Chargée de Recherche INSERM
I started my career in Lyon (EA1880, F. Mauguière) where I developed my research in the context of human drug-resistant partial epilepsies, and particularly the dysfunctions and the mechanisms that underlie the interictal state (irritative zone) in order to better localize the region from which the ictal discharges originate (epileptogenic zone).
My work was first oriented towards relating results from different exploration modalities, keeping the electrophysiological abnormalities recorded in scalp electroencephalography (EEG) as a common basis. The objective of these studies was to apply source localization approaches to identify the generators of interictal EEG activities and to test the concordance of these results with other abnormalities, morphological or functional, observed in Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) or Magnetoencephalography (MEG) [1-5]. My work has also focused on the more direct validation of source localization results using intracranial or intracerebral recordings as part of a Postdoctoral fellowship in the Department of Neurology and Neurosurgery at the Montreal Neurological Institute (J. Gotman)[6-9].
In the continuity of this work, I was also interested in abnormal in vivo 5-HT1A receptors binding in human partial epilepsy using a new radioligand, p-[18F]-MPPF, the synthesis of which had been optimized in our research team[10, 11]. This work involved upstream the establishment of a compartmental model for the binding of this radioligand[12], and a database of control subjects to serve as a reference for studies in patients [13].
I then joined F. Wendling's team (originally EPIC, SESAME and now cynetiks) of the LTSI in Rennes in 2005. Since then, my research work has focused on the development, validation, and use of EEG signal processing and modelling tools, mainly in drug-resistant epilepsies. This includes a panel of new source localization methods, exploiting realistic physiopathological hypotheses such as the hypersynchronization of activities within the distributed source or sparsiness [14-20] different source connectivity methods [21-23] and EEG denoising methods [21, 24-28] Part of my work is also related to the interpretation of surface EEG signals based on the use of computational models developed in the team. We have set up a processing chain for the simulation of electrophysiological signals at the level of virtual intracerebral (SEEG) or surface (EEG, MEG) sensors which takes into account different types of graphoelements and rhythms (alpha, theta, delta EEG waves, epileptic spikes, seizures, and high frequency oscillations) and makes it possible to study their observability at different levels according to the geometric configuration of the cortical sources [21, 29-32]. The realistic simulations of EEG/MEG/SEEG activity is used to validate the various source localization or denoising methods that we develop. In addition, this model also reproduces the cortical effects of non-invasive electrical stimulation [33-35], which makes it possible to predict changes in EEG induced by the application of electric currents.
Our projects focus on 3 main axes.
• The first is to keep developing new EEG signal processing methods to optimize the analysis of surface signals during the first phase of the presurgical evaluation of drug-resistant partial epilepsies. This optimization begins with improving the recording conditions of the EEG, in particular using dense electrode sytems (256 electrodes) both in adults and children with epilepsy. We are currently developing new denoising and source localization approaches optimized for these dense-EEG recordings.
• The second line of research focuses specifically on the study of new interictal markers of the epileptogenic zone, called high frequency oscillations (HFOs). I am interested in the spatial and temporal conditions necessary for their recording on the surface of the scalp, using the simulation tools of HFOs at the level of the scalp electrodes.
• The third area of research concerns the study of new therapeutical approaches for patients with epilepsy. Firstly we are currently investigating the effect of transcranial direct current stimulation (tDCS) and developing methods to optimize tDCS targeting of epileptogenic networks (Galvani project). Secondly, we study the effects of mindfulness meditation practice on epilepsy with comorbid depression/anxiety symptoms, with a specific focus on mindfulness-based modifications in interictal, ictal and default mode (DMN) networks (MiME project).
[1] I. Merlet, L. Garcia-Larrea, M.C. Gregoire, C. Pierre, F. Lavenne, L. Cinotti, J.C. Froment, F. Mauguiere, Source localization of paroxysmal activity in temporal lobe epilepsy: fitting of dipole model, MRI and PET data, 21st International epilepsy Congress, Epilepsia, Sydney, 1995, pp. s141.
[2] I. Merlet, L. Garcia-Larrea, M.C. Gregoire, F. Lavenne, F. Mauguiere, Source propagation of interictal spikes in temporal lobe epilepsy. Correlations between spike dipole modelling and [18F]fluorodeoxyglucose PET data, Brain, 119 (1996) 377-392.
[3] I. Merlet, R. Paetau, L. Garcia-Larrea, K. Uutela, M.L. Granstrom, F. Mauguiere, Apparent asynchrony between interictal electric and magnetic spikes, Neuroreport, 8 (1997) 1071-1076.
[4] P. Kahane, I. Merlet, M.C. Gregoire, C. Munari, J. Perret, F. Mauguiere, An H(2) (15)O-PET study of cerebral blood flow changes during focal epileptic discharges induced by intracerebral electrical stimulation, Brain, 122 ( Pt 10) (1999) 1851-1865.
[5] I. Merlet, L. Garcia-Larrea, J.C. Froment, F. Mauguiere, Simplified projection of EEG dipole sources onto human brain anatomy, Neurophysiol Clin, 29 (1999) 39-52.
[6] I. Merlet, L. Garcia-Larrea, P. Ryvlin, J. Isnard, M. Sindou, F. Mauguiere, Topographical reliability of mesio-temporal sources of interictal spikes in temporal lobe epilepsy, Electroencephalogr clin Neurophysiol, 107 (1998) 206-212.
[7] I. Merlet, J. Gotman, Reliability of dipole models of epileptic spikes, Clin Neurophysiol, 110 (1999) 1013-1028.
[8] K. Kobayashi, I. Merlet, J. Gotman, Separation of spikes from background by independent component analysis with dipole modeling and comparison to intracranial recording, 112 (2001) 405-413.
[9] I. Merlet, J. Gotman, Dipole modeling of scalp electroencephalogram epileptic discharges: correlation with intracerebral fields, 112 (2001) 414-430.
[10] I. Merlet, P. Ryvlin, N. Costes, D. Dufournel, J. Isnard, I. Faillenot, K. Ostrowsky, F. Lavenne, D. Le Bars, F. Mauguiere, Statistical parametric mapping of 5-HT(1A) receptor binding in temporal lobe epilepsy with hippocampal ictal onset on intracranial EEG, 22 (2004) 886-896.
[11] I. Merlet, K. Ostrowsky, N. Costes, P. Ryvlin, J. Isnard, I. Faillenot, F. Lavenne, D. Dufournel, D. Le Bars, F. Mauguiere, 5-HT1A receptor binding and intracerebral activity in temporal lobe epilepsy: an [18F]MPPF-PET study, Brain, 127 (2004) 900-913.
[12] N. Costes, I. Merlet, L. Zimmer, F. Lavenne, L. Cinotti, J. Delforge, A. Luxen, J.F. Pujol, D. Le Bars, Modeling [18 F]MPPF positron emission tomography kinetics for the determination of 5-hydroxytryptamine(1A) receptor concentration with multiinjection, 22 (2002) 753-765.
[13] N. Costes, I. Merlet, K. Ostrowsky, I. Faillenot, F. Lavenne, L. Zimmer, P. Ryvlin, D. Le Bars, A 18F-MPPF PET normative database of 5-HT1A receptor binding in men and women over aging, J Nucl Med, 46 (2005) 1980-1989.
[14] L. Albera, A. Ferreol, D. Cosandier-Rimele, I. Merlet, F. Wendling, Brain source localization using a fourth-order deflation scheme, IEEE Trans Biomed Eng, 55 (2008) 490-501.
[15] G. Birot, L. Albera, F. Wendling, I. Merlet, Localization of extended brain sources from EEG/MEG: The ExSo-MUSIC approach, Neuroimage, 56 (2011) 102-113.
[16] H. Becker, P. Comon, L. Albera, M. Haardt, I. Merlet, Multi-way space-time-wave-vector analysis for EEG source separation, Signal Processing, 92 (2012) 1021-1031.
[17] H. Becker, L. Albera, P. Comon, M. Haardt, G. Birot, F. Wendling, M. Gavaret, C.G. Benar, I. Merlet, EEG extended source localization: Tensor-based vs. conventional methods, Neuroimage, 96 (2014) 143-157.
[18] H. Becker, L. Albera, P. Comon, R. Gribonval, F. Wendling, I. Merlet, Brain-Source Imaging: From sparse to tensor models, IEEE Signal Processing Magazine, 32 (2015) 100-112.
[19] R.A. Chowdhury, I. Merlet, G. Birot, E. Kobayashi, A. Nica, A. Biraben, F. Wendling, J.M. Lina, L. Albera, C. Grova, Complex patterns of spatially extended generators of epileptic activity: Comparison of source localization methods cMEM and 4-ExSo-MUSIC on high resolution EEG and MEG data, Neuroimage, 143 (2016) 175-195.
[20] H. Becker, L. Albera, P. Comon, J.C. Nunes, R. Gribonval, J. Fleureau, P. Guillotel, I. Merlet, SISSY: An efficient and automatic algorithm for the analysis of EEG sources based on structured sparsity, Neuroimage, 157 (2017) 157-172.
[21] DOI Merlet, Isabelle/0000-0003-2590-6848.
[22] M. Hassan, O. Dufor, I. Merlet, C. Berrou, F. Wendling, EEG source connectivity analysis: from dense array recordings to brain networks, PLoS One, 9 (2014) e105041.
[23] M. Hassan, I. Merlet, A. Mheich, A. Kabbara, A. Biraben, A. Nica, F. Wendling, Identification of Interictal Epileptic Networks from Dense-EEG, Brain Topogr, 30 (2017) 60-76.
[24] D. Safieddine, A. Kachenoura, L. Albera, G. Birot, A. Karfoul, A. Pasnicu, A. Biraben, F. Wendling, L. Senhadji, I. Merlet, Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches, EURASIP Journal on Advances in Signal Processing, 2012 (2012) 127.
[25] S. Hajipour Sardouie, M. Bagher Shamsollahi, L. Albera, I. Merlet, Denoising of ictal EEG data using semi-blind source separation methods based on time-frequency priors, IEEE journal of biomedical and health informatics, DOI 10.1109/JBHI.2014.2336797(2014).
[26] S. Hajipour Sardouie, M. Bagher Shamsollahi, L. Albera, I. Merlet, Denoising of Ictal EEG Data Using Semi-Blind Source Separation Methods Based on Time-Frequency Priors, IEEE journal of biomedical and health informatics, 19 (2015) 839-847.
[27] H. Becker, L. Albera, P. Comon, A. Kachenoura, I. Merlet, A penalized semi-algebraic deflation ICA algorithm for the efficient extraction of interictal epileptic signals, IEEE journal of biomedical and health informatics, DOI 10.1109/JBHI.2015.2504126(2015).
[28] N. Taheri, A. Kachenoura, K. Ansari-Asl, A. Karfoul, L. Senhadji, L. Albera, I. Merlet, Feasibility of blind source separation methods for the denoising of dense-array EEG, Conf Proc IEEE Eng Med Biol Soc, 2015 (2015) 4773-4776.
[29] D. Cosandier-Rimele, I. Merlet, J.M. Badier, P. Chauvel, F. Wendling, The neuronal sources of EEG: modeling of simultaneous scalp and intracerebral recordings in epilepsy, Neuroimage, 42 (2008) 135-146.
[30] D. Cosandier-Rimele, I. Merlet, F. Bartolomei, J.M. Badier, F. Wendling, Computational modeling of epileptic activity: from cortical sources to EEG signals, J Clin Neurophysiol, 27 (2010) 465-470.
[31] D. Cosandier-Rimele, F. Bartolomei, I. Merlet, P. Chauvel, F. Wendling, Recording of fast activity at the onset of partial seizures: Depth EEG vs. scalp EEG, Neuroimage, 59 (2012) 3474-3487.
[32] M. Shamas, P. Benquet, I. Merlet, M. Khalil, W. El Falou, A. Nica, F. Wendling, On the origin of epileptic High Frequency Oscillations observed on clinical electrodes, Clin Neurophysiol, 129 (2018) 829-841.
[33] I. Merlet, G. Birot, R. Salvador, B. Molaee-Ardekani, A. Mekonnen, A. Soria-Frish, G. Ruffini, P.C. Miranda, F. Wendling, From oscillatory transcranial current stimulation to scalp EEG changes: a biophysical and physiological modeling study, PLoS One, 8 (2013) e57330.
[34] S. Bensaid, J. Modolo, I. Merlet, F. Wendling, P. Benquet, COALIA: A Computational Model of Human EEG for Consciousness Research, Frontiers in systems neuroscience, 13 (2019) 59.
[35] S. Marchesotti, J. Nicolle, I. Merlet, L.H. Arnal, J.P. Donoghue, A.L. Giraud, Selective enhancement of low-gamma activity by tACS improves phonemic processing and reading accuracy in dyslexia, PLoS Biol, 18 (2020) e3000833.