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Lesser RP, Webber WRS, Miglioretti DL. Pan-cortical electrophysiologic changes underlying attention. Sci Rep 2024; 14:2680. [PMID: 38302535 PMCID: PMC10834435 DOI: 10.1038/s41598-024-52717-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 01/23/2024] [Indexed: 02/03/2024] Open
Abstract
We previously reported that pan-cortical effects occur when cognitive tasks end afterdischarges. For this report, we analyzed wavelet cross-coherence changes during cognitive tasks used to terminate afterdischarges studying multiple time segments and multiple groups of inter-electrode-con distances. We studied 12 patients with intractable epilepsy, with 970 implanted electrode contacts, and 39,871 electrode contact combinations. When cognitive tasks ended afterdischarges, coherence varied similarly across the cortex throughout the tasks, but there were gradations with time, distance, and frequency: (1) They tended to progressively decrease relative to baseline with time and then to increase toward baseline when afterdischarges ended. (2) During most time segments, decreases from baseline were largest for the closest inter-contact distances, moderate for intermediate inter-contact distances, and smallest for the greatest inter-contact distances. With respect to our patients' intractable epilepsy, the changes found suggest that future therapies might treat regions beyond those closest to regions of seizure onset and treat later in a seizure's evolution. Similar considerations might apply to other disorders. Our findings also suggest that cognitive tasks can result in pan-cortical coherence changes that participate in underlying attention, perhaps complementing the better-known regional mechanisms.
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Affiliation(s)
- Ronald P Lesser
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
- Department of Neurological Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
| | - W R S Webber
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Diana L Miglioretti
- Department of Public Health Sciences, Davis, School of Medicine, University of California, Davis, CA, 95616, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA
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2
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Bröhl T, Rings T, Pukropski J, von Wrede R, Lehnertz K. The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1338864. [PMID: 38293249 PMCID: PMC10825060 DOI: 10.3389/fnetp.2023.1338864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024]
Abstract
Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.
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Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Jan Pukropski
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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3
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Baud MO, Proix T, Gregg NM, Brinkmann BH, Nurse ES, Cook MJ, Karoly PJ. Seizure forecasting: Bifurcations in the long and winding road. Epilepsia 2023; 64 Suppl 4:S78-S98. [PMID: 35604546 PMCID: PMC9681938 DOI: 10.1111/epi.17311] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/20/2022] [Accepted: 05/20/2022] [Indexed: 11/28/2022]
Abstract
To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.
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Affiliation(s)
- Maxime O Baud
- Sleep-Wake-Epilepsy Center, Center for Experimental Neurology, NeuroTec, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
- Wyss Center for Bio- and Neuro-Engineering, Geneva, Switzerland
| | - Timothée Proix
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nicholas M Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ewan S Nurse
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark J Cook
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Philippa J Karoly
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
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4
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Bröhl T, von Wrede R, Lehnertz K. Impact of biological rhythms on the importance hierarchy of constituents in time-dependent functional brain networks. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1237004. [PMID: 37705698 PMCID: PMC10497113 DOI: 10.3389/fnetp.2023.1237004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/09/2023] [Indexed: 09/15/2023]
Abstract
Biological rhythms are natural, endogenous cycles with period lengths ranging from less than 24 h (ultradian rhythms) to more than 24 h (infradian rhythms). The impact of the circadian rhythm (approximately 24 h) and ultradian rhythms on spectral characteristics of electroencephalographic (EEG) signals has been investigated for more than half a century. Yet, only little is known on how biological rhythms influence the properties of EEG-derived evolving functional brain networks. Here, we derive such networks from multiday, multichannel EEG recordings and use different centrality concepts to assess the time-varying importance hierarchy of the networks' vertices and edges as well as the various aspects of their structural integration in the network. We observe strong circadian and ultradian influences that highlight distinct subnetworks in the evolving functional brain networks. Our findings indicate the existence of a vital and fundamental subnetwork that is rather generally involved in ongoing brain activities during wakefulness and sleep.
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Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Center, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Center, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Center, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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5
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Abstract
Drug-resistant epilepsy is associated with poor health outcomes and increased economic burden. In the last three decades, various new antiseizure medications have been developed, but the proportion of people with drug-resistant epilepsy remains relatively unchanged. Developing strategies to address drug-resistant epilepsy is essential. Here, we define drug-resistant epilepsy and emphasize its relationship to the conceptualization of epilepsy as a symptom complex, delineate clinical risk factors, and characterize mechanisms based on current knowledge. We address the importance of ruling out pseudoresistance and consider the impact of nonadherence on determining whether an individual has drug-resistant epilepsy. We then review the principles of epilepsy drug therapy and briefly touch upon newly approved and experimental antiseizure medications.
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6
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Pegg EJ, McKavanagh A, Bracewell RM, Chen Y, Das K, Denby C, Kreilkamp BAK, Laiou P, Marson A, Mohanraj R, Taylor JR, Keller SS. Functional network topology in drug resistant and well-controlled idiopathic generalized epilepsy: a resting state functional MRI study. Brain Commun 2021; 3:fcab196. [PMID: 34514400 PMCID: PMC8417840 DOI: 10.1093/braincomms/fcab196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/18/2021] [Indexed: 11/23/2022] Open
Abstract
Despite an increasing number of drug treatment options for people with idiopathic generalized epilepsy (IGE), drug resistance remains a significant issue and the mechanisms underlying it remain poorly understood. Previous studies have largely focused on potential cellular or genetic explanations for drug resistance. However, epilepsy is understood to be a network disorder and there is a growing body of literature suggesting altered topology of large-scale resting networks in people with epilepsy compared with controls. We hypothesize that network alterations may also play a role in seizure control. The aim of this study was to compare resting state functional network structure between well-controlled IGE (WC-IGE), drug resistant IGE (DR-IGE) and healthy controls. Thirty-three participants with IGE (10 with WC-IGE and 23 with DR-IGE) and 34 controls were included. Resting state functional MRI networks were constructed using the Functional Connectivity Toolbox (CONN). Global graph theoretic network measures of average node strength (an equivalent measure to mean degree in a network that is fully connected), node strength distribution variance, characteristic path length, average clustering coefficient, small-world index and average betweenness centrality were computed. Graphs were constructed separately for positively weighted connections and for absolute values. Individual nodal values of strength and betweenness centrality were also measured and ‘hub nodes’ were compared between groups. Outcome measures were assessed across the three groups and between both groups with IGE and controls. The IGE group as a whole had a higher average node strength, characteristic path length and average betweenness centrality. There were no clear differences between groups according to seizure control. Outcome metrics were sensitive to whether negatively correlated connections were included in network construction. There were no clear differences in the location of ‘hub nodes’ between groups. The results suggest that, irrespective of seizure control, IGE interictal network topology is more regular and has a higher global connectivity compared to controls, with no alteration in hub node locations. These alterations may produce a resting state network that is more vulnerable to transitioning to the seizure state. It is possible that the lack of apparent influence of seizure control on network topology is limited by challenges in classifying drug response. It is also demonstrated that network topological features are influenced by the sign of connectivity weights and therefore future methodological work is warranted to account for anticorrelations in graph theoretic studies.
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Affiliation(s)
- Emily J Pegg
- Department of Neurology, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Salford, UK.,Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Andrea McKavanagh
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | | | - Yachin Chen
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Kumar Das
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | | | - Barbara A K Kreilkamp
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,Department of Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Petroula Laiou
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Anthony Marson
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Rajiv Mohanraj
- Department of Neurology, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Salford, UK.,Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Jason R Taylor
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.,Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
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7
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Perveen N, Ashraf W, Alqahtani F, Fawad Rasool M, Samad N, Imran I. Temporal Lobe Epilepsy: What do we understand about protein alterations? Chem Biol Drug Des 2021; 98:377-394. [PMID: 34132061 DOI: 10.1111/cbdd.13858] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/22/2021] [Accepted: 04/18/2021] [Indexed: 01/19/2023]
Abstract
During neuronal diseases, neuronal proteins get disturbed due to changes in the connections of neurons. As a result, neuronal proteins get disturbed and cause epilepsy. At the genetic level, many mutations may take place in proteins like axon guidance proteins, leucine-rich glioma inactivated 1 protein, microtubular protein, pore-forming, chromatin remodeling, and chemokine proteins which may lead toward temporal lobe epilepsy. These proteins can be targeted in the future for the treatment purpose of epilepsy. Novel avenues can be developed for therapeutic interventions by these new insights.
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Affiliation(s)
- Nadia Perveen
- Department of Pharmacology, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
| | - Waseem Ashraf
- Department of Pharmacology, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
| | - Faleh Alqahtani
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Muhammad Fawad Rasool
- Department of Pharmacy Practice, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
| | - Noreen Samad
- Department of Biochemistry, Faculty of Science, Bahauddin Zakariya University, Multan, Pakistan
| | - Imran Imran
- Department of Pharmacology, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
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8
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Saminu S, Xu G, Shuai Z, Abd El Kader I, Jabire AH, Ahmed YK, Karaye IA, Ahmad IS. A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal. Brain Sci 2021; 11:668. [PMID: 34065473 PMCID: PMC8160878 DOI: 10.3390/brainsci11050668] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 05/14/2021] [Accepted: 05/16/2021] [Indexed: 02/07/2023] Open
Abstract
The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.
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Affiliation(s)
- Sani Saminu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
- Biomedical Engineering Department, University of Ilorin, P.M.B 1515, Ilorin 240003, Nigeria;
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
| | - Zhang Shuai
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
| | - Isselmou Abd El Kader
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
| | - Adamu Halilu Jabire
- Department of Electrical and Electronics Engineering, Taraba State University, Jalingo 660242, Nigeria;
| | - Yusuf Kola Ahmed
- Biomedical Engineering Department, University of Ilorin, P.M.B 1515, Ilorin 240003, Nigeria;
| | - Ibrahim Abdullahi Karaye
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
| | - Isah Salim Ahmad
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; (Z.S.); (I.A.E.K.); (I.A.K.); (I.S.A.)
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9
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Juvale IIA, Che Has AT. Possible interplay between the theories of pharmacoresistant epilepsy. Eur J Neurosci 2020; 53:1998-2026. [PMID: 33306252 DOI: 10.1111/ejn.15079] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/22/2020] [Accepted: 12/04/2020] [Indexed: 02/06/2023]
Abstract
Epilepsy is one of the oldest known neurological disorders and is characterized by recurrent seizure activity. It has a high incidence rate, affecting a broad demographic in both developed and developing countries. Comorbid conditions are frequent in patients with epilepsy and have detrimental effects on their quality of life. Current management options for epilepsy include the use of anti-epileptic drugs, surgery, or a ketogenic diet. However, more than 30% of patients diagnosed with epilepsy exhibit drug resistance to anti-epileptic drugs. Further, surgery and ketogenic diets do little to alleviate the symptoms of patients with pharmacoresistant epilepsy. Thus, there is an urgent need to understand the underlying mechanisms of pharmacoresistant epilepsy to design newer and more effective anti-epileptic drugs. Several theories of pharmacoresistant epilepsy have been suggested over the years, the most common being the gene variant hypothesis, network hypothesis, multidrug transporter hypothesis, and target hypothesis. In our review, we discuss the main theories of pharmacoresistant epilepsy and highlight a possible interconnection between their mechanisms that could lead to the development of novel therapies for pharmacoresistant epilepsy.
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Affiliation(s)
- Iman Imtiyaz Ahmed Juvale
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Malaysia
| | - Ahmad Tarmizi Che Has
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Malaysia
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10
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Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities. Sci Rep 2020; 10:21921. [PMID: 33318564 PMCID: PMC7736584 DOI: 10.1038/s41598-020-78899-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 11/30/2020] [Indexed: 01/01/2023] Open
Abstract
Previous research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.
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11
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Zaveri HP, Schelter B, Schevon CA, Jiruska P, Jefferys JGR, Worrell G, Schulze-Bonhage A, Joshi RB, Jirsa V, Goodfellow M, Meisel C, Lehnertz K. Controversies on the network theory of epilepsy: Debates held during the ICTALS 2019 conference. Seizure 2020; 78:78-85. [PMID: 32272333 DOI: 10.1016/j.seizure.2020.03.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 03/13/2020] [Accepted: 03/15/2020] [Indexed: 12/21/2022] Open
Abstract
Debates on six controversial topics on the network theory of epilepsy were held during two debate sessions, as part of the International Conference for Technology and Analysis of Seizures, 2019 (ICTALS 2019) convened at the University of Exeter, UK, September 2-5 2019. The debate topics were (1) From pathologic to physiologic: is the epileptic network part of an existing large-scale brain network? (2) Are micro scale recordings pertinent for defining the epileptic network? (3) From seconds to years: do we need all temporal scales to define an epileptic network? (4) Is it necessary to fully define the epileptic network to control it? (5) Is controlling seizures sufficient to control the epileptic network? (6) Does the epileptic network want to be controlled? This article, written by the organizing committee for the debate sessions and the debaters, summarizes the arguments presented during the debates on these six topics.
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Affiliation(s)
- Hitten P Zaveri
- Department of Neurology, Yale University, New Haven, CT 06520, USA
| | - Björn Schelter
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3UE, UK
| | | | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - John G R Jefferys
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic; Department of Pharmacology, University of Oxford, Oxford OX1 3QT, UK
| | - Gregory Worrell
- Mayo Systems Electrophysiology Laboratory, Departments of Neurology and Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Rasesh B Joshi
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, Aix Marseille University, Marseille, France
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, UK; Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK; EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK
| | - Christian Meisel
- Department of Neurology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA; Department of Neurology, University Clinic Carl Gustav Carus, Fetscherstrasse 74, 01307 Dresden, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn, Venusberg Campus 1, 53127 Bonn, Germany; Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Str. 7, 53175 Bonn, Germany.
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12
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Kuhlmann L, Lehnertz K, Richardson MP, Schelter B, Zaveri HP. Seizure prediction - ready for a new era. Nat Rev Neurol 2019; 14:618-630. [PMID: 30131521 DOI: 10.1038/s41582-018-0055-2] [Citation(s) in RCA: 177] [Impact Index Per Article: 35.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Epilepsy is a common disorder characterized by recurrent seizures. An overwhelming majority of people with epilepsy regard the unpredictability of seizures as a major issue. More than 30 years of international effort have been devoted to the prediction of seizures, aiming to remove the burden of unpredictability and to couple novel, time-specific treatment to seizure prediction technology. A highly influential review published in 2007 concluded that insufficient evidence indicated that seizures could be predicted. Since then, several advances have been made, including successful prospective seizure prediction using intracranial EEG in a small number of people in a trial of a real-time seizure prediction device. In this Review, we examine advances in the field, including EEG databases, seizure prediction competitions, the prospective trial mentioned and advances in our understanding of the mechanisms of seizures. We argue that these advances, together with statistical evaluations, set the stage for a resurgence in efforts towards the development of seizure prediction methodologies. We propose new avenues of investigation involving a synergy between mechanisms, models, data, devices and algorithms and refine the existing guidelines for the development of seizure prediction technology to instigate development of a solution that removes the burden of the unpredictability of seizures.
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Affiliation(s)
- Levin Kuhlmann
- Centre for Human Psychopharmacology, Swinburne University of Technology, Melbourne, Victoria, Australia.,Department of Medicine - St. Vincent's, The University of Melbourne, Parkville, Victoria, Australia.,Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn, Bonn, Germany. .,Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany.
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Björn Schelter
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK
| | - Hitten P Zaveri
- Department of Neurology, Yale University, New Haven, CT, USA
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13
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Lesser RP, Webber WRS, Miglioretti DL, Pillai JJ, Agarwal S, Mori S, Morrison PF, Castagnola S, Lawal A, Lesser HJ. Cognitive effort decreases beta, alpha, and theta coherence and ends afterdischarges in human brain. Clin Neurophysiol 2019; 130:2169-2181. [PMID: 31399356 DOI: 10.1016/j.clinph.2019.07.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 07/11/2019] [Accepted: 07/12/2019] [Indexed: 12/22/2022]
Abstract
OBJECTIVE Mental activation has been reported to modify the occurrence of epileptiform activity. We studied its effect on afterdischarges. METHOD In 15 patients with implanted electrodes we presented cognitive tasks when afterdischarges occurred. We developed a wavelet cross-coherence function to analyze the electrocorticography before and after the tasks and compared findings when cognitive tasks did or did not result in afterdischarge termination. Six patients returned for functional MRI (fMRI) testing, using similar tasks. RESULTS Cognitive tasks often could terminate afterdischarges when direct abortive stimulation could not. Wavelet cross-coherence analysis showed that, when afterdischarges stopped, there was decreased coherence throughout the brain in the 7.13-22.53 Hz frequency ranges (p values 0.008-0.034). This occurred a) regardless of whether an area activated on fMRI and b) regardless of whether there were afterdischarges in the area. CONCLUSIONS It is known that cognitive tasks can alter localized or network synchronization. Our results show that they can change activity throughout the brain. These changes in turn can terminate localized epileptiform activity. SIGNIFICANCE Cognitive tasks result in diffuse brain changes that can modify focal brain activity. Combined with a seizure detection device, cognitive activation might provide a non-invasive method of terminating or modifying seizures.
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Affiliation(s)
- Ronald P Lesser
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
| | - W R S Webber
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Diana L Miglioretti
- Department of Public Health Sciences, University of California, Davis, School of Medicine, Davis, CA 95616, USA; Kaiser Permanente Washington Health Research Institute, Seattle, WA 98101, USA
| | - Jay J Pillai
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Shruti Agarwal
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Susumu Mori
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Peter F Morrison
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Stefano Castagnola
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Adeshola Lawal
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Helen J Lesser
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
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14
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Gummadavelli A, Zaveri HP, Spencer DD, Gerrard JL. Expanding Brain-Computer Interfaces for Controlling Epilepsy Networks: Novel Thalamic Responsive Neurostimulation in Refractory Epilepsy. Front Neurosci 2018; 12:474. [PMID: 30108472 PMCID: PMC6079216 DOI: 10.3389/fnins.2018.00474] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 06/22/2018] [Indexed: 01/01/2023] Open
Abstract
Seizures have traditionally been considered hypersynchronous excitatory events and epilepsy has been separated into focal and generalized epilepsy based largely on the spatial distribution of brain regions involved at seizure onset. Epilepsy, however, is increasingly recognized as a complex network disorder that may be distributed and dynamic. Responsive neurostimulation (RNS) is a recent technology that utilizes intracranial electroencephalography (EEG) to detect seizures and delivers stimulation to cortical and subcortical brain structures for seizure control. RNS has particular significance in the clinical treatment of medically refractory epilepsy and brain–computer interfaces in epilepsy. Closed loop RNS represents an important step forward to understand and target nodes in the seizure network. The thalamus is a central network node within several functional networks and regulates input to the cortex; clinically, several thalamic nuclei are safe and feasible targets. We highlight the network theory of epilepsy, potential targets for neuromodulation in epilepsy and the first reported use of RNS as a first generation brain–computer interface to detect and stimulate the centromedian intralaminar thalamic nucleus in a patient with bilateral cortical onset of seizures. We propose that advances in network analysis and neuromodulatory techniques using brain–computer interfaces will significantly improve outcomes in patients with epilepsy. There are numerous avenues of future direction in brain–computer interface devices including multi-modal sensors, flexible electrode arrays, multi-site targeting, and wireless communication.
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Affiliation(s)
- Abhijeet Gummadavelli
- Department of Neurosurgery, Yale University School of Medicine, Yale University, New Haven, CT, United States
| | - Hitten P Zaveri
- Department of Neurology, Yale University School of Medicine, Yale University, New Haven, CT, United States
| | - Dennis D Spencer
- Department of Neurosurgery, Yale University School of Medicine, Yale University, New Haven, CT, United States
| | - Jason L Gerrard
- Department of Neurosurgery, Yale University School of Medicine, Yale University, New Haven, CT, United States.,Department of Neuroscience, Yale University School of Medicine, Yale University, New Haven, CT, United States
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15
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Sanz-Garcia A, Rings T, Lehnertz K. Impact of type of intracranial EEG sensors on link strengths of evolving functional brain networks. Physiol Meas 2018; 39:074003. [PMID: 29932428 DOI: 10.1088/1361-6579/aace94] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Objective and Approach: Investigating properties of evolving functional brain networks has become a valuable tool to characterize the complex dynamics of the epileptic brain. Such networks are usually derived from electroencephalograms (EEG) recorded with sensors implanted chronically into deeper structures of the brain and/or placed onto the cortex. It is still unclear, however, whether the use of different sensors for an identification of network nodes affects properties of functional brain networks. We address this question by investigating properties of links of such networks that we characterize by assessing interactions in multi-sensor, multi-day EEG data recorded from 49 epilepsy patients during presurgical evaluation. These data allow us to study the impact of different types of sensors together with the impact of various physiologic and pathophysiologic activities on the properties of links. MAIN RESULTS We observe that different types of sensors differently impact on spatial means and temporal fluctuations of link strengths. Moreover, the impact depends on the relative anatomical location of sensors with respect to location and extent of sources of the prevailing activities. SIGNIFICANCE Type and location of sensors should be considered when constructing networks.
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Affiliation(s)
- Ancor Sanz-Garcia
- Instituto de Investigacion Sanitaria, Hospital Universitario De La Princesa, C/Diego de Leon 62, 28006 Madrid, Spain
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16
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Lamar KMJ, Carvill GL. Chromatin Remodeling Proteins in Epilepsy: Lessons From CHD2-Associated Epilepsy. Front Mol Neurosci 2018; 11:208. [PMID: 29962935 PMCID: PMC6013553 DOI: 10.3389/fnmol.2018.00208] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 05/25/2018] [Indexed: 12/24/2022] Open
Abstract
The chromodomain helicase DNA-binding (CHD) family of proteins are ATP-dependent chromatin remodelers that contribute to the reorganization of chromatin structure and deposition of histone variants necessary to regulate gene expression. CHD proteins play an important role in neurodevelopment, as pathogenic variants in CHD1, CHD2, CHD4, CHD7 and CHD8 have been associated with a range of neurological phenotypes, including autism spectrum disorder (ASD), intellectual disability (ID) and epilepsy. Pathogenic variants in CHD2 are associated with developmental epileptic encephalopathy (DEE) in humans, however little is known about how these variants contribute to this disorder. Of the nine CHD family members, CHD2 is the only one that leads to a brain-restricted phenotype when disrupted in humans. This suggests that despite being expressed ubiquitously, CHD2 has a unique role in human brain development and function. In this review, we will discuss the phenotypic spectrum of patients with pathogenic variants in CHD2, current animal models of CHD2 deficiency, and the role of CHD2 in proliferation, neurogenesis, neuronal differentiation, chromatin remodeling and DNA-repair. We also consider how CHD2 depletion can affect each of these biological mechanisms and how these defects may underpin neurodevelopmental disorders including epilepsy.
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Affiliation(s)
- Kay-Marie J Lamar
- Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Gemma L Carvill
- Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
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17
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The roles of surgery and technology in understanding focal epilepsy and its comorbidities. Lancet Neurol 2018; 17:373-382. [DOI: 10.1016/s1474-4422(18)30031-0] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Revised: 01/12/2018] [Accepted: 01/16/2018] [Indexed: 01/21/2023]
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18
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Fan H, Wang Y, Wang H, Lai YC, Wang X. Autapses promote synchronization in neuronal networks. Sci Rep 2018; 8:580. [PMID: 29330551 PMCID: PMC5766500 DOI: 10.1038/s41598-017-19028-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 12/20/2017] [Indexed: 11/09/2022] Open
Abstract
Neurological disorders such as epileptic seizures are believed to be caused by neuronal synchrony. However, to ascertain the causal role of neuronal synchronization in such diseases through the traditional approach of electrophysiological data analysis remains a controversial, challenging, and outstanding problem. We offer an alternative principle to assess the physiological role of neuronal synchrony based on identifying structural anomalies in the underlying network and studying their impacts on the collective dynamics. In particular, we focus on autapses - time delayed self-feedback links that exist on a small fraction of neurons in the network, and investigate their impacts on network synchronization through a detailed stability analysis. Our main finding is that the proper placement of a small number of autapses in the network can promote synchronization significantly, providing the computational and theoretical bases for hypothesizing a high degree of synchrony in real neuronal networks with autapses. Our result that autapses, the shortest possible links in any network, can effectively modulate the collective dynamics provides also a viable strategy for optimal control of complex network dynamics at minimal cost.
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Affiliation(s)
- Huawei Fan
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, 710062, China
| | - Yafeng Wang
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, 710062, China
| | - Hengtong Wang
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, 710062, China
| | - Ying-Cheng Lai
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, 710062, China.,School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, Arizona, 85287, USA
| | - Xingang Wang
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, 710062, China.
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19
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Afra P, Hanrahan SJ, Kellis SS, House P. Non ictal onset zone: A window to ictal dynamics. EPILEPSY & BEHAVIOR CASE REPORTS 2017; 8:123-127. [PMID: 29204348 PMCID: PMC5707213 DOI: 10.1016/j.ebcr.2017.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 10/03/2017] [Accepted: 10/11/2017] [Indexed: 11/24/2022]
Abstract
The focal and network concepts of epilepsy present different aspects of electroclinical phenomenon of seizures. Here, we present a 23-year-old man undergoing surgical evaluation with left fronto-temporal electrocorticography (ECoG) and microelectrode-array (MEA) in the middle temporal gyrus (MTG). We compare action-potential (AP) and local field potentials (LFP) recorded from MEA with ECoG. Seizure onset in the mesial-temporal lobe was characterized by changes in the pattern of AP-firing without clear changes in LFP or ECoG in MTG. This suggests simultaneous analysis of neuronal activity in differing spatial scales and frequency ranges provide complementary insights into how focal and network neurophysiological activity contribute to ictal activity.
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20
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Dickten H, Porz S, Elger CE, Lehnertz K. Weighted and directed interactions in evolving large-scale epileptic brain networks. Sci Rep 2016; 6:34824. [PMID: 27708381 PMCID: PMC5052583 DOI: 10.1038/srep34824] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Accepted: 09/21/2016] [Indexed: 01/03/2023] Open
Abstract
Epilepsy can be regarded as a network phenomenon with functionally and/or structurally aberrant connections in the brain. Over the past years, concepts and methods from network theory substantially contributed to improve the characterization of structure and function of these epileptic networks and thus to advance understanding of the dynamical disease epilepsy. We extend this promising line of research and assess-with high spatial and temporal resolution and using complementary analysis approaches that capture different characteristics of the complex dynamics-both strength and direction of interactions in evolving large-scale epileptic brain networks of 35 patients that suffered from drug-resistant focal seizures with different anatomical onset locations. Despite this heterogeneity, we find that even during the seizure-free interval the seizure onset zone is a brain region that, when averaged over time, exerts strongest directed influences over other brain regions being part of a large-scale network. This crucial role, however, manifested by averaging on the population-sample level only - in more than one third of patients, strongest directed interactions can be observed between brain regions far off the seizure onset zone. This may guide new developments for individualized diagnosis, treatment and control.
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Affiliation(s)
- Henning Dickten
- Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn, Germany.,Helmholtz-Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany.,Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53175 Bonn, Germany
| | - Stephan Porz
- Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn, Germany.,Helmholtz-Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany
| | - Christian E Elger
- Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn, Germany.,Helmholtz-Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany.,Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53175 Bonn, Germany
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21
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Slowing Down of Recovery as Generic Risk Marker for Acute Severity Transitions in Chronic Diseases. Crit Care Med 2016; 44:601-6. [PMID: 26765499 DOI: 10.1097/ccm.0000000000001564] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
OBJECTIVE We propose a novel paradigm to predict acute attacks and exacerbations in chronic episodic disorders such as asthma, cardiac arrhythmias, migraine, epilepsy, and depression. A better generic understanding of acute transitions in chronic dynamic diseases is increasingly important in critical care medicine because of the higher prevalence and incidence of these chronic diseases in our aging societies. DATA SOURCES PubMed, Medline, and Web of Science. STUDY SELECTION We selected studies from biology and medicine providing evidence of slowing down after a perturbation as a warning signal for critical transitions. DATA EXTRACTION Recent work in ecology, climate, and systems biology has shown that slowing down of recovery upon perturbations can indicate loss of resilience across complex, nonlinear biologic systems that are approaching a tipping point. This observation is supported by the empiric studies in pathophysiology and controlled laboratory experiments with other living systems, which can flip from one state of clinical balance to a contrasting one. We discuss examples of such evidence in bodily functions such as blood pressure, heart rate, mood, and respiratory regulation when a tipping point for a transition is near. CONCLUSIONS We hypothesize that in a range of chronic episodic diseases, indicators of critical slowing down, such as rising variance and temporal correlation, may be used to assess the risk of attacks, exacerbations, and even mortality. Identification of such early warning signals over a range of diseases will enhance the understanding of why, how, and when attacks and exacerbations will strike and may thus improve disease management in critical care medicine.
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22
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Malali A, Chaitanya G, Gowda S, Majumdar K. Analysis of cortical rhythms in intracranial EEG by temporal difference operators during epileptic seizures. Biomed Signal Process Control 2016. [DOI: 10.1016/j.bspc.2016.01.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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23
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Abstract
Technological advances are dramatically advancing translational research in Epilepsy. Neurophysiology, imaging, and metadata are now recorded digitally in most centers, enabling quantitative analysis. Basic and translational research opportunities to use these data are exploding, but academic and funding cultures prevent this potential from being realized. Research on epileptogenic networks, antiepileptic devices, and biomarkers could progress rapidly if collaborative efforts to digest this "big neuro data" could be organized. Higher temporal and spatial resolution data are driving the need for novel multidimensional visualization and analysis tools. Crowd-sourced science, the same that drives innovation in computer science, could easily be mobilized for these tasks, were it not for competition for funding, attribution, and lack of standard data formats and platforms. As these efforts mature, there is a great opportunity to advance Epilepsy research through data sharing and increase collaboration between the international research community.
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24
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Rotondi F, Franceschetti S, Avanzini G, Panzica F. Altered EEG resting-state effective connectivity in drug-naïve childhood absence epilepsy. Clin Neurophysiol 2016; 127:1130-1137. [DOI: 10.1016/j.clinph.2015.09.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Revised: 08/08/2015] [Accepted: 09/10/2015] [Indexed: 11/16/2022]
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25
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Vega-Zelaya L, Pastor J, de Sola RG, Ortega GJ. Disrupted Ipsilateral Network Connectivity in Temporal Lobe Epilepsy. PLoS One 2015; 10:e0140859. [PMID: 26489091 PMCID: PMC4619301 DOI: 10.1371/journal.pone.0140859] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Accepted: 10/01/2015] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE The current practice under which patients with refractory epilepsy are surgically treated is based mainly on the identification of specific cortical areas, mainly the epileptogenic zone, which is believed to be responsible for generation of seizures. A better understanding of the whole epileptic network and its components and properties is required before more effective and less invasive therapies can be developed. The aim of the present study was to partially characterize the evolution of the functional network during the preictal-ictal transition in partial seizures in patients with temporal lobe epilepsy (TLE). METHODS Scalp and foramen ovale (FOE) recordings from twenty-two TLE patients were analyzed under the complex network perspective. The density of links, average path length, average clustering coefficient, and modularity were calculated during the preictal and the ictal stages. Both linear-Pearson correlation-and non-linear-phase synchronization-measures were used as proxies of functional connectivity between the electrode locations areas. The transition from one stage to the other was evaluated in the whole network and in the mesial sub-networks. The results were compared with a voltage-dependent measure, namely, the spectral entropy. RESULTS Changes in the global functional network during the transition from the preictal to the ictal stage show, in the linear case, that in sixteen cases (72.7%) the density of the links increased during the seizure, with a decrease in the average path length in fifteen cases (68.1%). There was also a preictal and ictal imbalance in functional connectivity during both stages (77.2% to 86.3%). The SE dropped during the seizure in 95.4% of the cases, but did not show any tendency towards lateralization. When using the nonlinear measure of functional connectivity, the phase synchronization, similar results were obtained. CONCLUSIONS In TLE patients, the transition to the ictal stage is accompanied by increasing global synchronization and a more ordered spectral content of the signals, indicated by lower spectral entropy. The interictal connectivity imbalance (lower ipsilateral connectivity) is sustained during the seizure, irrespective of any appreciable imbalance in the spectral entropy of the mesial recordings.
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Affiliation(s)
- Lorena Vega-Zelaya
- Clinical Neurophysiology, Hospital Universitario la Princesa, Madrid, Spain
- Instituto de Investigación Sanitaria Hospital Universitario de la Princesa, Madrid, Spain
| | - Jesús Pastor
- Clinical Neurophysiology, Hospital Universitario la Princesa, Madrid, Spain
- Instituto de Investigación Sanitaria Hospital Universitario de la Princesa, Madrid, Spain
| | - Rafael G. de Sola
- Neurosurgery, Hospital Universitario la Princesa, Madrid, Spain
- Instituto de Investigación Sanitaria Hospital Universitario de la Princesa, Madrid, Spain
| | - Guillermo J. Ortega
- Neurosurgery, Hospital Universitario la Princesa, Madrid, Spain
- Instituto de Investigación Sanitaria Hospital Universitario de la Princesa, Madrid, Spain
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26
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Geier C, Lehnertz K, Bialonski S. Time-dependent degree-degree correlations in epileptic brain networks: from assortative to dissortative mixing. Front Hum Neurosci 2015; 9:462. [PMID: 26347641 PMCID: PMC4542502 DOI: 10.3389/fnhum.2015.00462] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2015] [Accepted: 08/06/2015] [Indexed: 11/30/2022] Open
Abstract
We investigate the long-term evolution of degree-degree correlations (assortativity) in functional brain networks from epilepsy patients. Functional networks are derived from continuous multi-day, multi-channel electroencephalographic data, which capture a wide range of physiological and pathophysiological activities. In contrast to previous studies which all reported functional brain networks to be assortative on average, even in case of various neurological and neurodegenerative disorders, we observe large fluctuations in time-resolved degree-degree correlations ranging from assortative to dissortative mixing. Moreover, in some patients these fluctuations exhibit some periodic temporal structure which can be attributed, to a large extent, to daily rhythms. Relevant aspects of the epileptic process, particularly possible pre-seizure alterations, contribute marginally to the observed long-term fluctuations. Our findings suggest that physiological and pathophysiological activity may modify functional brain networks in a different and process-specific way. We evaluate factors that possibly influence the long-term evolution of degree-degree correlations.
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Affiliation(s)
- Christian Geier
- Department of Epileptology, University of Bonn Bonn, Germany ; Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Bonn, Germany ; Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn Bonn, Germany ; Interdisciplinary Center for Complex Systems, University of Bonn Bonn, Germany
| | - Stephan Bialonski
- Max-Planck-Institute for the Physics of Complex Systems Dresden, Germany
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Vahabi Z, Amirfattahi R, Shayegh F, Ghassemi F. Online Epileptic Seizure Prediction Using Wavelet-Based Bi-Phase Correlation of Electrical Signals Tomography. Int J Neural Syst 2015; 25:1550028. [DOI: 10.1142/s0129065715500288] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Considerable efforts have been made in order to predict seizures. Among these methods, the ones that quantify synchronization between brain areas, are the most important methods. However, to date, a practically acceptable result has not been reported. In this paper, we use a synchronization measurement method that is derived according to the ability of bi-spectrum in determining the nonlinear properties of a system. In this method, first, temporal variation of the bi-spectrum of different channels of electro cardiography (ECoG) signals are obtained via an extended wavelet-based time-frequency analysis method; then, to compare different channels, the bi-phase correlation measure is introduced. Since, in this way, the temporal variation of the amount of nonlinear coupling between brain regions, which have not been considered yet, are taken into account, results are more reliable than the conventional phase-synchronization measures. It is shown that, for 21 patients of FSPEEG database, bi-phase correlation can discriminate the pre-ictal and ictal states, with very low false positive rates (FPRs) (average: 0.078/h) and high sensitivity (100%). However, the proposed seizure predictor still cannot significantly overcome the random predictor for all patients.
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Affiliation(s)
- Zahra Vahabi
- Digital Signal Processing Research Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Rasoul Amirfattahi
- Digital Signal Processing Research Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Farzaneh Shayegh
- Department of Electrical Engineering, Payame Noor University (PNU), Isfahan, Iran
- Medical Image and Signal Processing Research Center, Medical University of Isfahan, Isfahan, Iran
| | - Fahimeh Ghassemi
- Department of Advanced Medical Technologies, Medical University of Isfahan, Isfahan, Iran
- Medical Image and Signal Processing Research Center, Medical University of Isfahan, Isfahan, Iran
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28
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Hsiao FJ, Yu HY, Chen WT, Kwan SY, Chen C, Yen DJ, Yiu CH, Shih YH, Lin YY. Increased Intrinsic Connectivity of the Default Mode Network in Temporal Lobe Epilepsy: Evidence from Resting-State MEG Recordings. PLoS One 2015; 10:e0128787. [PMID: 26035750 PMCID: PMC4452781 DOI: 10.1371/journal.pone.0128787] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2014] [Accepted: 04/30/2015] [Indexed: 11/23/2022] Open
Abstract
The electrophysiological signature of resting state oscillatory functional connectivity within the default mode network (DMN) during spike-free periods in temporal lobe epilepsy (TLE) remains unclear. Using magnetoencephalographic (MEG) recordings, this study investigated how the connectivity within the DMN was altered in TLE, and we examined the effect of lateralized TLE on functional connectivity. Sixteen medically intractable TLE patients and 22 controls participated in this study. Whole-scalp 306-channel MEG epochs without interictal spikes generated from both MEG and EEG data were analyzed using a minimum norm estimate (MNE) and source-based imaginary coherence analysis. With this processing, we obtained the cortical activation and functional connectivity within the DMN. The functional connectivity was increased between DMN and the right medial temporal (MT) region at the delta band and between DMN and the bilateral anterior cingulate cortex (ACC) regions at the theta band. The functional change was associated with the lateralization of TLE. The right TLE showed enhanced DMN connectivity with the right MT while the left TLE demonstrated increased DMN connectivity with the bilateral MT. There was no lateralization effect of TLE upon the DMN connectivity with ACC. These findings suggest that the resting-state functional connectivity within the DMN is reinforced in temporal lobe epilepsy during spike-free periods. Future studies are needed to examine if the altered functional connectivity can be used as a biomarker for treatment responses, cognitive dysfunction and prognosis in patients with TLE.
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Affiliation(s)
- Fu-Jung Hsiao
- Institute of Brain Science, School of Medicine, National Yang-Ming University, Taipei, Taiwan
- Brain Research Center, National Yang-Ming University, Taipei, Taiwan
- Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
- Laboratory of Neurophysiology at Medical Research Division, Taipei Veterans General Hospital, Taipei, Taiwan
- * E-mail: (FJH); (YYL)
| | - Hsiang-Yu Yu
- Department of Neurology, School of Medicine, National Yang-Ming University, Taipei, Taiwan
- Department of Neurology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Wei-Ta Chen
- Institute of Brain Science, School of Medicine, National Yang-Ming University, Taipei, Taiwan
- Department of Neurology, School of Medicine, National Yang-Ming University, Taipei, Taiwan
- Laboratory of Neurophysiology at Medical Research Division, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Neurology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Shang-Yeong Kwan
- Department of Neurology, School of Medicine, National Yang-Ming University, Taipei, Taiwan
- Department of Neurology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chien Chen
- Department of Neurology, School of Medicine, National Yang-Ming University, Taipei, Taiwan
- Department of Neurology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Der-Jen Yen
- Department of Neurology, School of Medicine, National Yang-Ming University, Taipei, Taiwan
- Department of Neurology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chun-Hing Yiu
- Department of Neurology, School of Medicine, National Yang-Ming University, Taipei, Taiwan
- Department of Neurology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yang-Hsin Shih
- Institute of Brain Science, School of Medicine, National Yang-Ming University, Taipei, Taiwan
- Department of Neurology, School of Medicine, National Yang-Ming University, Taipei, Taiwan
- Department of Neurosurgery, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yung-Yang Lin
- Institute of Brain Science, School of Medicine, National Yang-Ming University, Taipei, Taiwan
- Department of Neurology, School of Medicine, National Yang-Ming University, Taipei, Taiwan
- Institute of Clinical Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan
- Institute of Physiology, School of Medicine, National Yang-Ming University, Taipei, Taiwan
- Brain Research Center, National Yang-Ming University, Taipei, Taiwan
- Laboratory of Neurophysiology at Medical Research Division, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Neurology, Taipei Veterans General Hospital, Taipei, Taiwan
- * E-mail: (FJH); (YYL)
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Bandarabadi M, Rasekhi J, Teixeira CA, Karami MR, Dourado A. On the proper selection of preictal period for seizure prediction. Epilepsy Behav 2015; 46:158-66. [PMID: 25944112 DOI: 10.1016/j.yebeh.2015.03.010] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2014] [Revised: 02/16/2015] [Accepted: 03/10/2015] [Indexed: 12/12/2022]
Abstract
Supervised machine learning-based seizure prediction methods consider preictal period as an important prerequisite parameter during training. However, the exact length of the preictal state is unclear and varies from seizure to seizure. We propose a novel statistical approach for proper selection of the preictal period, which can also be considered either as a measure of predictability of a seizure or as the prediction capability of an understudy feature. The optimal preictal periods (OPPs) obtained from the training samples can be used for building a more accurate classifier model. The proposed method uses amplitude distribution histograms of features extracted from electroencephalogram (EEG) recordings. To evaluate this method, we extract spectral power features in different frequency bands from monopolar and space-differential EEG signals of 18 patients suffering from pharmacoresistant epilepsy. Furthermore, comparisons among monopolar channels with space-differential channels, as well as intracranial EEG (iEEG) and surface EEG (sEEG) signals, indicate that while monopolar signals perform better in iEEG recordings, no significant difference is noticeable in sEEG recordings.
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Affiliation(s)
- Mojtaba Bandarabadi
- CISUC/DEI, Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Polo II, 3030-290 Coimbra, Portugal.
| | - Jalil Rasekhi
- Department of Biomedical Engineering, Faculty of Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - César A Teixeira
- CISUC/DEI, Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Polo II, 3030-290 Coimbra, Portugal
| | - Mohammad R Karami
- Department of Biomedical Engineering, Faculty of Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - António Dourado
- CISUC/DEI, Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Polo II, 3030-290 Coimbra, Portugal
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30
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Electrocorticographic evidence and surgical implications of different physiopathologic subtypes of temporal epilepsy. Clin Neurophysiol 2014; 125:2349-57. [DOI: 10.1016/j.clinph.2014.03.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2013] [Revised: 02/07/2014] [Accepted: 03/14/2014] [Indexed: 01/08/2023]
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EEG–EMG information flow in movement-activated myoclonus in patients with Unverricht–Lundborg disease. Clin Neurophysiol 2014; 125:1803-8. [DOI: 10.1016/j.clinph.2014.01.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2013] [Revised: 01/13/2014] [Accepted: 01/14/2014] [Indexed: 11/21/2022]
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32
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Shayegh F, Sadri S, Amirfattahi R, Ansari-Asl K, Bellanger JJ, Senhadji L. Hippocampal effective synchronization values are not pre-seizure indicator without considering the state of the onset channels. NETWORK (BRISTOL, ENGLAND) 2014; 25:139-167. [PMID: 25061815 PMCID: PMC5225267 DOI: 10.3109/0954898x.2014.940409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
In this paper, a model-based approach is presented to quantify the effective synchrony between hippocampal areas from depth-EEG signals. This approach is based on the parameter identification procedure of a realistic Multi-Source/Multi-Channel (MSMC) hippocampal model that simulates the function of different areas of hippocampus. In the model it is supposed that the observed signals recorded using intracranial electrodes are generated by some hidden neuronal sources, according to some parameters. An algorithm is proposed to extract the intrinsic (solely relative to one hippocampal area) and extrinsic (coupling coefficients between two areas) model parameters, simultaneously, by a Maximum Likelihood (ML) method. Coupling coefficients are considered as the measure of effective synchronization. This work can be considered as an application of Dynamic Causal Modeling (DCM) that enables us to understand effective synchronization changes during transition from inter-ictal to pre -ictal state. The algorithm is first validated by using some synthetic datasets. Then by extracting the coupling coefficients of real depth-EEG signals by the proposed approach, it is observed that the coupling values show no significant difference between ictal, pre-ictal and inter-ictal states, i.e. either the increase or decrease of coupling coefficients has been observed in all states. However, taking the value of intrinsic parameters into account, pre-seizure state can be distinguished from inter-ictal state. It is claimed that seizures start to appear when there are seizure-related physiological parameters on the onset channel, and its coupling coefficient toward other channels increases simultaneously. As a result of considering both intrinsic and extrinsic parameters as the feature vector, inter-ictal, pre-ictal and ictal activities are discriminated from each other with an accuracy of 91.33% accuracy.
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Affiliation(s)
- Farzaneh Shayegh
- Digital Signal Processing Research Lab.
Isfahan University of Technology - Isfahan, 84156‑83111
- Payame Noor University
Payame Noor University - Tehran
| | - Saeed Sadri
- Digital Signal Processing Research Lab.
Isfahan University of Technology - Isfahan, 84156‑83111
| | - Rassoul Amirfattahi
- Digital Signal Processing Research Lab.
Isfahan University of Technology - Isfahan, 84156‑83111
| | | | - Jean-Jacques Bellanger
- LTSI, Laboratoire Traitement du Signal et de l'Image
Institut National de la Santé et de la Recherche Médicale - U1099Université de Rennes 1 - Campus Universitaire de Beaulieu - Bât 22 - 35042 Rennes
| | - Lotfi Senhadji
- LTSI, Laboratoire Traitement du Signal et de l'Image
Institut National de la Santé et de la Recherche Médicale - U1099Université de Rennes 1 - Campus Universitaire de Beaulieu - Bât 22 - 35042 Rennes
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Korzeniewska A, Cervenka MC, Jouny CC, Perilla JR, Harezlak J, Bergey GK, Franaszczuk PJ, Crone NE. Ictal propagation of high frequency activity is recapitulated in interictal recordings: effective connectivity of epileptogenic networks recorded with intracranial EEG. Neuroimage 2014; 101:96-113. [PMID: 25003814 DOI: 10.1016/j.neuroimage.2014.06.078] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2014] [Revised: 06/08/2014] [Accepted: 06/30/2014] [Indexed: 01/08/2023] Open
Abstract
Seizures are increasingly understood to arise from epileptogenic networks across which ictal activity is propagated and sustained. In patients undergoing invasive monitoring for epilepsy surgery, high frequency oscillations have been observed within the seizure onset zone during both ictal and interictal intervals. We hypothesized that the patterns by which high frequency activity is propagated would help elucidate epileptogenic networks and thereby identify network nodes relevant for surgical planning. Intracranial EEG recordings were analyzed with a multivariate autoregressive modeling technique (short-time direct directed transfer function--SdDTF), based on the concept of Granger causality, to estimate the directionality and intensity of propagation of high frequency activity (70-175 Hz) during ictal and interictal recordings. These analyses revealed prominent divergence and convergence of high frequency activity propagation at sites identified by epileptologists as part of the ictal onset zone. In contrast, relatively little propagation of this activity was observed among the other analyzed sites. This pattern was observed in both subdural and depth electrode recordings of patients with focal ictal onset, but not in patients with a widely distributed ictal onset. In patients with focal ictal onsets, the patterns of propagation recorded during pre-ictal (up to 5 min immediately preceding ictal onset) and interictal (more than 24h before and after seizures) intervals were very similar to those recorded during seizures. The ability to characterize epileptogenic networks from interictal recordings could have important clinical implications for epilepsy surgery planning by reducing the need for prolonged invasive monitoring to record spontaneous seizures.
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Affiliation(s)
- A Korzeniewska
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St., Meyer 2-147, Baltimore, MD 21287, USA.
| | - M C Cervenka
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St., Meyer 2-147, Baltimore, MD 21287, USA
| | - C C Jouny
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St., Meyer 2-147, Baltimore, MD 21287, USA
| | - J R Perilla
- Beckman Institute and Department of Physics, University of Illinois Urbana-Champaign, 405 N. Mathews Ave., Urbana, IL 61801, USA
| | - J Harezlak
- Department of Biostatistics, Richard M. Fairbanks School of Public Health and School of Medicine Indiana University, 410 W 10th St., Suite 3000, Indianapolis, IN 46202, USA
| | - G K Bergey
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St., Meyer 2-147, Baltimore, MD 21287, USA
| | - P J Franaszczuk
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St., Meyer 2-147, Baltimore, MD 21287, USA; Human Research and Engineering Directorate, US Army Research Laboratory, 459 Mulberry Point Rd, Aberdeen Proving Ground, MD 21005, USA
| | - N E Crone
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe St., Meyer 2-147, Baltimore, MD 21287, USA
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Zhang T, Zhou J, Jiang R, Yang H, Carney PR, Jiang H. Pre-seizure state identified by diffuse optical tomography. Sci Rep 2014; 4:3798. [PMID: 24445927 PMCID: PMC3896905 DOI: 10.1038/srep03798] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2013] [Accepted: 12/30/2013] [Indexed: 11/23/2022] Open
Abstract
In epilepsy it has been challenging to detect early changes in brain activity that occurs prior to seizure onset and to map their origin and evolution for possible intervention. Here we demonstrate using a rat model of generalized epilepsy that diffuse optical tomography (DOT) provides a unique functional neuroimaging modality for noninvasively and continuously tracking such brain activities with high spatiotemporal resolution. We detected early hemodynamic responses with heterogeneous patterns, along with intracranial electroencephalogram gamma power changes, several minutes preceding the electroencephalographic seizure onset, supporting the presence of a "pre-seizure" state. We also observed the decoupling between local hemodynamic and neural activities. We found widespread hemodynamic changes evolving from local regions of the bilateral cortex and thalamus to the entire brain, indicating that the onset of generalized seizures may originate locally rather than diffusely. Together, these findings suggest DOT represents a powerful tool for mapping early seizure onset and propagation pathways.
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Affiliation(s)
- Tao Zhang
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida Gainesville, FL 32611, USA
| | - Junli Zhou
- Department of Pediatrics, University of Florida Gainesville, FL 32611, USA
| | - Ruixin Jiang
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida Gainesville, FL 32611, USA
| | - Hao Yang
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida Gainesville, FL 32611, USA
| | - Paul R. Carney
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida Gainesville, FL 32611, USA
- Department of Pediatrics, University of Florida Gainesville, FL 32611, USA
- Department of Neurology, University of Florida Gainesville, FL 32611, USA
- Department of Neuroscience, University of Florida Gainesville, FL 32611, USA
| | - Huabei Jiang
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida Gainesville, FL 32611, USA
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35
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Kuhnert MT, Bialonski S, Noennig N, Mai H, Hinrichs H, Helmstaedter C, Lehnertz K. Incidental and intentional learning of verbal episodic material differentially modifies functional brain networks. PLoS One 2013; 8:e80273. [PMID: 24260362 PMCID: PMC3832419 DOI: 10.1371/journal.pone.0080273] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2013] [Accepted: 10/11/2013] [Indexed: 11/18/2022] Open
Abstract
Learning- and memory-related processes are thought to result from dynamic interactions in large-scale brain networks that include lateral and mesial structures of the temporal lobes. We investigate the impact of incidental and intentional learning of verbal episodic material on functional brain networks that we derive from scalp-EEG recorded continuously from 33 subjects during a neuropsychological test schedule. Analyzing the networks' global statistical properties we observe that intentional but not incidental learning leads to a significantly increased clustering coefficient, and the average shortest path length remains unaffected. Moreover, network modifications correlate with subsequent recall performance: the more pronounced the modifications of the clustering coefficient, the higher the recall performance. Our findings provide novel insights into the relationship between topological aspects of functional brain networks and higher cognitive functions.
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Affiliation(s)
- Marie-Therese Kuhnert
- Department of Epileptology, University of Bonn, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
| | - Stephan Bialonski
- Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
| | - Nina Noennig
- Department of Neurology, University of Magdeburg, Magdeburg, Germany
| | - Heinke Mai
- Department of Neurology, University of Magdeburg, Magdeburg, Germany
| | - Hermann Hinrichs
- Department of Neurology, University of Magdeburg, Magdeburg, Germany
| | | | - Klaus Lehnertz
- Department of Epileptology, University of Bonn, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
- * E-mail:
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36
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Shayegh F, Sadri S, Amirfattahi R, Ansari-Asl K. A model-based method for computation of correlation dimension, Lyapunov exponents and synchronization from depth-EEG signals. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 113:323-337. [PMID: 24113422 DOI: 10.1016/j.cmpb.2013.08.014] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2013] [Revised: 08/14/2013] [Accepted: 08/28/2013] [Indexed: 06/02/2023]
Abstract
In order to predict epileptic seizures many precursory features, extracted from the EEG signals, have been introduced. Before checking out the performance of features in detection of pre-seizure state, it is required to see whether these features are accurately extracted. Evaluation of feature estimation methods has been less considered, mainly due to the lack of a ground truth for the real EEG signals' features. In this paper, some simulated long-term depth-EEG signals, with known state spaces, are generated via a realistic neural mass model with physiological parameters. Thanks to the known ground truth of these synthetic signals, they are suitable for evaluating different algorithms used to extract the features. It is shown that conventional methods of estimating correlation dimension, the largest Lyapunov exponent, and phase coherence have non-negligible errors. Then, a parameter identification-based method is introduced for estimating the features, which leads to better estimation results for synthetic signals. It is shown that the neural mass model is able to reproduce real depth-EEG signals accurately; thus, assuming this model underlying real depth-EEG signals, can improve the accuracy of features' estimation.
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Affiliation(s)
- F Shayegh
- Digital signal Processing Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, 84156-83111 Isfahan, Iran.
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37
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38
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Fujiwara H, Greiner HM, Lee KH, Holland-Bouley KD, Seo JH, Arthur T, Mangano FT, Leach JL, Rose DF. Resection of ictal high-frequency oscillations leads to favorable surgical outcome in pediatric epilepsy. Epilepsia 2012; 53:1607-17. [PMID: 22905734 DOI: 10.1111/j.1528-1167.2012.03629.x] [Citation(s) in RCA: 83] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
PURPOSE Intracranial electroencephalography (EEG) is performed as part of an epilepsy surgery evaluation when noninvasive tests are incongruent or the putative seizure-onset zone is near eloquent cortex. Determining the seizure-onset zone using intracranial EEG has been conventionally based on identification of specific ictal patterns with visual inspection. High-frequency oscillations (HFOs, >80 Hz) have been recognized recently as highly correlated with the epileptogenic zone. However, HFOs can be difficult to detect because of their low amplitude. Therefore, the prevalence of ictal HFOs and their role in localization of epileptogenic zone on intracranial EEG are unknown. METHODS We identified 48 patients who underwent surgical treatment after the surgical evaluation with intracranial EEG, and 44 patients met criteria for this retrospective study. Results were not used in surgical decision making. Intracranial EEG recordings were collected with a sampling rate of 2,000 Hz. Recordings were first inspected visually to determine ictal onset and then analyzed further with time-frequency analysis. Forty-one (93%) of 44 patients had ictal HFOs determined with time-frequency analysis of intracranial EEG. KEY FINDINGS Twenty-two (54%) of the 41 patients with ictal HFOs had complete resection of HFO regions, regardless of frequency bands. Complete resection of HFOs (n = 22) resulted in a seizure-free outcome in 18 (82%) of 22 patients, significantly higher than the seizure-free outcome with incomplete HFO resection (4/19, 21%). SIGNIFICANCE Our study shows that ictal HFOs are commonly found with intracranial EEG in our population largely of children with cortical dysplasia, and have localizing value. The use of ictal HFOs may add more promising information compared to interictal HFOs because of the evidence of ictal propagation and followed by clinical aspect of seizures. Complete resection of HFOs is a favorable prognostic indicator for surgical outcome.
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Affiliation(s)
- Hisako Fujiwara
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio 45229, USA
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Schindler K, Gast H, Goodfellow M, Rummel C. On seeing the trees and the forest: Single-signal and multisignal analysis of periictal intracranial EEG. Epilepsia 2012; 53:1658-68. [DOI: 10.1111/j.1528-1167.2012.03588.x] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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40
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Varotto G, Visani E, Canafoglia L, Franceschetti S, Avanzini G, Panzica F. Enhanced frontocentral EEG connectivity in photosensitive generalized epilepsies: A partial directed coherence study. Epilepsia 2011; 53:359-67. [DOI: 10.1111/j.1528-1167.2011.03352.x] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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41
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Osorio I, Lyubushin A, Sornette D. Toward a probabilistic definition of seizures. Epilepsy Behav 2011; 22 Suppl 1:S18-28. [PMID: 22078513 DOI: 10.1016/j.yebeh.2011.09.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2011] [Accepted: 09/07/2011] [Indexed: 12/26/2022]
Abstract
This writing (1) draws attention to the intricacies inherent to the pursuit of a universal seizure definition even when powerful, well-understood signal analysis methods are used to this end; (2) identifies this aim as a multi-objective optimization problem and discusses the advantages and disadvantages of adopting or rejecting a unitary seizure definition; and (3) introduces a probabilistic measure of seizure activity to manage this thorny issue. The challenges posed by the attempt to define seizures unitarily may be partly related to their fractal properties and understood through a simplistic analogy to the so-called "Richardson effect." A revision of the time-honored conceptualization of seizures may be warranted to further advance epileptology. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.
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Affiliation(s)
- Ivan Osorio
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS 66160, USA.
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42
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Automated seizure detection: unrecognized challenges, unexpected insights. Epilepsy Behav 2011; 22 Suppl 1:S7-17. [PMID: 22078522 DOI: 10.1016/j.yebeh.2011.09.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2011] [Accepted: 09/07/2011] [Indexed: 12/14/2022]
Abstract
One of epileptology's fundamental aims is the formulation of a universal, internally consistent seizure definition. To assess this aim's feasibility three signal analysis methods were applied to a seizure time series and performance comparisons were undertaken among them and with respect to a validated algorithm. One of the methods uses a Fisher's matrix weighted measure of the rate of parameters change of a 2nd order auto-regressive model, another is based on the Wavelet Transform Maximum Modulus for quantification of changes in the logarithm of the standard deviation of ECoG power and yet another employs the ratio of short-to-long term averages computed from cortical signals. The central finding, fluctuating concordance among all methods' output as a function of seizure duration, uncovers unexpected hurdles in the path to a universal definition, while furnishing relevant knowledge in the dynamical (spectral non-stationarity/varying ictal signal complexity) and clinical (potential un-attainability of consensus) domains. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.
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Zhang ZJ, Valiante TA, Carlen PL. Transition to seizure: from "macro"- to "micro"-mysteries. Epilepsy Res 2011; 97:290-9. [PMID: 22075227 DOI: 10.1016/j.eplepsyres.2011.09.025] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2011] [Revised: 09/25/2011] [Accepted: 09/27/2011] [Indexed: 01/25/2023]
Abstract
One of the most terrifying aspects of epilepsy is the sudden and apparently unpredictable transition of the brain into the pathological state of an epileptic seizure. The pathophysiology of the transition to seizure still remains mysterious. Herein we review some of the key concepts and relevant literatures dealing with this enigmatic transitioning of brain states. At the "MACRO" level, electroencephalographic (EEG) recordings at time display preictal phenomena followed by pathological high-frequency oscillations at the seizure onset. Numerous seizure prediction algorithms predicated on identifying changes prior to seizure onset have met with little success, underscoring our lack of understanding of the dynamics of transition to seizure, amongst other inherent limitation. We then discuss the concept of synchronized hyperexcited oscillatory networks underlying seizure generation. We consider these networks as weakly coupled oscillators, a concept which forms the basis of some relevant mathematical modeling of seizure transitions. Next, the underlying "MICRO" processes involved in seizure generation are discussed. The depolarization of the GABA(A) chloride reversal potential is a major concept, facilitating epileptogenesis, particularly in immature brain. Also the balance of inhibitory and excitatory local neuronal networks plays an important role in the process of transitioning to seizure. Gap junctional communication, including that which occurs between glia, as well as ephaptic interactions are increasingly recognized as critical for seizure generation. In brief, this review examines the evidence regarding the characterization of the transition to seizure at both the "MACRO" and "MICRO" levels, trying to characterize this mysterious yet critical problem of the brain state transitioning into a seizure.
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Affiliation(s)
- Z J Zhang
- Division of Fundamental Neurobiology, Toronto Western Research Institute, Toronto Western Hospital, Toronto, ON, Canada.
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Zhang Z, Liao W, Chen H, Mantini D, Ding JR, Xu Q, Wang Z, Yuan C, Chen G, Jiao Q, Lu G. Altered functional–structural coupling of large-scale brain networks in idiopathic generalized epilepsy. Brain 2011; 134:2912-28. [PMID: 21975588 DOI: 10.1093/brain/awr223] [Citation(s) in RCA: 410] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Affiliation(s)
- Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, PR China
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45
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Altered GABA signaling in early life epilepsies. Neural Plast 2011; 2011:527605. [PMID: 21826277 PMCID: PMC3150203 DOI: 10.1155/2011/527605] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2011] [Revised: 05/04/2011] [Accepted: 05/27/2011] [Indexed: 01/13/2023] Open
Abstract
The incidence of seizures is particularly high in the early ages of life. The immaturity of inhibitory systems, such as GABA, during normal brain development and its further dysregulation under pathological conditions that predispose to seizures have been speculated to play a major role in facilitating seizures. Seizures can further impair or disrupt GABAA signaling by reshuffling the subunit composition of its receptors or causing aberrant reappearance of depolarizing or hyperpolarizing GABAA receptor currents. Such effects may not result in epileptogenesis as frequently as they do in adults. Given the central role of GABAA signaling in brain function and development, perturbation of its physiological role may interfere with neuronal morphology, differentiation, and connectivity, manifesting as cognitive or neurodevelopmental deficits. The current GABAergic antiepileptic drugs, while often effective for adults, are not always capable of stopping seizures and preventing their sequelae in neonates. Recent studies have explored the therapeutic potential of chloride cotransporter inhibitors, such as bumetanide, as adjunctive therapies of neonatal seizures. However, more needs to be known so as to develop therapies capable of stopping seizures while preserving the age- and sex-appropriate development of the brain.
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Lévesque M, Bortel A, Gotman J, Avoli M. High-frequency (80-500 Hz) oscillations and epileptogenesis in temporal lobe epilepsy. Neurobiol Dis 2011; 42:231-41. [PMID: 21238589 PMCID: PMC4873283 DOI: 10.1016/j.nbd.2011.01.007] [Citation(s) in RCA: 77] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2010] [Revised: 11/17/2010] [Accepted: 01/02/2011] [Indexed: 11/25/2022] Open
Abstract
High-frequency oscillations (HFOs), termed ripples (80-200 Hz) and fast ripples (250-600 Hz), are recorded in the EEG of epileptic patients and in animal epilepsy models; HFOs are thought to reflect pathological activity and seizure onset zones. Here, we analyzed the temporal and spatial evolution of interictal spikes with and without HFOs in the rat pilocarpine model of temporal lobe epilepsy. Depth electrode recordings from dentate gyrus (DG), CA3 region, subiculum and entorhinal cortex (EC), were obtained from rats between the 4th and 15th day after a status epilepticus (SE) induced by i.p. injection of pilocarpine. The first seizure occurred 6.1 ± 2.5 days after SE (n = 7 rats). Five of 7 animals exhibited interictal spikes that co-occurred with fast ripples accounting for 4.9 ± 4.6% of all analyzed interictal spikes (n = 12,886) while all rats showed interictal spikes co-occurring with ripples, accounting for 14.3 ± 3.4% of all events. Increased rates of interictal spikes without HFOs in the EC predicted upcoming seizures on the following day, while rates of interictal spikes with fast ripples in CA3 reflected periods of high seizure occurrence. Finally, interictal spikes co-occurring with ripples did not show any specific relation to seizure occurrence. Our findings identify different temporal and spatial developmental patterns for the rates of interictal spikes with or without HFOs in relation with seizure occurrence. These distinct categories of interictal spikes point at dynamic processes that should bring neuronal networks close to seizure generation.
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Affiliation(s)
- Maxime Lévesque
- Montreal Neurological Institute and Department of Neurology & Neurosurgery, McGill University, 3801 University Street, Montreal, Qc, Canada H3A 2B4
| | - Aleksandra Bortel
- Montreal Neurological Institute and Department of Neurology & Neurosurgery, McGill University, 3801 University Street, Montreal, Qc, Canada H3A 2B4
| | - Jean Gotman
- Montreal Neurological Institute and Department of Neurology & Neurosurgery, McGill University, 3801 University Street, Montreal, Qc, Canada H3A 2B4
| | - Massimo Avoli
- Montreal Neurological Institute and Department of Neurology & Neurosurgery, McGill University, 3801 University Street, Montreal, Qc, Canada H3A 2B4
- Dipartimento di Medicina Sperimentale, Sapienza Università di Roma, Viale del Castro Laurenziano 9, 00185 Roma, Italy
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