How important are inter-dataset interactions for large scale analysis of fMRI data: A multi-dimensional comparison

Abstract

Analyzing multi-subject functional magnetic resonance imaging (fMRI) data requires methods that can jointly capture shared and individual patterns of brain activity across participants. Joint blind source separation (JBSS) techniques, such as independent vector analysis (IVA), provide a principled framework for this purpose by modeling dependencies across subjects while identifying distinct functional networks. Constrained IVA variants, including adaptive-reverse cIVA-G (ar-cIVA-G) and threshold-free cIVA-G (tf-cIVA-G), further enhance interpretability through the use of reference templates and inter-subject correlation constraints. Alternatively, regression-based methods like IVA-G regression (regIVA-G) and reference-guided component analysis (RGCA) process subjects individually, aligning their components to references with improved computational efficiency. Despite their potential, systematic evaluations of reference-based JBSS approaches for fMRI analysis remain limited. In this work, we present a comparative study of these methods to assess their capacity for identifying schizophrenia-related biomarkers using real fMRI data from subjects with schizophrenia and healthy controls. Our results demonstrate that both constrained IVA and regression-based methods effectively extract meaningful biomarkers while the latter achieve comparable performance at substantially reduced computational cost.