Zhongming Liu

This paper develops a non-invasive MRI protocol to capture whole-stomach dynamics in anesthetized rats with high spatiotemporal resolution.

The paper investigates how visual speech modulates different frequency bands in auditory cortex, revealing distinct temporal and spatial patterns across theta, beta, and high-gamma bands.

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.

This paper uses a variational autoencoder to model and decode fMRI activity in the visual cortex, finding lower prediction accuracy compared to CNNs but successful decoding of latent variables.

This study develops methods to transfer and generalize deep-learning-based encoding models across subjects using Bayesian inference and incremental data training, applied to fMRI data from naturalistic video watching.