Olivier David A

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.

This study uses EEG-fMRI and simulations in awake rats to show that absence seizures suppress brain responsiveness to sensory stimuli.

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

This study compares hemodynamic response functions from resting-state fMRI and invasive electrophysiological recordings in rats, finding similar HRF parameters between ground truth and estimated values.

The paper investigates reward and punishment prediction errors in humans using intracerebral recordings, finding distinct brain regions sensitive to each type of error.

This study investigates the role of the anterior nucleus of the thalamus (ANT) in seizures of temporal lobe epilepsy, finding that ANT recruitment is more common in seizures affecting mesial temporal regions and correlates with seizure duration and consciousness impairment.