Maciej Jedynak

This study evaluates microstructure-weighted connectomes using diffusion MRI data and finds that DePerp provides the most consistent structure-function coupling across spatial scales.

This paper introduces directed connectomics using bicommunities to reveal asymmetric pathways in brain networks and validates them with electrophysiological data.

This paper develops a whole-brain model to predict conduction velocity from MRI-derived microstructural features, explaining 29% of variance in neurophysiological conduction velocity.

This paper compares non-invasive and invasive methods to estimate brain connectivity and signal propagation delays, finding they yield similar results.

The paper uses intracranial EEG data to show that structural connectivity models can predict the propagation of electrical brain stimulation across the cortex.

The paper develops a mean-field formalism to predict the collective dynamics of large networks of complex spiking neurons, linking population models to electrophysiology.

This study shows that coupling between cortical columns in a neuronal model leads to stochastic initiation and noise-independent durations of sustained collective activity, interpreted as epileptic events.