Jung‐Hoon Kim

This paper shows that the low-dimensional manifold of spontaneous brain activity can represent responses to naturalistic stimuli, using a deep variational autoencoder trained on resting-state fMRI data.

FSINC improves EEG-fMRI source imaging by integrating frequency-resolved EEG with fMRI via a data-driven neurovascular coupling model, achieving better spatial and temporal accuracy in both simulated and real data.

This study uses a variational autoencoder to embed functional connectivity profiles into a latent space for simultaneous comparison across anatomical locations and groups.

This paper uses a variational autoencoder to model non-linear fetal-neonatal brain development, improving age prediction and revealing functional brain networks.

This paper uses a variational autoencoder to disentangle sources of variation in resting state fMRI data and shows that latent representations can reliably identify subjects from a large group.