Large-scale Resting-State Fmri Analysis Via Regression-Assisted Framework.

Resting-state fMRI has been widely used to investigate the neurobiology of psychiatric disorders. However, analyzing large-scale fMRI data poses significant challenges due to data complexity and subject heterogeneity. Independent vector analysis (IVA) has been effectively utilized to identify biomarkers and detect homogeneous subgroups in resting-state fMRI data. Compared to other joint blind source separation methods like group ICA, IVA preserves subject variability by incorporating multivariate information across subjects. However, the computational cost of IVA increases exponentially with the number of subjects, limiting its applicability to large-scale datasets. In this paper, we propose a regression-assisted framework for large-scale resting-state fMRI analysis. This framework addresses the computational limitations of traditional blind source separation algorithms, such as IVA, by leveraging results from multilinear regression as an initialization for constrained IVA, while maintaining strong source separation performance. Additionally, we introduce the coreset selection method, which enables efficient and deterministic selection of a representative subset of data. We apply the proposed framework to 352 subjects from the B-SNIP study. The results demonstrate its advantages in preserving subject variability, reducing computational costs, and producing more reproducible estimation results. Clinically, the framework detects significant and reproducible differences in functional network interactions between healthy controls and schizophrenia patients across multiple brain regions. Notably, it identifies abnormal interactions between the dorsolateral prefrontal cortex and the dorsal posterior cingulate cortex, aligning with previous clinical research. These findings suggest that the proposed framework has the potential to serve as a valuable biomarker detection tool for various psychiatric disorders.