Processing of depth-EEG signals during
transition to ictal activity in human mesial TLE
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
transition to ictal activity in human mesial TLE
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
Models of depth-EEG activity
Physiologically-plausible models are developped and
used to explain the conditions (related to model
parameters) for the emergence of epileptic activity.
Signal processing and optimization techniques are
developped to identify model parameters from real
observations
Physiologically-plausible models are developped and
used to explain the conditions (related to model
parameters) for the emergence of epileptic activity.
Signal processing and optimization techniques are
developped to identify model parameters from real
observations
The second research axis (interpretation) aims at explaining observed signals using physiologically-relevant computational
models of neuronal systems at the origin of recorded activity. In particluar, we developped macroscopic models (neuronal
population level) for both the hippocampus and the entorhinal cortex which are two limbic strcutures often involved in
Temporal Lobe Epilepsy (TLE).
models of neuronal systems at the origin of recorded activity. In particluar, we developped macroscopic models (neuronal
population level) for both the hippocampus and the entorhinal cortex which are two limbic strcutures often involved in
Temporal Lobe Epilepsy (TLE).
Selected publications
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]
Labyt E, Frogerais P, Uva L, Bellanger JJ, Wendling F, Modeling of entorhinal cortex and simulation of epileptic activity: insights into the role of
inhibition-related parameters., IEEE Trans Inf Technol Biomed. 2007 Jul;11(4):450-61 [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]
Wendling F. Neurocomputational models in the study of epileptic phenomena. J Clin Neurophysiol. 2005 Oct;22(5):285-7. [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]
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]
Labyt E, Frogerais P, Uva L, Bellanger JJ, Wendling F, Modeling of entorhinal cortex and simulation of epileptic activity: insights into the role of
inhibition-related parameters., IEEE Trans Inf Technol Biomed. 2007 Jul;11(4):450-61 [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]
Wendling F. Neurocomputational models in the study of epileptic phenomena. J Clin Neurophysiol. 2005 Oct;22(5):285-7. [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]
| CV |
| Collaborations |
| Contact |
| Publications |
My research activities focus on the analysis and the interpretation of electrophysiological signals
recorded in patients suffering from drug-resistant partial epilepsy. Clinical objectives are the localization
of the epileptogenic zone and the definition of its organization, prior to surgery.
The first research axis (analysis) aims at developing methods able to quantify the information
conveyed by electrophysiological observations (EEG, MEG and intracerebral EEG). Tackled questions
relate to signal processing and statistics, in nonstationary and nonlinear situations. Many aspects are
dealt with such as the detection of transient events like epileptic spikes, the measurement of statistical
couplings between signals acquired from multiple sensors, the classification of signals based on their
time-frequency or time-scale representation.
recorded in patients suffering from drug-resistant partial epilepsy. Clinical objectives are the localization
of the epileptogenic zone and the definition of its organization, prior to surgery.
The first research axis (analysis) aims at developing methods able to quantify the information
conveyed by electrophysiological observations (EEG, MEG and intracerebral EEG). Tackled questions
relate to signal processing and statistics, in nonstationary and nonlinear situations. Many aspects are
dealt with such as the detection of transient events like epileptic spikes, the measurement of statistical
couplings between signals acquired from multiple sensors, the classification of signals based on their
time-frequency or time-scale representation.
Fabrice Wendling
Directeur de recherche Inserm (Research Scientist)
Head of the team SESAME: "Epileptogenic Systems: Signals and Models"
Inserm 1099 - Signal and Image Processing Laboratory - University of Rennes 1
Directeur de recherche Inserm (Research Scientist)
Head of the team SESAME: "Epileptogenic Systems: Signals and Models"
Inserm 1099 - Signal and Image Processing Laboratory - University of Rennes 1
Copyright LTSI - Inserm - Université de Rennes 1 (all rights reserved)