Adapting β-TCVAE to real fMRI data recovers biologically relevant nonlinear spatial components and coherent functional network patterns.
Compared with lin- ear ICA (InfoMax) [7], the learned representations are more spatially coherent and exhibit improved correspondence with established functional networks
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Isolating Nonlinear Independent Sources in fMRI with $\beta$-TCVAE Models
Adapting β-TCVAE to real fMRI data recovers biologically relevant nonlinear spatial components and coherent functional network patterns.