Oleksandr V. Popovych

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

This paper evaluates ODF reconstruction methods for single-shell dMRI, finding SS3T provides reliable and predictive WM bundle segmentation with lower angular resolution data.

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

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