A Flexible Constrained ICA Approach for Multisubject fMRI Analysis

Large-scale analysis of functional connectivity within intrinsic brain networks using functional magnetic resonance imaging (fMRI) data has been widely used for identifying biomarkers in various psychiatric disorders. While the emerging access to large neuroimaging datasets provides unprecedented opportunities for exploring brain functions, they also pose significant computational complexity challenges due to the large amount of inherent variability across individuals and the complexity of brain activity patterns. To address these challenges, this paper introduces two novel constrained ICA methods, arc-EBM and minc-EBM, designed to overcome the computational complexity issue by incorporating prior information into the analysis framework. The proposed methods preserve the subject variability by adaptively selecting the constrained parameters for different functional networks and individuals, while also allowing estimation flexibility for activities not covered by the prior information through the concept of free components. Our methods are shown to enhance the precision of functional network estimation and improve the capture of subject variability across different cohorts. We evaluate the proposed methods using both synthetic and real fMRI data. By applying the proposed methods to a resting-state fMRI dataset including 179 subjects, both algorithms successfully reveal significant group differences in functional network connectivity between healthy controls and schizophrenia patients. The observed group differences, particularly the abnormal connectivity alterations in networks involving the thalamus, subthalamus/hypothalamus, and superior temporal gyrus, align with findings from previous clinical studies. Furthermore, our results demonstrate that the constraint parameters adaptively selected by arc-EBM reveal more diverse resting-state network structures in individuals with schizophrenia compared with healthy controls. This finding is consistent with prior studies and suggests that the selected constraint parameters could serve as potential biomarkers for mental disorder diagnosis.