identified from fMRI data can provide potential biomarkers for brain disorders. Group independent component analysis (GICA) is popular for extracting brain functional networks from multiple subjects. In GICA, different strategies exist for reconstructing subject-specific networks from the group-level networks. However, it is unknown whether these strategies have different sensitivities to group differences and abilities in distinguishing patients. Among GICA, spatio-temporal regression (STR) and spatially constrained ICA approaches such as group information guided ICA (GIG-ICA) can be used to propagate components (indicating networks) to a new subject that is not included in the original subjects. In this study, based on the same a priori network maps, we reconstructed subject-specific networks using these two methods separately from resting-state fMRI data of 151 schizophrenia patients (SZs) and 163 healthy controls (HCs). We investigated group differences in the estimated functional networks and the functional network connectivity (FNC) obtained by each method. The networks were also used as features in a cross-validated support vector machine (SVM) for classifying SZs and HCs. We selected features using different strategies to provide a comprehensive comparison between the two methods. GIG-ICA generally showed greater sensitivity in statistical analysis and better classification performance (accuracy 76.45 ± 8.9%, sensitivity 0.74 ± 0.11, specificity 0.79 ± 0.11) than STR (accuracy 67.45 ± 8.13%, sensitivity 0.65 ± 0.11, specificity 0.71 ± 0.11). Importantly, results were also consistent when applied to an independent dataset including 82 HCs and 82 SZs. Our work suggests that the functional networks estimated by GIG-ICA are more sensitive to group differences, and GIG-ICA is promising for identifying image-derived biomarkers of brain disease. •We investigate which group ICA method is more sensitive to group differences in networks and FNC.•We evaluate which group ICA method can result in better classification performance.•We compare the spatio-temporal regression (STR) and GIG-ICA using different feature selection and model building strategies and independent samples.•We use different feature selection and model building strategies and classification using independent samples.•Using fMRI data including schizophrenia patients, GIG-ICA shows better performance than the traditional STR method.