Trang-Anh E. Nghiem

The paper develops white-matter-microstructure-informed whole-brain models to identify excitation-inhibition imbalance in schizophrenia, improving model performance and diagnostic prediction.

The paper investigates excitation-inhibition balance dynamics in brain networks during naturalistic behavior in rats, finding coordinated E-I dynamics and transient imbalances linked to behavioral changes.

This paper uses computational modeling to show how excitatory-inhibitory balance and cholinergic modulation affect default mode network (DMN) function and dysfunction in mice.

This paper validates an explainable AI framework to identify brain regions affected by excitation/inhibition imbalance using simulations and empirical data from autism.

This paper investigates how fixational eye movements (FEM) affect visual acuity by modeling retinal coding and comparing it to human performance in a visual acuity task.

This study investigates the default mode network (DMN) in freely exploring rats, finding it dynamically encodes spatial exploration behaviors and challenges the notion of the DMN being task-negative.

This paper develops a mean-field neural model of brain states that reproduces empirical dynamics across scales and brain states, using the human connectome and the Virtual Brain simulator.

The paper develops a brain-wide model using biologically realistic population dynamics to simulate slow-wave synchrony and asynchronous states, showing their distinct responses to stimulation.

The paper identifies two types of slow waves in the cerebral cortex, showing they differ in neural dynamics and sensitivity to stimuli, explaining differences in memory consolidation under sleep and anesthesia.

This study uses Maximum Entropy models to analyze neural activity in human and monkey cortex, revealing that wakefulness involves pairwise interactions while sleep involves population-wide interactions.