Reference-Guided Parallel Independent Component Analysis: Estimating Cognition Associated Multimodal Patterns In Schizophrenia

Multimodal fusion provides cross-modality information to understand the human brain from different perspectives that may be missed in single modality analysis. Supervised fusion focuses on extracting multimodal patterns related to specific clinical measures by further incorporating a prior interested reference. However, existing supervised fusion methods cannot extract component that have weak correlations with the reference, which may be lost during the optimization process. Here, we propose a reference-guided parallel independent component analysis (RG-PICA) aiming at identifying multimodal covarying features related to interested reference through global optimization. The intra-modality independence, the inter-modality correlation, and the correlation between modalities and the reference are maximized globally. Simulations show that RG-PICA can accurately extract multimodal features correlated with the weak related reference while keeping cross-modality linkage comparing with seven fusion methods. In real data application, RG-PICA reveals co-varying patterns in schizophrenia (SZ) that links with cognition and correlates between modalities. These results demonstrate RG-PICA can jointly optimize for target components that correlate with the reference while keeping cross-modality linkage. This approach can improve the meaningful detection of reliable reference-linked multimodal brain patterns for brain disorders.