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Adeyeye A, Mirsadeghi S, Gutierrez M, Hsieh J. Integrating adult neurogenesis and human brain organoid models to advance epilepsy and associated behavioral research. Epilepsy Behav 2024; 159:109982. [PMID: 39181108 DOI: 10.1016/j.yebeh.2024.109982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 08/02/2024] [Accepted: 08/04/2024] [Indexed: 08/27/2024]
Abstract
Epilepsy is a chronic neurological disorder characterized by recurring, unprovoked seizures, asymmetrical electroencephalogram patterns, and other pathological abnormalities. The hippocampus plays a pivotal role in learning, memory consolidation, attentional control, and pattern separation. Impairment of hippocampal network circuitry can induce long-term cognitive and memory dysfunction. In this review, we discuss how aberrant adult neurogenesis and plasticity collectively alter the network balance for information processing within the hippocampal neural network. Subsequently, we explore the potential of human brain organoids integrated into microelectrode array technology as an electrophysiological tool. We also discuss the utilization of a closed-loop platform that connects the brain organoid to a mobile robot in a virtual environment. While in vivo models provide valuable insights into some aspects of epileptogenesis, such as the impact of adult neurogenesis on hippocampal function, brain organoids are indispensable for comprehensively studying epileptogenesis involving genetic mutations that underlie human epilepsy. More importantly, a combinational approach using brain organoids on MEA paves the way for studying impaired plasticity and abnormal information processing within epileptic neural networks. This innovative in vitro approach may provide a new pathway for investigating the behavioral outcomes of aberrant neural networks when integrated with a mobile robot, closing the loop between the neural network in brain organoids and the mobile robot. In this review, we aim to discuss the use of each model to study the behavioral changes in epilepsy and highlight the benefits of both in vivo and in vitro models for understanding the behavioral aspects of epilepsy.
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Affiliation(s)
- Adebayo Adeyeye
- Department of Neuroscience, Developmental and Regenerative Biology, The University of Texas at San Antonio, San Antonio, TX, USA; Brain Health Consortium, The University of Texas at San Antonio, San Antonio, TX, USA
| | - Sara Mirsadeghi
- Department of Neuroscience, Developmental and Regenerative Biology, The University of Texas at San Antonio, San Antonio, TX, USA; Brain Health Consortium, The University of Texas at San Antonio, San Antonio, TX, USA
| | - Maryfer Gutierrez
- Department of Neuroscience, Developmental and Regenerative Biology, The University of Texas at San Antonio, San Antonio, TX, USA; Brain Health Consortium, The University of Texas at San Antonio, San Antonio, TX, USA
| | - Jenny Hsieh
- Department of Neuroscience, Developmental and Regenerative Biology, The University of Texas at San Antonio, San Antonio, TX, USA; Brain Health Consortium, The University of Texas at San Antonio, San Antonio, TX, USA.
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Mofakham S, Robertson J, Lubin N, Cleri NA, Mikell CB. An Unpredictable Brain Is a Conscious, Responsive Brain. J Cogn Neurosci 2024; 36:1643-1652. [PMID: 38579270 DOI: 10.1162/jocn_a_02154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2024]
Abstract
Severe traumatic brain injuries typically result in loss of consciousness or coma. In deeply comatose patients with traumatic brain injury, cortical dynamics become simple, repetitive, and predictable. We review evidence that this low-complexity, high-predictability state results from a passive cortical state, represented by a stable repetitive attractor, that hinders the flexible formation of neuronal ensembles necessary for conscious experience. Our data and those from other groups support the hypothesis that this cortical passive state is because of the loss of thalamocortical input. We identify the unpredictability and complexity of cortical dynamics captured by local field potential as a sign of recovery from this passive coma attractor. In this Perspective article, we discuss how these electrophysiological biomarkers of the recovery of consciousness could inform the design of closed-loop stimulation paradigms to treat disorders of consciousness.
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Bratu I, Medina Villalon S, Garnier E, Bénar C, Bartolomei F. How fast does the brain recover after an epileptic seizure? Ann Clin Transl Neurol 2024; 11:1630-1635. [PMID: 38730560 PMCID: PMC11187945 DOI: 10.1002/acn3.52073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Accepted: 04/10/2024] [Indexed: 05/13/2024] Open
Abstract
The postictal state, an abnormal cerebral condition following a seizure until the return to the interictal baseline, is frequently overlooked, despite often exceeding ictal duration and significantly impacting patients' lives. This study analyzes stereo-EEG (SEEG) signal dynamics using permutation entropy to quantify recovery time (postictal alteration time - PAT) in focal epilepsy and its clinical correlations. The average PAT was 4.5 min, extending up to an hour and was highest in temporal epilepsy and hippocampal sclerosis. Correlating with age at seizure onset and at SEEG, PAT provides a solution for operationally defining the postictal state and guiding interventions.
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Affiliation(s)
- Ionuț‐Flavius Bratu
- Epileptology and Cerebral RhythmologyAPHM, Timone HospitalMarseilleFrance
- INSERM, INS, Institut de Neurosciences des SystèmesAix‐Marseille UniversiteMarseilleFrance
| | - Samuel Medina Villalon
- Epileptology and Cerebral RhythmologyAPHM, Timone HospitalMarseilleFrance
- INSERM, INS, Institut de Neurosciences des SystèmesAix‐Marseille UniversiteMarseilleFrance
| | - Elodie Garnier
- INSERM, INS, Institut de Neurosciences des SystèmesAix‐Marseille UniversiteMarseilleFrance
| | - Christian‐G. Bénar
- INSERM, INS, Institut de Neurosciences des SystèmesAix‐Marseille UniversiteMarseilleFrance
| | - Fabrice Bartolomei
- Epileptology and Cerebral RhythmologyAPHM, Timone HospitalMarseilleFrance
- INSERM, INS, Institut de Neurosciences des SystèmesAix‐Marseille UniversiteMarseilleFrance
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Naccache L, Munoz-Musat E. A global neuronal workspace model of functional neurological disorders. DIALOGUES IN CLINICAL NEUROSCIENCE 2024; 26:1-23. [PMID: 38767966 PMCID: PMC11107854 DOI: 10.1080/19585969.2024.2340131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 04/03/2024] [Indexed: 05/22/2024]
Abstract
We introduce here a general model of Functional Neurological Disorders based on the following hypothesis: a Functional Neurological Disorder could correspond to a consciously initiated voluntary top-down process causing involuntary lasting consequences that are consciously experienced and subjectively interpreted by the patient as involuntary. We develop this central hypothesis according to Global Neuronal Workspace theory of consciousness, that is particularly suited to describe interactions between conscious and non-conscious cognitive processes. We then present a list of predictions defining a research program aimed at empirically testing their validity. Finally, this general model leads us to reinterpret the long-debated links between hypnotic suggestion and functional neurological disorders. Driven by both scientific and therapeutic goals, this theoretical paper aims at bringing closer the psychiatric and neurological worlds of functional neurological disorders with the latest developments of cognitive neuroscience of consciousness.
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Affiliation(s)
- Lionel Naccache
- Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Paris, France- Sorbonne Université, UPMC Univ Paris 06, Faculté de Médecine Pitié-Salpêtrière, Sorbonne Université, Paris, France
- Department of Neurology, AP-HP, Hôpital Groupe hospitalier Pitié-Salpêtrière, DMU Neurosciences, Paris, France
- Department of Clinical Neurophysiology, AP-HP, Hôpital Groupe hospitalier Pitié-Salpêtrière, DMU Neurosciences, Paris, France
| | - Esteban Munoz-Musat
- Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Paris, France- Sorbonne Université, UPMC Univ Paris 06, Faculté de Médecine Pitié-Salpêtrière, Sorbonne Université, Paris, France
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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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Baglivo FH, Campora N, Mininni CJ, Kochen S, Lew S. Consciousness transitions during epilepsy seizures through the lens of integrated information theory. Sci Rep 2024; 14:5355. [PMID: 38438478 PMCID: PMC10912751 DOI: 10.1038/s41598-024-56045-x] [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: 10/18/2023] [Accepted: 03/01/2024] [Indexed: 03/06/2024] Open
Abstract
Consciousness is one of the most complex aspects of human experience. Studying the mechanisms involved in the transitions among different levels of consciousness remains as one of the greatest challenges in neuroscience. In this study we use a measure of integrated information (ΦAR) to evaluate dynamic changes during consciousness transitions. We applied the measure to intracranial electroencephalography (SEEG) recordings collected from 6 patients that suffer from refractory epilepsy, taking into account inter-ictal, pre-ictal and ictal periods. We analyzed the dynamical evolution of ΦAR in groups of electrode contacts outside the epileptogenic region and compared it with the Consciousness Seizure Scale (CCS). We show that changes on ΦAR are significantly correlated with changes in the reported states of consciousness.
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Affiliation(s)
- F H Baglivo
- Universidad de Buenos Aires, Instituto de Ingeniería Biomédica, Buenos Aires, Argentina
| | - N Campora
- Estudios en Neurociencias y Sistemas Complejos, CONICET, Buenos Aires, Argentina
| | - C J Mininni
- Universidad de Buenos Aires, Instituto de Ingeniería Biomédica, Buenos Aires, Argentina
- Instituto de Biología y Medicina Experimental, CONICET, Buenos Aires, Argentina
| | - S Kochen
- Estudios en Neurociencias y Sistemas Complejos, CONICET, Buenos Aires, Argentina
| | - S Lew
- Universidad de Buenos Aires, Instituto de Ingeniería Biomédica, Buenos Aires, Argentina.
- Instituto de Biología y Medicina Experimental, CONICET, Buenos Aires, Argentina.
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Bratu IF, Makhalova J, Garnier E, Villalon SM, Jegou A, Bonini F, Lagarde S, Pizzo F, Trébuchon A, Scavarda D, Carron R, Bénar C, Bartolomei F. Permutation entropy-derived parameters to estimate the epileptogenic zone network. Epilepsia 2024; 65:389-401. [PMID: 38041564 DOI: 10.1111/epi.17849] [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: 10/07/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 12/03/2023]
Abstract
OBJECTIVE Quantification of the epileptogenic zone network (EZN) most frequently implies analysis of seizure onset. However, important information can also be obtained from the postictal period, characterized by prominent changes in the EZN. We used permutation entropy (PE), a measure of signal complexity, to analyze the peri-ictal stereoelectroencephalography (SEEG) signal changes with emphasis on the postictal state. We sought to determine the best PE-derived parameter (PEDP) for identifying the EZN. METHODS Several PEDPs were computed retrospectively on SEEG-recorded seizures of 86 patients operated on for drug-resistant epilepsy: mean baseline preictal entropy, minimum ictal entropy, maximum postictal entropy, the ratio between the maximum postictal and the minimum ictal entropy, and the ratio between the maximum postictal and the baseline preictal entropy. The performance of each biomarker was assessed by comparing the identified epileptogenic contacts or brain regions against the EZN defined by clinical analysis incorporating the Epileptogenicity Index and the connectivity epileptogenicity index methods (EZNc), using the receiver-operating characteristic and precision-recall. RESULTS The ratio between the maximum postictal and the minimum ictal entropy (defined as the Permutation Entropy Index [PEI]) proved to be the best-performing PEDP to identify the EZNC . It demonstrated the highest area under the curve (AUC) and F1 score at the contact level (AUC 0.72; F1 0.39) and at the region level (AUC 0.78; F1 0.47). PEI values gradually decreased between the EZN, the propagation network, and the non-involved regions. PEI showed higher performance in patients with slow seizure-onset patterns than in those with fast seizure-onset patterns. The percentage of resected epileptogenic regions defined by PEI was significantly correlated with surgical outcome. SIGNIFICANCE PEI is a promising tool to improve the delineation of the EZN. PEI combines ease and robustness in a routine clinical setting with high sensitivity for seizures without fast activity at seizure onset.
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Affiliation(s)
- Ionuț-Flavius Bratu
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Julia Makhalova
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
- APHM, Timone Hospital, CEMEREM, Marseille, France
| | - Elodie Garnier
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Samuel Medina Villalon
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Aude Jegou
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Francesca Bonini
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Stanislas Lagarde
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Francesca Pizzo
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Agnès Trébuchon
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Didier Scavarda
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- APHM Paediatric Neurosurgery Department, Marseille, France
| | - Romain Carron
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- APHM Functional Neurosurgery Department, Marseille, France
| | - Christian Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Fabrice Bartolomei
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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Troyas C, Sleigh J. Electroencephalographic signatures of consciousness: uncovering the fake news. Br J Anaesth 2024; 132:218-219. [PMID: 38104006 DOI: 10.1016/j.bja.2023.11.038] [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: 10/11/2023] [Revised: 11/19/2023] [Accepted: 11/24/2023] [Indexed: 12/19/2023] Open
Abstract
Amongst electroencephalographic markers of anaesthetic-induced unresponsiveness, those that estimate loss of frontoparietal functional connectivity detect loss of sensory perceptual connection with the outside world, rather than full phenomenological unconsciousness. This transition to unconsciousness is manifest as further incremental changes in indices of electroencephalographic complexity.
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Affiliation(s)
- Carla Troyas
- Department of Anesthesiology and Intensive Care Medicine, Technical University of Munich, School of Medicine, Munich, Germany
| | - Jamie Sleigh
- Department of Anaesthesia and Pain Medicine, Waikato Clinical School, University of Auckland, Hamilton, New Zealand.
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Annen J, Frasso G, van der Lande GJM, Bonin EAC, Vitello MM, Panda R, Sala A, Cavaliere C, Raimondo F, Bahri MA, Schiff ND, Gosseries O, Thibaut A, Laureys S. Cerebral electrometabolic coupling in disordered and normal states of consciousness. Cell Rep 2023; 42:112854. [PMID: 37498745 DOI: 10.1016/j.celrep.2023.112854] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 06/02/2023] [Accepted: 07/08/2023] [Indexed: 07/29/2023] Open
Abstract
We assess cerebral integrity with cortical and subcortical FDG-PET and cortical electroencephalography (EEG) within the mesocircuit model framework in patients with disorders of consciousness (DoCs). The mesocircuit hypothesis proposes that subcortical activation facilitates cortical function. We find that the metabolic balance of subcortical mesocircuit areas is informative for diagnosis and is associated with four EEG-based power spectral density patterns, cortical metabolism, and α power in healthy controls and patients with a DoC. Last, regional electrometabolic coupling at the cortical level can be identified in the θ and α ranges, showing positive and negative relations with glucose uptake, respectively. This relation is inverted in patients with a DoC, potentially related to altered orchestration of neural activity, and may underlie suboptimal excitability states in patients with a DoC. By understanding the neurobiological basis of the pathophysiology underlying DoCs, we foresee translational value for diagnosis and treatment of patients with a DoC.
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Affiliation(s)
- Jitka Annen
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium.
| | | | - Glenn J M van der Lande
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Estelle A C Bonin
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Marie M Vitello
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Rajanikant Panda
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Arianna Sala
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | | | - Federico Raimondo
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Mohamed Ali Bahri
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | | | - Olivia Gosseries
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Aurore Thibaut
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Steven Laureys
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium; Joint International Research Unit on Consciousness, CERVO Brain Research Centre, University Laval, Quebec City, QC, Canada
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