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Wendling F, Koksal-Ersoz E, Al-Harrach M, Yochum M, Merlet I, Ruffini G, Bartolomei F, Benquet P. Multiscale neuro-inspired models for interpretation of EEG signals in patients with epilepsy. Clin Neurophysiol 2024; 161:198-210. [PMID: 38520800 DOI: 10.1016/j.clinph.2024.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 03/06/2024] [Accepted: 03/11/2024] [Indexed: 03/25/2024]
Abstract
OBJECTIVE The aim is to gain insight into the pathophysiological mechanisms underlying interictal epileptiform discharges observed in electroencephalographic (EEG) and stereo-EEG (SEEG, depth electrodes) recordings performed during pre-surgical evaluation of patients with drug-resistant epilepsy. METHODS We developed novel neuro-inspired computational models of the human cerebral cortex at three different levels of description: i) microscale (detailed neuron models), ii) mesoscale (neuronal mass models) and iii) macroscale (whole brain models). Although conceptually different, micro- and mesoscale models share some similar features, such as the typology of neurons (pyramidal cells and three types of interneurons), their spatial arrangement in cortical layers, and their synaptic connectivity (excitatory and inhibitory). The whole brain model consists of a large-scale network of interconnected neuronal masses, with connectivity based on the human connectome. RESULTS For these three levels of description, the fine-tuning of free parameters and the quantitative comparison with real data allowed us to reproduce interictal epileptiform discharges with a high degree of fidelity and to formulate hypotheses about the cell- and network-related mechanisms underlying the generation of fast ripples and SEEG-recorded epileptic spikes and spike-waves. CONCLUSIONS The proposed models provide valuable insights into the pathophysiological mechanisms underlying the generation of epileptic events. The knowledge gained from these models effectively complements the clinical analysis of SEEG data collected during the evaluation of patients with epilepsy. SIGNIFICANCE These models are likely to play a key role in the mechanistic interpretation of epileptiform activity.
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Affiliation(s)
| | | | | | | | | | | | - Fabrice Bartolomei
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology Department, Marseille, France; Univ Aix Marseille, INSERM, INS, Inst Neurosci Syst, Marseille, France
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Medrano J, Friston K, Zeidman P. Linking fast and slow: The case for generative models. Netw Neurosci 2024; 8:24-43. [PMID: 38562283 PMCID: PMC10861163 DOI: 10.1162/netn_a_00343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 10/11/2023] [Indexed: 04/04/2024] Open
Abstract
A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days, or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multiscale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.
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Affiliation(s)
- Johan Medrano
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
| | - Karl Friston
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
| | - Peter Zeidman
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
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Dallmer-Zerbe I, Jajcay N, Chvojka J, Janca R, Jezdik P, Krsek P, Marusic P, Jiruska P, Hlinka J. Computational modeling allows unsupervised classification of epileptic brain states across species. Sci Rep 2023; 13:13436. [PMID: 37596382 PMCID: PMC10439162 DOI: 10.1038/s41598-023-39867-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 08/01/2023] [Indexed: 08/20/2023] Open
Abstract
Current advances in epilepsy treatment aim to personalize and responsively adjust treatment parameters to overcome patient heterogeneity in treatment efficiency. For tailoring treatment to the individual and the current brain state, tools are required that help to identify the patient- and time-point-specific parameters of epilepsy. Computational modeling has long proven its utility in gaining mechanistic insight. Recently, the technique has been introduced as a diagnostic tool to predict individual treatment outcomes. In this article, the Wendling model, an established computational model of epilepsy dynamics, is used to automatically classify epileptic brain states in intracranial EEG from patients (n = 4) and local field potential recordings from in vitro rat data (high-potassium model of epilepsy, n = 3). Five-second signal segments are classified to four types of brain state in epilepsy (interictal, preonset, onset, ictal) by comparing a vector of signal features for each data segment to four prototypical feature vectors obtained by Wendling model simulations. The classification result is validated against expert visual assessment. Model-driven brain state classification achieved a classification performance significantly above chance level (mean sensitivity 0.99 on model data, 0.77 on rat data, 0.56 on human data in a four-way classification task). Model-driven prototypes showed similarity with data-driven prototypes, which we obtained from real data for rats and humans. Our results indicate similar electrophysiological patterns of epileptic states in the human brain and the animal model that are well-reproduced by the computational model, and captured by a key set of signal features, enabling fully automated and unsupervised brain state classification in epilepsy.
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Affiliation(s)
- Isa Dallmer-Zerbe
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, 182 00, Prague, Czech Republic
- Department of Physiology, Second Faculty of Medicine, Charles University, 150 06, Prague, Czech Republic
| | - Nikola Jajcay
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, 182 00, Prague, Czech Republic
- National Institute of Mental Health, 250 67, Klecany, Czech Republic
| | - Jan Chvojka
- Department of Physiology, Second Faculty of Medicine, Charles University, 150 06, Prague, Czech Republic
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27, Prague, Czech Republic
| | - Radek Janca
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27, Prague, Czech Republic
| | - Petr Jezdik
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, 166 27, Prague, Czech Republic
| | - Pavel Krsek
- Department of Paediatric Neurology, Second Faculty of Medicine, Motol University Hospital, Charles University, 150 06, Prague, Czech Republic
| | - Petr Marusic
- Department of Neurology, Second Faculty of Medicine, Motol University Hospital, Charles University, 150 06, Prague, Czech Republic
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, 150 06, Prague, Czech Republic
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, 182 00, Prague, Czech Republic.
- National Institute of Mental Health, 250 67, Klecany, Czech Republic.
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4
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Sanchez-Todo R, Bastos AM, Lopez-Sola E, Mercadal B, Santarnecchi E, Miller EK, Deco G, Ruffini G. A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings. Neuroimage 2023; 270:119938. [PMID: 36775081 DOI: 10.1016/j.neuroimage.2023.119938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 01/13/2023] [Accepted: 02/09/2023] [Indexed: 02/12/2023] Open
Abstract
Cortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM) that represent the mean activity of large numbers of neurons. Here, we provide first a new framework called laminar NMM, or LaNMM for short, where we combine conduction physics with NMMs to simulate electrophysiological measurements. Then, we employ this framework to infer the location of oscillatory generators from laminar-resolved data collected from the prefrontal cortex in the macaque monkey. We define a minimal model capable of generating coupled slow and fast oscillations, and we optimize LaNMM-specific parameters to fit multi-contact recordings. We rank the candidate models using an optimization function that evaluates the match between the functional connectivity (FC) of the model and data, where FC is defined by the covariance between bipolar voltage measurements at different cortical depths. The family of best solutions reproduces the FC of the observed electrophysiology by selecting locations of pyramidal cells and their synapses that result in the generation of fast activity at superficial layers and slow activity across most depths, in line with recent literature proposals. In closing, we discuss how this hybrid modeling framework can be more generally used to infer cortical circuitry.
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Affiliation(s)
- Roser Sanchez-Todo
- Department of Brain Modeling, Neuroelectrics SL, Av. Tibidabo 47b, 08035 Barcelona, Spain; Center of Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - André M Bastos
- Department of Psychology and Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, United States
| | - Edmundo Lopez-Sola
- Department of Brain Modeling, Neuroelectrics SL, Av. Tibidabo 47b, 08035 Barcelona, Spain
| | - Borja Mercadal
- Department of Brain Modeling, Neuroelectrics SL, Av. Tibidabo 47b, 08035 Barcelona, Spain
| | - Emiliano Santarnecchi
- Precision Neuroscience & Neuromodulation Program, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Earl K Miller
- The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Gustavo Deco
- Center of Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Instituci'o Catalana de la Recerca i Estudis Avan,ats (ICREA), Passeig Llu's Companys 23, Barcelona, 08010, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC 3800, Australia
| | - Giulio Ruffini
- Department of Brain Modeling, Neuroelectrics SL, Av. Tibidabo 47b, 08035 Barcelona, Spain; Starlab Barcelona, Av. Tibidabo 47b, 08035 Barcelona, Spain; Haskins Laboratories, 300 George Street, New Haven, CT, 06511, USA.
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Clusella P, Köksal-Ersöz E, Garcia-Ojalvo J, Ruffini G. Comparison between an exact and a heuristic neural mass model with second-order synapses. BIOLOGICAL CYBERNETICS 2023; 117:5-19. [PMID: 36454267 PMCID: PMC10160168 DOI: 10.1007/s00422-022-00952-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 10/23/2022] [Indexed: 05/05/2023]
Abstract
Neural mass models (NMMs) are designed to reproduce the collective dynamics of neuronal populations. A common framework for NMMs assumes heuristically that the output firing rate of a neural population can be described by a static nonlinear transfer function (NMM1). However, a recent exact mean-field theory for quadratic integrate-and-fire (QIF) neurons challenges this view by showing that the mean firing rate is not a static function of the neuronal state but follows two coupled nonlinear differential equations (NMM2). Here we analyze and compare these two descriptions in the presence of second-order synaptic dynamics. First, we derive the mathematical equivalence between the two models in the infinitely slow synapse limit, i.e., we show that NMM1 is an approximation of NMM2 in this regime. Next, we evaluate the applicability of this limit in the context of realistic physiological parameter values by analyzing the dynamics of models with inhibitory or excitatory synapses. We show that NMM1 fails to reproduce important dynamical features of the exact model, such as the self-sustained oscillations of an inhibitory interneuron QIF network. Furthermore, in the exact model but not in the limit one, stimulation of a pyramidal cell population induces resonant oscillatory activity whose peak frequency and amplitude increase with the self-coupling gain and the external excitatory input. This may play a role in the enhanced response of densely connected networks to weak uniform inputs, such as the electric fields produced by noninvasive brain stimulation.
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Affiliation(s)
- Pau Clusella
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, 08003, Barcelona, Spain.
| | - Elif Köksal-Ersöz
- LTSI - UMR 1099, INSERM, Univ Rennes, Campus Beaulieu, 35000, Rennes, France
| | - Jordi Garcia-Ojalvo
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, 08003, Barcelona, Spain
| | - Giulio Ruffini
- Brain Modeling Department, Neuroelectrics, Av. Tibidabo, 47b, 08035, Barcelona, Spain.
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Galan-Gadea A, Salvador R, Bartolomei F, Wendling F, Ruffini G. Spherical harmonics representation of the steady-state membrane potential shift induced by tDCS in realistic neuron models. J Neural Eng 2023; 20. [PMID: 36758230 DOI: 10.1088/1741-2552/acbabd] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 02/09/2023] [Indexed: 02/11/2023]
Abstract
Objective.We provide a systematic framework for quantifying the effect of externally applied weak electric fields on realistic neuron compartment models as captured by physiologically relevant quantities such as the membrane potential or transmembrane current as a function of the orientation of the field.Approach.We define a response function as the steady-state change of the membrane potential induced by a canonical external field of 1 V m-1as a function of its orientation. We estimate the function values through simulations employing reconstructions of the rat somatosensory cortex from the Blue Brain Project. The response of different cell types is simulated using the NEURON simulation environment. We represent and analyze the angular response as an expansion in spherical harmonics.Main results.We report membrane perturbation values comparable to those in the literature, extend them to different cell types, and provide their profiles as spherical harmonic coefficients. We show that at rest, responses are dominated by their dipole terms (ℓ=1), in agreement with experimental findings and compartment theory. Indeed, we show analytically that for a passive cell, only the dipole term is nonzero. However, while minor, other terms are relevant for states different from resting. In particular, we show howℓ=0andℓ=2terms can modify the function to induce asymmetries in the response.Significance.This work provides a practical framework for the representation of the effects of weak electric fields on different neuron types and their main regions-an important milestone for developing micro- and mesoscale models and optimizing brain stimulation solutions.
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Affiliation(s)
| | | | - Fabrice Bartolomei
- Clinical Physiology Department, INSERM, UMR 1106 and Timone University Hospital, Aix-Marseille Université, Marseille, France
| | - Fabrice Wendling
- Univ Rennes, INSERM, LTSI (Laboratoire de Traitement du Signal et de l'Image) U1099, 35000 Rennes, France
| | - Giulio Ruffini
- Neuroelectrics, Av. Tibidabo 47b, 08035 Barcelona, Spain
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Mercadal B, Lopez-Sola E, Galan-Gadea A, Al Harrach M, Sanchez-Todo R, Salvador R, Bartolomei F, Wendling F, Ruffini G. Towards a mesoscale physical modeling framework for stereotactic-EEG recordings. J Neural Eng 2023; 20. [PMID: 36548999 DOI: 10.1088/1741-2552/acae0c] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 12/22/2022] [Indexed: 12/24/2022]
Abstract
Objective.Stereotactic-electroencephalography (SEEG) and scalp EEG recordings can be modeled using mesoscale neural mass population models (NMMs). However, the relationship between those mathematical models and the physics of the measurements is unclear. In addition, it is challenging to represent SEEG data by combining NMMs and volume conductor models due to the intermediate spatial scale represented by these measurements.Approach.We provide a framework combining the multi-compartmental modeling formalism and a detailed geometrical model to simulate the transmembrane currents that appear in layer 3, 5 and 6 pyramidal cells due to a synaptic input. With this approach, it is possible to realistically simulate the current source density (CSD) depth profile inside a cortical patch due to inputs localized into a single cortical layer and the induced voltage measured by two SEEG contacts using a volume conductor model. Based on this approach, we built a framework to connect the activity of a NMM with a volume conductor model and we simulated an example of SEEG signal as a proof of concept.Main results.CSD depends strongly on the distribution of the synaptic inputs onto the different cortical layers and the equivalent current dipole strengths display substantial differences (of up to a factor of four in magnitude in our example). Thus, the inputs coming from different neural populations do not contribute equally to the electrophysiological recordings. A direct consequence of this is that the raw output of NMMs is not a good proxy for electrical recordings. We also show that the simplest CSD model that can accurately reproduce SEEG measurements can be constructed from discrete monopolar sources (one per cortical layer).Significance.Our results highlight the importance of including a physical model in NMMs to represent measurements. We provide a framework connecting microscale neuron models with the neural mass formalism and with physical models of the measurement process that can improve the accuracy of predicted electrophysiological recordings.
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Affiliation(s)
- Borja Mercadal
- Neuroelectrics, Av. Tibidabo 47b, 08035 Barcelona, Spain
| | | | | | - Mariam Al Harrach
- Université de Rennes, INSERM, LTSI (Laboratoire de Traitement du Signal et de l'Image) U1099, 35000 Rennes, France
| | | | | | - Fabrice Bartolomei
- Clinical Physiology Department, INSERM, UMR 1106 and Timone University Hospital, Aix-Marseille Université, Marseille, France
| | - Fabrice Wendling
- Université de Rennes, INSERM, LTSI (Laboratoire de Traitement du Signal et de l'Image) U1099, 35000 Rennes, France
| | - Giulio Ruffini
- Neuroelectrics, Av. Tibidabo 47b, 08035 Barcelona, Spain
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Boosting psychological change: Combining non-invasive brain stimulation with psychotherapy. Neurosci Biobehav Rev 2022; 142:104867. [PMID: 36122739 DOI: 10.1016/j.neubiorev.2022.104867] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 09/01/2022] [Accepted: 09/07/2022] [Indexed: 11/21/2022]
Abstract
Mental health disorders and substance use disorders are a leading cause of morbidity and mortality worldwide, and one of the most important challenges for public health systems. While evidence-based psychotherapy is generally pursued to address mental health challenges, psychological change is often hampered by non-adherence to treatments, relapses, and practical barriers (e.g., time, cost). In recent decades, Non-invasive brain stimulation (NIBS) techniques have emerged as promising tools to directly target dysfunctional neural circuitry and promote long-lasting plastic changes. While the therapeutic efficacy of NIBS protocols for mental illnesses has been established, neuromodulatory interventions might also be employed to support the processes activated by psychotherapy. Indeed, combining psychotherapy with NIBS might help tailor the treatment to the patient's unique characteristics and therapeutic goal, and would allow more direct control of the neuronal changes induced by therapy. Herein, we overview emerging evidence on the use of NIBS to enhance the psychotherapeutic effect, while highlighting the next steps in advancing clinical and research methods toward personalized intervention approaches.
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Köksal Ersöz E, Lazazzera R, Yochum M, Merlet I, Makhalova J, Mercadal B, Sanchez-Todo R, Ruffini G, Bartolomei F, Benquet P, Wendling F. Signal processing and computational modeling for interpretation of SEEG-recorded interictal epileptiform discharges in epileptogenic and non-epileptogenic zones. J Neural Eng 2022; 19. [PMID: 36067727 DOI: 10.1088/1741-2552/ac8fb4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 09/06/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE In partial epilepsies, interictal epileptiform discharges (IEDs) are paroxysmal events observed in epileptogenic and non-epileptogenic zones. IEDs' generation and recurrence are subject to different hypotheses: they appear through glutamatergic and GABAergic processes; they may trigger seizures or prevent seizure propagation. This paper focuses on a specific class of IEDs, spike-waves (SWs), characterized by a short-duration spike followed by a longer duration wave, both of the same polarity. Signal analysis and neurophysiological mathematical models are used to interpret puzzling IED generation. APPROACH Interictal activity was recorded by intracranial stereo-electroencephalography (SEEG) electrodes in five different patients. SEEG experts identified the epileptic and non-epileptic zones in which IEDs were detected. After quantifying spatial and temporal features of the detected IEDs, the most significant features for classifying epileptic and non-epileptic zones were determined. A neurophysiologically-plausible mathematical model was then introduced to simulate the IEDs and understand the underlying differences observed in epileptic and non-epileptic zone IEDs. MAIN RESULTS Two classes of SWs were identified according to subtle differences in morphology and timing of the spike and wave component. Results showed that type-1 SWs were generated in epileptogenic regions also involved at seizure onset, while type-2 SWs were produced in the propagation or non-involved areas. The modeling study indicated that synaptic kinetics, cortical organization, and network interactions determined the morphology of the simulated SEEG signals. Modeling results suggested that the IED morphologies were linked to the degree of preserved inhibition. SIGNIFICANCE This work contributes to the understanding of different mechanisms generating IEDs in epileptic networks. The combination of signal analysis and computational models provides an efficient framework for exploring IEDs in partial epilepsies and classifying epileptogenic and non-epileptogenic zones.
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Affiliation(s)
- Elif Köksal Ersöz
- INSERM, LTSI - UMR 1099, Universite de Rennes 1, Campus de Beaulieu, Rennes, Bretagne, 35042 , FRANCE
| | - Remo Lazazzera
- INSERM, LTSI - UMR 1099, Universite de Rennes 1, Campus de Beaulieu, Rennes, Bretagne, 35042 , FRANCE
| | - Maxime Yochum
- INSERM, LTSI - UMR 1099, Universite de Rennes 1, Campus de Beaulieu, Rennes, Bretagne, 35042 , FRANCE
| | - Isabelle Merlet
- INSERM, LTSI - UMR 1099, Universite de Rennes 1, Campus de Beaulieu, Rennes, Bretagne, 35042 , FRANCE
| | - Julia Makhalova
- Neurophysiologie clinique, Service d'Epileptologie et de Rythmologie Cerebrale, Assistance Publique Hopitaux de Marseille, Hôpital de la Timone, Marseille, Provence-Alpes-Côte d'Azu, 13354, FRANCE
| | - Borja Mercadal
- Neuroelectrics Barcelona SL, Av. Tibidabo, 47b, Barcelona, 08035, SPAIN
| | - Roser Sanchez-Todo
- Neuroelectrics Barcelona SL, Avda Tibidabo, 47 bis, Barcelona, Catalunya, 08035, SPAIN
| | - Giulio Ruffini
- Neuroelectrics Barcelona SL, Av. Tibidabo, 47b, Barcelona, Catalunya, 08035, SPAIN
| | - Fabrice Bartolomei
- Neurophysiologie clinique, Service d'Epileptologie et de Rythmologie Cerebrale, Assistance Publique Hopitaux de Marseille, Hôpital de la Timone, Marseille, Provence-Alpes-Côte d'Azu, 13354, FRANCE
| | - Pascal Benquet
- INSERM, LTSI - UMR 1099, Universite de Rennes 1, Campus de Beaulieu, Rennes, Bretagne, 35042 , FRANCE
| | - Fabrice Wendling
- INSERM, LTSI - UMR 1099, Universite de Rennes 1, Campus Beaulieu, Rennes, Bretagne, 35042, FRANCE
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