Subgroup Identification in Resting State fMRI using Common Subspace Independent Vectors Analysis

Joint blind source separation (JBSS) is commonly used to uncover latent structures across multiple datasets. However, it often struggles with high-dimensional data and inaccurate latent dimensionality estimation, limiting its scalability and separation accuracy. To address these challenges, we propose a novel common subspace independent vector analysis model based on a bounded multivariate generalized Gaussian mixture distribution, referred to as BMIVA-CS. The model extracts low-rank common sources across datasets while modeling subject-specific variations. It incorporates a bounded indicator function and leverages spatial correlations to improve robustness, particularly under noisy, high-dimensional conditions. We validate the model through simulations and experiments on resting-state fMRI datasets from individuals with schizophrenia and autism. BMIVA-CS reliably identifies clinically relevant sources and consistently localizes affected brain regions across subjects. These results demonstrate the effectiveness of BMIVA-CS and its potential as a diagnostic tool for neurological and psychiatric disorders using rs-fMRI data.