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Kopf M, Martini J, Stier C, Ethofer S, Braun C, Li Hegner Y, Focke NK, Marquetand J, Helfrich RF. Aperiodic Activity Indexes Neural Hyperexcitability in Generalized Epilepsy. eNeuro 2024; 11:ENEURO.0242-24.2024. [PMID: 39137987 PMCID: PMC11376430 DOI: 10.1523/eneuro.0242-24.2024] [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: 06/07/2024] [Revised: 07/22/2024] [Accepted: 07/25/2024] [Indexed: 08/15/2024] Open
Abstract
Generalized epilepsy (GE) encompasses a heterogeneous group of hyperexcitability disorders that clinically manifest as seizures. At the whole-brain level, distinct seizure patterns as well as interictal epileptic discharges (IEDs) reflect key signatures of hyperexcitability in magneto- and electroencephalographic (M/EEG) recordings. Moreover, it had been suggested that aperiodic activity, specifically the slope of the 1/ƒx decay function of the power spectrum, might index neural excitability. However, it remained unclear if hyperexcitability as encountered at the cellular level directly translates to putative large-scale excitability signatures, amenable to M/EEG. In order to test whether the power spectrum is altered in hyperexcitable states, we recorded resting-state MEG from male and female GE patients (n = 51; 29 females; 28.82 ± 12.18 years; mean ± SD) and age-matched healthy controls (n = 49; 22 females; 32.10 ± 12.09 years). We parametrized the power spectra using FOOOF ("fitting oscillations and one over f") to separate oscillatory from aperiodic activity to directly test whether aperiodic activity is systematically altered in GE patients. We further identified IEDs to quantify the temporal dynamics of aperiodic activity around overt epileptic activity. The results demonstrate that aperiodic activity indexes hyperexcitability in GE at the whole-brain level, especially during epochs when no IEDs were present (p = 0.0130; d = 0.52). Upon IEDs, large-scale circuits transiently shifted to a less excitable network state (p = 0.001; d = 0.68). In sum, these results uncover that MEG background activity might index hyperexcitability based on the current brain state and does not rely on the presence of epileptic waveforms.
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Affiliation(s)
- Markus Kopf
- Hertie Institute for Clinical Brain Research, University Medical Center Tübingen, Tübingen 72076, Germany
| | - Jan Martini
- Hertie Institute for Clinical Brain Research, University Medical Center Tübingen, Tübingen 72076, Germany
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tübingen, Tübingen 72076, Germany
| | - Christina Stier
- Institute for Biomagnetism and Biosignal Analysis, University of Münster, Münster 48149, Germany
| | - Silke Ethofer
- Department of Neurosurgery, University Medical Center Tübingen, Tübingen 72076, Germany
| | - Christoph Braun
- Hertie Institute for Clinical Brain Research, University Medical Center Tübingen, Tübingen 72076, Germany
- Magnetoencephalography (MEG) Center, University of Tübingen, Tübingen 72076, Germany
- CIMeC Center for Mind/Brain Sciences, University of Trento, Rovereto 38068, Italy
- Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen 72076, Germany
| | - Yiwen Li Hegner
- Hertie Institute for Clinical Brain Research, University Medical Center Tübingen, Tübingen 72076, Germany
- Magnetoencephalography (MEG) Center, University of Tübingen, Tübingen 72076, Germany
| | - Niels K Focke
- Department of Neurology, University Medical Center Göttingen, Göttingen 37075, Germany
| | - Justus Marquetand
- Hertie Institute for Clinical Brain Research, University Medical Center Tübingen, Tübingen 72076, Germany
- Magnetoencephalography (MEG) Center, University of Tübingen, Tübingen 72076, Germany
- Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen 72076, Germany
- Department of Neurology and Epileptology, University Medical Center Tübingen, Tübingen 72076, Germany
- Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart 70569, Germany
| | - Randolph F Helfrich
- Hertie Institute for Clinical Brain Research, University Medical Center Tübingen, Tübingen 72076, Germany
- Department of Neurology and Epileptology, University Medical Center Tübingen, Tübingen 72076, Germany
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Al Harrach M, Yochum M, Ruffini G, Bartolomei F, Wendling F, Benquet P. NeoCoMM: A neocortical neuroinspired computational model for the reconstruction and simulation of epileptiform events. Comput Biol Med 2024; 180:108934. [PMID: 39079417 DOI: 10.1016/j.compbiomed.2024.108934] [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: 01/18/2024] [Revised: 06/13/2024] [Accepted: 07/20/2024] [Indexed: 08/29/2024]
Abstract
BACKGROUND Understanding the pathophysiological dynamics that underline Interictal Epileptiform Events (IEEs) such as epileptic spikes, spike-and-waves or High-Frequency Oscillations (HFOs) is of major importance in the context of neocortical refractory epilepsy, as it paves the way for the development of novel therapies. Typically, these events are detected in Local Field Potential (LFP) recordings obtained through depth electrodes during pre-surgical investigations. Although essential, the underlying pathophysiological mechanisms for the generation of these epileptic neuromarkers remain unclear. The aim of this paper is to propose a novel neurophysiologically relevant reconstruction of the neocortical microcircuitry in the context of epilepsy. This reconstruction intends to facilitate the analysis of a comprehensive set of parameters encompassing physiological, morphological, and biophysical aspects that directly impact the generation and recording of different IEEs. METHOD a novel microscale computational model of an epileptic neocortical column was introduced. This model incorporates the intricate multilayered structure of the cortex and allows for the simulation of realistic interictal epileptic signals. The proposed model was validated through comparisons with real IEEs recorded using intracranial stereo-electroencephalography (SEEG) signals from both humans and animals. Using the model, the user can recreate epileptiform patterns observed in different species (human, rodent, and mouse) and study the intracellular activity associated with these patterns. RESULTS Our model allowed us to unravel the relationship between glutamatergic and GABAergic synaptic transmission of the epileptic neural network and the type of generated IEE. Moreover, sensitivity analyses allowed for the exploration of the pathophysiological parameters responsible for the transitions between these events. Finally, the presented modeling framework also provides an Electrode Tissue Model (ETI) that adds realism to the simulated signals and offers the possibility of studying their sensitivity to the electrode characteristics. CONCLUSION The model (NeoCoMM) presented in this work can be of great use in different applications since it offers an in silico framework for sensitivity analysis and hypothesis testing. It can also be used as a starting point for more complex studies.
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Affiliation(s)
- M Al Harrach
- University of Rennes, INSERM, LTSI-U1099, 35000 Rennes, France.
| | - M Yochum
- Neuroelectrics, Av. Tibidabo 47b, 08035 Barcelona, Spain
| | - G Ruffini
- Neuroelectrics, Av. Tibidabo 47b, 08035 Barcelona, Spain
| | - F Bartolomei
- Hopitaux de Marseille, Service d'Epileptologie et de Rythmologie Cerebrale, Hopital La Timone, Marseille, France
| | - F Wendling
- University of Rennes, INSERM, LTSI-U1099, 35000 Rennes, France
| | - P Benquet
- University of Rennes, INSERM, LTSI-U1099, 35000 Rennes, France
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Bourdillon P, Ren L, Halgren M, Paulk AC, Salami P, Ulbert I, Fabó D, King JR, Sjoberg KM, Eskandar EN, Madsen JR, Halgren E, Cash SS. Differential cortical layer engagement during seizure initiation and spread in humans. Nat Commun 2024; 15:5153. [PMID: 38886376 PMCID: PMC11183216 DOI: 10.1038/s41467-024-48746-8] [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: 11/22/2022] [Accepted: 05/10/2024] [Indexed: 06/20/2024] Open
Abstract
Despite decades of research, we still do not understand how spontaneous human seizures start and spread - especially at the level of neuronal microcircuits. In this study, we used laminar arrays of micro-electrodes to simultaneously record the local field potentials and multi-unit neural activities across the six layers of the neocortex during focal seizures in humans. We found that, within the ictal onset zone, the discharges generated during a seizure consisted of current sinks and sources only within the infra-granular and granular layers. Outside of the seizure onset zone, ictal discharges reflected current flow in the supra-granular layers. Interestingly, these patterns of current flow evolved during the course of the seizure - especially outside the seizure onset zone where superficial sinks and sources extended into the deeper layers. Based on these observations, a framework describing cortical-cortical dynamics of seizures is proposed with implications for seizure localization, surgical targeting, and neuromodulation techniques to block the generation and propagation of seizures.
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Affiliation(s)
- Pierre Bourdillon
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Neurosurgery, Hospital Foundation Adolphe de Rothschild, Paris, France.
- Integrative Neuroscience and Cognition Center, Paris Cité University, Paris, France.
| | - Liankun Ren
- Department of Neurology, Xuanwu Hospital, National Center for Neurological Disorders, Clinical Center for Epilepsy, Capital Medical University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Mila Halgren
- Brain and Cognitive Sciences Department and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Angelique C Paulk
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Pariya Salami
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - István Ulbert
- HUN-REN, Research Center for Natural Sciences, Institute of Cognitive Neuroscience and Psychology, Budapest, Hungary
- Faculty of Information Technology and Bionics, Péter Pázmány Catholic University, Budapest, Hungary
- Department of Neurosurgery and Neurointervention, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Dániel Fabó
- Department of Neurosurgery and Neurointervention, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Jean-Rémi King
- Laboratoire des Systèmes Perceptifs, Département d'études cognitives, École normale supérieure, PSL University, CNRS, Paris, France
| | - Kane M Sjoberg
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Harvard College, Cambridge, MA, 02138, USA
| | - Emad N Eskandar
- Department of Neurological Surgery, Albert Einstein College of Medicine - Montefiore Medical Center, Bronx, NY, USA
| | - Joseph R Madsen
- Department of Neurosurgery, Boston Children Hospital, Harvard Medical School, Boston, MA, USA
| | - Eric Halgren
- Departments of Radiology and, Neurosciences, University of California, San Diego, San Diego, CA, USA
| | - Sydney S Cash
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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Olenin S, Stasenko S, Levanova T. Spiral attractors in a reduced mean-field model of neuron-glial interaction. CHAOS (WOODBURY, N.Y.) 2024; 34:063112. [PMID: 38829793 DOI: 10.1063/5.0211051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 05/07/2024] [Indexed: 06/05/2024]
Abstract
This paper investigates various bifurcation scenarios of the appearance of bursting activity in the phenomenological mean-field model of neuron-glial interactions. In particular, we show that the homoclinic spiral attractors in this system can be the source of several types of bursting activity with different properties.
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Affiliation(s)
- S Olenin
- Control Theory Department, Lobachevsky University, Gagarin Avenue, 23, Nizhny Novgorod 603022, Russia
| | - S Stasenko
- Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky University, Gagarin Avenue, 23, Nizhny Novgorod 603022, Russia
| | - T Levanova
- Control Theory Department, Lobachevsky University, Gagarin Avenue, 23, Nizhny Novgorod 603022, Russia
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Abdi-Sargezeh B, Shirani S, Sanei S, Took CC, Geman O, Alarcon G, Valentin A. A review of signal processing and machine learning techniques for interictal epileptiform discharge detection. Comput Biol Med 2024; 168:107782. [PMID: 38070202 DOI: 10.1016/j.compbiomed.2023.107782] [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: 06/24/2023] [Revised: 11/15/2023] [Accepted: 11/28/2023] [Indexed: 01/10/2024]
Abstract
Brain interictal epileptiform discharges (IEDs), as one of the hallmarks of epileptic brain, are transient events captured by electroencephalogram (EEG). IEDs are generated by seizure networks, and they occur between seizures (interictal periods). The development of a robust method for IED detection could be highly informative for clinical treatment procedures and epileptic patient management. Since 1972, different machine learning techniques, from template matching to deep learning, have been developed to automatically detect IEDs from scalp EEG (scEEG) and intracranial EEG (iEEG). While the scEEG signals suffer from low information details and high attenuation of IEDs due to the high skull electrical impedance, the iEEG signals recorded using implanted electrodes enjoy higher details and are more suitable for identifying the IEDs. In this review paper, we group IED detection techniques into six categories: (1) template matching, (2) feature representation (mimetic, time-frequency, and nonlinear features), (3) matrix decomposition, (4) tensor factorization, (5) neural networks, and (6) estimation of the iEEG from the concurrent scEEG followed by detection and classification. The methods are compared quantitatively (e.g., in terms of accuracy, sensitivity, and specificity), and their general advantages and limitations are described. Finally, current limitations and possible future research paths related to this field are mentioned.
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Affiliation(s)
- Bahman Abdi-Sargezeh
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; School of Science and Technology, Nottingham Trent University, Nottingham, UK.
| | - Sepehr Shirani
- School of Science and Technology, Nottingham Trent University, Nottingham, UK
| | - Saeid Sanei
- School of Science and Technology, Nottingham Trent University, Nottingham, UK
| | - Clive Cheong Took
- Department of Electronic Engineering, Royal Holloway, University of London, London, UK
| | - Oana Geman
- Computer, Electronics and Automation Department, University Stefan cel Mare, Suceava, Romania
| | - Gonzalo Alarcon
- Department of Clinical Neurophysiology, Royal Manchester Children's Hospital, Manchester, UK
| | - Antonio Valentin
- Department of Clinical Neuroscience, King's College London, London, UK
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Bod R, Tóth K, Essam N, Tóth EZ, Erõss L, Entz L, Bagó AG, Fabó D, Ulbert I, Wittner L. Synaptic alterations and neuronal firing in human epileptic neocortical excitatory networks. Front Synaptic Neurosci 2023; 15:1233569. [PMID: 37635750 PMCID: PMC10450510 DOI: 10.3389/fnsyn.2023.1233569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 07/25/2023] [Indexed: 08/29/2023] Open
Abstract
Epilepsy is a prevalent neurological condition, with underlying neuronal mechanisms involving hyperexcitability and hypersynchrony. Imbalance between excitatory and inhibitory circuits, as well as histological reorganization are relatively well-documented in animal models or even in the human hippocampus, but less is known about human neocortical epileptic activity. Our knowledge about changes in the excitatory signaling is especially scarce, compared to that about the inhibitory cell population. This study investigated the firing properties of single neurons in the human neocortex in vitro, during pharmacological blockade of glutamate receptors, and additionally evaluated anatomical changes in the excitatory circuit in tissue samples from epileptic and non-epileptic patients. Both epileptic and non-epileptic tissues exhibited spontaneous population activity (SPA), NMDA receptor antagonization reduced SPA recurrence only in epileptic tissue, whereas further blockade of AMPA/kainate receptors reversibly abolished SPA emergence regardless of epilepsy. Firing rates did not significantly change in excitatory principal cells and inhibitory interneurons during pharmacological experiments. Granular layer (L4) neurons showed an increased firing rate in epileptic compared to non-epileptic tissue. The burstiness of neurons remained unchanged, except for that of inhibitory cells in epileptic recordings, which decreased during blockade of glutamate receptors. Crosscorrelograms computed from single neuron discharge revealed both mono- and polysynaptic connections, particularly involving intrinsically bursting principal cells. Histological investigations found similar densities of SMI-32-immunopositive long-range projecting pyramidal cells in both groups, and shorter excitatory synaptic active zones with a higher proportion of perforated synapses in the epileptic group. These findings provide insights into epileptic modifications from the perspective of the excitatory system and highlight discrete alterations in firing patterns and synaptic structure. Our data suggest that NMDA-dependent glutamatergic signaling, as well as the excitatory synaptic machinery are perturbed in epilepsy, which might contribute to epileptic activity in the human neocortex.
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Affiliation(s)
- Réka Bod
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Eötvös Loránd Research Network, Budapest, Hungary
- Semmelweis University Doctoral School, Budapest, Hungary
| | - Kinga Tóth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Eötvös Loránd Research Network, Budapest, Hungary
| | - Nour Essam
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Eötvös Loránd Research Network, Budapest, Hungary
| | - Estilla Zsófia Tóth
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Eötvös Loránd Research Network, Budapest, Hungary
- Semmelweis University Doctoral School, Budapest, Hungary
| | - Loránd Erõss
- National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
| | - László Entz
- National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
| | - Attila G. Bagó
- National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
| | - Dániel Fabó
- National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
| | - István Ulbert
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Eötvös Loránd Research Network, Budapest, Hungary
- Semmelweis University Doctoral School, Budapest, Hungary
- National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Lucia Wittner
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Eötvös Loránd Research Network, Budapest, Hungary
- Semmelweis University Doctoral School, Budapest, Hungary
- National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
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Fabo D, Bokodi V, Szabó JP, Tóth E, Salami P, Keller CJ, Hajnal B, Thesen T, Devinsky O, Doyle W, Mehta A, Madsen J, Eskandar E, Erőss L, Ulbert I, Halgren E, Cash SS. The role of superficial and deep layers in the generation of high frequency oscillations and interictal epileptiform discharges in the human cortex. Sci Rep 2023; 13:9620. [PMID: 37316509 DOI: 10.1038/s41598-022-22497-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023] Open
Abstract
Describing intracortical laminar organization of interictal epileptiform discharges (IED) and high frequency oscillations (HFOs), also known as ripples. Defining the frequency limits of slow and fast ripples. We recorded potential gradients with laminar multielectrode arrays (LME) for current source density (CSD) and multi-unit activity (MUA) analysis of interictal epileptiform discharges IEDs and HFOs in the neocortex and mesial temporal lobe of focal epilepsy patients. IEDs were observed in 20/29, while ripples only in 9/29 patients. Ripples were all detected within the seizure onset zone (SOZ). Compared to hippocampal HFOs, neocortical ripples proved to be longer, lower in frequency and amplitude, and presented non-uniform cycles. A subset of ripples (≈ 50%) co-occurred with IEDs, while IEDs were shown to contain variable high-frequency activity, even below HFO detection threshold. The limit between slow and fast ripples was defined at 150 Hz, while IEDs' high frequency components form clusters separated at 185 Hz. CSD analysis of IEDs and ripples revealed an alternating sink-source pair in the supragranular cortical layers, although fast ripple CSD appeared lower and engaged a wider cortical domain than slow ripples MUA analysis suggested a possible role of infragranularly located neural populations in ripple and IED generation. Laminar distribution of peak frequencies derived from HFOs and IEDs, respectively, showed that supragranular layers were dominated by slower (< 150 Hz) components. Our findings suggest that cortical slow ripples are generated primarily in upper layers while fast ripples and associated MUA in deeper layers. The dissociation of macro- and microdomains suggests that microelectrode recordings may be more selective for SOZ-linked ripples. We found a complex interplay between neural activity in the neocortical laminae during ripple and IED formation. We observed a potential leading role of cortical neurons in deeper layers, suggesting a refined utilization of LMEs in SOZ localization.
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Affiliation(s)
- Daniel Fabo
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary.
| | - Virag Bokodi
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- Roska Tamás Doctoral School of Sciences and Technologies, Budapest, Hungary
| | - Johanna-Petra Szabó
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Budapest, Hungary
| | - Emilia Tóth
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- Department of Neurology, University of Texas, McGovern Medical School, Houston, TX, USA
| | - Pariya Salami
- Epilepsy Division, Department of Neurology, Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Corey J Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Boglárka Hajnal
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Budapest, Hungary
| | - Thomas Thesen
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, USA
- Department of Biomedical Sciences, College of Medicine, University of Houston, Houston, TX, USA
| | - Orrin Devinsky
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, USA
| | - Werner Doyle
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, USA
| | - Ashesh Mehta
- Department of Neurosurgery, Zucker School of Medicine at Hofstra/Northwell and Feinstein Institute for Medical Research, Manhasset, NY, USA
| | | | - Emad Eskandar
- Massachusetts General Hospital Neurosurgery Research, Boston, MA, USA
| | - Lorand Erőss
- Department of Functional Neurosurgery, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
| | - István Ulbert
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- Institute of Psychology, Eötvös Loránd Research Network, Budapest, Hungary
| | - Eric Halgren
- Department of Radiology, Neurosciences and Psychiatry, University of California, San Diego, San Diego, CA, USA
| | - Sydney S Cash
- Epilepsy Division, Department of Neurology, Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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