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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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2
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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: 1] [Impact Index Per Article: 1.0] [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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3
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Schroeder GM, Karoly PJ, Maturana M, Panagiotopoulou M, Taylor PN, Cook MJ, Wang Y. Chronic intracranial EEG recordings and interictal spike rate reveal multiscale temporal modulations in seizure states. Brain Commun 2023; 5:fcad205. [PMID: 37693811 PMCID: PMC10484289 DOI: 10.1093/braincomms/fcad205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 06/07/2023] [Accepted: 07/18/2023] [Indexed: 09/12/2023] Open
Abstract
Many biological processes are modulated by rhythms on circadian and multidien timescales. In focal epilepsy, various seizure features, such as spread and duration, can change from one seizure to the next within the same patient. However, the specific timescales of this variability, as well as the specific seizure characteristics that change over time, are unclear. Here, in a cross-sectional observational study, we analysed within-patient seizure variability in 10 patients with chronic intracranial EEG recordings (185-767 days of recording time, 57-452 analysed seizures/patient). We characterized the seizure evolutions as sequences of a finite number of patient-specific functional seizure network states. We then compared seizure network state occurrence and duration to (1) time since implantation and (2) patient-specific circadian and multidien cycles in interictal spike rate. In most patients, the occurrence or duration of at least one seizure network state was associated with the time since implantation. Some patients had one or more seizure network states that were associated with phases of circadian and/or multidien spike rate cycles. A given seizure network state's occurrence and duration were usually not associated with the same timescale. Our results suggest that different time-varying factors modulate within-patient seizure evolutions over multiple timescales, with separate processes modulating a seizure network state's occurrence and duration. These findings imply that the development of time-adaptive treatments in epilepsy must account for several separate properties of epileptic seizures and similar principles likely apply to other neurological conditions.
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Affiliation(s)
- Gabrielle M Schroeder
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Philippa J Karoly
- Graeme Clark Institute and St Vincent’s Hospital, University of Melbourne, Parkville, Victoria 3010, Australia
- Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Matias Maturana
- Graeme Clark Institute and St Vincent’s Hospital, University of Melbourne, Parkville, Victoria 3010, Australia
- Department of Biomedical Engineering, University of Melbourne, Parkville, Victoria 3010, Australia
- Research Department, Seer Medical Pty Ltd., Melbourne, Victoria 3000, Australia
| | - Mariella Panagiotopoulou
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
- UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Mark J Cook
- Graeme Clark Institute and St Vincent’s Hospital, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
- UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, UK
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Lehnertz H, Broehl T, Rings T, von Wrede R, Lehnertz K. Modifying functional brain networks in focal epilepsy by manual visceral-osteopathic stimulation of the vagus nerve at the abdomen. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1205476. [PMID: 37520657 PMCID: PMC10374317 DOI: 10.3389/fnetp.2023.1205476] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/04/2023] [Indexed: 08/01/2023]
Abstract
Non-invasive transcutaneous vagus nerve stimulation elicits similar therapeutic effects as invasive vagus nerve stimulation, offering a potential treatment alternative for a wide range of diseases, including epilepsy. Here, we present a novel, non-invasive stimulation of the vagus nerve, which is performed manually viscero-osteopathically on the abdomen (voVNS). We explore the impact of short-term voVNS on various local and global characteristics of EEG-derived, large-scale evolving functional brain networks from a group of 20 subjects with and without epilepsy. We observe differential voVNS-mediated alterations of these characteristics that can be interpreted as a reconfiguration and modification of networks and their stability and robustness properties. Clearly, future studies are necessary to assess the impact of such a non-pharmaceutical intervention on clinical decision-making in the treatment of epilepsy. However, our findings may add to the current discussion on the importance of the gut-brain axis in health and disease. Clinical Trial Registration: https://drks.de/search/en/trial/DRKS00029914, identifier DRKS00029914.
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Affiliation(s)
- Hendrik Lehnertz
- BMT Internationale Akademie für Biodynamische Manuelle Therapie GmbH, Bühler, Switzerland
| | - Timo Broehl
- 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
| | - 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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5
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Lehnertz K, Bröhl T, Wrede RV. Epileptic-network-based prediction and control of seizures in humans. Neurobiol Dis 2023; 181:106098. [PMID: 36997129 DOI: 10.1016/j.nbd.2023.106098] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/08/2023] [Accepted: 03/22/2023] [Indexed: 03/30/2023] Open
Abstract
Epilepsy is now conceptualized as a network disease. The epileptic brain network comprises structurally and functionally connected cortical and subcortical brain regions - spanning lobes and hemispheres -, whose connections and dynamics evolve in time. With this concept, focal and generalized seizures as well as other related pathophysiological phenomena are thought to emerge from, spread via, and be terminated by network vertices and edges that also generate and sustain normal, physiological brain dynamics. Research over the last years has advanced concepts and techniques to identify and characterize the evolving epileptic brain network and its constituents on various spatial and temporal scales. Network-based approaches further our understanding of how seizures emerge from the evolving epileptic brain network, and they provide both novel insights into pre-seizure dynamics and important clues for success or failure of measures for network-based seizure control and prevention. In this review, we summarize the current state of knowledge and address several important challenges that would need to be addressed to move network-based prediction and control of seizures closer to clinical translation.
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Affiliation(s)
- Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 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.
| | - Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany; Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany
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6
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Laiou P, Biondi A, Bruno E, Viana PF, Winston JS, Rashid Z, Ranjan Y, Conde P, Stewart C, Sun S, Zhang Y, Folarin A, Dobson RJB, Schulze-Bonhage A, Dümpelmann M, Richardson MP. Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence. Biomedicines 2022; 10:2662. [PMID: 36289925 PMCID: PMC9599905 DOI: 10.3390/biomedicines10102662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/10/2022] [Accepted: 10/11/2022] [Indexed: 12/02/2022] Open
Abstract
Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.
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Affiliation(s)
- Petroula Laiou
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Andrea Biondi
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Elisa Bruno
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Pedro F. Viana
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- Faculty of Medicine, University of Lisbon, 1649-028 Lisbon, Portugal
| | - Joel S. Winston
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Zulqarnain Rashid
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Yatharth Ranjan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Pauline Conde
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Callum Stewart
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Shaoxiong Sun
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Yuezhou Zhang
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Amos Folarin
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- Institute of Health Informatics, University College London, London WC1E 6BT, UK
- NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, King’s College London, London SE5 8AF, UK
- Health Data Research UK London, University College London, London WC1E 6BT, UK
- NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London W1T 7DN, UK
| | - Richard J. B. Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- Institute of Health Informatics, University College London, London WC1E 6BT, UK
- NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, King’s College London, London SE5 8AF, UK
- Health Data Research UK London, University College London, London WC1E 6BT, UK
- NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London W1T 7DN, UK
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
- Department of Microsystems Engineering (IMTEK), University of Freiburg, 79110 Freiburg, Germany
| | - Mark P. Richardson
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, King’s College London, London SE5 8AF, UK
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7
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Helmstaedter C, Rings T, Buscher L, Janssen B, Alaeddin S, Krause V, Knecht S, Lehnertz K. Stimulation-related modifications of evolving functional brain networks in unresponsive wakefulness. Sci Rep 2022; 12:11586. [PMID: 35803974 PMCID: PMC9270393 DOI: 10.1038/s41598-022-15803-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 06/29/2022] [Indexed: 11/09/2022] Open
Abstract
Recent advances in neurophysiological brain network analysis have demonstrated novel potential for diagnosis and prognosis of disorders of consciousness. While most progress has been achieved on the population-sample level, time-economic and easy-to-apply personalized solutions are missing. This prospective controlled study combined EEG recordings, basal stimulation, and daily behavioral assessment as applied routinely during complex early rehabilitation treatment. We investigated global characteristics of EEG-derived evolving functional brain networks during the repeated (3–6 weeks apart) evaluation of brain dynamics at rest as well as during and after multisensory stimulation in ten patients who were diagnosed with an unresponsive wakefulness syndrome (UWS). The age-corrected average clustering coefficient C* allowed to discriminate between individual patients at first (three patients) and second assessment (all patients). Clinically, only two patients changed from UWS to minimally conscious state. Of note, most patients presented with significant changes of C* due to stimulations, along with treatment, and with an increasing temporal distance to injury. These changes tended towards the levels of nine healthy controls. Our approach allowed to monitor both, short-term effects of individual therapy sessions and possibly long-term recovery. Future studies will need to assess its full potential for disease monitoring and control of individualized treatment decisions.
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Affiliation(s)
- Christoph Helmstaedter
- St. Mauritius Therapieklinik GmbH, Strümper Str. 111, 40670, Meerbusch, Germany. .,Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127, Bonn, Germany.
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127, Bonn, Germany.,Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115, Bonn, Germany
| | - Lara Buscher
- St. Mauritius Therapieklinik GmbH, Strümper Str. 111, 40670, Meerbusch, Germany
| | - Benedikt Janssen
- St. Mauritius Therapieklinik GmbH, Strümper Str. 111, 40670, Meerbusch, Germany
| | - Sara Alaeddin
- St. Mauritius Therapieklinik GmbH, Strümper Str. 111, 40670, Meerbusch, Germany
| | - Vanessa Krause
- St. Mauritius Therapieklinik GmbH, Strümper Str. 111, 40670, Meerbusch, Germany
| | - Stefan Knecht
- St. Mauritius Therapieklinik GmbH, Strümper Str. 111, 40670, Meerbusch, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127, 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 Str. 7, 53175, Bonn, Germany
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8
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Schach S, Rings T, Bregulla M, Witt JA, Bröhl T, Surges R, von Wrede R, Lehnertz K, Helmstaedter C. Electrodermal Activity Biofeedback Alters Evolving Functional Brain Networks in People With Epilepsy, but in a Non-specific Manner. Front Neurosci 2022; 16:828283. [PMID: 35310086 PMCID: PMC8927283 DOI: 10.3389/fnins.2022.828283] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/10/2022] [Indexed: 11/13/2022] Open
Abstract
There is evidence that biofeedback of electrodermal activity (EDA) can reduce seizure frequency in people with epilepsy. Prior studies have linked EDA biofeedback to a diffuse brain activation as a potential functional mechanism. Here, we investigated whether short-term EDA biofeedback alters EEG-derived large-scale functional brain networks in people with epilepsy. In this prospective controlled trial, thirty participants were quasi-randomly assigned to one of three biofeedback conditions (arousal, sham, or relaxation) and performed a single, 30-min biofeedback training while undergoing continuous EEG recordings. Based on the EEG, we derived evolving functional brain networks and examined their topological, robustness, and stability properties over time. Potential effects on attentional-executive functions and mood were monitored via a neuropsychological assessment and subjective self-ratings. Participants assigned to the relaxation group seemed to be most successful in meeting the task requirements for this specific control condition (i.e., decreasing EDA). Participants in the sham group were more successful in increasing EDA than participants in the arousal group. However, only the arousal biofeedback training was associated with a prolonged robustness-enhancing effect on networks. Effects on other network properties were mostly unspecific for the different groups. None of the biofeedback conditions affected attentional-executive functions or subjective behavioral measures. Our results suggest that global characteristics of evolving functional brain networks are modified by EDA biofeedback. Some alterations persisted after the single training session; however, the effects were largely unspecific across the different biofeedback protocols. Further research should address changes of local network characteristics and whether multiple training sessions will result in more specific network modifications.
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Affiliation(s)
- Sophia Schach
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- *Correspondence: Sophia Schach,
| | - Thorsten Rings
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | | | | | - Timo Bröhl
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Rainer Surges
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University Hospital 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
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9
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Müller M, Dekkers M, Wiest R, Schindler K, Rummel C. More Than Spikes: On the Added Value of Non-linear Intracranial EEG Analysis for Surgery Planning in Temporal Lobe Epilepsy. Front Neurol 2022; 12:741450. [PMID: 35095712 PMCID: PMC8793863 DOI: 10.3389/fneur.2021.741450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 10/18/2021] [Indexed: 11/13/2022] Open
Abstract
Epilepsy surgery can be a very effective therapy in medication refractory patients. During patient evaluation intracranial EEG is analyzed by clinical experts to identify the brain tissue generating epileptiform events. Quantitative EEG analysis increasingly complements this approach in research settings, but not yet in clinical routine. We investigate the correspondence between epileptiform events and a specific quantitative EEG marker. We analyzed 99 preictal epochs of multichannel intracranial EEG of 40 patients with mixed etiologies. Time and channel of occurrence of epileptiform events (spikes, slow waves, sharp waves, fast oscillations) were annotated by a human expert and non-linear excess interrelations were calculated as a quantitative EEG marker. We assessed whether the visually identified preictal events predicted channels that belonged to the seizure onset zone, that were later resected or that showed strong non-linear interrelations. We also investigated whether the seizure onset zone or the resection were predicted by channels with strong non-linear interrelations. In patients with temporal lobe epilepsy (32 of 40), epileptic spikes and the seizure onset zone predicted the resected brain tissue much better in patients with favorable seizure control after surgery than in unfavorable outcomes. Beyond that, our analysis did not reveal any significant associations with epileptiform EEG events. Specifically, none of the epileptiform event types did predict non-linear interrelations. In contrast, channels with strong non-linear excess EEG interrelations predicted the resected channels better in patients with temporal lobe epilepsy and favorable outcome. Also in the small number of patients with seizure onset in the frontal and parietal lobes, no association between epileptiform events and channels with strong non-linear excess EEG interrelations was detectable. In contrast to patients with temporal seizure onset, EEG channels with strong non-linear excess interrelations did neither predict the seizure onset zone nor the resection of these patients or allow separation between patients with favorable and unfavorable seizure control. Our study indicates that non-linear excess EEG interrelations are not strictly associated with epileptiform events, which are one key concept of current clinical EEG assessment. Rather, they may provide information relevant for surgery planning in temporal lobe epilepsy. Our study suggests to incorporate quantitative EEG analysis in the workup of clinical cases. We make the EEG epochs and expert annotations publicly available in anonymized form to foster similar analyses for other quantitative EEG methods.
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Affiliation(s)
- Michael Müller
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
| | - Martijn Dekkers
- Department of Neurology, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
| | - Kaspar Schindler
- Department of Neurology, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
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10
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Rings T, von Wrede R, Bröhl T, Schach S, Helmstaedter C, Lehnertz K. Impact of Transcutaneous Auricular Vagus Nerve Stimulation on Large-Scale Functional Brain Networks: From Local to Global. Front Physiol 2021; 12:700261. [PMID: 34489724 PMCID: PMC8417898 DOI: 10.3389/fphys.2021.700261] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 07/28/2021] [Indexed: 11/13/2022] Open
Abstract
Transcutaneous auricular vagus nerve stimulation (taVNS) is a novel non-invasive brain stimulation technique considered as a potential supplementary treatment option for a wide range of diseases. Although first promising findings were obtained so far, the exact mode of action of taVNS is not fully understood yet. We recently developed an examination schedule to probe for immediate taVNS-induced modifications of large-scale epileptic brain networks. With this schedule, we observed short-term taVNS to have a topology-modifying, robustness- and stability-enhancing immediate effect on large-scale functional brain networks from subjects with focal epilepsies. We here expand on this study and investigate the impact of short-term taVNS on various local and global characteristics of large-scale evolving functional brain networks from a group of 30 subjects with and without central nervous system diseases. Our findings point to differential, at first glance counterintuitive, taVNS-mediated alterations of local and global topological network characteristics that result in a reconfiguration of networks and a modification of their stability and robustness properties. We propose a model of a stimulation-related stretching and compression of evolving functional brain networks that may help to better understand the mode of action of taVNS.
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Affiliation(s)
- Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Sophia Schach
- 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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11
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Tait L, Lopes MA, Stothart G, Baker J, Kazanina N, Zhang J, Goodfellow M. A large-scale brain network mechanism for increased seizure propensity in Alzheimer's disease. PLoS Comput Biol 2021; 17:e1009252. [PMID: 34379638 PMCID: PMC8382184 DOI: 10.1371/journal.pcbi.1009252] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 08/23/2021] [Accepted: 07/06/2021] [Indexed: 11/19/2022] Open
Abstract
People with Alzheimer's disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider hyperexcitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, in the general population of people with epilepsy, large-scale brain network organization additionally plays a role in determining seizure likelihood and phenotype. Here, we propose that alterations to large-scale brain network organization seen in AD may contribute to increased seizure likelihood. To test this hypothesis, we combine computational modelling with electrophysiological data using an approach that has proved informative in clinical epilepsy cohorts without AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions. As cortical tissue excitability was increased in the simulations, AD simulations were more likely to transition into seizures than simulations from healthy controls, suggesting an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with AD pathologies.
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Affiliation(s)
- Luke Tait
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Marinho A. Lopes
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - George Stothart
- Department of Psychology, University of Bath, Bath, United Kingdom
| | - John Baker
- Dementia Research Centre, Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Nina Kazanina
- School of Psychological Science, University of Bristol, Bristol, United Kingdom
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, United Kingdom
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12
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Smith RJ, Alipourjeddi E, Garner C, Maser AL, Shrey DW, Lopour BA. Infant functional networks are modulated by state of consciousness and circadian rhythm. Netw Neurosci 2021; 5:614-630. [PMID: 34189380 PMCID: PMC8233111 DOI: 10.1162/netn_a_00194] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 03/22/2021] [Indexed: 01/05/2023] Open
Abstract
Functional connectivity networks are valuable tools for studying development, cognition, and disease in the infant brain. In adults, such networks are modulated by the state of consciousness and the circadian rhythm; however, it is unknown if infant brain networks exhibit similar variation, given the unique temporal properties of infant sleep and circadian patterning. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings (average duration 20.8 hr) from 19 healthy infants. Networks were subject specific, as intersubject correlations between weighted adjacency matrices were low. However, within individual subjects, both sleep and wake networks were stable over time, with stronger functional connectivity during sleep than wakefulness. Principal component analysis revealed the presence of two dominant networks; visual sleep scoring confirmed that these corresponded to sleep and wakefulness. Lastly, we found that network strength, degree, clustering coefficient, and path length significantly varied with time of day, when measured in either wakefulness or sleep at the group level. Together, these results suggest that modulation of healthy functional networks occurs over ∼24 hr and is robust and repeatable. Accounting for such temporal periodicities may improve the physiological interpretation and use of functional connectivity analysis to investigate brain function in health and disease.
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Affiliation(s)
- Rachel J. Smith
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
| | - Ehsan Alipourjeddi
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
| | - Cristal Garner
- Division of Neurology, Children’s Hospital of Orange County, Orange, CA, USA
| | - Amy L. Maser
- Department of Psychology, Children’s Hospital of Orange County, Orange, CA, USA
| | - Daniel W. Shrey
- Division of Neurology, Children’s Hospital of Orange County, Orange, CA, USA
- Department of Pediatrics, University of California, Irvine, Irvine, CA, USA
| | - Beth A. Lopour
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
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13
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Transcutaneous auricular vagus nerve stimulation induces stabilizing modifications in large-scale functional brain networks: towards understanding the effects of taVNS in subjects with epilepsy. Sci Rep 2021; 11:7906. [PMID: 33846432 PMCID: PMC8042037 DOI: 10.1038/s41598-021-87032-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 03/22/2021] [Indexed: 02/01/2023] Open
Abstract
Transcutaneous auricular vagus nerve stimulation (taVNS) is a novel non-invasive brain stimulation technique considered as a potential supplementary treatment option for subjects with refractory epilepsy. Its exact mechanism of action is not yet fully understood. We developed an examination schedule to probe for immediate taVNS-induced modifications of large-scale epileptic brain networks and accompanying changes of cognition and behaviour. In this prospective trial, we applied short-term (1 h) taVNS to 14 subjects with epilepsy during a continuous 3-h EEG recording which was embedded in two standardized neuropsychological assessments. From these EEG, we derived evolving epileptic brain networks and tracked important topological, robustness, and stability properties of networks over time. In the majority of investigated subjects, taVNS induced measurable and persisting modifications in network properties that point to a more resilient epileptic brain network without negatively impacting cognition, behaviour, or mood. The stimulation was well tolerated and the usability of the device was rated good. Short-term taVNS has a topology-modifying, robustness- and stability-enhancing immediate effect on large-scale epileptic brain networks. It has no detrimental effects on cognition and behaviour. Translation into clinical practice requires further studies to detail knowledge about the exact mechanisms by which taVNS prevents or inhibits seizures.
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14
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Li R, Wang H, Wang L, Zhang L, Zou T, Wang X, Liao W, Zhang Z, Lu G, Chen H. Shared and distinct global signal topography disturbances in subcortical and cortical networks in human epilepsy. Hum Brain Mapp 2021; 42:412-426. [PMID: 33073893 PMCID: PMC7776006 DOI: 10.1002/hbm.25231] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/08/2020] [Accepted: 09/29/2020] [Indexed: 01/21/2023] Open
Abstract
Epilepsy is a common brain network disorder associated with disrupted large-scale excitatory and inhibitory neural interactions. Recent resting-state fMRI evidence indicates that global signal (GS) fluctuations that have commonly been ignored are linked to neural activity. However, the mechanisms underlying the altered global pattern of fMRI spontaneous fluctuations in epilepsy remain unclear. Here, we quantified GS topography using beta weights obtained from a multiple regression model in a large group of epilepsy with different subtypes (98 focal temporal epilepsy; 116 generalized epilepsy) and healthy population (n = 151). We revealed that the nonuniformly distributed GS topography across association and sensory areas in healthy controls was significantly shifted in patients. Particularly, such shifts of GS topography disturbances were more widespread and bilaterally distributed in the midbrain, cerebellum, visual cortex, and medial and orbital cortex in generalized epilepsy, whereas in focal temporal epilepsy, these networks spread beyond the temporal areas but mainly remain lateralized. Moreover, we found that these abnormal GS topography patterns were likely to evolve over the course of a longer epilepsy disease. Our study demonstrates that epileptic processes can potentially affect global excitation/inhibition balance and shift the normal GS topological distribution. These progressive topographical GS disturbances in subcortical-cortical networks may underlie pathophysiological mechanisms of global fluctuations in human epilepsy.
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Affiliation(s)
- Rong Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Hongyu Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Liangcheng Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Leiyao Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Ting Zou
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Xuyang Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Zhiqiang Zhang
- Department of Medical ImagingJinling Hospital, Nanjing University School of MedicineNanjingChina
| | - Guangming Lu
- Department of Medical ImagingJinling Hospital, Nanjing University School of MedicineNanjingChina
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Laboratory for Neuroinformation, High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
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15
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Lehnertz K, Rings T, Bröhl T. Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks. FRONTIERS IN NETWORK PHYSIOLOGY 2021; 1:755016. [PMID: 36925573 PMCID: PMC10013076 DOI: 10.3389/fnetp.2021.755016] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 09/13/2021] [Indexed: 11/13/2022]
Abstract
Electroencephalography (EEG) is a widely employed tool for exploring brain dynamics and is used extensively in various domains, ranging from clinical diagnosis via neuroscience, cognitive science, cognitive psychology, psychophysiology, neuromarketing, neurolinguistics, and pharmacology to research on brain computer interfaces. EEG is the only technique that enables the continuous recording of brain dynamics over periods of time that range from a few seconds to hours and days and beyond. When taking long-term recordings, various endogenous and exogenous biological rhythms may impinge on characteristics of EEG signals. While the impact of the circadian rhythm and of ultradian rhythms on spectral characteristics of EEG signals has been investigated for more than half a century, only little is known on how biological rhythms influence characteristics of brain dynamics assessed with modern EEG analysis techniques. At the example of multiday, multichannel non-invasive and invasive EEG recordings, we here discuss the impact of biological rhythms on temporal changes of various characteristics of human brain dynamics: higher-order statistical moments and interaction properties of multichannel EEG signals as well as local and global characteristics of EEG-derived evolving functional brain networks. Our findings emphasize the need to take into account the impact of biological rhythms in order to avoid erroneous statements about brain dynamics and about evolving functional brain networks.
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Affiliation(s)
- 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
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany.,Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany.,Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
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16
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Bröhl T, Rings T, Lehnertz K. Von Interaktionen zu Interaktionsnetzwerken: Zeitabhängige
funktionelle Netzwerke am Beispiel der Epilepsie. KLIN NEUROPHYSIOL 2020. [DOI: 10.1055/a-1195-9190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
ZusammenfassungDas menschliche Gehirn ist ein komplexes Netzwerk aus interagierenden
nichtstationären Subsystemen (Netzwerk von Netzwerken), deren
komplizierte räumlich-zeitliche Dynamiken bis heute nur unzureichend
verstanden sind. Dabei versprechen aktuelle Entwicklungen im Bereich der
Zeitreihenanalyse sowie der Theorie komplexer Netzwerke neue und verbesserte
Einblicke in die Dynamiken von Hirnnetzwerken auf verschiedenen
räumlich-zeitlichen Skalen. Wir geben einen Überblick
über diese Entwicklungen und besprechen am Beispiel
zeitabhängiger epileptischer Hirnnetzwerke Fortschritte im
Verständnis von Hirndynamiken, die über multiple Skalen
hinweg variieren.
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Affiliation(s)
- Timo Bröhl
- Klinik und Poliklinik für Epileptologie,
Universitätsklinikum Bonn, Bonn
- Helmholtz Institut für Strahlen- und Kernphysik,
Bonn
| | - Thorsten Rings
- Klinik und Poliklinik für Epileptologie,
Universitätsklinikum Bonn, Bonn
- Helmholtz Institut für Strahlen- und Kernphysik,
Bonn
| | - Klaus Lehnertz
- Klinik und Poliklinik für Epileptologie,
Universitätsklinikum Bonn, Bonn
- Helmholtz Institut für Strahlen- und Kernphysik,
Bonn
- Interdisziplinäres Zentrum für komplexe Systeme,
Bonn
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17
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Wang Y, Sinha N, Schroeder GM, Ramaraju S, McEvoy AW, Miserocchi A, de Tisi J, Chowdhury FA, Diehl B, Duncan JS, Taylor PN. Interictal intracranial electroencephalography for predicting surgical success: The importance of space and time. Epilepsia 2020; 61:1417-1426. [PMID: 32589284 PMCID: PMC7611164 DOI: 10.1111/epi.16580] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 05/21/2020] [Accepted: 05/21/2020] [Indexed: 12/14/2022]
Abstract
Objective Predicting postoperative seizure freedom using functional correlation networks derived from interictal intracranial electroencephalography (EEG) has shown some success. However, there are important challenges to consider: (1) electrodes physically closer to each other naturally tend to be more correlated, causing a spatial bias; (2) implantation location and number of electrodes differ between patients, making cross-subject comparisons difficult; and (3) functional correlation networks can vary over time but are currently assumed to be static. Methods In this study, we address these three challenges using intracranial EEG data from 55 patients with intractable focal epilepsy. Patients additionally underwent preoperative magnetic resonance imaging (MRI), intraoperative computed tomography, and postoperative MRI, allowing accurate localization of electrodes and delineation of the removed tissue. Results We show that normalizing for spatial proximity between nearby electrodes improves prediction of postsurgery seizure outcomes. Moreover, patients with more extensive electrode coverage were more likely to have their outcome predicted correctly (area under the receiver operating characteristic curve > 0.9, P « 0.05) but not necessarily more likely to have a better outcome. Finally, our predictions are robust regardless of the time segment analyzed. Significance Future studies should account for the spatial proximity of electrodes in functional network construction to improve prediction of postsurgical seizure outcomes. Greater coverage of both removed and spared tissue allows for predictions with higher accuracy.
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Affiliation(s)
- Yujiang Wang
- CNNP lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK.,Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK.,Institute of Neurology, University College London, London, UK
| | - Nishant Sinha
- CNNP lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK.,Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
| | - Gabrielle M Schroeder
- CNNP lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK
| | - Sriharsha Ramaraju
- CNNP lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK
| | - Andrew W McEvoy
- Institute of Neurology, University College London, London, UK
| | - Anna Miserocchi
- Institute of Neurology, University College London, London, UK
| | - Jane de Tisi
- Institute of Neurology, University College London, London, UK
| | | | - Beate Diehl
- Institute of Neurology, University College London, London, UK
| | - John S Duncan
- Institute of Neurology, University College London, London, UK
| | - Peter N Taylor
- CNNP lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, UK.,Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK.,Institute of Neurology, University College London, London, UK
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18
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Mitsis GD, Anastasiadou MN, Christodoulakis M, Papathanasiou ES, Papacostas SS, Hadjipapas A. Functional brain networks of patients with epilepsy exhibit pronounced multiscale periodicities, which correlate with seizure onset. Hum Brain Mapp 2020; 41:2059-2076. [PMID: 31977145 PMCID: PMC7268013 DOI: 10.1002/hbm.24930] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 12/11/2019] [Accepted: 01/07/2020] [Indexed: 11/08/2022] Open
Abstract
Epileptic seizure detection and prediction by using noninvasive measurements such as scalp EEG signals or invasive, intracranial recordings, has been at the heart of epilepsy studies for at least three decades. To this end, the most common approach has been to consider short‐length recordings (several seconds to a few minutes) around a seizure, aiming to identify significant changes that occur before or during seizures. An inherent assumption in this approach is the presence of a relatively constant EEG activity in the interictal period, which is interrupted by seizure occurrence. Here, we examine this assumption by using long‐duration scalp EEG data (21–94 hr) in nine patients with epilepsy, based on which we construct functional brain networks. Our results reveal that these networks vary over time in a periodic fashion, exhibiting multiple peaks at periods ranging between 1 and 24 hr. The effects of seizure onset on the functional brain network properties were found to be considerably smaller in magnitude compared to the changes due to these inherent periodic cycles. Importantly, the properties of the identified network periodic components (instantaneous phase) were found to be strongly correlated to seizure onset, especially for the periodicities around 3 and 5 hr. These correlations were found to be largely absent between EEG signal periodicities and seizure onset, suggesting that higher specificity may be achieved by using network‐based metrics. In turn, this implies that more robust seizure detection and prediction can be achieved if longer term underlying functional brain network periodic variations are taken into account.
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Affiliation(s)
| | | | | | | | - Savvas S Papacostas
- Neurology Clinic B, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
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19
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Müller M, Caporro M, Gast H, Pollo C, Wiest R, Schindler K, Rummel C. Linear and nonlinear interrelations show fundamentally distinct network structure in preictal intracranial EEG of epilepsy patients. Hum Brain Mapp 2019; 41:467-483. [PMID: 31625670 PMCID: PMC7268049 DOI: 10.1002/hbm.24816] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Revised: 09/18/2019] [Accepted: 09/20/2019] [Indexed: 12/24/2022] Open
Abstract
Resection of the seizure generating tissue can be highly beneficial in patients with drug-resistant epilepsy. However, only about half of all patients undergoing surgery get permanently and completely seizure free. Investigating the dependences between intracranial EEG signals adds a multivariate perspective largely unavailable to visual EEG analysis, which is the current clinical practice. We examined linear and nonlinear interrelations between intracranial EEG signals regarding their spatial distribution and network characteristics. The analyzed signals were recorded immediately before clinical seizure onset in epilepsy patients who received a standardized electrode implantation targeting the mesiotemporal structures. The linear interrelation networks were predominantly locally connected and highly reproducible between patients. In contrast, the nonlinear networks had a clearly centralized structure, which was specific for the individual pathology. The nonlinear interrelations were overrepresented in the focal hemisphere and in patients with no or only rare seizures after surgery specifically in the resected tissue. Connections to the outside were predominantly nonlinear. In all patients without worthwhile improvement after resective treatment, tissue producing strong nonlinear interrelations was left untouched by surgery. Our findings indicate that linear and nonlinear interrelations play fundamentally different roles in preictal intracranial EEG. Moreover, they suggest nonlinear signal interrelations to be a marker of epileptogenic tissue and not a characteristic of the mesiotemporal structures. Our results corroborate the network-based nature of epilepsy and suggest the application of network analysis to support the planning of resective epilepsy surgery.
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Affiliation(s)
- Michael Müller
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland.,Department of Neurology, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland
| | - Matteo Caporro
- Department of Neurology, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland
| | - Heidemarie Gast
- Department of Neurology, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland
| | - Claudio Pollo
- Department of Neurosurgery, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
| | - Kaspar Schindler
- Department of Neurology, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
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20
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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: 191] [Impact Index Per Article: 38.2] [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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21
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Precursors of seizures due to specific spatial-temporal modifications of evolving large-scale epileptic brain networks. Sci Rep 2019; 9:10623. [PMID: 31337840 PMCID: PMC6650408 DOI: 10.1038/s41598-019-47092-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 07/10/2019] [Indexed: 12/25/2022] Open
Abstract
Knowing when, where, and how seizures are initiated in large-scale epileptic brain networks remains a widely unsolved problem. Seizure precursors – changes in brain dynamics predictive of an impending seizure – can now be identified well ahead of clinical manifestations, but either the seizure onset zone or remote brain areas are reported as network nodes from which seizure precursors emerge. We aimed to shed more light on the role of constituents of evolving epileptic networks that recurrently transit into and out of seizures. We constructed such networks from more than 3200 hours of continuous intracranial electroencephalograms recorded in 38 patients with medication refractory epilepsy. We succeeded in singling out predictive edges and predictive nodes. Their particular characteristics, namely edge weight respectively node centrality (a fundamental concept of network theory), from the pre-ictal periods of 78 out of 97 seizures differed significantly from the characteristics seen during inter-ictal periods. The vast majority of predictive nodes were connected by most of the predictive edges, but these nodes never played a central role in the evolving epileptic networks. Interestingly, predictive nodes were entirely associated with brain regions deemed unaffected by the focal epileptic process. We propose a network mechanism for a transition into the pre-seizure state, which puts into perspective the role of the seizure onset zone in this transition and highlights the necessity to reassess current concepts for seizure generation and seizure prevention.
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22
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Anastasiadou MN, Christodoulakis M, Papathanasiou ES, Papacostas SS, Hadjipapas A, Mitsis GD. Graph Theoretical Characteristics of EEG-Based Functional Brain Networks in Patients With Epilepsy: The Effect of Reference Choice and Volume Conduction. Front Neurosci 2019; 13:221. [PMID: 30949021 PMCID: PMC6436604 DOI: 10.3389/fnins.2019.00221] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Accepted: 02/26/2019] [Indexed: 12/22/2022] Open
Abstract
It is well-established that both volume conduction and the choice of recording reference (montage) affect the correlation measures obtained from scalp EEG, both in the time and frequency domains. As a result, a number of correlation measures have been proposed aiming to reduce these effects. In our previous work, we have showed that scalp-EEG based functional brain networks in patients with epilepsy exhibit clear periodic patterns at different time scales and that these patterns are strongly correlated to seizure onset, particularly at shorter time scales (around 3 and 5 h), which has important clinical implications. In the present work, we use the same long-duration clinical scalp EEG data (multiple days) to investigate the extent to which the aforementioned results are affected by the choice of reference choice and correlation measure, by considering several widely used montages as well as correlation metrics that are differentially sensitive to the effects of volume conduction. Specifically, we compare two standard and commonly used linear correlation measures, cross-correlation in the time domain, and coherence in the frequency domain, with measures that account for zero-lag correlations: corrected cross-correlation, imaginary coherence, phase lag index, and weighted phase lag index. We show that the graphs constructed with corrected cross-correlation and WPLI are more stable across different choices of reference. Also, we demonstrate that all the examined correlation measures revealed similar periodic patterns in the obtained graph measures when the bipolar and common reference (Cz) montage were used. This includes circadian-related periodicities (e.g., a clear increase in connectivity during sleep periods as compared to awake periods), as well as periodicities at shorter time scales (around 3 and 5 h). On the other hand, these results were affected to a large degree when the average reference montage was used in combination with standard cross-correlation, coherence, imaginary coherence, and PLI, which is likely due to the low number of electrodes and inadequate electrode coverage of the scalp. Finally, we demonstrate that the correlation between seizure onset and the brain network periodicities is preserved when corrected cross-correlation and WPLI were used for all the examined montages. This suggests that, even in the standard clinical setting of EEG recording in epilepsy where only a limited number of scalp EEG measurements are available, graph-theoretic quantification of periodic patterns using appropriate montage, and correlation measures corrected for volume conduction provides useful insights into seizure onset.
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Affiliation(s)
- Maria N Anastasiadou
- KIOS Research and Innovation Centre of Excellence, Faculty of Engineering, University of Cyprus, Nicosia, Cyprus
| | - Manolis Christodoulakis
- KIOS Research and Innovation Centre of Excellence, Faculty of Engineering, University of Cyprus, Nicosia, Cyprus
| | - Eleftherios S Papathanasiou
- Laboratory of Clinical Neurophysiology, Clinic B, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Savvas S Papacostas
- Laboratory of Clinical Neurophysiology, Clinic B, Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | | | - Georgios D Mitsis
- Department of Bioengineering, McGill University, Montreal, QC, Canada.,Department of Electrical and Computer Engineering, KIOS Research Center, University of Cyprus, Nicosia, Cyprus
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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: 10] [Impact Index Per Article: 1.7] [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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24
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Tauste Campo A, Principe A, Ley M, Rocamora R, Deco G. Degenerate time-dependent network dynamics anticipate seizures in human epileptic brain. PLoS Biol 2018; 16:e2002580. [PMID: 29621233 PMCID: PMC5886392 DOI: 10.1371/journal.pbio.2002580] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 03/05/2018] [Indexed: 01/24/2023] Open
Abstract
Epileptic seizures are known to follow specific changes in brain dynamics. While some algorithms can nowadays robustly detect these changes, a clear understanding of the mechanism by which these alterations occur and generate seizures is still lacking. Here, we provide crossvalidated evidence that such changes are initiated by an alteration of physiological network state dynamics. Specifically, our analysis of long intracranial electroencephalography (iEEG) recordings from a group of 10 patients identifies a critical phase of a few hours in which time-dependent network states become less variable ("degenerate"), and this phase is followed by a global functional connectivity reduction before seizure onset. This critical phase is characterized by an abnormal occurrence of highly correlated network instances and is shown to be particularly associated with the activity of the resected regions in patients with validated postsurgical outcome. Our approach characterizes preseizure network dynamics as a cascade of 2 sequential events providing new insights into seizure prediction and control. Understanding and predicting the generation of seizures in epileptic patients is fundamental to improving the quality of life of the more than 1% of the world population who suffer from this illness. Although seizure prediction has made important advances over the last decade, there is a lack of understanding of the common principles explaining the transitions that brain activity undergoes before a seizure. In this study, we characterized this transition from a novel perspective grounded on the mathematical analysis of continuous recordings inside the brains of epileptic patients over several days using depth electrodes. We show that the critical period preceding a seizure unfolds in a two-stage process. It begins with a phase of several hours when the highly correlated activity in the preceding days is altered, and it proceeds with a second, shorter phase of decrease in global connectivity before the seizure onset. Furthermore, our analysis reveals that these global alterations are more locally manifested in areas that are selected for surgical treatment. Our study suggests that preseizure activity might follow global stereotyped dynamics that could be targeted and modulated to prevent epileptic seizures.
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Affiliation(s)
- Adrià Tauste Campo
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Epilepsy Unit, Department of Neurology, Hospital del Mar-Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- * E-mail:
| | - Alessandro Principe
- Epilepsy Unit, Department of Neurology, Hospital del Mar-Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Miguel Ley
- Epilepsy Unit, Department of Neurology, Hospital del Mar-Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Rodrigo Rocamora
- Epilepsy Unit, Department of Neurology, Hospital del Mar-Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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25
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Abstract
Pharmaco-electroencephalography (pharmaco-EEG) has never gained great popularity in epilepsy research. Nevertheless, the electroencephalogram (EEG) is the most important neurological examination technique in this patient population. Development and investigation of antiepileptic drugs (AEDs) involves EEG for diagnosis and outcome evaluation. In contrast to the common use of the EEG for documenting the effect of AEDs on the presence of interictal epileptiform activities or seizures, quantitative analysis of drug responses in the EEG are not yet standard in pharmacological studies. We provide an overview of dedicated pharmaco-EEG studies with AEDs in humans. A systematic search in PubMed yielded 43 articles, which were reviewed for their relevance. After excluding studies according to our exclusion criteria, nine studies remained. These studies plus the retrieved references from the bibliographies of the identified studies yielded 37 studies to be included in the review. The most prominent method in pharmaco-EEG research for AEDs was analysis of the frequency content in response to drug intake, often with quantitative methods such as spectral analysis. Despite documenting the effect of the drug on brain activity, some studies were conducted in order to document treatment response, detect neurotoxic effects, and measure reversibility of AED-induced changes. There were some attempts to predict treatment response or side effects. We suggest that pharmaco-EEG deserves more attention in AED research, specifically because the newest drugs and techniques have not yet been subject to investigation.
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Affiliation(s)
- Yvonne Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Ignaz Harrer Str. 79, 5020, Salzburg, Austria. .,Department of Psychology, University of Akureyri, Norðurslóð 2, 600, Akureyri, Iceland.
| | - Christoph Helmstaedter
- 0000 0001 2240 3300grid.10388.32Department of Epileptology, University of Bonn, Sigmund Freud Straße 25, 53105 Bonn, Germany
| | - Klaus Lehnertz
- 0000 0001 2240 3300grid.10388.32Department of Epileptology, University of Bonn, Sigmund Freud Straße 25, 53105 Bonn, Germany ,0000 0001 2240 3300grid.10388.32Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53175 Bonn, Germany
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26
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27
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Anastasiadou M, Hadjipapas A, Christodoulakis M, Papathanasiou ES, Papacostas SS, Mitsis GD. Epileptic seizure onset correlates with long term EEG functional brain network properties. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:2822-2825. [PMID: 28268905 DOI: 10.1109/embc.2016.7591317] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
We investigated the correlation of epileptic seizure onset times with long term EEG functional brain network properties. To do so, we constructed binary functional brain networks from long-term, multichannel electroencephalographic data recorded from nine patients with epilepsy. The corresponding network properties were quantified using the average network degree. It was found that the network degree (as well as other network properties such as the network efficiency and clustering coefficient) exhibited large fluctuations over time; however, it also exhibited specific periodic temporal structure over different time scales (1.5hr-24hr periods) that was consistent across subjects. We investigated the correlation of the phases of these network periodicities with the seizure onset by using circular statistics. The results showed that the instantaneous phases of the 3.5hr, 5.5hr, 12hr and 24hr network degree periodic components are not uniformly distributed, suggesting that functional network properties are related to seizure generation and occurrence.
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28
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Kinjo ER, Rodríguez PXR, Dos Santos BA, Higa GSV, Ferraz MSA, Schmeltzer C, Rüdiger S, Kihara AH. New Insights on Temporal Lobe Epilepsy Based on Plasticity-Related Network Changes and High-Order Statistics. Mol Neurobiol 2017; 55:3990-3998. [PMID: 28555345 DOI: 10.1007/s12035-017-0623-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 05/16/2017] [Indexed: 12/21/2022]
Abstract
Epilepsy is a disorder of the brain characterized by the predisposition to generate recurrent unprovoked seizures, which involves reshaping of neuronal circuitries based on intense neuronal activity. In this review, we first detailed the regulation of plasticity-associated genes, such as ARC, GAP-43, PSD-95, synapsin, and synaptophysin. Indeed, reshaping of neuronal connectivity after the primary, acute epileptogenesis event increases the excitability of the temporal lobe. Herein, we also discussed the heterogeneity of neuronal populations regarding the number of synaptic connections, which in the theoretical field is commonly referred as degree. Employing integrate-and-fire neuronal model, we determined that in addition to increased synaptic strength, degree correlations might play essential and unsuspected roles in the control of network activity. Indeed, assortativity, which can be described as a condition where high-degree correlations are observed, increases the excitability of neural networks. In this review, we summarized recent topics in the field, and data were discussed according to newly developed or unusual tools, as provided by mathematical graph analysis and high-order statistics. With this, we were able to present new foundations for the pathological activity observed in temporal lobe epilepsy.
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Affiliation(s)
- Erika Reime Kinjo
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
| | - Pedro Xavier Royero Rodríguez
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
| | - Bianca Araújo Dos Santos
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
| | - Guilherme Shigueto Vilar Higa
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
- Departamento de Fisiologia e Biofísica, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Mariana Sacrini Ayres Ferraz
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
| | - Christian Schmeltzer
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
- Institute of Physics, Humboldt University at Berlin, Berlin, Germany
| | - Sten Rüdiger
- Institute of Physics, Humboldt University at Berlin, Berlin, Germany
| | - Alexandre Hiroaki Kihara
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil.
- Departamento de Fisiologia e Biofísica, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo, SP, Brazil.
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29
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Geier C, Lehnertz K. Long-term variability of importance of brain regions in evolving epileptic brain networks. CHAOS (WOODBURY, N.Y.) 2017; 27:043112. [PMID: 28456162 DOI: 10.1063/1.4979796] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
We investigate the temporal and spatial variability of the importance of brain regions in evolving epileptic brain networks. We construct these networks from multiday, multichannel electroencephalographic data recorded from 17 epilepsy patients and use centrality indices to assess the importance of brain regions. Time-resolved indications of highest importance fluctuate over time to a greater or lesser extent, however, with some periodic temporal structure that can mostly be attributed to phenomena unrelated to the disease. In contrast, relevant aspects of the epileptic process contribute only marginally. Indications of highest importance also exhibit pronounced alternations between various brain regions that are of relevance for studies aiming at an improved understanding of the epileptic process with graph-theoretical approaches. Nonetheless, these findings may guide new developments for individualized diagnosis, treatment, and control.
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Affiliation(s)
- Christian Geier
- 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
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30
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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: 29] [Impact Index Per Article: 3.6] [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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31
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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: 26] [Impact Index Per Article: 2.9] [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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32
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van Diessen E, Numan T, van Dellen E, van der Kooi AW, Boersma M, Hofman D, van Lutterveld R, van Dijk BW, van Straaten ECW, Hillebrand A, Stam CJ. Opportunities and methodological challenges in EEG and MEG resting state functional brain network research. Clin Neurophysiol 2015; 126:1468-81. [PMID: 25511636 DOI: 10.1016/j.clinph.2014.11.018] [Citation(s) in RCA: 241] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2014] [Revised: 10/30/2014] [Accepted: 11/20/2014] [Indexed: 12/17/2022]
Affiliation(s)
- E van Diessen
- Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, The Netherlands.
| | - T Numan
- Department of Intensive Care, University Medical Center Utrecht, The Netherlands
| | - E van Dellen
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, The Netherlands; Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - A W van der Kooi
- Department of Intensive Care, University Medical Center Utrecht, The Netherlands
| | - M Boersma
- Department of Experimental Psychology, Utrecht University, The Netherlands
| | - D Hofman
- Department of Experimental Psychology, Utrecht University, The Netherlands
| | - R van Lutterveld
- Center for Mindfulness, University of Massachusetts School of Medicine, Worcester, Massachusetts, USA
| | - B W van Dijk
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - E C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - A Hillebrand
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - C J Stam
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
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33
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Geier C, Bialonski S, Elger CE, Lehnertz K. How important is the seizure onset zone for seizure dynamics? Seizure 2015; 25:160-6. [DOI: 10.1016/j.seizure.2014.10.013] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2014] [Revised: 10/09/2014] [Accepted: 10/21/2014] [Indexed: 11/29/2022] Open
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34
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Abstract
Modern network science has revealed fundamental aspects of normal brain-network organization, such as small-world and scale-free patterns, hierarchical modularity, hubs and rich clubs. The next challenge is to use this knowledge to gain a better understanding of brain disease. Recent developments in the application of network science to conditions such as Alzheimer's disease, multiple sclerosis, traumatic brain injury and epilepsy have challenged the classical concept of neurological disorders being either 'local' or 'global', and have pointed to the overload and failure of hubs as a possible final common pathway in neurological disorders.
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Affiliation(s)
- Cornelis J Stam
- Department of Neurology and Clinical Neurophysiology, MEG Center, VU University Medical Center, De Boelelaan 1118, 1081HV Amsterdam, The Netherlands
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35
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Onias H, Viol A, Palhano-Fontes F, Andrade KC, Sturzbecher M, Viswanathan G, de Araujo DB. Brain complex network analysis by means of resting state fMRI and graph analysis: will it be helpful in clinical epilepsy? Epilepsy Behav 2014; 38:71-80. [PMID: 24374054 DOI: 10.1016/j.yebeh.2013.11.019] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2013] [Revised: 10/11/2013] [Accepted: 11/16/2013] [Indexed: 10/25/2022]
Abstract
Functional magnetic resonance imaging (fMRI) has just completed 20 years of existence. It currently serves as a research tool in a broad range of human brain studies in normal and pathological conditions, as is the case of epilepsy. To date, most fMRI studies aimed at characterizing brain activity in response to various active paradigms. More recently, a number of strategies have been used to characterize the low-frequency oscillations of the ongoing fMRI signals when individuals are at rest. These datasets have been largely analyzed in the context of functional connectivity, which inspects the covariance of fMRI signals from different areas of the brain. In addition, resting state fMRI is progressively being used to evaluate complex network features of the brain. These strategies have been applied to a number of different problems in neuroscience, which include diseases such as Alzheimer's, schizophrenia, and epilepsy. Hence, we herein aimed at introducing the subject of complex network and how to use it for the analysis of fMRI data. This appears to be a promising strategy to be used in clinical epilepsy. Therefore, we also review the recent literature that has applied these ideas to the analysis of fMRI data in patients with epilepsy.
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Affiliation(s)
- Heloisa Onias
- Brain Institute/Onofre Lopes University Hospital, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
| | - Aline Viol
- Department of Physics, UFRN, Natal, Brazil
| | - Fernanda Palhano-Fontes
- Brain Institute/Onofre Lopes University Hospital, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
| | - Katia C Andrade
- Brain Institute/Onofre Lopes University Hospital, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil
| | | | | | - Draulio B de Araujo
- Brain Institute/Onofre Lopes University Hospital, Federal University of Rio Grande do Norte (UFRN), Natal, Brazil.
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36
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van Diessen E, Otte WM, Braun KPJ, Stam CJ, Jansen FE. Does sleep deprivation alter functional EEG networks in children with focal epilepsy? Front Syst Neurosci 2014; 8:67. [PMID: 24808832 PMCID: PMC4010773 DOI: 10.3389/fnsys.2014.00067] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2013] [Accepted: 04/08/2014] [Indexed: 11/29/2022] Open
Abstract
Electroencephalography (EEG) recordings after sleep deprivation increase the diagnostic yield in patients suspected of epilepsy if the routine EEG remains inconclusive. Sleep deprivation is associated with increased interictal EEG abnormalities in patients with epilepsy, but the exact mechanism is unknown. In this feasibility study, we used a network analytical approach to provide novel insights into this clinical observation. The aim was to characterize the effect of sleep deprivation on the interictal functional network organization using a unique dataset of paired routine and sleep deprivation recordings in patients and controls. We included 21 children referred to the first seizure clinic of our center with suspected new onset focal epilepsy in whom a routine interictal and a sleep deprivation EEG (SD-EEG) were performed. Seventeen children, in whom the diagnosis of epilepsy was excluded, served as controls. For both time points weighted functional networks were constructed based on interictal artifact free time-series. Routine and sleep deprivation networks were characterized at different frequency bands using minimum spanning tree (MST) measures (leaf number and diameter) and classical measures of integration (path length) and segregation (clustering coefficient). A significant interaction was found for leaf number and diameter between patients and controls after sleep deprivation: patients showed a shift toward a more path-like MST network whereas controls showed a shift toward a more star-like MST network. This shift in network organization after sleep deprivation in patients is in accordance with previous studies showing a more regular network organization in the ictal state and might relate to the increased epileptiform abnormalities found in patients after sleep deprivation. Larger studies are needed to verify these results. Finally, MST measures were more sensitive in detecting network changes as compared to the classical measures of integration and segregation.
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Affiliation(s)
- Eric van Diessen
- Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht Utrecht, Netherlands
| | - Willem M Otte
- Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht Utrecht, Netherlands ; Biomedical MR Imaging and Spectroscopy Group, Image Sciences Institute, University Medical Center Utrecht Utrecht, Netherlands
| | - Kees P J Braun
- Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht Utrecht, Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology, VU University Medical Center Amsterdam, Netherlands
| | - Floor E Jansen
- Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht Utrecht, Netherlands
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37
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Radebach A, Donner RV, Runge J, Donges JF, Kurths J. Disentangling different types of El Niño episodes by evolving climate network analysis. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2013; 88:052807. [PMID: 24329318 DOI: 10.1103/physreve.88.052807] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2011] [Revised: 07/19/2013] [Indexed: 06/03/2023]
Abstract
Complex network theory provides a powerful toolbox for studying the structure of statistical interrelationships between multiple time series in various scientific disciplines. In this work, we apply the recently proposed climate network approach for characterizing the evolving correlation structure of the Earth's climate system based on reanalysis data for surface air temperatures. We provide a detailed study of the temporal variability of several global climate network characteristics. Based on a simple conceptual view of red climate networks (i.e., networks with a comparably low number of edges), we give a thorough interpretation of our evolving climate network characteristics, which allows a functional discrimination between recently recognized different types of El Niño episodes. Our analysis provides deep insights into the Earth's climate system, particularly its global response to strong volcanic eruptions and large-scale impacts of different phases of the El Niño Southern Oscillation.
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van Diessen E, Diederen SJH, Braun KPJ, Jansen FE, Stam CJ. Functional and structural brain networks in epilepsy: what have we learned? Epilepsia 2013; 54:1855-65. [PMID: 24032627 DOI: 10.1111/epi.12350] [Citation(s) in RCA: 221] [Impact Index Per Article: 20.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/28/2013] [Indexed: 02/06/2023]
Abstract
Brain functioning is increasingly seen as a complex interplay of dynamic neural systems that rely on the integrity of structural and functional networks. Recent studies that have investigated functional and structural networks in epilepsy have revealed specific disruptions in connectivity and network topology and, consequently, have led to a shift from "focus" to "networks" in modern epilepsy research. Disruptions in these networks may be associated with cognitive and behavioral impairments often seen in patients with chronic epilepsy. In this review, we aim to provide an overview that would introduce the clinical neurologist and epileptologist to this new theoretical paradigm. We focus on the application of a theory, called "network analysis," to characterize resting-state functional and structural networks and discuss current and future clinical applications of network analysis in patients with epilepsy.
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Affiliation(s)
- Eric van Diessen
- Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
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39
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Bialonski S, Lehnertz K. Assortative mixing in functional brain networks during epileptic seizures. CHAOS (WOODBURY, N.Y.) 2013; 23:033139. [PMID: 24089975 DOI: 10.1063/1.4821915] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
We investigate assortativity of functional brain networks before, during, and after one-hundred epileptic seizures with different anatomical onset locations. We construct binary functional networks from multi-channel electroencephalographic data recorded from 60 epilepsy patients; and from time-resolved estimates of the assortativity coefficient, we conclude that positive degree-degree correlations are inherent to seizure dynamics. While seizures evolve, an increasing assortativity indicates a segregation of the underlying functional network into groups of brain regions that are only sparsely interconnected, if at all. Interestingly, assortativity decreases already prior to seizure end. Together with previous observations of characteristic temporal evolutions of global statistical properties and synchronizability of epileptic brain networks, our findings may help to gain deeper insights into the complicated dynamics underlying generation, propagation, and termination of seizures.
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Affiliation(s)
- Stephan Bialonski
- Max Planck Institute for the Physics of Complex Systems, Nöthnitzer Straße 38, 01187 Dresden, Germany
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40
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Direito B, Teixeira C, Ribeiro B, Castelo-Branco M, Sales F, Dourado A. Modeling epileptic brain states using EEG spectral analysis and topographic mapping. J Neurosci Methods 2012; 210:220-9. [DOI: 10.1016/j.jneumeth.2012.07.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2012] [Revised: 07/02/2012] [Accepted: 07/10/2012] [Indexed: 10/28/2022]
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41
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Vaessen MJ, Braakman HMH, Heerink JS, Jansen JFA, Debeij-van Hall MHJA, Hofman PAM, Aldenkamp AP, Backes WH. Abnormal modular organization of functional networks in cognitively impaired children with frontal lobe epilepsy. ACTA ACUST UNITED AC 2012; 23:1997-2006. [PMID: 22772649 DOI: 10.1093/cercor/bhs186] [Citation(s) in RCA: 68] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Many children with frontal lobe epilepsy (FLE) have significant cognitive comorbidity, for which the underlying mechanism has not yet been unraveled, but is likely related to disturbed cerebral network integrity. Using resting-state fMRI, we investigated whether cerebral network characteristics are associated with epilepsy and cognitive comorbidity. We included 37 children with FLE and 41 healthy age-matched controls. Cognitive performance was determined by means of a computerized visual searching task. A connectivity matrix for 82 cortical and subcortical brain regions was generated for each subject by calculating the inter-regional correlation of the fMRI time signals. From the connectivity matrix, graph metrics were calculated and the anatomical configuration of aberrant connections and modular organization was investigated. Both patients and controls displayed efficiently organized networks. However, FLE patients displayed a higher modularity, implying that subnetworks are less interconnected. Impaired cognition was associated with higher modularity scores and abnormal modular organization of the brain, which was mainly expressed as a decrease in long-range and an increase in interhemispheric connectivity in patients. We showed that network modularity analysis provides a sensitive marker for cognitive impairment in FLE and suggest that abnormally interconnected functional subnetworks of the brain might underlie the cognitive problems in children with FLE.
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Affiliation(s)
- M J Vaessen
- Department of Radiology, Maastricht University Medical Centre, Maastricht 6202 AZ, The Netherlands
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42
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Ansmann G, Lehnertz K. Surrogate-assisted analysis of weighted functional brain networks. J Neurosci Methods 2012; 208:165-72. [DOI: 10.1016/j.jneumeth.2012.05.008] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2012] [Revised: 05/02/2012] [Accepted: 05/04/2012] [Indexed: 10/28/2022]
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Scaling effects and spatio-temporal multilevel dynamics in epileptic seizures. PLoS One 2012; 7:e30371. [PMID: 22363431 PMCID: PMC3281841 DOI: 10.1371/journal.pone.0030371] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2011] [Accepted: 12/19/2011] [Indexed: 11/19/2022] Open
Abstract
Epileptic seizures are one of the most well-known dysfunctions of the nervous system. During a seizure, a highly synchronized behavior of neural activity is observed that can cause symptoms ranging from mild sensual malfunctions to the complete loss of body control. In this paper, we aim to contribute towards a better understanding of the dynamical systems phenomena that cause seizures. Based on data analysis and modelling, seizure dynamics can be identified to possess multiple spatial scales and on each spatial scale also multiple time scales. At each scale, we reach several novel insights. On the smallest spatial scale we consider single model neurons and investigate early-warning signs of spiking. This introduces the theory of critical transitions to excitable systems. For clusters of neurons (or neuronal regions) we use patient data and find oscillatory behavior and new scaling laws near the seizure onset. These scalings lead to substantiate the conjecture obtained from mean-field models that a Hopf bifurcation could be involved near seizure onset. On the largest spatial scale we introduce a measure based on phase-locking intervals and wavelets into seizure modelling. It is used to resolve synchronization between different regions in the brain and identifies time-shifted scaling laws at different wavelet scales. We also compare our wavelet-based multiscale approach with maximum linear cross-correlation and mean-phase coherence measures.
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Abstract
Epilepsy is characterized by intermittent, paroxysmal, hypersynchronous electrical activity that may remain localized and/or spread and severely disrupt the brain's normal multitask and multiprocessing function. Epileptic seizures are the hallmarks of such activity. The ability to issue warnings in real time of impending seizures may lead to novel diagnostic tools and treatments for epilepsy. Applications may range from a warning to the patient to avert seizure-associated injuries, to automatic timely administration of an appropriate stimulus. Seizure prediction could become an integral part of the treatment of epilepsy through neuromodulation, especially in the new generation of closed-loop seizure control systems.
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Affiliation(s)
- Leon D Iasemidis
- The Harrington Department of Biomedical Engineering, School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287-9709, USA.
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Feldwisch-Drentrup H, Staniek M, Schulze-Bonhage A, Timmer J, Dickten H, Elger CE, Schelter B, Lehnertz K. Identification of preseizure States in epilepsy: a data-driven approach for multichannel EEG recordings. Front Comput Neurosci 2011; 5:32. [PMID: 21779241 PMCID: PMC3133837 DOI: 10.3389/fncom.2011.00032] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2011] [Accepted: 06/23/2011] [Indexed: 12/25/2022] Open
Abstract
The retrospective identification of preseizure states usually bases on a time-resolved characterization of dynamical aspects of multichannel neurophysiologic recordings that can be assessed with measures from linear or non-linear time series analysis. This approach renders time profiles of a characterizing measure – so-called measure profiles – for different recording sites or combinations thereof. Various downstream evaluation techniques have been proposed to single out measure profiles that carry potential information about preseizure states. These techniques, however, rely on assumptions about seizure precursor dynamics that might not be generally valid or face the statistical problem of multiple testing. Addressing these issues, we have developed a method to preselect measure profiles that carry potential information about preseizure states, and to identify brain regions associated with seizure precursor dynamics. Our data-driven method is based on the ratio S of the global to local temporal variance of measure profiles. We evaluated its suitability by retrospectively analyzing long-lasting multichannel intracranial EEG recordings from 18 patients that included 133 focal onset seizures, using a bivariate measure for the strength of interactions. In 17/18 patients, we observed S to be significantly correlated with the predictive performance of measure profiles assessed retrospectively by means of receiver-operating-characteristic statistics. Predictive performance was higher for measure profiles preselected with S than for a manual selection using information about onset and spread of seizures. Across patients, highest predictive performance was not restricted to recordings from focal areas, thus supporting the notion of an extended epileptic network in which even distant brain regions contribute to seizure generation. We expect our method to provide further insight into the complex spatial and temporal aspects of the seizure generating process.
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