Fabrice Bartoloméi

The paper uses multivariate information theory to analyze higher-order network interactions in drug-resistant focal epilepsy patients using resting-state fMRI, finding that redundant information-sharing dominates epileptogenic networks and correlates with surgical outcomes.

This study uses invasive brain stimulation to identify cortical regions and networks involved in vestibular self-motion perception in humans.

The paper investigates how musical memorability relates to neural responses to surprise and expectation, finding a U-shaped relationship between surprise and memory performance.

This paper introduces a variational autoencoder for detecting seizure onset phases in SEEG data, achieving high recall and interpretable latent features.

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

The paper introduces PolyRNN, a model of musical expectations in polyphonic music, and shows it aligns with human brain responses recorded via MEG and intracranial EEG.

This study identifies shared and distinct ultra-rare genetic risk factors for epilepsy subtypes using whole-exome sequencing of 20,979 patients and 33,444 controls.

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.

This study demonstrates that gene therapy targeting GluK2 reduces seizures in TLE models and patient-derived slices.

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 study investigates the role of the thalamus in human reinforcement learning using intra-thalamic electrophysiological recordings and identifies temporally structured oscillations as encoding key learning signals.

This study identifies multi-scale structural changes in the thalamus and basal ganglia of focal epilepsy patients using 7T MRI, showing their dependence on epileptogenic zones and clinical features.

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.

Temporal interference stimulation suppresses epileptic biomarkers in mice and shows translatability to humans.

This study uses MEG and blind source separation to identify mesial brain activity linked to memory tasks, validated with intracerebral EEG.

This study links seizure spread on a patient-specific cortical surface to theta-alpha activity patterns using computational modeling in The Virtual Brain.

This paper demonstrates orientation-tunable temporally-interfering electric fields using surface electrodes to evoke seizure-like events in the mouse hippocampus without implanted electrodes.

This study investigates whether theta-alpha activity patterns in seizure recordings are caused by spreading seizures, finding support for this hypothesis through simulations and comparisons with real patient data.

The paper uses a neural mass model combined with patient-specific brain networks to simulate seizure propagation and test clinical hypotheses about epileptic seizure dynamics.

This paper develops a seizure control strategy using patient-specific brain network models to predict and modulate seizure propagation patterns in epilepsy patients.