1
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Lainscsek C, Salami P, Carvalho VR, Mendes EMAM, Fan M, Cash SS, Sejnowski TJ. Network-motif delay differential analysis of brain activity during seizures. CHAOS (WOODBURY, N.Y.) 2023; 33:123136. [PMID: 38156987 PMCID: PMC10757649 DOI: 10.1063/5.0165904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 11/28/2023] [Indexed: 01/03/2024]
Abstract
Delay Differential Analysis (DDA) is a nonlinear method for analyzing time series based on principles from nonlinear dynamical systems. DDA is extended here to incorporate network aspects to improve the dynamical characterization of complex systems. To demonstrate its effectiveness, DDA with network capabilities was first applied to the well-known Rössler system under different parameter regimes and noise conditions. Network-motif DDA, based on cortical regions, was then applied to invasive intracranial electroencephalographic data from drug-resistant epilepsy patients undergoing presurgical monitoring. The directional network motifs between brain areas that emerge from this analysis change dramatically before, during, and after seizures. Neural systems provide a rich source of complex data, arising from varying internal states generated by network interactions.
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Affiliation(s)
| | - Pariya Salami
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
| | | | - Eduardo M. A. M. Mendes
- Laboratório de Modelagem, Análise e Controle de Sistemas Não Lineares, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, 31270-901 Belo Horizonte, MG, Brazil
| | - Miaolin Fan
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
| | - Sydney S. Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
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2
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Combining the neural mass model and Hodgkin–Huxley formalism: Neuronal dynamics modelling. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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3
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Hu B, Wang Z, Xu M, Zhang D, Wang D. The adjustment mechanism of the spike and wave discharges in thalamic neurons: a simulation analysis. Cogn Neurodyn 2022; 16:1449-1460. [PMID: 36408065 PMCID: PMC9666587 DOI: 10.1007/s11571-022-09788-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 01/18/2022] [Accepted: 01/25/2022] [Indexed: 11/28/2022] Open
Abstract
Different from many previous theoretical studies, this paper explores the regulatory mechanism of the spike and wave discharges (SWDs) in the reticular thalamic nucleus (TRN) by a dynamic computational model. We observe that the SWDs appears in the TRN by changing the coupling weights and delays in the thalamocortical circuit. The abundant poly-spikes wave discharges is also induced when the delay increases to large enough. These discharges can be inhibited by tuning the inhibitory output from the basal ganglia to the thalamus. The mechanisms of these waves can be explained in this model together with simulation results, which are different from the mechanisms in the cortex. The TRN is an important target in treating epilepsy, and the results may be a theoretical evidence for experimental study in the future.
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Affiliation(s)
- Bing Hu
- Department of Applied Mathematics, Zhejiang University of Technology, Hangzhou, 310023 China
| | - Zhizhi Wang
- Department of Applied Mathematics, Zhejiang University of Technology, Hangzhou, 310023 China
| | - Minbo Xu
- Department of Applied Mathematics, Zhejiang University of Technology, Hangzhou, 310023 China
| | - Dongmei Zhang
- Department of Applied Mathematics, Zhejiang University of Technology, Hangzhou, 310023 China
| | - Dingjiang Wang
- Department of Applied Mathematics, Zhejiang University of Technology, Hangzhou, 310023 China
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4
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González-Ramírez LR. Fractional-Order Traveling Wave Approximations for a Fractional-Order Neural Field Model. Front Comput Neurosci 2022; 16:788924. [PMID: 35399918 PMCID: PMC8987931 DOI: 10.3389/fncom.2022.788924] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 02/24/2022] [Indexed: 11/18/2022] Open
Abstract
In this work, we establish a fractional-order neural field mathematical model with Caputo's fractional derivative temporal order α considering 0 < α < 2, to analyze the effect of fractional-order on cortical wave features observed preceding seizure termination. The importance of this incorporation relies on the theoretical framework established by fractional-order derivatives in which memory and hereditary properties of a system are considered. Employing Mittag-Leffler functions, we first obtain approximate fractional-order solutions that provide information about the initial wave dynamics in a fractional-order frame. We then consider the Adomian decomposition method to approximate pulse solutions in a wider range of orders and longer times. The former approach establishes a direct way to investigate the initial relationships between fractional-order and wave features, such as wave speed and wave width. In contrast, the latter approach displays wave propagation dynamics in different fractional orders for longer times. Using the previous two approaches, we establish approximate wave solutions with characteristics consistent with in vivo cortical waves preceding seizure termination. In our analysis, we find consistent differences in the initial effect of the fractional-order on the features of wave speed and wave width, depending on whether α <1 or α>1. Both cases can model the shape of cortical wave propagation for different fractional-orders at the cost of modifying the wave speed. Our results also show that the effect of fractional-order on wave width depends on the synaptic threshold and the synaptic connectivity extent. Fractional-order derivatives have been interpreted as the memory trace of the system. This property and the results of our analysis suggest that fractional-order derivatives and neuronal collective memory modify cortical wave features.
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5
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Földi T, Lőrincz ML, Berényi A. Temporally Targeted Interactions With Pathologic Oscillations as Therapeutical Targets in Epilepsy and Beyond. Front Neural Circuits 2021; 15:784085. [PMID: 34955760 PMCID: PMC8693222 DOI: 10.3389/fncir.2021.784085] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/10/2021] [Indexed: 11/13/2022] Open
Abstract
Self-organized neuronal oscillations rely on precisely orchestrated ensemble activity in reverberating neuronal networks. Chronic, non-malignant disorders of the brain are often coupled to pathological neuronal activity patterns. In addition to the characteristic behavioral symptoms, these disturbances are giving rise to both transient and persistent changes of various brain rhythms. Increasing evidence support the causal role of these "oscillopathies" in the phenotypic emergence of the disease symptoms, identifying neuronal network oscillations as potential therapeutic targets. While the kinetics of pharmacological therapy is not suitable to compensate the disease related fine-scale disturbances of network oscillations, external biophysical modalities (e.g., electrical stimulation) can alter spike timing in a temporally precise manner. These perturbations can warp rhythmic oscillatory patterns via resonance or entrainment. Properly timed phasic stimuli can even switch between the stable states of networks acting as multistable oscillators, substantially changing the emergent oscillatory patterns. Novel transcranial electric stimulation (TES) approaches offer more reliable neuronal control by allowing higher intensities with tolerable side-effect profiles. This precise temporal steerability combined with the non- or minimally invasive nature of these novel TES interventions make them promising therapeutic candidates for functional disorders of the brain. Here we review the key experimental findings and theoretical background concerning various pathological aspects of neuronal network activity leading to the generation of epileptic seizures. The conceptual and practical state of the art of temporally targeted brain stimulation is discussed focusing on the prevention and early termination of epileptic seizures.
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Affiliation(s)
- Tamás Földi
- MTA-SZTE "Momentum" Oscillatory Neuronal Networks Research Group, Department of Physiology, University of Szeged, Szeged, Hungary.,Neurocybernetics Excellence Center, University of Szeged, Szeged, Hungary.,HCEMM-USZ Magnetotherapeutics Research Group, University of Szeged, Szeged, Hungary.,Child and Adolescent Psychiatry, Department of the Child Health Center, University of Szeged, Szeged, Hungary
| | - Magor L Lőrincz
- MTA-SZTE "Momentum" Oscillatory Neuronal Networks Research Group, Department of Physiology, University of Szeged, Szeged, Hungary.,Neurocybernetics Excellence Center, University of Szeged, Szeged, Hungary.,Department of Physiology, Anatomy and Neuroscience, Faculty of Sciences University of Szeged, Szeged, Hungary.,Neuroscience Division, Cardiff University, Cardiff, United Kingdom
| | - Antal Berényi
- MTA-SZTE "Momentum" Oscillatory Neuronal Networks Research Group, Department of Physiology, University of Szeged, Szeged, Hungary.,Neurocybernetics Excellence Center, University of Szeged, Szeged, Hungary.,HCEMM-USZ Magnetotherapeutics Research Group, University of Szeged, Szeged, Hungary.,Neuroscience Institute, New York University, New York, NY, United States
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6
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Bhattacharya A, Baweja T, Karri SPK. Epileptic Seizure Prediction Using Deep Transformer Model. Int J Neural Syst 2021; 32:2150058. [PMID: 34720065 DOI: 10.1142/s0129065721500581] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The electroencephalogram (EEG) is the most promising and efficient technique to study epilepsy and record all the electrical activity going in our brain. Automated screening of epilepsy through data-driven algorithms reduces the manual workload of doctors to diagnose epilepsy. New algorithms are biased either towards signal processing or deep learning, which holds subjective advantages and disadvantages. The proposed pipeline is an end-to-end automated seizure prediction framework with a Fourier transform feature extraction and deep learning-based transformer model, a blend of signal processing and deep learning - this imbibes the potential features to automatically identify the attentive regions in EEG signals for effective screening. The proposed pipeline has demonstrated superior performance on the benchmark dataset with average sensitivity and false-positive rate per hour (FPR/h) as 98.46%, 94.83% and 0.12439, 0, respectively. The proposed work shows great results on the benchmark datasets and a big potential for clinics as a support system with medical experts monitoring the patients.
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Affiliation(s)
- Abhijeet Bhattacharya
- Electrical and Electronics Engineering, Bharati Vidyapeeth's College of Engineering, A-4 Block, Baba Ramdev Marg, Shiva Enclave, Paschim Vihar, New Delhi, 110063, India
| | - Tanmay Baweja
- Electrical and Electronics Engineering, Bharati Vidyapeeth's College of Engineering, A-4 Block, Baba Ramdev Marg, Shiva Enclave, Paschim Vihar, New Delhi, 110063, India
| | - S P K Karri
- Department of Electrical Engineering, National Institute of Technology, Andhra Pradesh, Tadepalligudem - 534101, India
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7
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Depannemaecker D, Destexhe A, Jirsa V, Bernard C. Modeling seizures: From single neurons to networks. Seizure 2021; 90:4-8. [PMID: 34219016 DOI: 10.1016/j.seizure.2021.06.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 06/11/2021] [Accepted: 06/11/2021] [Indexed: 11/26/2022] Open
Abstract
Dynamical system tools offer a complementary approach to detailed biophysical seizure modeling, with a high potential for clinical applications. This review describes the theoretical framework that provides a basis for theorizing certain properties of seizures and for their classification according to their dynamical properties at onset and offset. We describe various modeling approaches spanning different scales, from single neurons to large-scale networks. This narrative review provides an accessible overview of this field, including non-exhaustive examples of key recent works.
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Affiliation(s)
- Damien Depannemaecker
- Paris-Saclay University, French National Centre for Scientific Research (CNRS), Institute of Neuroscience (NeuroPSI), 91198 Gif sur Yvette, France.
| | - Alain Destexhe
- Paris-Saclay University, French National Centre for Scientific Research (CNRS), Institute of Neuroscience (NeuroPSI), 91198 Gif sur Yvette, France.
| | - Viktor Jirsa
- Aix Marseille Univ, INSERM, INS, Institut des Neurosciences des Systèmes, Marseille, France.
| | - Christophe Bernard
- Aix Marseille Univ, INSERM, INS, Institut des Neurosciences des Systèmes, Marseille, France.
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8
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Rasheed K, Qayyum A, Qadir J, Sivathamboo S, Kwan P, Kuhlmann L, O'Brien T, Razi A. Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review. IEEE Rev Biomed Eng 2021; 14:139-155. [PMID: 32746369 DOI: 10.1109/rbme.2020.3008792] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. This is especially the case for epilepsy, which is characterized by recurrent and unpredictable seizures. Patients can be relieved from the adverse consequences of epileptic seizures if it could somehow be predicted in advance. Despite decades of research, seizure prediction remains an unsolved problem. This is likely to remain at least partly because of the inadequate amount of data to resolve the problem. There have been exciting new developments in ML-based algorithms that have the potential to deliver a paradigm shift in the early and accurate prediction of epileptic seizures. Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals. We will identify the gaps, challenges, and pitfalls in the current research and recommend future directions.
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9
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Burrows DRW, Samarut É, Liu J, Baraban SC, Richardson MP, Meyer MP, Rosch RE. Imaging epilepsy in larval zebrafish. Eur J Paediatr Neurol 2020; 24:70-80. [PMID: 31982307 PMCID: PMC7035958 DOI: 10.1016/j.ejpn.2020.01.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 01/03/2020] [Accepted: 01/04/2020] [Indexed: 12/19/2022]
Abstract
Our understanding of the genetic aetiology of paediatric epilepsies has grown substantially over the last decade. However, in order to translate improved diagnostics to personalised treatments, there is an urgent need to link molecular pathophysiology in epilepsy to whole-brain dynamics in seizures. Zebrafish have emerged as a promising new animal model for epileptic seizure disorders, with particular relevance for genetic and developmental epilepsies. As a novel model organism for epilepsy research they combine key advantages: the small size of larval zebrafish allows high throughput in vivo experiments; the availability of advanced genetic tools allows targeted modification to model specific human genetic disorders (including genetic epilepsies) in a vertebrate system; and optical access to the entire central nervous system has provided the basis for advanced microscopy technologies to image structure and function in the intact larval zebrafish brain. There is a growing body of literature describing and characterising features of epileptic seizures and epilepsy in larval zebrafish. Recently genetically encoded calcium indicators have been used to investigate the neurobiological basis of these seizures with light microscopy. This approach offers a unique window into the multiscale dynamics of epileptic seizures, capturing both whole-brain dynamics and single-cell behaviour concurrently. At the same time, linking observations made using calcium imaging in the larval zebrafish brain back to an understanding of epileptic seizures largely derived from cortical electrophysiological recordings in human patients and mammalian animal models is non-trivial. In this review we briefly illustrate the state of the art of epilepsy research in zebrafish with particular focus on calcium imaging of epileptic seizures in the larval zebrafish. We illustrate the utility of a dynamic systems perspective on the epileptic brain for providing a principled approach to linking observations across species and identifying those features of brain dynamics that are most relevant to epilepsy. In the following section we survey the literature for imaging features associated with epilepsy and epileptic seizures and link these to observations made from humans and other more traditional animal models. We conclude by identifying the key challenges still facing epilepsy research in the larval zebrafish and indicate strategies for future research to address these and integrate more directly with the themes and questions that emerge from investigating epilepsy in other model systems and human patients.
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Affiliation(s)
- D R W Burrows
- MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - É Samarut
- Department of Neurosciences, Research Center of the University of Montreal Hospital Center, Montreal, Quebec, Canada
| | - J Liu
- Department of Neurological Surgery and Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA, USA
| | - S C Baraban
- Department of Neurological Surgery and Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA, USA
| | - M P Richardson
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - M P Meyer
- MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - R E Rosch
- MRC Centre for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Department of Paediatric Neurology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
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10
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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: 190] [Impact Index Per Article: 38.0] [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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11
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Kuhlmann L, Karoly P, Freestone DR, Brinkmann BH, Temko A, Barachant A, Li F, Titericz G, Lang BW, Lavery D, Roman K, Broadhead D, Dobson S, Jones G, Tang Q, Ivanenko I, Panichev O, Proix T, Náhlík M, Grunberg DB, Reuben C, Worrell G, Litt B, Liley DTJ, Grayden DB, Cook MJ. Epilepsyecosystem.org: crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG. Brain 2019; 141:2619-2630. [PMID: 30101347 DOI: 10.1093/brain/awy210] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 06/25/2018] [Indexed: 11/13/2022] Open
Abstract
Accurate seizure prediction will transform epilepsy management by offering warnings to patients or triggering interventions. However, state-of-the-art algorithm design relies on accessing adequate long-term data. Crowd-sourcing ecosystems leverage quality data to enable cost-effective, rapid development of predictive algorithms. A crowd-sourcing ecosystem for seizure prediction is presented involving an international competition, a follow-up held-out data evaluation, and an online platform, Epilepsyecosystem.org, for yielding further improvements in prediction performance. Crowd-sourced algorithms were obtained via the 'Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge' conducted at kaggle.com. Long-term continuous intracranial electroencephalography (iEEG) data (442 days of recordings and 211 lead seizures per patient) from prediction-resistant patients who had the lowest seizure prediction performances from the NeuroVista Seizure Advisory System clinical trial were analysed. Contestants (646 individuals in 478 teams) from around the world developed algorithms to distinguish between 10-min inter-seizure versus pre-seizure data clips. Over 10 000 algorithms were submitted. The top algorithms as determined by using the contest data were evaluated on a much larger held-out dataset. The data and top algorithms are available online for further investigation and development. The top performing contest entry scored 0.81 area under the classification curve. The performance reduced by only 6.7% on held-out data. Many other teams also showed high prediction reproducibility. Pseudo-prospective evaluation demonstrated that many algorithms, when used alone or weighted by circadian information, performed better than the benchmarks, including an average increase in sensitivity of 1.9 times the original clinical trial sensitivity for matched time in warning. These results indicate that clinically-relevant seizure prediction is possible in a wider range of patients than previously thought possible. Moreover, different algorithms performed best for different patients, supporting the use of patient-specific algorithms and long-term monitoring. The crowd-sourcing ecosystem for seizure prediction will enable further worldwide community study of the data to yield greater improvements in prediction performance by way of competition, collaboration and synergism.10.1093/brain/awy210_video1awy210media15817489051001.
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Affiliation(s)
- Levin Kuhlmann
- Department of Medicine - St. Vincent's, The University of Melbourne, Parkville VIC, Australia.,NeuroEngineering Lab, Department of Biomedical Engineering, The University of Melbourne, Parkville VIC, Australia.,Brain Dynamics Lab, Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn VIC, Australia
| | - Philippa Karoly
- Department of Medicine - St. Vincent's, The University of Melbourne, Parkville VIC, Australia.,NeuroEngineering Lab, Department of Biomedical Engineering, The University of Melbourne, Parkville VIC, Australia
| | - Dean R Freestone
- Department of Medicine - St. Vincent's, The University of Melbourne, Parkville VIC, Australia
| | - Benjamin H Brinkmann
- Mayo Systems Electrophysiology Laboratory, Departments of Neurology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Andriy Temko
- Irish Centre for Fetal and Neonatal Translational Research, University College Cork, Cork, Ireland
| | | | | | | | - Brian W Lang
- Areté Associates, 1550 Crystal Drive, Suite 703, Arlington, VA, USA
| | - Daniel Lavery
- Areté Associates, 1550 Crystal Drive, Suite 703, Arlington, VA, USA
| | - Kelly Roman
- Areté Associates, 1550 Crystal Drive, Suite 703, Arlington, VA, USA
| | - Derek Broadhead
- Areté Associates, 1550 Crystal Drive, Suite 703, Arlington, VA, USA
| | - Scott Dobson
- Areté Associates, 1550 Crystal Drive, Suite 703, Arlington, VA, USA
| | - Gareth Jones
- UCL Ear Institute, 332 Gray's Inn Road, London, UK
| | - Qingnan Tang
- Department of Physics, National University of Singapore, Singapore
| | | | | | - Timothée Proix
- Department of Neuroscience, Brown University, Providence, Rhode Island, USA.,Center for Neurorestoration and Neurotechnology, U.S. Department of Veterans Affairs, Providence, Rhode Island, USA
| | | | - Daniel B Grunberg
- Solverworld, Suite 140, 1337 Mass. Ave, Arlington, Massachusetts, USA
| | | | - Gregory Worrell
- Mayo Systems Electrophysiology Laboratory, Departments of Neurology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Brian Litt
- University of Pennsylvania, Penn Center for Neuroengineering and Therapeutics, Philadelphia, PA, USA
| | - David T J Liley
- Department of Medicine - St. Vincent's, The University of Melbourne, Parkville VIC, Australia.,Brain Dynamics Lab, Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn VIC, Australia
| | - David B Grayden
- Department of Medicine - St. Vincent's, The University of Melbourne, Parkville VIC, Australia.,NeuroEngineering Lab, Department of Biomedical Engineering, The University of Melbourne, Parkville VIC, Australia
| | - Mark J Cook
- Department of Medicine - St. Vincent's, The University of Melbourne, Parkville VIC, Australia
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12
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Mäki-Marttunen T, Kaufmann T, Elvsåshagen T, Devor A, Djurovic S, Westlye LT, Linne ML, Rietschel M, Schubert D, Borgwardt S, Efrim-Budisteanu M, Bettella F, Halnes G, Hagen E, Næss S, Ness TV, Moberget T, Metzner C, Edwards AG, Fyhn M, Dale AM, Einevoll GT, Andreassen OA. Biophysical Psychiatry-How Computational Neuroscience Can Help to Understand the Complex Mechanisms of Mental Disorders. Front Psychiatry 2019; 10:534. [PMID: 31440172 PMCID: PMC6691488 DOI: 10.3389/fpsyt.2019.00534] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 07/10/2019] [Indexed: 12/11/2022] Open
Abstract
The brain is the most complex of human organs, and the pathophysiology underlying abnormal brain function in psychiatric disorders is largely unknown. Despite the rapid development of diagnostic tools and treatments in most areas of medicine, our understanding of mental disorders and their treatment has made limited progress during the last decades. While recent advances in genetics and neuroscience have a large potential, the complexity and multidimensionality of the brain processes hinder the discovery of disease mechanisms that would link genetic findings to clinical symptoms and behavior. This applies also to schizophrenia, for which genome-wide association studies have identified a large number of genetic risk loci, spanning hundreds of genes with diverse functionalities. Importantly, the multitude of the associated variants and their prevalence in the healthy population limit the potential of a reductionist functional genetics approach as a stand-alone solution to discover the disease pathology. In this review, we outline the key concepts of a "biophysical psychiatry," an approach that employs large-scale mechanistic, biophysics-founded computational modelling to increase transdisciplinary understanding of the pathophysiology and strive toward robust predictions. We discuss recent scientific advances that allow a synthesis of previously disparate fields of psychiatry, neurophysiology, functional genomics, and computational modelling to tackle open questions regarding the pathophysiology of heritable mental disorders. We argue that the complexity of the increasing amount of genetic data exceeds the capabilities of classical experimental assays and requires computational approaches. Biophysical psychiatry, based on modelling diseased brain networks using existing and future knowledge of basic genetic, biochemical, and functional properties on a single neuron to a microcircuit level, may allow a leap forward in deriving interpretable biomarkers and move the field toward novel treatment options.
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Affiliation(s)
- Tuomo Mäki-Marttunen
- Department of Computational Physiology, Simula Research Laboratory, Oslo, Norway.,NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Torbjørn Elvsåshagen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Anna Devor
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States.,Department of Radiology, University of California, San Diego, La Jolla, CA, United States.,Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway.,NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Psychology, University of Oslo, Oslo, Norway
| | - Marja-Leena Linne
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Dirk Schubert
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Stefan Borgwardt
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Magdalena Efrim-Budisteanu
- Prof. Dr. Alex. Obregia Clinical Hospital of Psychiatry, Bucharest, Romania.,Victor Babes National Institute of Pathology, Bucharest, Romania.,Faculty of Medicine, Titu Maiorescu University, Bucharest, Romania
| | - Francesco Bettella
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Geir Halnes
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Espen Hagen
- Department of Physics, University of Oslo, Oslo, Norway
| | - Solveig Næss
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Torbjørn V Ness
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Torgeir Moberget
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Christoph Metzner
- Centre for Computer Science and Informatics Research, University of Hertfordshire, Hatfield, United Kingdom.,Institute of Software Engineering and Theoretical Computer Science, Technische Universität zu Berlin, Berlin, Germany
| | - Andrew G Edwards
- Department of Computational Physiology, Simula Research Laboratory, Oslo, Norway
| | - Marianne Fyhn
- Department of Biosciences, University of Oslo, Oslo, Norway
| | - Anders M Dale
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States.,Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Gaute T Einevoll
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.,Department of Physics, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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13
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Abstract
The current paradigm for treatment of epilepsy begins with trials of antiepileptic drugs, followed by evaluation for resective brain surgery in drug-resistant patients. If surgery is not possible or fails to control seizures, some patients benefit from implanted neurostimulation devices. In addition to their therapeutic benefit, some of these devices have diagnostic capability enabling recordings of brain activity with unprecedented chronicity. Two recent studies using different devices for chronic EEG (i.e., over months to years) yielded convergent findings of daily and multiday cycles of brain activity that help explain seizure timing. Knowledge of these patient-specific cycles can be leveraged to gauge and forecast seizure risk, empowering patients to adopt risk-stratified treatment strategies and behavioral modifications. We review evidence that epilepsy is a cyclical disorder, and we argue that implanted monitoring devices should be offered earlier in the treatment paradigm. Chronic EEG would allow pharmacologic treatments tailored to days of high seizure risk-here termed chronotherapy-and would help characterize long timescale seizure dynamics to improve subsequent surgical planning. Coupled with neuromodulation, the proposed approach could improve quality of life for patients and decrease the number ultimately requiring resective surgery. We outline challenges for chronic monitoring and seizure forecasting that demand close collaboration among engineers, neurosurgeons, and neurologists.
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Affiliation(s)
- Maxime O Baud
- From the Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Department of Neurology (M.O.B.), Inselspital, Bern University Hospital, University of Bern; Wyss Center for Bio- and Neuro-engineering (M.O.B.), Geneva, Switzerland; and Department of Neurology and Weill Institute for Neurosciences (V.R.R.), University of California, San Francisco.
| | - Vikram R Rao
- From the Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Department of Neurology (M.O.B.), Inselspital, Bern University Hospital, University of Bern; Wyss Center for Bio- and Neuro-engineering (M.O.B.), Geneva, Switzerland; and Department of Neurology and Weill Institute for Neurosciences (V.R.R.), University of California, San Francisco
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14
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Karoly PJ, Kuhlmann L, Soudry D, Grayden DB, Cook MJ, Freestone DR. Seizure pathways: A model-based investigation. PLoS Comput Biol 2018; 14:e1006403. [PMID: 30307937 PMCID: PMC6199000 DOI: 10.1371/journal.pcbi.1006403] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Revised: 10/23/2018] [Accepted: 07/26/2018] [Indexed: 02/06/2023] Open
Abstract
We present the results of a model inversion algorithm for electrocorticography (ECoG) data recorded during epileptic seizures. The states and parameters of neural mass models were tracked during a total of over 3000 seizures from twelve patients with focal epilepsy. These models provide an estimate of the effective connectivity within intracortical circuits over the time course of seizures. Observing the dynamics of effective connectivity provides insight into mechanisms of seizures. Estimation of patients seizure dynamics revealed: 1) a highly stereotyped pattern of evolution for each patient, 2) distinct sub-groups of onset mechanisms amongst patients, and 3) different offset mechanisms for long and short seizures. Stereotypical dynamics suggest that, once initiated, seizures follow a deterministic path through the parameter space of a neural model. Furthermore, distinct sub-populations of patients were identified based on characteristic motifs in the dynamics at seizure onset. There were also distinct patterns between long and short duration seizures that were related to seizure offset. Understanding how these different patterns of seizure evolution arise may provide new insights into brain function and guide treatment for epilepsy, since specific therapies may have preferential effects on the various parameters that could potentially be individualized. Methods that unite computational models with data provide a powerful means to generate testable hypotheses for further experimental research. This work provides a demonstration that the hidden connectivity parameters of a neural mass model can be dynamically inferred from data. Our results underscore the power of theoretical models to inform epilepsy management. It is our hope that this work guides further efforts to apply computational models to clinical data.
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Affiliation(s)
- Philippa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, Australia
| | - Levin Kuhlmann
- Brain Dynamics Lab, Swinburne University of Technology, Hawthorn, Australia
- Centre for Neural Engineering, The University of Melbourne, Parkville, Australia
| | - Daniel Soudry
- Department of Electrical Engineering, Technion, Haifa, Israel
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia
- Centre for Neural Engineering, The University of Melbourne, Parkville, Australia
| | - Mark J Cook
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, Australia
| | - Dean R Freestone
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, Australia
- Seer Medical Pty, Melbourne, Australia
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15
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Fan X, Gaspard N, Legros B, Lucchetti F, Ercek R, Nonclercq A. Seizure evolution can be characterized as path through synaptic gain space of a neural mass model. Eur J Neurosci 2018; 48:3097-3112. [PMID: 30194874 DOI: 10.1111/ejn.14142] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 08/08/2018] [Accepted: 08/29/2018] [Indexed: 11/30/2022]
Abstract
Physiologically based models could facilitate better understanding of mechanisms underlying epileptic seizures. In this paper, we attempt to reveal the dynamic evolution of intracranial EEG activity during epileptic seizures based on synaptic gain identification procedure of a neural mass model. The distribution of average excitatory, slow and fast inhibitory synaptic gain in the parameter space and their temporal evolution, i.e., the path through the model parameter space, were analyzed in thirty seizures from ten temporal lobe epileptic patients. Results showed that the synaptic gain values located roughly on a plane before seizure onset, dispersed during seizure and returned to the plane when seizure terminated. Cluster analysis was performed on seizure paths and demonstrated consistency in synaptic gain evolution across different seizures from the individual patient. Furthermore, two patient groups were identified, each one corresponding to a specific synaptic gain evolution in the parameter space during a seizure. Results were validated by a bootstrapping approach based on comparison with random paths. The differences in the path revealed variations in EEG dynamics for patients despite showing identical seizure onset pattern. Our approach may have the potential to classify the epileptic patients into subgroups based on different mechanisms revealed by subtle changes in synaptic gains and further enable more robust decisions regarding treatment strategy.
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Affiliation(s)
- Xiaoya Fan
- Bio, Electro And Mechanical Systems (BEAMS), Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicolas Gaspard
- Department of Neurology, Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Benjamin Legros
- Department of Neurology, Hôpital Erasme, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Federico Lucchetti
- Bio, Electro And Mechanical Systems (BEAMS), Université Libre de Bruxelles (ULB), Brussels, Belgium.,Laboratoire de Neurophysiologie Sensorielle et Cognitive, Hôpital Brugmann, Brussels, Belgium
| | - Rudy Ercek
- Laboratories of Image, Signal Processing and Acoustics (LISA), Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Antoine Nonclercq
- Bio, Electro And Mechanical Systems (BEAMS), Université Libre de Bruxelles (ULB), Brussels, Belgium
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16
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Truong ND, Nguyen AD, Kuhlmann L, Bonyadi MR, Yang J, Ippolito S, Kavehei O. Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Netw 2018; 105:104-111. [DOI: 10.1016/j.neunet.2018.04.018] [Citation(s) in RCA: 246] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Revised: 04/24/2018] [Accepted: 04/26/2018] [Indexed: 11/24/2022]
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17
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Rosch RE, Hunter PR, Baldeweg T, Friston KJ, Meyer MP. Calcium imaging and dynamic causal modelling reveal brain-wide changes in effective connectivity and synaptic dynamics during epileptic seizures. PLoS Comput Biol 2018; 14:e1006375. [PMID: 30138336 PMCID: PMC6124808 DOI: 10.1371/journal.pcbi.1006375] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 09/05/2018] [Accepted: 07/18/2018] [Indexed: 12/31/2022] Open
Abstract
Pathophysiological explanations of epilepsy typically focus on either the micro/mesoscale (e.g. excitation-inhibition imbalance), or on the macroscale (e.g. network architecture). Linking abnormalities across spatial scales remains difficult, partly because of technical limitations in measuring neuronal signatures concurrently at the scales involved. Here we use light sheet imaging of the larval zebrafish brain during acute epileptic seizure induced with pentylenetetrazole. Spectral changes of spontaneous neuronal activity during the seizure are then modelled using neural mass models, allowing Bayesian inference on changes in effective network connectivity and their underlying synaptic dynamics. This dynamic causal modelling of seizures in the zebrafish brain reveals concurrent changes in synaptic coupling at macro- and mesoscale. Fluctuations of both synaptic connection strength and their temporal dynamics are required to explain observed seizure patterns. These findings highlight distinct changes in local (intrinsic) and long-range (extrinsic) synaptic transmission dynamics as a possible seizure pathomechanism and illustrate how our Bayesian model inversion approach can be used to link existing neural mass models of seizure activity and novel experimental methods.
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Affiliation(s)
- Richard E. Rosch
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
- Developmental Neurosciences Programme, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Paul R. Hunter
- Department of Developmental Neurobiology & MRC Center for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Torsten Baldeweg
- Developmental Neurosciences Programme, UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Karl J. Friston
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
| | - Martin P. Meyer
- Department of Developmental Neurobiology & MRC Center for Neurodevelopmental Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
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18
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Proix T, Jirsa VK, Bartolomei F, Guye M, Truccolo W. Predicting the spatiotemporal diversity of seizure propagation and termination in human focal epilepsy. Nat Commun 2018. [PMID: 29540685 PMCID: PMC5852068 DOI: 10.1038/s41467-018-02973-y] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Recent studies have shown that seizures can spread and terminate across brain areas via a rich diversity of spatiotemporal patterns. In particular, while the location of the seizure onset area is usually invariant across seizures in an individual patient, the source of traveling (2–3 Hz) spike-and-wave discharges during seizures can either move with the slower propagating ictal wavefront or remain stationary at the seizure onset area. Furthermore, although many focal seizures terminate synchronously across brain areas, some evolve into distinct ictal clusters and terminate asynchronously. Here, we introduce a unifying perspective based on a new neural field model of epileptic seizure dynamics. Two main mechanisms, the co-existence of wave propagation in excitable media and coupled-oscillator dynamics, together with the interaction of multiple time scales, account for the reported diversity. We confirm our predictions in seizures and tractography data obtained from patients with pharmacologically resistant epilepsy. Our results contribute toward patient-specific seizure modeling. A major goal of epilepsy research is understanding the spatiotemporal dynamics of seizure. Here, the authors extend the Epileptor neural mass model into a neural field model, in order to provide a unified and patient-specific model of seizure initiation, propagation, and termination.
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Affiliation(s)
- Timothée Proix
- Department of Neuroscience, Brown University, Providence, RI, 02912, USA.,Institute for Brain Science, Brown University, Providence, RI, 02912, USA.,Center for Neurorestoration & Neurotechnology, U.S. Department of Veterans Affairs, Providence, RI, 02912, USA
| | - Viktor K Jirsa
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix Marseille Univ, Marseille, 13005, France
| | - Fabrice Bartolomei
- Institut de Neurosciences des Systèmes (INS), Inserm, Aix Marseille Univ, Marseille, 13005, France
| | - Maxime Guye
- CNRS, CRMBM UMR 7339, Aix Marseille Univ, Marseille, 13005, France
| | - Wilson Truccolo
- Department of Neuroscience, Brown University, Providence, RI, 02912, USA. .,Institute for Brain Science, Brown University, Providence, RI, 02912, USA. .,Center for Neurorestoration & Neurotechnology, U.S. Department of Veterans Affairs, Providence, RI, 02912, USA.
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19
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Eissa TL, Dijkstra K, Brune C, Emerson RG, van Putten MJAM, Goodman RR, McKhann GM, Schevon CA, van Drongelen W, van Gils SA. Cross-scale effects of neural interactions during human neocortical seizure activity. Proc Natl Acad Sci U S A 2017; 114:10761-10766. [PMID: 28923948 PMCID: PMC5635869 DOI: 10.1073/pnas.1702490114] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Small-scale neuronal networks may impose widespread effects on large network dynamics. To unravel this relationship, we analyzed eight multiscale recordings of spontaneous seizures from four patients with epilepsy. During seizures, multiunit spike activity organizes into a submillimeter-sized wavefront, and this activity correlates significantly with low-frequency rhythms from electrocorticographic recordings across a 10-cm-sized neocortical network. Notably, this correlation effect is specific to the ictal wavefront and is absent interictally or from action potential activity outside the wavefront territory. To examine the multiscale interactions, we created a model using a multiscale, nonlinear system and found evidence for a dual role for feedforward inhibition in seizures: while inhibition at the wavefront fails, allowing seizure propagation, feedforward inhibition of the surrounding centimeter-scale networks is activated via long-range excitatory connections. Bifurcation analysis revealed that distinct dynamical pathways for seizure termination depend on the surrounding inhibition strength. Using our model, we found that the mesoscopic, local wavefront acts as the forcing term of the ictal process, while the macroscopic, centimeter-sized network modulates the oscillatory seizure activity.
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Affiliation(s)
- Tahra L Eissa
- Department of Pediatrics, University of Chicago, Chicago, IL 60637;
| | - Koen Dijkstra
- Department of Applied Mathematics, MIRA Institute for Biomedical Engineering and Technical Medicine, University of Twente, Enschede 7500AE, The Netherlands;
| | - Christoph Brune
- Department of Applied Mathematics, MIRA Institute for Biomedical Engineering and Technical Medicine, University of Twente, Enschede 7500AE, The Netherlands
| | - Ronald G Emerson
- Department of Neurology, Columbia University, New York, NY 10032
| | - Michel J A M van Putten
- Deptartment of Neurology and Clinical Neurophysiolgy, Medisch Spectrum Twente, Enschede 7500AE, The Netherlands
- Clinical Neurophysiology Group, MIRA Institute for Biomedical Engineering and Technical Medicine, University of Twente, Enschede 7500AE, The Netherlands
| | - Robert R Goodman
- Department of Neurological Surgery, Columbia University, New York, NY 10032
| | - Guy M McKhann
- Department of Neurological Surgery, Columbia University, New York, NY 10032
| | | | | | - Stephan A van Gils
- Department of Applied Mathematics, MIRA Institute for Biomedical Engineering and Technical Medicine, University of Twente, Enschede 7500AE, The Netherlands
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20
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Karoly PJ, Ung H, Grayden DB, Kuhlmann L, Leyde K, Cook MJ, Freestone DR. The circadian profile of epilepsy improves seizure forecasting. Brain 2017; 140:2169-2182. [PMID: 28899023 DOI: 10.1093/brain/awx173] [Citation(s) in RCA: 121] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Accepted: 05/20/2017] [Indexed: 01/20/2023] Open
Abstract
It is now established that epilepsy is characterized by periodic dynamics that increase seizure likelihood at certain times of day, and which are highly patient-specific. However, these dynamics are not typically incorporated into seizure prediction algorithms due to the difficulty of estimating patient-specific rhythms from relatively short-term or unreliable data sources. This work outlines a novel framework to develop and assess seizure forecasts, and demonstrates that the predictive power of forecasting models is improved by circadian information. The analyses used long-term, continuous electrocorticography from nine subjects, recorded for an average of 320 days each. We used a large amount of out-of-sample data (a total of 900 days for algorithm training, and 2879 days for testing), enabling the most extensive post hoc investigation into seizure forecasting. We compared the results of an electrocorticography-based logistic regression model, a circadian probability, and a combined electrocorticography and circadian model. For all subjects, clinically relevant seizure prediction results were significant, and the addition of circadian information (combined model) maximized performance across a range of outcome measures. These results represent a proof-of-concept for implementing a circadian forecasting framework, and provide insight into new approaches for improving seizure prediction algorithms. The circadian framework adds very little computational complexity to existing prediction algorithms, and can be implemented using current-generation implant devices, or even non-invasively via surface electrodes using a wearable application. The ability to improve seizure prediction algorithms through straightforward, patient-specific modifications provides promise for increased quality of life and improved safety for patients with epilepsy.
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Affiliation(s)
- Philippa J Karoly
- Department of Medicine, The University of Melbourne, St. Vincent's Hospital, Fitzroy VIC 3065, Australia.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.,NeuroEngineering Research Laboratory, Department of Biomedical Engineering, The University of Melbourne, Parkville VIC 3010, Australia
| | - Hoameng Ung
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - David B Grayden
- NeuroEngineering Research Laboratory, Department of Biomedical Engineering, The University of Melbourne, Parkville VIC 3010, Australia
| | - Levin Kuhlmann
- Department of Medicine, The University of Melbourne, St. Vincent's Hospital, Fitzroy VIC 3065, Australia.,Brain Dynamics Lab, Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorne VIC 3122, Australia
| | - Kent Leyde
- Cascade Neuroscience, Seattle, WA 98109, USA
| | - Mark J Cook
- Department of Medicine, The University of Melbourne, St. Vincent's Hospital, Fitzroy VIC 3065, Australia.,Graeme Clark Institute for Biomedical Engineering, The University of Melbourne
| | - Dean R Freestone
- Department of Medicine, The University of Melbourne, St. Vincent's Hospital, Fitzroy VIC 3065, Australia
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21
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Freestone DR, Layton KJ, Kuhlmann L, Cook MJ. Statistical Performance Analysis of Data-Driven Neural Models. Int J Neural Syst 2016; 27:1650045. [DOI: 10.1142/s0129065716500453] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Data-driven model-based analysis of electrophysiological data is an emerging technique for understanding the mechanisms of seizures. Model-based analysis enables tracking of hidden brain states that are represented by the dynamics of neural mass models. Neural mass models describe the mean firing rates and mean membrane potentials of populations of neurons. Various neural mass models exist with different levels of complexity and realism. An ideal data-driven model-based analysis framework will incorporate the most realistic model possible, enabling accurate imaging of the physiological variables. However, models must be sufficiently parsimonious to enable tracking of important variables using data. This paper provides tools to inform the realism versus parsimony trade-off, the Bayesian Cramer-Rao (lower) Bound (BCRB). We demonstrate how the BCRB can be used to assess the feasibility of using various popular neural mass models to track epilepsy-related dynamics via stochastic filtering methods. A series of simulations show how optimal state estimates relate to measurement noise, model error and initial state uncertainty. We also demonstrate that state estimation accuracy will vary between seizure-like and normal rhythms. The performance of the extended Kalman filter (EKF) is assessed against the BCRB. This work lays a foundation for assessing feasibility of model-based analysis. We discuss how the framework can be used to design experiments to better understand epilepsy.
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Affiliation(s)
- Dean R. Freestone
- Department of Medicine St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy, Victoria, 3065, Australia
- Department of Statistics, Columbia University, New York, New York, 10027, United States
| | - Kelvin J. Layton
- Institute for Telecommunications Research, University of South Australia, Mawson Lakes, SA 5095, Australia
| | - Levin Kuhlmann
- Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Mark J. Cook
- Department of Medicine St Vincent’s Hospital Melbourne, The University of Melbourne, Fitzroy, Victoria, 3065, Australia
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22
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Koppert M, Kalitzin S, Velis D, Lopes Da Silva F, Viergever MA. Preventive and Abortive Strategies for Stimulation Based Control of Epilepsy: A Computational Model Study. Int J Neural Syst 2016; 26:1650028. [DOI: 10.1142/s0129065716500283] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Epilepsy is a condition in which periods of ongoing normal EEG activity alternate with periods of oscillatory behavior characteristic of epileptic seizures. The dynamics of the transitions between the two states are still unclear. Computational models provide a powerful tool to explore the underlying mechanisms of such transitions, with the purpose of eventually finding therapeutic interventions for this debilitating condition. In this study, the possibility to postpone seizures elicited by a decrease of inhibition is investigated by using external stimulation in a realistic bistable neuronal model consisting of two interconnected neuronal populations representing pyramidal cells and interneurons. In the simulations, seizures are induced by slowly decreasing the conductivity of GABA[Formula: see text] synaptic channels over time. Since the model is bistable, the system will change state from the initial steady state (SS) to the limit cycle (LS) state because of internal noise, when the inhibition falls below a certain threshold. Several state-independent stimulations paradigms are simulated. Their effectiveness is analyzed for various stimulation frequencies and intensities in combination with periodic and random stimulation sequences. The distributions of the time to first seizure in the presence of stimulation are compared with the situation without stimulation. In addition, stimulation protocols targeted to specific subsystems are applied with the objective of counteracting the baseline shift due to decreased inhibition in the system. Furthermore, an analytical model is used to investigate the effects of random noise. The relation between the strength of random noise stimulation, the control parameter of the system and the transitions between steady state and limit cycle are investigated. The study shows that it is possible to postpone epileptic activity by targeted stimulation in a realistic neuronal model featuring bistability and that it is possible to stop seizures by random noise in an analytical model.
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Affiliation(s)
- Marc Koppert
- Foundation Epilepsy Institutes Netherlands (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
| | - Stiliyan Kalitzin
- Foundation Epilepsy Institutes Netherlands (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
- Image Sciences Institute, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands
| | - Demetrios Velis
- Foundation Epilepsy Institutes Netherlands (SEIN), Achterweg 5, 2103 SW Heemstede, The Netherlands
- Department of Neurosurgery, Free University Medical Center Amsterdam, 1007 MB Amsterdam, The Netherlands
| | - Fernando Lopes Da Silva
- Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 SM Amsterdam, The Netherlands
- Department of Bioengineering, Instituto Superior Técnico, University of Lisbon, 1049-001, Lisboa, Portugal
| | - Max A. Viergever
- Image Sciences Institute, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands
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23
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Kuhlmann L, Freestone DR, Manton JH, Heyse B, Vereecke HE, Lipping T, Struys MM, Liley DT. Neural mass model-based tracking of anesthetic brain states. Neuroimage 2016; 133:438-456. [DOI: 10.1016/j.neuroimage.2016.03.039] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Revised: 02/26/2016] [Accepted: 03/18/2016] [Indexed: 01/22/2023] Open
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24
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O'Sullivan-Greene E, Kuhlmann L, Nurse ES, Freestone DR, Grayden DB, Cook M, Burkitt A, Mareels I. Probing to Observe Neural Dynamics Investigated with Networked Kuramoto Oscillators. Int J Neural Syst 2016; 27:1650038. [PMID: 27596927 DOI: 10.1142/s0129065716500386] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The expansion of frontiers in neural engineering is dependent on the ability to track, detect and predict dynamics in neural tissue. Recent innovations to elucidate information from electrical recordings of brain dynamics, such as epileptic seizure prediction, have involved switching to an active probing paradigm using electrically evoked recordings rather than traditional passive measurements. This paper positions the advantage of probing in terms of information extraction, by using a coupled oscillator Kuramoto model to represent brain dynamics. While active probing performs better at observing underlying system synchrony in Kuramoto networks, especially in non-Gaussian measurement environments, the benefits diminish with increasing relative size of electrode spatial resolution compared to synchrony area. This suggests probing will be useful for improved characterization of synchrony for suitably dense electrode recordings.
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Affiliation(s)
- Elma O'Sullivan-Greene
- * Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Levin Kuhlmann
- * Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.,† Brain & Psychological Sciences Research Centre, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
| | - Ewan S Nurse
- * Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.,‡ Department of Medicine, The University of Melbourne, Parkville, VIC 3010, Australia.,§ St. Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - Dean R Freestone
- * Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.,‡ Department of Medicine, The University of Melbourne, Parkville, VIC 3010, Australia.,§ St. Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - David B Grayden
- * Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Mark Cook
- ‡ Department of Medicine, The University of Melbourne, Parkville, VIC 3010, Australia.,§ St. Vincent's Hospital Melbourne, Fitzroy, VIC 3065, Australia
| | - Anthony Burkitt
- * Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Iven Mareels
- * Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia
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25
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Seizure Prediction 6: [LINE SEPARATOR]From Mechanisms to Engineered Interventions for Epilepsy. J Clin Neurophysiol 2016; 32:181-7. [PMID: 26035671 DOI: 10.1097/wnp.0000000000000184] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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