Deep Independent Vector Analysis

We introduce Deep Independent Vector Analysis (DeepIVA), a multivariate latent variable model for learning linked and identifiable latent sources from multimodal data. DeepIVA jointly identifies sources within each modality and aligns sources across modalities. We propose a rigorous set of performance metrics measuring unimodal identifiability, cross-modal linkage, and cross-segment consistency. We validate DeepIVA using synthetic data and then apply it to a large multimodal neuroimaging dataset with structural and functional magnetic resonance imaging. In the synthetic data experiment, DeepIVA successfully recovers nonlinearly mixed multimodal sources, outperforming baseline methods. In the neuroimaging experiment, DeepIVA reveals linked imaging biomarkers associated with sex and age, shedding light on coupled structural-functional brain relationships.