Kyesam Jung

cuBNM accelerates brain network simulations using GPUs, enabling large-scale studies of neural processes previously limited by computational costs.

This study develops an individualized in vivo marker of excitation-inhibition balance in adolescents using biophysical network models and finds increased inhibition in association cortices during development.

This study uses simulated brain networks from MRI data to show differences between Parkinson's patients and healthy controls, correlating with disease severity and duration.

This commentary critiques the use of geometric eigenmodes as a model for brain function, arguing that they may overfit and fail to explain functional mechanisms beyond geometry.

This study uses simulated whole-brain connectomes to classify Parkinson's disease patients against healthy controls, showing improved classification when combining empirical and simulated data.

The paper evaluates how different levels of co-fluctuation in functional connectivity predict individual-level phenotypes using machine learning.

This study evaluates how tractography density impacts structural brain architecture and resting-state dynamical modeling, finding optimal parameter settings for different brain parcellations.

This study shows that schizophrenia is associated with a compressed cortical hierarchy organization, reducing the separation between sensory and cognitive systems.

The paper introduces a dynamic causal model for calcium imaging data to analyze effective connectivity in sensory processing, finding differential connectivity between hit and error trials in mice.