Computational models of epileptiform activity. Adaped from Wendling et al. 2016.
Mechanistic insights about (patho)physiological phenomena are derived from model-based interpretation of real data
I develop methods for the detection of transient events (epileptic spikes, HFOs), the estimation of statistical couplings between signals (brain connectivity), the classification of signals based on their time-frequency or time-scale representation.
Top: Depth-EEG signals recorded from human hippocampus and entorhinal cortex during transition to seizure activity in TLE.
Bottom: Time-frequency representation of depth-EEG signals showing a narrow-band fast activity (20-30 Hz) at seizure onset
I develop neuro-inspired models:
- To study the conditions for emergence of epileptic activity
- To predict the impact of neurostimulations
My second research axis aims at explaining observed signals using physiologically-relevant computational models of neuronal systems at the origin of recorded activity.
I develop computational models at various levels of description, from microscopic (detailed networks) to macroscopic (neural mass and neural fields) level.
Selected publications (PDF provided)
Hassan M, Wendling F Electroencephalography Source Connectivity: Aiming for High Resolution of Brain Networks in Time and Space. IEEE Signal Process. Mag. 2018 [PDF]
Wendling F., Gerber U., Cosandier D., Nica A, De Montigny J, Raineteau O., Kalitzin S, Lopes da Silva F., Benquet P., Brain (Hyper)Excitability Revealed by Optimal Electrical Stimulation of GABAergic Interneurons, Brain Stimulation, 2016 [PDF]
Wendling, F., Benquet, P., Bartolomei, F. and Jirsa, V., (2016). Computational models of epileptiform activity. J Neurosci Methods [PDF]
Wendling F, Bartolomei F, Mina F, Huneau C, and Benquet P, Interictal spikes, fast ripples and seizures in partial epilepsies - combining multi-level computational models with experimental data, Eur J Neurosci, vol. 36, pp. 2164-77, 2012 [PDF]
Wendling F, Bartolomei F, and Senhadji L, Spatial analysis of intracerebral electroencephalographic signals in the time and frequency domain: identification of epileptogenic networks in partial epilepsy, Philos Transact A Math Phys Eng Sci, vol. 367, pp. 297-316, 2009. [PDF]
Wendling F, Ansari-Asl K, Bartolomei F, and Senhadji L., From EEG signals to brain connectivity: A model-based evaluation of interdependence measures, J Neurosci Methods, 2009. [PDF]
Wendling F, Computational models of epileptic activity: a bridge between observation and pathophysiolocial interpretation, Expert Review of Neurotherapeutics, Jun;8(6):889-96 , 2008 [PDF]
Cosandier-Rimélé D, Merlet I, Badier JM, Chauvel P, Wendling F, The neuronal sources of EEG: modeling of simultaneous scalp and intracerebral recordings in epilepsy, Neuroimage, vol. 42, pp. 135-46, 2008 [PDF]
Cosandier-Rimele D, Badier JM, Chauvel P, Wendling F. A physiologically plausible spatio-temporal model for EEG signals recorded with intracerebral electrodes in human partial epilepsy. IEEE Trans Biomed Eng. 2007 Mar;54(3):380-8. [PDF]
Gnatkovsky V, Wendling F, de Curtis M. Cellular correlates of spontaneous periodic events in the medial entorhinal cortex of the in vitro isolated guinea pig brain, Eur J Neurosci. 2007 Jul;26(2):302-11. [PDF]
Guye M, Regis J, Tamura M, Wendling F, McGonigal A, Chauvel P, Bartolomei F. The role of corticothalamic coupling in human temporal lobe epilepsy, Brain. 2006 Jul;129(Pt 7):1917-28 [PDF]
Ansari-Asl K, Senhadji L, Bellanger JJ, Wendling F. Quantitative evaluation of linear and nonlinear methods characterizing interdependencies between brain signals. Phys Rev E Stat Nonlin Soft Matter Phys. 2006 Sep;74(3 Pt 1):031916. [PDF]
Suffczynski P, Wendling F, Bellanger J-J, Lopes Da Silva FH, Some insights into computational models of (Patho)physiological brain activity. Proceedings of the IEEE 94(4):784- 804, 2006 [PDF]
Labyt E, Uva L, de Curtis M, Wendling F. Realistic modeling of entorhinal cortex field potentials and interpretation of epileptic activity in the guinea pig isolated brain preparation. J Neurophysiol. 2006 Jul;96(1):363-77. [PDF]
Wendling F, Hernandez A, Bellanger JJ, Chauvel P, Bartolomei F. Interictal to ictal transition in human temporal lobe epilepsy: insights from a computational model of intracerebral EEG. J Clin Neurophysiol. 2005 Oct;22(5):343-56. [PDF]
Bourien J, Bartolomei F, Bellanger JJ, Gavaret M, Chauvel P, Wendling F. A method to identify reproducible subsets of co-activated structures during interictal spikes. Application to intracerebral EEG in temporal lobe epilepsy, Clin Neurophysiol. 2005 Feb;116(2):443-55. [PDF]
Bartolomei F, Wendling F, Regis J, Gavaret M, Guye M, Chauvel P. Pre-ictal synchronicity in limbic networks of mesial temporal lobe epilepsy, Epilepsy Res. 2004 Sep-Oct;61(1-3):89-104 [PDF].
Wendling F, Bartolomei F, Bellanger JJ, Bourien J, Chauvel P. Epileptic fast intracerebral EEG activity: evidence for spatial decorrelation at seizure onset. Brain. 2003 Jun;126(Pt 6):1449-59. [PDF]
My research activity focuses on the analysis and on the interpretation of electrophysiological signals recorded in patients with drug-resistant partial epilepsy. Clinical objectives are the localization of the epileptogenic zone and the definition of its organization, prior to surgery.
My first research axis aims at developing methods able to quantify the information conveyed by electrophysiological observations (from LFPs to scalp EEG).
Copyright LTSI - Inserm - Université de Rennes 1 (all rights reserved)
Fabrice Wendling
"Epileptogenic Systems: Signals and Models"
Research director (DR1 Inserm)
LTSI, Inserm U1099, University of Rennes