Deep Generative Modeling for Latent Source Separation and Psychosis Continuum Estimation from Neuroimaging Data

In this talk, we will introduce Deep Multidataset Independent Subspace Analysis (DeepMISA), a unified framework that encompasses multiple linear and nonlinear multivariate methods, including Multimodal Independent Vector Analysis (MMIVA) (Silva et al. 2024), Multimodal Subspace Independent Vector Analysis (MSIVA) (Li et al. 2024a), and Deep Independent Vector Analysis (DeepIVA) (Li et al., in preparation). We demonstrate that DeepMISA methods successfully recover multimodal sources that are linearly or nonlinearly mixed from various synthetic datasets, significantly outperforming baseline methods. We then show that DeepMISA methods reveal linked sources associated with phenotypic measures such as age, sex and psychosis in large-scale multimodal neuroimaging datasets. Next, we will present a functional network connectivity (FNC) interpolation framework (Li et al. 2024b), which uses an unsupervised generative model to capture the neuropsychiatric continuum and heterogeneity. We apply this framework to interpolate static FNC (sFNC) and dynamic FNC (dFNC) data from controls and patients with schizophrenia or autism spectrum disorder. Our results show that the proposed framework captures individual variability, sFNC progression patterns, and group-specific dFNC states, providing new insights into personalized mental disorder characterization and progression prediction. Finally, we highlight the advantages of deep generative models in neuroimaging analysis and discuss future directions.