Fabrice Wendling

This study uses a neuroinspired model to investigate how tACS and tDCS affect neural activity in healthy and epileptic brain states, showing frequency entrainment in healthy networks and reduced hyperexcitability in epileptic networks.

This paper develops personalized whole-brain models of seizure propagation using multimodal clinical data to simulate and evaluate individualized therapeutic strategies.

This paper introduces a computational model of an epileptic neocortical column to simulate and analyze interictal epileptiform events, revealing relationships between synaptic transmission and event types.

NeoCoMM simulates neocortical activity across species and visualizes neural responses, field potentials, and electrode effects to study brain pathologies and hypotheses.

The paper models how different cortical cell types respond to tACS at various frequencies, finding frequency-dependent polarization and entrainment patterns.

The paper evaluates biophysical models to characterize electric fields from intracranial electrodes in DRE patients, finding that electrode geometry and tissue properties influence field distribution.

The paper quantifies how weak electric fields affect neuron models using spherical harmonics, showing dipole dominance and other terms' roles in non-resting states.

The paper develops a framework linking neural mass models with physical models to simulate SEEG recordings more accurately by incorporating current source density and volume conductor effects.

This paper introduces a laminar neural mass model that realistically reproduces SEEG recordings during epileptic seizures and demonstrates its ability to capture key electrophysiological features.

This study investigates dynamic functional brain networks during cognitive action control in Parkinson's disease patients and healthy controls using HD-EEG, finding differences in network composition and temporal organization.

The paper evaluates EEG source-space networks using a large-scale brain activity model called COALIA, showing that high electrode density and specific inverse solution/connectivity measure combinations improve network estimation.

COALIA is a computational model of human EEG that simulates brain dynamics to study consciousness, reproducing EEG patterns and explaining clinical results in DOC patients and healthy volunteers.

This study evaluates information-based metrics for measuring brain complexity in fetal and neonatal fMEG data to assess consciousness, finding LZW compression as the most usable metric with mixed results for fetal data.

The paper evaluates how different functional connectivity methods affect EEG source space networks at rest and finds that methods retaining zero-lag connections correlate better with fMRI networks.

This paper estimates excitatory and inhibitory post-synaptic potentials from local field potentials using a physiologically-grounded neural mass model, validated on simulated and in vivo data.

This study examines functional brain network changes in disorders of consciousness using EEG, finding reduced network integration and increased segregation in patients compared to healthy controls.

This paper introduces a framework to detect fast modular brain states during rest and task, validated on MEG and EEG data from healthy subjects and Parkinson's patients.

This paper develops a hybrid brain model personalized with neuroimaging and electrophysiology data to simulate individual brain activity and functional connectivity.