Isolating Nonlinear Independent Sources in fMRI with $\beta$-TCVAE Models

ArXi:2605.16708v1 Announce Type: new Learning meaningful latent representations from nonlinear fMRI data remains a fundamental challenge in neuroimaging analysis. Traditional independent component analysis, widely used due to its ability to estimate interpretable functional brain networks, relies on a linear mixing assumption for latent sources, limiting its ability to capture the inherently nonlinear and complex organization of brain dynamics. recently, deep representation learning methods have emerged as promising alternatives for modeling nonlinear latent structure.