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Aud'hui M, Kachenoura A, Yochum M, Kaminska A, Nabbout R, Wendling F, Kuchenbuch M, Benquet P. Detection of seizure onset in childhood absence epilepsy. Clin Neurophysiol 2024; 163:267-279. [PMID: 38644110 DOI: 10.1016/j.clinph.2024.03.034] [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: 10/16/2023] [Revised: 03/19/2024] [Accepted: 03/26/2024] [Indexed: 04/23/2024]
Abstract
OBJECTIVE This study aims to detect the seizure onset, in childhood absence epilepsy, as early as possible. Indeed, interfering with absence seizures with sensory simulation has been shown to be possible on the condition that the stimulation occurs soon enough after the seizure onset. METHODS We present four variations (two supervised, two unsupervised) of an algorithm designed to detect the onset of absence seizures from 4 scalp electrodes, and compare their performance with that of a state-of-the-art algorithm. We exploit the characteristic shape of spike-wave discharges to detect the seizure onset. Their performance is assessed on clinical electroencephalograms from 63 patients with confirmed childhood absence epilepsy. RESULTS The proposed approaches succeed in early detection of the seizure onset, contrary to the classical detection algorithm. Indeed, the results clearly show the superiority of the proposed methods for small delays of detection, under 750 ms from the onset. CONCLUSION The performance of the proposed unsupervised methods is equivalent to that of the supervised ones. The use of only four electrodes makes the pipeline suitable to be embedded in a wearable device. SIGNIFICANCE The proposed pipelines perform early detection of absence seizures, which constitutes a prerequisite for a closed-loop system.
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Affiliation(s)
- M Aud'hui
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France
| | - A Kachenoura
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France
| | - M Yochum
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France.
| | - A Kaminska
- Department of Clinical Neurophysiology, Hôpital Necker Enfants Malades, AP-HP, Université de Paris, Paris, France
| | - R Nabbout
- Department of Clinical Neurophysiology, Hôpital Necker Enfants Malades, AP-HP, Université de Paris, Paris, France; Reference Center for Rare Epilepsies, Department of Pediatric Neurology, Member of EPICARE Network, Institute Imagine INSERM 1163, Université de Paris, Paris, France
| | - F Wendling
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France
| | - M Kuchenbuch
- Pediatric and Genetic Department, CHU, Nancy, France
| | - P Benquet
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France
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Awuah WA, Ahluwalia A, Darko K, Sanker V, Tan JK, Tenkorang PO, Ben-Jaafar A, Ranganathan S, Aderinto N, Mehta A, Shah MH, Lee Boon Chun K, Abdul-Rahman T, Atallah O. Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications. World Neurosurg 2024; 189:138-153. [PMID: 38789029 DOI: 10.1016/j.wneu.2024.05.104] [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: 01/22/2024] [Revised: 05/16/2024] [Accepted: 05/17/2024] [Indexed: 05/26/2024]
Abstract
Brain-computer interfaces (BCIs), a remarkable technological advancement in neurology and neurosurgery, mark a significant leap since the inception of electroencephalography in 1924. These interfaces effectively convert central nervous system signals into commands for external devices, offering revolutionary benefits to patients with severe communication and motor impairments due to a myriad of neurological conditions like stroke, spinal cord injuries, and neurodegenerative disorders. BCIs enable these individuals to communicate and interact with their environment, using their brain signals to operate interfaces for communication and environmental control. This technology is especially crucial for those completely locked in, providing a communication lifeline where other methods fall short. The advantages of BCIs are profound, offering autonomy and an improved quality of life for patients with severe disabilities. They allow for direct interaction with various devices and prostheses, bypassing damaged or nonfunctional neural pathways. However, challenges persist, including the complexity of accurately interpreting brain signals, the need for individual calibration, and ensuring reliable, long-term use. Additionally, ethical considerations arise regarding autonomy, consent, and the potential for dependence on technology. Despite these challenges, BCIs represent a transformative development in neurotechnology, promising enhanced patient outcomes and a deeper understanding of brain-machine interfaces.
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Affiliation(s)
| | - Arjun Ahluwalia
- School of Medicine, Queen's University Belfast, Belfast, United Kingdom
| | - Kwadwo Darko
- Department of Neurosurgery, Korle Bu Teaching Hospital, Accra, Ghana
| | - Vivek Sanker
- Department of Neurosurgery, Trivandrum Medical College, India
| | - Joecelyn Kirani Tan
- Faculty of Medicine, University of St Andrews, St. Andrews, Scotland, United Kingdom.
| | | | - Adam Ben-Jaafar
- University College Dublin, School of Medicine, Belfield, Dublin, Ireland
| | - Sruthi Ranganathan
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Nicholas Aderinto
- Internal Medicine Department, LAUTECH Teaching Hospital, Ogbomoso, Nigeria
| | - Aashna Mehta
- University of Debrecen-Faculty of Medicine, Debrecen, Hungary
| | | | | | | | - Oday Atallah
- Department of Neurosurgery, Hannover Medical School, Hannover, Germany
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Chai X, Cao T, He Q, Wang N, Zhang X, Shan X, Lv Z, Tu W, Yang Y, Zhao J. Brain-computer interface digital prescription for neurological disorders. CNS Neurosci Ther 2024; 30:e14615. [PMID: 38358054 PMCID: PMC10867871 DOI: 10.1111/cns.14615] [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: 07/17/2023] [Revised: 12/13/2023] [Accepted: 01/09/2024] [Indexed: 02/16/2024] Open
Abstract
Neurological and psychiatric diseases can lead to motor, language, emotional disorder, and cognitive, hearing or visual impairment By decoding the intention of the brain in real time, the Brain-computer interface (BCI) can first assist in the diagnosis of diseases, and can also compensate for its damaged function by directly interacting with the environment; In addition, provide output signals in various forms, such as actual motion, tactile or visual feedback, to assist in rehabilitation training; Further intervention in brain disorders is achieved by close-looped neural modulation. In this article, we envision the future BCI digital prescription system for patients with different functional disorders and discuss the key contents in the prescription the brain signals, coding and decoding protocols and interaction paradigms, and assistive technology. Then, we discuss the details that need to be specially included in the digital prescription for different intervention technologies. The third part summarizes previous examples of intervention, focusing on how to select appropriate interaction paradigms for patients with different functional impairments. For the last part, we discussed the indicators and influencing factors in evaluating the therapeutic effect of BCI as intervention.
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Affiliation(s)
- Xiaoke Chai
- Brain Computer Interface Transitional Research Center, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Center for Neurological DisordersBeijingChina
- Translation Laboratory of Clinical MedicineChinese Institute for Brain Research & Beijing Tiantan HospitalBeijingChina
| | - Tianqing Cao
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Qiheng He
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Nan Wang
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Xuemin Zhang
- National Research Center for Rehabilitation Technical AidsBeijingChina
| | - Xinying Shan
- National Research Center for Rehabilitation Technical AidsBeijingChina
| | - Zeping Lv
- National Research Center for Rehabilitation Technical AidsBeijingChina
| | - Wenjun Tu
- Translation Laboratory of Clinical MedicineChinese Institute for Brain Research & Beijing Tiantan HospitalBeijingChina
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Yi Yang
- Brain Computer Interface Transitional Research Center, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Center for Neurological DisordersBeijingChina
- Translation Laboratory of Clinical MedicineChinese Institute for Brain Research & Beijing Tiantan HospitalBeijingChina
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
- National Research Center for Rehabilitation Technical AidsBeijingChina
- Beijing Institute of Brain DisordersBeijingChina
- Chinese Institute for Brain ResearchBeijingChina
| | - Jizong Zhao
- Brain Computer Interface Transitional Research Center, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Center for Neurological DisordersBeijingChina
- Translation Laboratory of Clinical MedicineChinese Institute for Brain Research & Beijing Tiantan HospitalBeijingChina
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
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Li D, Zhang X, Liu R, Long M, Zhou S, Lin J, Zhang L. Kainic acid induced hyperexcitability in thalamic reticular nucleus that initiates an inflammatory response through the HMGB1/TLR4 pathway. Neurotoxicology 2023; 95:94-106. [PMID: 36669621 DOI: 10.1016/j.neuro.2023.01.007] [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: 10/24/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 01/20/2023]
Abstract
OBJECTIVE To explore the relationship between the proinflammatory factor high-mobility group box 1 (HMGB1) and glutamatergic alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors in the development of epilepsy. METHODS Thalamic reticular nucleus (TRN) slices were treated with kainic acid (KA) to simulate seizures. Action potentials and spontaneous inhibitory postsynaptic currents (sIPSCs) were recorded within TRN slices using whole-cell patch clamp techniques. The translocation of HMGB1 was detected by immunofluorescence. The HMGB1/TLR4 signaling pathway and its downstream inflammatory factors (IL-1β and NF-κB) were detected by RTPCR, Western blot and ELISA. RESULTS KA-evoked spikings were observed in TRN slices and blocked by perampanel. sIPSCs in the TRN were enhanced by KA and reduced by perampanel. The translocation of HMGB1 in the TRN was promoted by KA and inhibited by perampanel. The expression of the HMGB1/TLR4 signaling pathway was promoted by KA and suppressed by perampanel. CONCLUSION KA induced hyperexcitability activates the HMGB1/TLR4 pathway, which potentially leading to neuroinflammation in epilepsy.
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Affiliation(s)
- Dongbin Li
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China; First Department of Neurology and Neuroscience Center, Heilongjiang Provincial Hospital, Harbin, China
| | - Xiaosi Zhang
- Metro-Medic Clinic, 1538 sherbrooke Ouest, suite 100, Montreal, QC H3G 1L5, Canada
| | - Ruoshi Liu
- Department of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Meixin Long
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Shanshan Zhou
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jinghan Lin
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Liming Zhang
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
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5
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Bibliometric analysis on Brain-computer interfaces in a 30-year period. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04226-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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6
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Valeriani D, Santoro F, Ienca M. The present and future of neural interfaces. Front Neurorobot 2022; 16:953968. [PMID: 36304780 PMCID: PMC9592849 DOI: 10.3389/fnbot.2022.953968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/13/2022] [Indexed: 11/18/2022] Open
Abstract
The 2020's decade will likely witness an unprecedented development and deployment of neurotechnologies for human rehabilitation, personalized use, and cognitive or other enhancement. New materials and algorithms are already enabling active brain monitoring and are allowing the development of biohybrid and neuromorphic systems that can adapt to the brain. Novel brain-computer interfaces (BCIs) have been proposed to tackle a variety of enhancement and therapeutic challenges, from improving decision-making to modulating mood disorders. While these BCIs have generally been developed in an open-loop modality to optimize their internal neural decoders, this decade will increasingly witness their validation in closed-loop systems that are able to continuously adapt to the user's mental states. Therefore, a proactive ethical approach is needed to ensure that these new technological developments go hand in hand with the development of a sound ethical framework. In this perspective article, we summarize recent developments in neural interfaces, ranging from neurohybrid synapses to closed-loop BCIs, and thereby identify the most promising macro-trends in BCI research, such as simulating vs. interfacing the brain, brain recording vs. brain stimulation, and hardware vs. software technology. Particular attention is devoted to central nervous system interfaces, especially those with application in healthcare and human enhancement. Finally, we critically assess the possible futures of neural interfacing and analyze the short- and long-term implications of such neurotechnologies.
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Affiliation(s)
| | - Francesca Santoro
- Institute for Biological Information Processing - Bioelectronics, IBI-3, Forschungszentrum Juelich, Juelich, Germany
- Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Aachen, Germany
| | - Marcello Ienca
- College of Humanities, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
- *Correspondence: Marcello Ienca
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Du Y, Liu J. IENet: a robust convolutional neural network for EEG based brain-computer interfaces. J Neural Eng 2022; 19. [PMID: 35605585 DOI: 10.1088/1741-2552/ac7257] [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: 12/06/2021] [Accepted: 05/22/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) develop into novel application areas with more complex scenarios, which put forward higher requirements for the robustness of EEG signal processing algorithms. Deep learning can automatically extract discriminative features and potential dependencies via deep structures, demonstrating strong analytical capabilities in numerous domains such as computer vision (CV) and natural language processing (NLP). Making full use of deep learning technology to design a robust algorithm that is capable of analyzing EEG across BCI paradigms is our main work in this paper. APPROACH Inspired by InceptionV4 and InceptionTime architecture, we introduce a neural network ensemble named InceptionEEG-Net (IENet), where multi-scale convolutional layer and convolution of length 1 enable model to extract rich high-dimensional features with limited parameters. In addition, we propose the average receptive field gain for convolutional neural networks (CNNs), which optimizes IENet to detect long patterns at a smaller cost. We compare with the current state-of-the-art method across five EEG-BCI paradigms: steady-state visual evoked potentials, epilepsy EEG, overt attention P300 visual-evoked potentials, covert attention P300 visual-evoked potentials and movement-related cortical potentials. MAIN RESULTS The classification results show that the generalizability of IENet is on par with the state-of-the-art paradigm-agnostic models on test datasets. Furthermore, the feature explainability analysis of IENet illustrates its capability to extract neurophysiologically interpretable features for different BCI paradigms, ensuring the reliability of algorithm. Significance. It can be seen from our results that IENet can generalize to different BCI paradigms. And it is essential for deep CNNs to increase the receptive field size using average receptive field gain.
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Affiliation(s)
- Yipeng Du
- SCCE, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083 P. R.China, Beijing, Beijing, 100083, CHINA
| | - Jian Liu
- SCCE, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083 P. R.China, Beijing, 100083, CHINA
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Zabcikova M, Koudelkova Z, Jasek R, Navarro JJL. Recent Advances and Current Trends in Brain-Computer Interface (BCI) Research and Their Applications. Int J Dev Neurosci 2021; 82:107-123. [PMID: 34939217 DOI: 10.1002/jdn.10166] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 11/16/2021] [Accepted: 12/18/2021] [Indexed: 11/06/2022] Open
Abstract
Brain-Computer Interface (BCI) provides direct communication between the brain and an external device. BCI systems have become a trendy field of research in recent years. These systems can be used in a variety of applications to help both disabled and healthy people. Concerning significant BCI progress, we may assume that these systems are not very far from real-world applications. This review has taken into account current trends in BCI research. In this survey, one hundred most cited articles from the WOS database were selected over the last four years. This survey is divided into several sectors. These sectors are Medicine, Communication and Control, Entertainment, and Other BCI applications. The application area, recording method, signal acquisition types, and countries of origin have been identified in each article. This survey provides an overview of the BCI articles published from 2016 to 2020 and their current trends and advances in different application areas.
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Affiliation(s)
- Martina Zabcikova
- Department of Informatics and Artificial Intelligence, Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic
| | - Zuzana Koudelkova
- Department of Informatics and Artificial Intelligence, Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic
| | - Roman Jasek
- Department of Informatics and Artificial Intelligence, Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic
| | - José Javier Lorenzo Navarro
- Departamento de Informática y Sistemas, Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
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do Canto AM, Donatti A, Geraldis JC, Godoi AB, da Rosa DC, Lopes-Cendes I. Neuroproteomics in Epilepsy: What Do We Know so Far? Front Mol Neurosci 2021; 13:604158. [PMID: 33488359 PMCID: PMC7817846 DOI: 10.3389/fnmol.2020.604158] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 12/09/2020] [Indexed: 12/12/2022] Open
Abstract
Epilepsies are chronic neurological diseases that affect approximately 2% of the world population. In addition to being one of the most frequent neurological disorders, treatment for patients with epilepsy remains a challenge, because a proportion of patients do not respond to the antiseizure medications that are currently available. This results in a severe economic and social burden for patients, families, and the healthcare system. A characteristic common to all forms of epilepsy is the occurrence of epileptic seizures that are caused by abnormal neuronal discharges, leading to a clinical manifestation that is dependent on the affected brain region. It is generally accepted that an imbalance between neuronal excitation and inhibition generates the synchronic electrical activity leading to seizures. However, it is still unclear how a normal neural circuit becomes susceptible to the generation of seizures or how epileptogenesis is induced. Herein, we review the results of recent proteomic studies applied to investigate the underlying mechanisms leading to epilepsies and how these findings may impact research and treatment for these disorders.
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Affiliation(s)
- Amanda M. do Canto
- Department of Medical Genetics and Genomic Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Amanda Donatti
- Department of Medical Genetics and Genomic Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Jaqueline C. Geraldis
- Department of Medical Genetics and Genomic Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Alexandre B. Godoi
- Department of Medical Genetics and Genomic Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Douglas C. da Rosa
- Department of Medical Genetics and Genomic Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Iscia Lopes-Cendes
- Department of Medical Genetics and Genomic Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
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Crunelli V, Lőrincz ML, McCafferty C, Lambert RC, Leresche N, Di Giovanni G, David F. Clinical and experimental insight into pathophysiology, comorbidity and therapy of absence seizures. Brain 2020; 143:2341-2368. [PMID: 32437558 PMCID: PMC7447525 DOI: 10.1093/brain/awaa072] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 12/19/2019] [Accepted: 01/31/2020] [Indexed: 12/24/2022] Open
Abstract
Absence seizures in children and teenagers are generally considered relatively benign because of their non-convulsive nature and the large incidence of remittance in early adulthood. Recent studies, however, show that 30% of children with absence seizures are pharmaco-resistant and 60% are affected by severe neuropsychiatric comorbid conditions, including impairments in attention, cognition, memory and mood. In particular, attention deficits can be detected before the epilepsy diagnosis, may persist even when seizures are pharmacologically controlled and are aggravated by valproic acid monotherapy. New functional MRI-magnetoencephalography and functional MRI-EEG studies provide conclusive evidence that changes in blood oxygenation level-dependent signal amplitude and frequency in children with absence seizures can be detected in specific cortical networks at least 1 min before the start of a seizure, spike-wave discharges are not generalized at seizure onset and abnormal cortical network states remain during interictal periods. From a neurobiological perspective, recent electrical recordings and imaging of large neuronal ensembles with single-cell resolution in non-anaesthetized models show that, in contrast to the predominant opinion, cortical mechanisms, rather than an exclusively thalamic rhythmogenesis, are key in driving seizure ictogenesis and determining spike-wave frequency. Though synchronous ictal firing characterizes cortical and thalamic activity at the population level, individual cortico-thalamic and thalamocortical neurons are sparsely recruited to successive seizures and consecutive paroxysmal cycles within a seizure. New evidence strengthens previous findings on the essential role for basal ganglia networks in absence seizures, in particular the ictal increase in firing of substantia nigra GABAergic neurons. Thus, a key feature of thalamic ictogenesis is the powerful increase in the inhibition of thalamocortical neurons that originates at least from two sources, substantia nigra and thalamic reticular nucleus. This undoubtedly provides a major contribution to the ictal decrease in total firing and the ictal increase of T-type calcium channel-mediated burst firing of thalamocortical neurons, though the latter is not essential for seizure expression. Moreover, in some children and animal models with absence seizures, the ictal increase in thalamic inhibition is enhanced by the loss-of-function of the astrocytic GABA transporter GAT-1 that does not necessarily derive from a mutation in its gene. Together, these novel clinical and experimental findings bring about paradigm-shifting views of our understanding of absence seizures and demand careful choice of initial monotherapy and continuous neuropsychiatric evaluation of affected children. These issues are discussed here to focus future clinical and experimental research and help to identify novel therapeutic targets for treating both absence seizures and their comorbidities.
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Affiliation(s)
- Vincenzo Crunelli
- Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta, Msida, Malta.,Neuroscience Division, School of Bioscience, Cardiff University, Museum Avenue, Cardiff, UK
| | - Magor L Lőrincz
- Neuroscience Division, School of Bioscience, Cardiff University, Museum Avenue, Cardiff, UK.,Department of Physiology, Faculty of Medicine, University of Szeged, Szeged, Hungary.,Department of Physiology, Anatomy and Neuroscience, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary
| | - Cian McCafferty
- Department of Anatomy and Neuroscience, University College Cork, Cork, Ireland
| | - Régis C Lambert
- Sorbonne Université, CNRS, INSERM, Neuroscience Paris Seine and Institut de Biologie Paris Seine (NPS - IBPS), Paris, France
| | - Nathalie Leresche
- Sorbonne Université, CNRS, INSERM, Neuroscience Paris Seine and Institut de Biologie Paris Seine (NPS - IBPS), Paris, France
| | - Giuseppe Di Giovanni
- Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta, Msida, Malta.,Neuroscience Division, School of Bioscience, Cardiff University, Museum Avenue, Cardiff, UK
| | - François David
- Cerebral dynamics, learning and plasticity, Integrative Neuroscience and Cognition Center - UMR 8002, Paris, France
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Gallagher MJ. Reconsidering Network Mechanisms in Absence Seizures: Unhitching the Wave Cart From the Spike Horse. Epilepsy Curr 2020; 21:64-66. [PMID: 34025278 PMCID: PMC7863300 DOI: 10.1177/1535759720975418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
[Box: see text]
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12
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Terlau J, Yang J, Khastkhodaei Z, Seidenbecher T, Luhmann HJ, Pape H, Lüttjohann A. Spike‐wave discharges in absence epilepsy: segregation of electrographic components reveals distinct pathways of seizure activity. J Physiol 2020; 598:2397-2414. [DOI: 10.1113/jp279483] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 02/24/2020] [Indexed: 12/15/2022] Open
Affiliation(s)
- Jonas Terlau
- Institute of Physiology IWestfälische Wilhelms University Münster Münster Germany
| | - Jenq‐Wei Yang
- Institute of PhysiologyUniversity Medical Center of the Johannes Gutenberg University Mainz Mainz Germany
| | - Zeinab Khastkhodaei
- Institute of PhysiologyUniversity Medical Center of the Johannes Gutenberg University Mainz Mainz Germany
| | - Thomas Seidenbecher
- Institute of Physiology IWestfälische Wilhelms University Münster Münster Germany
| | - Heiko J. Luhmann
- Institute of PhysiologyUniversity Medical Center of the Johannes Gutenberg University Mainz Mainz Germany
| | - Hans‐Christian Pape
- Institute of Physiology IWestfälische Wilhelms University Münster Münster Germany
| | - Annika Lüttjohann
- Institute of Physiology IWestfälische Wilhelms University Münster Münster Germany
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13
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Smyk MK, van Luijtelaar G. Circadian Rhythms and Epilepsy: A Suitable Case for Absence Epilepsy. Front Neurol 2020; 11:245. [PMID: 32411068 PMCID: PMC7198737 DOI: 10.3389/fneur.2020.00245] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 03/13/2020] [Indexed: 11/16/2022] Open
Abstract
Many physiological processes such as sleep, hormonal secretion, or thermoregulation, are expressed as daily rhythms orchestrated by the circadian timing system. A powerful internal clock mechanism ensures proper synchronization of vital functions within an organism on the one hand, and between the organism and the external environment on the other. Some of the pathological processes developing in the brain and body are subjected to circadian modulation as well. Epilepsy is one of the conditions which symptoms often worsen at a very specific time of a day. Variation in peak occurrence depends on the syndrome and localization of the epileptic focus. Moreover, the timing of some types of seizures is closely related to the sleep-wake cycle, one of the most prominent circadian rhythms. This review focuses on childhood absence epilepsy (CAE), a genetic generalized epilepsy syndrome, in which both, the circadian and sleep influences play a significant role in manifestation of symptoms. Human and animal studies report rhythmical occurrence of spike-wave discharges (SWDs), an EEG hallmark of CAE. The endogenous nature of the SWDs rhythm has been confirmed experimentally in a genetic animal model of the disease, rats of the WAG/Rij strain. Well-known detrimental effects of circadian misalignment were demonstrated to impact the severity of ongoing epileptic activity. SWDs are vigilance-dependent in both humans and animal models, occurring most frequently during passive behavioral states and light slow-wave sleep. The relationship with the sleep-wake cycle seems to be bidirectional, while sleep shapes the rhythm of seizures, epileptic phenotype changes sleep architecture. Circadian factors and the sleep-wake states dependency have a potential as add-ons in seizures' forecasting. Stability of the rhythm of recurrent seizures in individual patients has been already used as a variable which refines existing algorithms for seizures' prediction. On the other hand, apart from successful pharmacological approach, circadian hygiene including sufficient sleep and avoidance of internal desynchronization or sleep loss, may be beneficial for patients with epilepsy in everyday management of seizures.
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Affiliation(s)
- Magdalena K Smyk
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, Netherlands
| | - Gilles van Luijtelaar
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
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14
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Kögel J, Jox RJ, Friedrich O. What is it like to use a BCI? - insights from an interview study with brain-computer interface users. BMC Med Ethics 2020; 21:2. [PMID: 31906947 PMCID: PMC6945485 DOI: 10.1186/s12910-019-0442-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 12/23/2019] [Indexed: 11/25/2022] Open
Abstract
Background The neurotechnology behind brain-computer interfaces (BCIs) raises various ethical questions. The ethical literature has pinpointed several issues concerning safety, autonomy, responsibility and accountability, psychosocial identity, consent, privacy and data security. This study aims to assess BCI users’ experiences, self-observations and attitudes in their own right and looks for social and ethical implications. Methods We conducted nine semi-structured interviews with BCI users, who used the technology for medical reasons. The transcribed interviews were analyzed according to the Grounded Theory coding method. Results BCI users perceive themselves as active operators of a technology that offers them social participation and impacts their self-definition. Each of these aspects bears its own opportunities and risks. BCIs can contribute to retaining or regaining human capabilities. At the same time, BCI use contains elements that challenge common experiences, for example when the technology is in conflict with the affective side of BCI users. The potential benefits of BCIs are regarded as outweighing the risks in that BCI use is considered to promote valuable qualities and capabilities. BCI users appreciate the opportunity to regain lost capabilities as well as to gain new ones. Conclusions BCI users appreciate the technology for various reasons. The technology is highly appreciated in cases where it is beneficial in terms of agency, participation and self-definitions. Rather than questioning human nature, the technology can retain and restore characteristics and abilities which enrich our lives.
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Affiliation(s)
- Johannes Kögel
- Institute of Ethics, History and Theory of Medicine, LMU Munich, Lessingstr. 2, 80336, Munich, Germany.
| | - Ralf J Jox
- Clinical Ethics Unit and Institute of Humanities in Medicine, Lausanne University Hospital and Faculty of Biology and Medicine, University of Lausanne, Avenue de Provence 82, CH-1007, Lausanne, Switzerland
| | - Orsolya Friedrich
- Institute of Philosophy, Faculty of Cultural and Social Sciences, FernUniversität in Hagen, Universitätsstr. 33, 58097, Hagen, Germany
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15
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Pisarchik AN, Maksimenko VA, Hramov AE. From Novel Technology to Novel Applications: Comment on "An Integrated Brain-Machine Interface Platform With Thousands of Channels" by Elon Musk and Neuralink. J Med Internet Res 2019; 21:e16356. [PMID: 31674923 PMCID: PMC6914250 DOI: 10.2196/16356] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 10/16/2019] [Accepted: 10/20/2019] [Indexed: 01/20/2023] Open
Affiliation(s)
- Alexander N Pisarchik
- Center for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, Madrid, Spain.,Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russian Federation
| | - Vladimir A Maksimenko
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russian Federation
| | - Alexander E Hramov
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russian Federation
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16
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Hramov AE, Maksimenko V, Koronovskii A, Runnova AE, Zhuravlev M, Pisarchik AN, Kurths J. Percept-related EEG classification using machine learning approach and features of functional brain connectivity. CHAOS (WOODBURY, N.Y.) 2019; 29:093110. [PMID: 31575147 DOI: 10.1063/1.5113844] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Accepted: 08/08/2019] [Indexed: 06/10/2023]
Abstract
Machine learning is a promising approach for electroencephalographic (EEG) trials classification. Its efficiency is largely determined by the feature extraction and selection techniques reducing the dimensionality of input data. Dimensionality reduction is usually implemented via the mathematical approaches (e.g., principal component analysis, linear discriminant analysis, etc.) regardless of the origin of analyzed data. We hypothesize that since EEG features are determined by certain neurophysiological processes, they should have distinctive characteristics in spatiotemporal domain. If so, it is possible to specify the set of EEG principal features based on the prior knowledge about underlying neurophysiological processes. To test this hypothesis, we consider the classification of EEG trials related to the perception of ambiguous visual stimuli. We observe that EEG features, underlying the different ambiguous stimuli interpretations, are defined by the network properties of neuronal activity. Having analyzed functional neural interactions, we specify the brain area in which neural network architecture exhibits differences for different classes of EEG trials. We optimize the feedforward multilayer perceptron and develop a strategy for the training set selection to maximize the classification accuracy, being 85% when all channels are used. The revealed localization of the percept-related features allows about 95% accuracy, when the number of channels is reduced up to 90%. Obtained results can be used for classification of EEG trials associated with more complex cognitive tasks. Taking into account that cognitive activity is subserved by a distributed functional cortical network, its topological properties have to be considered when selecting optimal features for EEG trial classification.
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Affiliation(s)
- Alexander E Hramov
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Innopolis, The Republic of Tatarstan, Russia
| | - Vladimir Maksimenko
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Innopolis, The Republic of Tatarstan, Russia
| | - Alexey Koronovskii
- Faculty of Nonlinear Processes, Saratov State University, 410012 Saratov, Russia
| | - Anastasiya E Runnova
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Innopolis, The Republic of Tatarstan, Russia
| | - Maxim Zhuravlev
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Innopolis, The Republic of Tatarstan, Russia
| | - Alexander N Pisarchik
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Innopolis, The Republic of Tatarstan, Russia
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany
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Tangwiriyasakul C, Perani S, Centeno M, Yaakub SN, Abela E, Carmichael DW, Richardson MP. Dynamic brain network states in human generalized spike-wave discharges. Brain 2019; 141:2981-2994. [PMID: 30169608 PMCID: PMC6158757 DOI: 10.1093/brain/awy223] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2018] [Accepted: 07/15/2018] [Indexed: 12/21/2022] Open
Abstract
Generalized spike-wave discharges in idiopathic generalized epilepsy are conventionally assumed to have abrupt onset and offset. However, in rodent models, discharges emerge during a dynamic evolution of brain network states, extending several seconds before and after the discharge. In human idiopathic generalized epilepsy, simultaneous EEG and functional MRI shows cortical regions may be active before discharges, and network connectivity around discharges may not be normal. Here, in human idiopathic generalized epilepsy, we investigated whether generalized spike-wave discharges emerge during a dynamic evolution of brain network states. Using EEG-functional MRI, we studied 43 patients and 34 healthy control subjects. We obtained 95 discharges from 20 patients. We compared data from patients with discharges with data from patients without discharges and healthy controls. Changes in MRI (blood oxygenation level-dependent) signal amplitude in discharge epochs were observed only at and after EEG onset, involving a sequence of parietal and frontal cortical regions then thalamus (P < 0.01, across all regions and measurement time points). Examining MRI signal phase synchrony as a measure of functional connectivity between each pair of 90 brain regions, we found significant connections (P < 0.01, across all connections and measurement time points) involving frontal, parietal and occipital cortex during discharges, and for 20 s after EEG offset. This network prominent during discharges showed significantly low synchrony (below 99% confidence interval for synchrony in this network in non-discharge epochs in patients) from 16 s to 10 s before discharges, then ramped up steeply to a significantly high level of synchrony 2 s before discharge onset. Significant connections were seen in a sensorimotor network in the minute before discharge onset. This network also showed elevated synchrony in patients without discharges compared to healthy controls (P = 0.004). During 6 s prior to discharges, additional significant connections to this sensorimotor network were observed, involving prefrontal and precuneus regions. In healthy subjects, significant connections involved a posterior cortical network. In patients with discharges, this posterior network showed significantly low synchrony during the minute prior to discharge onset. In patients without discharges, this network showed the same level of synchrony as in healthy controls. Our findings suggest persistently high sensorimotor network synchrony, coupled with transiently (at least 1 min) low posterior network synchrony, may be a state predisposing to generalized spike-wave discharge onset. Our findings also show that EEG onset and associated MRI signal amplitude change is embedded in a considerably longer period of evolving brain network states before and after discharge events.
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Affiliation(s)
- Chayanin Tangwiriyasakul
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
| | - Suejen Perani
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK.,Developmental Imaging and Biophysics Section, Developmental Neurosciences Program, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Maria Centeno
- Developmental Imaging and Biophysics Section, Developmental Neurosciences Program, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Siti Nurbaya Yaakub
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
| | - Eugenio Abela
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
| | - David W Carmichael
- Developmental Imaging and Biophysics Section, Developmental Neurosciences Program, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Mark P Richardson
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK.,King's College Hospital, London, UK
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Can absence seizures be predicted by vigilance states?: Advanced analysis of sleep-wake states and spike-wave discharges' occurrence in rats. Epilepsy Behav 2019; 96:200-209. [PMID: 31153123 DOI: 10.1016/j.yebeh.2019.04.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 03/27/2019] [Accepted: 04/08/2019] [Indexed: 01/14/2023]
Abstract
Spike-wave discharges (SWDs) are the main manifestation of absence epilepsy. Their occurrence is dependent on the behavioral state, and they preferentially occur during unstable vigilance periods. The present study investigated whether the occurrence of SWDs can be predicted by the preceding behavioral state and whether this relationship is different between the light and the dark phases of the 24-h day. Twenty-four-hour (12:12 light/dark phases) electroencephalographic (EEG) recordings of 12 Wistar Albino Glaxo, originally bred in Rijswijk (WAG/Rij) rats, a well-known genetic model of absence epilepsy, were analyzed and transformed into sequences of 2-s length intervals of the following 6 possible states: active wakefulness (AW), passive wakefulness (PW), deep slow-wave sleep (DSWS), light slow-wave sleep (LSWS), rapid eye movement (REM) sleep, and SWDs, given discrete series of categorical data. Probabilities of all transitions between states and Shannon entropy of transitions were calculated for the light and dark phases separately and statistically analyzed. Common differences between the light and the dark phases were found regarding the time spent in AW, LSWS, DSWS, and SWDs. The most probable transitions were that AW was preceded and followed by PW and vice versa regardless of the phase of the photoperiod. A similar relationship was found for light and deep slow-wave sleep. The most probable transitions to and from SWDs were AW and LSWS, respectively, with these transition likelihoods being consistent across both circadian phases. The second most probable transitions around SWDs appeared more variable between light and dark. During the light phase, SWDs occurred around PW and participated exclusively in sleep initiation; in the dark phase, SWDs were seen on both, ascending and descending steps towards and from sleep. Conditional Shannon entropy showed that AW and DSWS are the most predictable events, while the possible prediction horizon of SWDs is not larger than 4 s and despite the higher occurrence of SWDs in the dark phase, did not differ between phases. It can be concluded that although SWDs show a stable, strong circadian rhythm with a peak in number during the dark phase, their occurrence cannot be reliably predicted by the preceding behavioral state, except at a very short time base.
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19
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Ge Y, Cao Y, Yi G, Han C, Qin Y, Wang J, Che Y. Robust closed-loop control of spike-and-wave discharges in a thalamocortical computational model of absence epilepsy. Sci Rep 2019; 9:9093. [PMID: 31235838 PMCID: PMC6591255 DOI: 10.1038/s41598-019-45639-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 06/07/2019] [Indexed: 01/24/2023] Open
Abstract
In this paper, we investigate the abatement of spike-and-wave discharges in a thalamocortical model using a closed-loop brain stimulation method. We first explore the complex states and various transitions in the thalamocortical computational model of absence epilepsy by using bifurcation analysis. We demonstrate that the Hopf and double cycle bifurcations are the key dynamical mechanisms of the experimental observed bidirectional communications during absence seizures through top-down cortical excitation and thalamic feedforward inhibition. Then, we formulate the abatement of epileptic seizures to a closed-loop tracking control problem. Finally, we propose a neural network based sliding mode feedback control system to drive the dynamics of pathological cortical area to track the desired normal background activities. The control system is robust to uncertainties and disturbances, and its stability is guaranteed by Lyapunov stability theorem. Our results suggest that the seizure abatement can be modeled as a tracking control problem and solved by a robust closed-loop control method, which provides a promising brain stimulation strategy.
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Affiliation(s)
- Yafang Ge
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, 300072, P. R. China
| | - Yuzhen Cao
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, 300072, P. R. China
| | - Guosheng Yi
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, P. R. China
| | - Chunxiao Han
- Tianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, 300222, P. R. China.
| | - Yingmei Qin
- Tianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, 300222, P. R. China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, P. R. China.
| | - Yanqiu Che
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, 17033, USA. .,Center for Neural Engineering, Penn State, University Park, PA, 16802, USA.
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20
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Statistical Properties and Predictability of Extreme Epileptic Events. Sci Rep 2019; 9:7243. [PMID: 31076609 PMCID: PMC6510789 DOI: 10.1038/s41598-019-43619-3] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Accepted: 04/26/2019] [Indexed: 01/03/2023] Open
Abstract
The use of extreme events theory for the analysis of spontaneous epileptic brain activity is a relevant multidisciplinary problem. It allows deeper understanding of pathological brain functioning and unraveling mechanisms underlying the epileptic seizure emergence along with its predictability. The latter is a desired goal in epileptology which might open the way for new therapies to control and prevent epileptic attacks. With this goal in mind, we applied the extreme event theory for studying statistical properties of electroencephalographic (EEG) recordings of WAG/Rij rats with genetic predisposition to absence epilepsy. Our approach allowed us to reveal extreme events inherent in this pathological spiking activity, highly pronounced in a particular frequency range. The return interval analysis showed that the epileptic seizures exhibit a highly-structural behavior during the active phase of the spiking activity. Obtained results evidenced a possibility for early (up to 7 s) prediction of epileptic seizures based on consideration of EEG statistical properties.
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Abstract
Interictal stereotactic-EEG functional connectivity in refractory focal epilepsies Lagarde S, Roehri N, Lambert I, et al. Brain. 2018;141(10):2966-2980. doi:10.1093/brain/awy214 Drug-refractory focal epilepsies are network diseases associated with functional connectivity alterations both during ictal and interictal periods. A large majority of studies on the interictal/resting state have focused on functional magnetic resonance imaging (MRI)-based functional connectivity. Few studies have used electrophysiology, despite its high-temporal capacities. In particular, stereotactic-electroencephalogram (EEG) is highly suitable to study functional connectivity because it permits direct intracranial electrophysiological recordings with relative large-scale sampling. Most previous studies in stereotactic-EEG have been directed toward temporal lobe epilepsy, which does not represent the whole spectrum of drug-refractory epilepsies. The present study aims at filling this gap, investigating interictal functional connectivity alterations behind cortical epileptic organization and its association with postsurgical prognosis. To this purpose, we studied a large cohort of 59 patients with malformation of cortical development explored by stereotactic-EEG with a wide spatial sampling (76 distinct brain areas were recorded, median of 13.2 per patient). We computed functional connectivity using nonlinear correlation. We focused on 3 zones defined by stereotactic-EEG ictal activity: the epileptogenic zone (EZ), the propagation zone (PZ), and the noninvolved zone. First, we compared within-zone and between-zones functional connectivity. Second, we analyzed the directionality of functional connectivity between these zones. Third, we measured the associations between functional connectivity measures and clinical variables, especially postsurgical prognosis. Our study confirms that functional connectivity differs according to the zone under investigation. We found: (1) a gradual decrease in the within-zone functional connectivity with higher values for EZ and PZ, and lower for noninvolved zones; (2) preferential coupling between structures of the EZ; (3) preferential coupling between EZ and PZ; and (4) poorer postsurgical outcome in patients with higher functional connectivity of non-involved zone (within-noninvolved zone, between noninvolved zone, and PZ functional connectivity). Our work suggests that, even during the interictal state, functional connectivity is reinforced within epileptic cortices (EZ and PZ) with a gradual organization. Moreover, larger functional connectivity alterations, suggesting more diffuse disease, are associated with poorer postsurgical prognosis. This is consistent with computational studies suggesting that connectivity is crucial in order to model the spatiotemporal dynamics of seizures. Dynamic brain network states in human generalized spike-wave discharges Tangwiriyasakul C, Perani S, Centeno M, et al. Brain. 2018;141(10):2981-2994. Generalized spike-wave discharges in idiopathic generalized epilepsy are conventionally assumed to have abrupt onset and offset. However, in rodent models, discharges emerge during a dynamic evolution of brain network states, extending several seconds before and after the discharge. In human idiopathic generalized epilepsy, simultaneous EEG and functional MRI shows cortical regions may be active before discharges, and network connectivity around discharges may not be normal. Here, in human idiopathic generalized epilepsy, we investigated whether generalized spike-wave discharges emerge during a dynamic evolution of brain network states. Using EEG-functional MRI, we studied 43 patients and 34 healthy control subjects. We obtained 95 discharges from 20 patients. We compared data from patients with discharges with data from patients without discharges and healthy controls. Changes in MRI (blood oxygenation level dependent) signal amplitude in discharge epochs were observed only at and after EEG onset, involving a sequence of parietal and frontal cortical regions then thalamus (P < .01, across all regions and measurement time points). Examining MRI signal phase synchrony as a measure of functional connectivity between each pair of 90 brain regions, we found significant connections (P < .01, across all connections and measurement time points) involving frontal, parietal and occipital cortex during discharges, and for 20 seconds after EEG offset. This network prominent during discharges showed significantly low synchrony (below 99% confidence interval for synchrony in this network in nondischarge epochs in patients) from 16 seconds to 10 seconds before discharges, then ramped up steeply to a significantly high level of synchrony 2 seconds before discharge onset. Significant connections were seen in a sensorimotor network in the minute before discharge onset. This network also showed elevated synchrony in patients without discharges compared to healthy controls (P = .004). During 6 seconds prior to discharges, additional significant connections to this sensorimotor network were observed, involving prefrontal, and precuneus regions. In healthy subjects, significant connections involved a posterior cortical network. In patients with discharges, this posterior network showed significantly low synchrony during the minute prior to discharge onset. In patients without discharges, this network showed the same level of synchrony as in healthy controls. Our findings suggest persistently high sensorimotor network synchrony, coupled with transiently (at least 1 minute) low posterior network synchrony, may be a state predisposing to generalized spike-wave discharge onset. Our findings also show that EEG onset and associated MRI signal amplitude change is embedded in a considerably longer period of evolving brain network states before and after discharge events.
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Kögel J, Schmid JR, Jox RJ, Friedrich O. Using brain-computer interfaces: a scoping review of studies employing social research methods. BMC Med Ethics 2019; 20:18. [PMID: 30845952 PMCID: PMC6407281 DOI: 10.1186/s12910-019-0354-1] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 02/22/2019] [Indexed: 12/11/2022] Open
Abstract
Background The rapid expansion of research on Brain-Computer Interfaces (BCIs) is not only due to the promising solutions offered for persons with physical impairments. There is also a heightened need for understanding BCIs due to the challenges regarding ethics presented by new technology, especially in its impact on the relationship between man and machine. Here we endeavor to present a scoping review of current studies in the field to gain insight into the complexity of BCI use. By examining studies related to BCIs that employ social research methods, we seek to demonstrate the multitude of approaches and concerns from various angles in considering the social and human impact of BCI technology. Methods For this scoping review of research on BCIs’ social and ethical implications, we systematically analyzed six databases, encompassing the fields of medicine, psychology, and the social sciences, in order to identify empirical studies on BCIs. The search yielded 73 publications that employ quantitative, qualitative, or mixed methods. Results Of the 73 publications, 71 studies address the user perspective. Some studies extend to consideration of other BCI stakeholders such as medical technology experts, caregivers, or health care professionals. The majority of the studies employ quantitative methods. Recurring themes across the studies examined were general user opinion towards BCI, central technical or social issues reported, requests/demands made by users of the technology, the potential/future of BCIs, and ethical aspects of BCIs. Conclusions Our findings indicate that while technical aspects of BCIs such as usability or feasibility are being studied extensively, comparatively little in-depth research has been done on the self-image and self-experience of the BCI user. In general there is also a lack of focus or examination of the caregiver’s perspective. Electronic supplementary material The online version of this article (10.1186/s12910-019-0354-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Johannes Kögel
- Institute of Ethics, History and Theory of Medicine, LMU Munich, Lessingstr. 2, D-80336, Munich, Germany.
| | - Jennifer R Schmid
- Institute of Ethics, History and Theory of Medicine, LMU Munich, Lessingstr. 2, D-80336, Munich, Germany
| | - Ralf J Jox
- Institute of Ethics, History and Theory of Medicine, LMU Munich, Lessingstr. 2, D-80336, Munich, Germany
| | - Orsolya Friedrich
- Institute of Ethics, History and Theory of Medicine, LMU Munich, Lessingstr. 2, D-80336, Munich, Germany
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Maksimenko VA, Hramov AE, Frolov NS, Lüttjohann A, Nedaivozov VO, Grubov VV, Runnova AE, Makarov VV, Kurths J, Pisarchik AN. Increasing Human Performance by Sharing Cognitive Load Using Brain-to-Brain Interface. Front Neurosci 2018; 12:949. [PMID: 30631262 PMCID: PMC6315120 DOI: 10.3389/fnins.2018.00949] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Accepted: 11/29/2018] [Indexed: 11/13/2022] Open
Abstract
Brain-computer interfaces (BCIs) attract a lot of attention because of their ability to improve the brain's efficiency in performing complex tasks using a computer. Furthermore, BCIs can increase human's performance not only due to human-machine interactions, but also thanks to an optimal distribution of cognitive load among all members of a group working on a common task, i.e., due to human-human interaction. The latter is of particular importance when sustained attention and alertness are required. In every day practice, this is a common occurrence, for example, among office workers, pilots of a military or a civil aircraft, power plant operators, etc. Their routinely work includes continuous monitoring of instrument readings and implies a heavy cognitive load due to processing large amounts of visual information. In this paper, we propose a brain-to-brain interface (BBI) which estimates brain states of every participant and distributes a cognitive load among all members of the group accomplishing together a common task. The BBI allows sharing the whole workload between all participants depending on their current cognitive performance estimated from their electrical brain activity. We show that the team efficiency can be increased due to redistribution of the work between participants so that the most difficult workload falls on the operator who exhibits maximum performance. Finally, we demonstrate that the human-to-human interaction is more efficient in the presence of a certain delay determined by brain rhythms. The obtained results are promising for the development of a new generation of communication systems based on neurophysiological brain activity of interacting people. Such BBIs will distribute a common task between all group members according to their individual physical conditions.
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Affiliation(s)
- Vladimir A Maksimenko
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Alexander E Hramov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Nikita S Frolov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | | | - Vladimir O Nedaivozov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Vadim V Grubov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Anastasia E Runnova
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Vladimir V Makarov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research, Potsdam, Germany.,Department of Physics, Humboldt University, Berlin, Germany.,Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, United Kingdom
| | - Alexander N Pisarchik
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia.,Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain
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Colominas MA, Jomaa MESH, Jrad N, Humeau-Heurtier A, Van Bogaert P. Time-Varying Time–Frequency Complexity Measures for Epileptic EEG Data Analysis. IEEE Trans Biomed Eng 2018; 65:1681-1688. [DOI: 10.1109/tbme.2017.2761982] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Hramov AE, Maksimenko VA, Pchelintseva SV, Runnova AE, Grubov VV, Musatov VY, Zhuravlev MO, Koronovskii AA, Pisarchik AN. Classifying the Perceptual Interpretations of a Bistable Image Using EEG and Artificial Neural Networks. Front Neurosci 2017; 11:674. [PMID: 29255403 PMCID: PMC5722852 DOI: 10.3389/fnins.2017.00674] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Accepted: 11/20/2017] [Indexed: 01/04/2023] Open
Abstract
In order to classify different human brain states related to visual perception of ambiguous images, we use an artificial neural network (ANN) to analyze multichannel EEG. The classifier built on the basis of a multilayer perceptron achieves up to 95% accuracy in classifying EEG patterns corresponding to two different interpretations of the Necker cube. The important feature of our classifier is that trained on one subject it can be used for the classification of EEG traces of other subjects. This result suggests the existence of common features in the EEG structure associated with distinct interpretations of bistable objects. We firmly believe that the significance of our results is not limited to visual perception of the Necker cube images; the proposed experimental approach and developed computational technique based on ANN can also be applied to study and classify different brain states using neurophysiological data recordings. This may give new directions for future research in the field of cognitive and pathological brain activity, and for the development of brain-computer interfaces.
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Affiliation(s)
- Alexander E Hramov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia.,Faculty of Nonlinear Processes, Saratov State University, Saratov, Russia
| | - Vladimir A Maksimenko
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Svetlana V Pchelintseva
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Anastasiya E Runnova
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Vadim V Grubov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Vyacheslav Yu Musatov
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia
| | - Maksim O Zhuravlev
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia.,Faculty of Nonlinear Processes, Saratov State University, Saratov, Russia
| | - Alexey A Koronovskii
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia.,Faculty of Nonlinear Processes, Saratov State University, Saratov, Russia
| | - Alexander N Pisarchik
- REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia.,Center for Biomedical Technology, Technical University of Madrid, Madrid, Spain
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Lüttjohann A. Disclosing hidden information in the electroencephalogram using advanced signal analytical techniques. J Physiol 2017; 595:7021-7022. [PMID: 28994124 PMCID: PMC5709326 DOI: 10.1113/jp275132] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Affiliation(s)
- Annika Lüttjohann
- Institute of Physiology IWestfälische Wilhelms University MünsterRobert‐Koch Str. 27a48149MünsterGermany
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