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Harrington EG, Kissack P, Terry JR, Woldman W, Junges L. Treatment effects in epilepsy: a mathematical framework for understanding response over time. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1308501. [PMID: 38988793 PMCID: PMC11233745 DOI: 10.3389/fnetp.2024.1308501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 05/30/2024] [Indexed: 07/12/2024]
Abstract
Epilepsy is a neurological disorder characterized by recurrent seizures, affecting over 65 million people worldwide. Treatment typically commences with the use of anti-seizure medications, including both mono- and poly-therapy. Should these fail, more invasive therapies such as surgery, electrical stimulation and focal drug delivery are often considered in an attempt to render the person seizure free. Although a significant portion ultimately benefit from these treatment options, treatment responses often fluctuate over time. The physiological mechanisms underlying these temporal variations are poorly understood, making prognosis a significant challenge when treating epilepsy. Here we use a dynamic network model of seizure transition to understand how seizure propensity may vary over time as a consequence of changes in excitability. Through computer simulations, we explore the relationship between the impact of treatment on dynamic network properties and their vulnerability over time that permit a return to states of high seizure propensity. For small networks we show vulnerability can be fully characterised by the size of the first transitive component (FTC). For larger networks, we find measures of network efficiency, incoherence and heterogeneity (degree variance) correlate with robustness of networks to increasing excitability. These results provide a set of potential prognostic markers for therapeutic interventions in epilepsy. Such markers could be used to support the development of personalized treatment strategies, ultimately contributing to understanding of long-term seizure freedom.
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Affiliation(s)
- Elanor G. Harrington
- School of Mathematics, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
| | - Peter Kissack
- School of Mathematics, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
| | - John R. Terry
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
- Neuronostics Ltd, Engine Shed, Station Approach, Bristol, United Kingdom
| | - Wessel Woldman
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
- Neuronostics Ltd, Engine Shed, Station Approach, Bristol, United Kingdom
| | - Leandro Junges
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
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Dallmer-Zerbe I, Jiruska P, Hlinka J. Personalized dynamic network models of the human brain as a future tool for planning and optimizing epilepsy therapy. Epilepsia 2023; 64:2221-2238. [PMID: 37340565 DOI: 10.1111/epi.17690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/17/2023] [Accepted: 06/19/2023] [Indexed: 06/22/2023]
Abstract
Epilepsy is a common neurological disorder, with one third of patients not responding to currently available antiepileptic drugs. The proportion of pharmacoresistant epilepsies has remained unchanged for many decades. To cure epilepsy and control seizures requires a paradigm shift in the development of new approaches to epilepsy diagnosis and treatment. Contemporary medicine has benefited from the exponential growth of computational modeling, and the application of network dynamics theory to understanding and treating human brain disorders. In epilepsy, the introduction of these approaches has led to personalized epileptic network modeling that can explore the patient's seizure genesis and predict the functional impact of resection on its individual network's propensity to seize. The application of the dynamic systems approach to neurostimulation therapy of epilepsy allows designing stimulation strategies that consider the patient's seizure dynamics and long-term fluctuations in the stability of their epileptic networks. In this article, we review, in a nontechnical fashion suitable for a broad neuroscientific audience, recent progress in personalized dynamic brain network modeling that is shaping the future approach to the diagnosis and treatment of epilepsy.
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Affiliation(s)
- Isa Dallmer-Zerbe
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
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Bowles KR, Pugh DA, Pedicone C, Oja L, Weitzman SA, Liu Y, Chen JL, Disney MD, Goate AM. Development of MAPT S305 mutation models exhibiting elevated 4R tau expression, resulting in altered neuronal and astrocytic function. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.02.543224. [PMID: 37333200 PMCID: PMC10274740 DOI: 10.1101/2023.06.02.543224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Due to the importance of 4R tau in the pathogenicity of primary tauopathies, it has been challenging to model these diseases in iPSC-derived neurons, which express very low levels of 4R tau. To address this problem we have developed a panel of isogenic iPSC lines carrying the MAPT splice-site mutations S305S, S305I or S305N, derived from four different donors. All three mutations significantly increased the proportion of 4R tau expression in iPSC-neurons and astrocytes, with up to 80% 4R transcripts in S305N neurons from as early as 4 weeks of differentiation. Transcriptomic and functional analyses of S305 mutant neurons revealed shared disruption in glutamate signaling and synaptic maturity, but divergent effects on mitochondrial bioenergetics. In iPSC-astrocytes, S305 mutations induced lysosomal disruption and inflammation and exacerbated internalization of exogenous tau that may be a precursor to the glial pathologies observed in many tauopathies. In conclusion, we present a novel panel of human iPSC lines that express unprecedented levels of 4R tau in neurons and astrocytes. These lines recapitulate previously characterized tauopathy-relevant phenotypes, but also highlight functional differences between the wild type 4R and mutant 4R proteins. We also highlight the functional importance of MAPT expression in astrocytes. These lines will be highly beneficial to tauopathy researchers enabling a more complete understanding of the pathogenic mechanisms underlying 4R tauopathies across different cell types.
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Affiliation(s)
- KR Bowles
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- Ronald M. Loeb Center for Alzheimer’s disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - DA Pugh
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- Ronald M. Loeb Center for Alzheimer’s disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - C Pedicone
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- Ronald M. Loeb Center for Alzheimer’s disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - L Oja
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- Ronald M. Loeb Center for Alzheimer’s disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - SA Weitzman
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- Ronald M. Loeb Center for Alzheimer’s disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - Y Liu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- Ronald M. Loeb Center for Alzheimer’s disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
| | - JL Chen
- Department of Chemistry, Scripps Research Institute, Jupiter, FL, United States of America
| | - MD Disney
- Department of Chemistry, Scripps Research Institute, Jupiter, FL, United States of America
| | - AM Goate
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
- Ronald M. Loeb Center for Alzheimer’s disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
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Mason SL, Junges L, Woldman W, Facer-Childs ER, de Campos BM, Bagshaw AP, Terry JR. Classification of human chronotype based on fMRI network-based statistics. Front Neurosci 2023; 17:1147219. [PMID: 37342462 PMCID: PMC10277557 DOI: 10.3389/fnins.2023.1147219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023] Open
Abstract
Chronotype-the relationship between the internal circadian physiology of an individual and the external 24-h light-dark cycle-is increasingly implicated in mental health and cognition. Individuals presenting with a late chronotype have an increased likelihood of developing depression, and can display reduced cognitive performance during the societal 9-5 day. However, the interplay between physiological rhythms and the brain networks that underpin cognition and mental health is not well-understood. To address this issue, we use rs-fMRI collected from 16 people with an early chronotype and 22 people with a late chronotype over three scanning sessions. We develop a classification framework utilizing the Network Based-Statistic methodology, to understand if differentiable information about chronotype is embedded in functional brain networks and how this changes throughout the day. We find evidence of subnetworks throughout the day that differ between extreme chronotypes such that high accuracy can occur, describe rigorous threshold criteria for achieving 97.3% accuracy in the Evening and investigate how the same conditions hinder accuracy for other scanning sessions. Revealing differences in functional brain networks based on extreme chronotype suggests future avenues of research that may ultimately better characterize the relationship between internal physiology, external perturbations, brain networks, and disease.
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Affiliation(s)
- Sophie L. Mason
- School of Mathematics, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
| | - Leandro Junges
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
| | - Wessel Woldman
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
| | - Elise R. Facer-Childs
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, VIC, Australia
- Danny Frawley Centre for Health and Wellbeing, Melbourne, VIC, Australia
- Centre for Human Brain Health, College of Life and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
- Faculty of Health and Medical Sciences, University of Surrey, Surrey, United Kingdom
| | | | - Andrew P. Bagshaw
- Centre for Human Brain Health, College of Life and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - John R. Terry
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
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Fan D, Wu H, Luan G, Wang Q. The distribution and heterogeneity of excitability in focal epileptic network potentially contribute to the seizure propagation. Front Psychiatry 2023; 14:1137704. [PMID: 36998622 PMCID: PMC10043226 DOI: 10.3389/fpsyt.2023.1137704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 02/21/2023] [Indexed: 03/17/2023] Open
Abstract
IntroductionExisting dynamical models can explain the transmigration mechanisms involved in seizures but are limited to a single modality. Combining models with networks can reproduce scaled epileptic dynamics. And the structure and coupling interactions of the network, as well as the heterogeneity of both the node and network activities, may influence the final state of the network model.MethodsWe built a fully connected network with focal nodes prominently interacting and established a timescale separated epileptic network model. The factors affecting epileptic network seizure were explored by varying the connectivity patterns of focal network nodes and modulating the distribution of network excitability.ResultsThe whole brain network topology as the brain activity foundation affects the consistent delayed clustering seizure propagation. In addition, the network size and distribution heterogeneity of the focal excitatory nodes can influence seizure frequency. With the increasing of the network size and averaged excitability level of focal network, the seizure period decreases. In contrast, the larger heterogeneity of excitability for focal network nodes can lower the functional activity level (average degree) of focal network. There are also subtle effects of focal network topologies (connection patterns of excitatory nodes) that cannot be ignored along with non-focal nodes.DiscussionUnraveling the role of excitatory factors in seizure onset and propagation can be used to understand the dynamic mechanisms and neuromodulation of epilepsy, with profound implications for the treatment of epilepsy and even for the understanding of the brain.
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Affiliation(s)
- Denggui Fan
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China
| | - Hongyu Wu
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China
| | - Guoming Luan
- Epilepsy Center, Sanbo Brain Hospital, Capital Medical University, Beijing, China
- *Correspondence: Guoming Luan, ; Qingyun Wang,
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing, China
- *Correspondence: Guoming Luan, ; Qingyun Wang,
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Lopes MA, Hamandi K, Zhang J, Creaser JL. The role of additive and diffusive coupling on the dynamics of neural populations. Sci Rep 2023; 13:4115. [PMID: 36914685 PMCID: PMC10011566 DOI: 10.1038/s41598-023-30172-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 02/17/2023] [Indexed: 03/16/2023] Open
Abstract
Dynamical models consisting of networks of neural masses commonly assume that the interactions between neural populations are via additive or diffusive coupling. When using the additive coupling, a population's activity is affected by the sum of the activities of neighbouring populations. In contrast, when using the diffusive coupling a neural population is affected by the sum of the differences between its activity and the activity of its neighbours. These two coupling functions have been used interchangeably for similar applications. In this study, we show that the choice of coupling can lead to strikingly different brain network dynamics. We focus on a phenomenological model of seizure transitions that has been used both with additive and diffusive coupling in the literature. We consider small networks with two and three nodes, as well as large random and scale-free networks with 64 nodes. We further assess resting-state functional networks inferred from magnetoencephalography (MEG) from people with juvenile myoclonic epilepsy (JME) and healthy controls. To characterize the seizure dynamics on these networks, we use the escape time, the brain network ictogenicity (BNI) and the node ictogenicity (NI), which are measures of the network's global and local ability to generate seizure activity. Our main result is that the level of ictogenicity of a network is strongly dependent on the coupling function. Overall, we show that networks with additive coupling have a higher propensity to generate seizures than those with diffusive coupling. We find that people with JME have higher additive BNI than controls, which is the hypothesized BNI deviation between groups, while the diffusive BNI provides opposite results. Moreover, we find that the nodes that are more likely to drive seizures in the additive coupling case are more likely to prevent seizures in the diffusive coupling case, and that these features correlate to the node's number of connections. Consequently, previous results in the literature involving such models to interrogate functional or structural brain networks could be highly dependent on the choice of coupling. Our results on the MEG functional networks and evidence from the literature suggest that the additive coupling may be a better modeling choice than the diffusive coupling, at least for BNI and NI studies. Thus, we highlight the need to motivate and validate the choice of coupling in future studies involving network models of brain activity.
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Affiliation(s)
- Marinho A Lopes
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF24 4HQ, United Kingdom.
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF24 4HQ, United Kingdom
- The Welsh Epilepsy Unit, Department of Neurology, University Hospital of Wales, Cardiff, CF14 4XW, United Kingdom
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF24 4HQ, United Kingdom
- Department of Computer Science, Swansea University, Swansea, SA1 8EN, United Kingdom
| | - Jennifer L Creaser
- Department of Mathematics, University of Exeter, Exeter, EX4 4QJ, United Kingdom
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Kieninger S, Xiao T, Weisschuh N, Kohl S, Rüther K, Kroisel PM, Brockmann T, Knappe S, Kellner U, Lagrèze W, Mazzola P, Haack TB, Wissinger B, Tonagel F. DNAJC30 disease-causing gene variants in a large Central European cohort of patients with suspected Leber's hereditary optic neuropathy and optic atrophy. J Med Genet 2022; 59:1027-1034. [PMID: 35091433 PMCID: PMC9554085 DOI: 10.1136/jmedgenet-2021-108235] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 01/07/2022] [Indexed: 01/18/2023]
Abstract
BACKGROUND Leber's hereditary optic neuropathy (LHON) has been considered a prototypical mitochondriopathy and a textbook example for maternal inheritance linked to certain disease-causing variants in the mitochondrial genome. Recently, an autosomal recessive form of LHON (arLHON) has been described, caused by disease-causing variants in the nuclear encoded gene DNAJC30. METHODS AND RESULTS In this study, we screened the DNAJC30 gene in a large Central European cohort of patients with a clinical diagnosis of LHON or other autosomal inherited optic atrophies (OA). We identified likely pathogenic variants in 35/1202 patients, corresponding to a detection rate of 2.9%. The previously described missense variant c.152A>G;p.(Tyr51Cys) accounts for 90% of disease-associated alleles in our cohort and we confirmed a strong founder effect. Furthermore, we identified two novel pathogenic variants in DNAJC30: the nonsense variant c.610G>T;p.(Glu204*) and the in-frame deletion c.230_232del;p.(His77del). Clinical investigation of the patients with arLHON revealed a younger age of onset, a more frequent bilateral onset and an increased clinically relevant recovery compared with LHON associated with disease-causing variants in the mitochondrial DNA. CONCLUSION This study expands previous findings on arLHON and emphasises the importance of DNAJC30 in the genetic diagnostics of LHON and OA in European patients.
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Affiliation(s)
- Sinja Kieninger
- Molecular Genetics Laboratory, Institute for Ophthalmic Research, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Ting Xiao
- Molecular Genetics Laboratory, Institute for Ophthalmic Research, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Nicole Weisschuh
- Molecular Genetics Laboratory, Institute for Ophthalmic Research, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Susanne Kohl
- Molecular Genetics Laboratory, Institute for Ophthalmic Research, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Klaus Rüther
- Facharztpraxis für Augenheilkunde, Berlin-Mitte, Germany
| | - Peter Michael Kroisel
- Diagnostic & Research Institute of Human Genetics, Diagnostic & Research Centre for Molecular BioMedicine, Medical University of Graz, Graz, Austria
| | - Tobias Brockmann
- Department of Ophthalmology, Universitätsmedizin Rostock, University of Rostock, Rostock, Germany
| | - Steffi Knappe
- Department of Ophthalmology, Universitätsmedizin Rostock, University of Rostock, Rostock, Germany
| | - Ulrich Kellner
- Zentrum für Seltene Netzhauterkrankungen, AugenZentrum Siegburg, MVZ Augenärztliches Diagnostik- und Therapiecentrum Siegburg GmbH, Siegburg, Germany
- RetinaScience, Bonn, Germany
| | - Wolf Lagrèze
- Eye Centre, Medical Centre - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Pascale Mazzola
- Institute of Human Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Tobias B Haack
- Institute of Human Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Centre for Rare Diseases, University of Tübingen, Tübingen, Germany
| | - Bernd Wissinger
- Molecular Genetics Laboratory, Institute for Ophthalmic Research, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - Felix Tonagel
- Centre for Ophthalmology, University of Tübingen, Tübingen, Germany
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Rozeman A, Hund H, Boiten J, Vos JA, Schonewille W, Wermer M, Lycklama a Nijeholt G, Algra A. Circle of Willis variation and outcome after intra-arterial treatment. BMJ Neurol Open 2022; 4:e000340. [PMID: 36160689 PMCID: PMC9490629 DOI: 10.1136/bmjno-2022-000340] [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: 06/26/2022] [Accepted: 09/08/2022] [Indexed: 11/03/2022] Open
Abstract
BackgroundIntra-arterial treatment (IAT) improves outcomes in acute ischaemic stroke. Presence of collaterals increases likelihood of good outcome. We investigated whether variations in the circle of Willis (CoW) and contributing carotid arteries influence outcome in patients who had a stroke treated with IAT.MethodsCT angiography data on patients who had an acute stroke treated with IAT were retrospectively collected. CoW was regarded complete if the contralateral A1 segment, anterior communicating artery and ipsilateral posterior communicating artery were fully developed, and the P1 segment was visible. Carotid artery contribution was studied with a self-developed carotid artery score ranging from 0 to 2 depending on the number of arteries supplying the occluded side of the CoW. Good clinical outcome was defined as modified Rankin Score ≤2 and measured at discharge and 3 months. We calculated risk ratios for the relation between completeness of the CoW, carotid score and good outcome, and performed a trend analysis for good outcome according to the carotid score.Results126 patients were included for analysis. Patients with a complete and incomplete CoW had a comparable risk for good outcome at discharge and 3 months. A higher carotid score was associated with a higher likelihood of good clinical outcome (p for trend 0.24 at discharge and 0.05 at 3 months).ConclusionIn patients with acute ischaemic stroke treated with IAT, chances of good clinical outcome tended to improve with number of carotid arteries supplying the cerebral circulation. Completeness of the CoW was not related to clinical outcome.
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Affiliation(s)
- Anouk Rozeman
- Neurology, Albert Schweitzer Hospital, Dordrecht, The Netherlands
| | - Hajo Hund
- Radiology, Haaglanden Medical Center Bronovo, Den Haag, The Netherlands
| | - Jelis Boiten
- Radiology, Haaglanden Medical Center Bronovo, Den Haag, The Netherlands
- Neurology, Haaglanden Medisch Center Bronovo, Den Haag, The Netherlands
| | - Jan-Albert Vos
- Radiology, Sint Antonius Ziekenhuis, Nieuwegein, The Netherlands
| | | | | | | | - Ale Algra
- Brain Center Rudolf Magnus, UMC Utrecht, Utrecht, The Netherlands
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9
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Shakeshaft A, Laiou P, Abela E, Stavropoulos I, Richardson MP, Pal DK, Howell A, Hyde A, McQueen A, Duran A, Gaurav A, Collingwood A, Kitching A, Shakeshaft A, Papathanasiou A, Clough A, Gribbin A, Swain A, Needle A, Hall A, Smith A, Macleod A, Chhibda A, Fonferko-Shadrach B, Camara B, Petrova B, Stuart C, Hamilton C, Peacey C, Campbell C, Cotter C, Edwards C, Picton C, Busby C, Quamina C, Waite C, West C, Ng CC, Giavasi C, Backhouse C, Holliday C, Mewies C, Thow C, Egginton D, Dickerson D, Rice D, Mullan D, Daly D, Mcaleer D, Gardella E, Stephen E, Irvine E, Sacre E, Lin F, Castle G, Mackay G, Salim H, Cock H, Collier H, Cockerill H, Navarra H, Mhandu H, Crudgington H, Hayes I, Stavropoulos I, Daglish J, Smith J, Bartholomew J, Cotta J, Ceballos JP, Natarajan J, Crooks J, Quirk J, Bland J, Sidebottom J, Gesche J, Glenton J, Henry J, Davis J, Ball J, Selmer KK, Rhodes K, Holroyd K, Lim KS, O’Brien K, Thrasyvoulou L, Makawa L, Charles L, Richardson L, Nelson L, Walding L, Woodhead L, Ehiorobo L, Hawkins L, Adams L, Connon M, Home M, Baker M, Mencias M, Richardson MP, Sargent M, Syvertsen M, Milner M, Recto M, Chang M, O'Donoghue M, Young M, Ray M, Panjwani N, Ghaus N, Sudarsan N, Said N, Pickrell O, Easton P, Frattaroli P, McAlinden P, Harrison R, Swingler R, Wane R, Ramsay R, Møller RS, McDowall R, Clegg R, Uka S, White S, Truscott S, Francis S, Tittensor S, Sharman SJ, Chung SK, Patel S, Ellawela S, Begum S, Kempson S, Raj S, Bayley S, Warriner S, Kilroy S, MacFarlane S, Brown T, Samakomva T, Nortcliffe T, Calder V, Collins V, Parker V, Richmond V, Stern W, Haslam Z, Šobíšková Z, Agrawal A, Whiting A, Pratico A, Desurkar A, Saraswatula A, MacDonald B, Fong CY, Beier CP, Andrade D, Pauldhas D, Greenberg DA, Deekollu D, Pal DK, Jayachandran D, Lozsadi D, Galizia E, Scott F, Rubboli G, Angus-Leppan H, Talvik I, Takon I, Zarubova J, Koht J, Aram J, Lanyon K, Irwin K, Hamandi K, Yeung L, Strug LJ, Rees M, Reuber M, Kirkpatrick M, Taylor M, Maguire M, Koutroumanidis M, Khan M, Moran N, Striano P, Bala P, Bharat R, Pandey R, Mohanraj R, Thomas R, Belderbos R, Slaght SJ, Delamont S, Sastry S, Mariguddi S, Kumar S, Kumar S, Majeed T, Jegathasan U, Whitehouse W. Heterogeneity of resting-state EEG features in juvenile myoclonic epilepsy and controls. Brain Commun 2022; 4:fcac180. [PMID: 35873918 PMCID: PMC9301584 DOI: 10.1093/braincomms/fcac180] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 05/18/2022] [Accepted: 07/07/2022] [Indexed: 11/12/2022] Open
Abstract
Abnormal EEG features are a hallmark of epilepsy, and abnormal frequency and network features are apparent in EEGs from people with idiopathic generalized epilepsy in both ictal and interictal states. Here, we characterize differences in the resting-state EEG of individuals with juvenile myoclonic epilepsy and assess factors influencing the heterogeneity of EEG features. We collected EEG data from 147 participants with juvenile myoclonic epilepsy through the Biology of Juvenile Myoclonic Epilepsy study. Ninety-five control EEGs were acquired from two independent studies [Chowdhury et al. (2014) and EU-AIMS Longitudinal European Autism Project]. We extracted frequency and functional network-based features from 10 to 20 s epochs of resting-state EEG, including relative power spectral density, peak alpha frequency, network topology measures and brain network ictogenicity: a computational measure of the propensity of networks to generate seizure dynamics. We tested for differences between epilepsy and control EEGs using univariate, multivariable and receiver operating curve analysis. In addition, we explored the heterogeneity of EEG features within and between cohorts by testing for associations with potentially influential factors such as age, sex, epoch length and time, as well as testing for associations with clinical phenotypes including anti-seizure medication, and seizure characteristics in the epilepsy cohort. P-values were corrected for multiple comparisons. Univariate analysis showed significant differences in power spectral density in delta (2-5 Hz) (P = 0.0007, hedges' g = 0.55) and low-alpha (6-9 Hz) (P = 2.9 × 10-8, g = 0.80) frequency bands, peak alpha frequency (P = 0.000007, g = 0.66), functional network mean degree (P = 0.0006, g = 0.48) and brain network ictogenicity (P = 0.00006, g = 0.56) between epilepsy and controls. Since age (P = 0.009) and epoch length (P = 1.7 × 10-8) differed between the two groups and were potential confounders, we controlled for these covariates in multivariable analysis where disparities in EEG features between epilepsy and controls remained. Receiver operating curve analysis showed low-alpha power spectral density was optimal at distinguishing epilepsy from controls, with an area under the curve of 0.72. Lower average normalized clustering coefficient and shorter average normalized path length were associated with poorer seizure control in epilepsy patients. To conclude, individuals with juvenile myoclonic epilepsy have increased power of neural oscillatory activity at low-alpha frequencies, and increased brain network ictogenicity compared with controls, supporting evidence from studies in other epilepsies with considerable external validity. In addition, the impact of confounders on different frequency-based and network-based EEG features observed in this study highlights the need for careful consideration and control of these factors in future EEG research in idiopathic generalized epilepsy particularly for their use as biomarkers.
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Affiliation(s)
- Amy Shakeshaft
- Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK,MRC Centre for Neurodevelopmental Disorders, King’s College London, London, UK
| | - Petroula Laiou
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Eugenio Abela
- Department of Basic & Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | | | - Mark P Richardson
- Correspondence may also be addressed to: Professor Mark P Richardson Maurice Wohl Clinical Neurosciences Institute Institute of Psychiatry, Psychology & Neuroscience King’s College London, 5 Cutcombe Road, London SE5 9RX, UK E-mail:
| | - Deb K Pal
- Correspondence to: Professor Deb K Pal Maurice Wohl Clinical Neurosciences Institute Institute of Psychiatry, Psychology & Neuroscience King’s College London 5 Cutcombe Road, London SE5 9RX, UK E-mail:
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10
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Lau LA, Staley KJ, Lillis KP. In vitro ictogenesis is stochastic at the single neuron level. Brain 2022; 145:531-541. [PMID: 34431994 PMCID: PMC9014754 DOI: 10.1093/brain/awab312] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 07/13/2021] [Accepted: 07/30/2021] [Indexed: 11/14/2022] Open
Abstract
Seizure initiation is the least understood and most disabling element of epilepsy. Studies of ictogenesis require high speed recordings at cellular resolution in the area of seizure onset. However, in vivo seizure onset areas cannot be determined at the level of resolution necessary to enable such studies. To circumvent these challenges, we used novel GCaMP7-based calcium imaging in the organotypic hippocampal slice culture model of post-traumatic epilepsy in mice. Organotypic hippocampal slice cultures generate spontaneous, recurrent seizures in a preparation in which it is feasible to image the activity of the entire network (with no unseen inputs existing). Chronic calcium imaging of the entire hippocampal network, with paired electrophysiology, revealed three patterns of seizure onset: (i) low amplitude fast activity; (ii) sentinel spike; and (iii) spike burst and low amplitude fast activity onset. These patterns recapitulate common features of human seizure onset, including low voltage fast activity and spike discharges. Weeks-long imaging of seizure activity showed a characteristic evolution in onset type and a refinement of the seizure onset zone. Longitudinal tracking of individual neurons revealed that seizure onset is stochastic at the single neuron level, suggesting that seizure initiation activates neurons in non-stereotyped sequences seizure to seizure. This study demonstrates for the first time that transitions to seizure are not initiated by a small number of neuronal 'bad actors' (such as overly connected hub cells), but rather by network changes which enable the onset of pathology among large populations of neurons.
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Affiliation(s)
- Lauren A Lau
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.,Harvard Medical School, Boston, MA 02115, USA
| | - Kevin J Staley
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.,Harvard Medical School, Boston, MA 02115, USA
| | - Kyle P Lillis
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.,Harvard Medical School, Boston, MA 02115, USA
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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] [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. People with Alzheimer’s disease (AD) are more likely to develop seizures than cognitively healthy people. In this study, we aimed to understand whether whole-brain network structure is related to this increased seizure likelihood. We used electroencephalography (EEG) to estimate brain networks from people with AD and healthy controls. We subsequently inserted these networks into a model brain and simulated disease progression by increasing the excitability of brain tissue. We found the simulated AD brains were more likely to develop seizures than the simulated control brains. No participants had seizures when we collected data, so our results suggest an increased probability of developing seizures at a future time for AD participants. Therefore functional brain network structure may play a role in increased seizure likelihood in AD. We also used the model to examine which brain regions were most important for generating seizures, and found that the seizure-generating regions corresponded to those typically affected in early AD. Our results also provide a potential explanation for why people with AD are more likely to have generalized seizures (i.e. seizures involving the whole brain, as opposed to ‘focal’ seizures which only involve certain areas) than the general population with epilepsy.
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Affiliation(s)
- Luke Tait
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- * E-mail:
| | - 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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Ictal gamma-band interactions localize ictogenic nodes of the epileptic network in focal cortical dysplasia. Clin Neurophysiol 2021; 132:1927-1936. [PMID: 34157635 DOI: 10.1016/j.clinph.2021.04.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 03/18/2021] [Accepted: 04/05/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE Epilepsy surgery fails in > 30% of patients with focal cortical dysplasia (FCD). The seizure persistence after surgery can be attributed to the inability to precisely localize the tissue with an endogenous potential to generate seizures. In this study, we aimed to identify the critical components of the epileptic network that were actively involved in seizure genesis. METHODS The directed transfer function was applied to intracranial EEG recordings and the effective connectivity was determined with a high temporal and frequency resolution. Pre-ictal network properties were compared with ictal epochs to identify regions actively generating ictal activity and discriminate them from the areas of propagation. RESULTS Analysis of 276 seizures from 30 patients revealed the existence of a seizure-related network reconfiguration in the gamma-band (25-170 Hz; p < 0.005) - ictogenic nodes. Unlike seizure onset zone, resecting the majority of ictogenic nodes correlated with favorable outcomes (p < 0.012). CONCLUSION The prerequisite to successful epilepsy surgery is the accurate identification of brain areas from which seizures arise. We show that in FCD-related epilepsy, gamma-band network markers can reliably identify and distinguish ictogenic areas in macroelectrode recordings, improve intracranial EEG interpretation and better delineate the epileptogenic zone. SIGNIFICANCE Ictogenic nodes localize the critical parts of the epileptogenic tissue and increase the diagnostic yield of intracranial evaluation.
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13
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Lacuata JA, Tirona-Remulla A, Cabungcal AC, Santos RM. Combined external and endoscopic transnasal approach with use of a diamond burr in the removal of a fishing harpoon hook traversing bilateral sphenoid sinuses in a 22-year-old man. BMJ Case Rep 2021; 14:14/5/e239055. [PMID: 34016625 DOI: 10.1136/bcr-2020-239055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
A 22-year old construction worker was shot with a fishing harpoon gun on the left side of his face. He consulted at the emergency room 12 days postinjury, stable but with blurring of vision on the right. The shaft of the harpoon was protruding at the left preauricular area; the tip was neither visible nor palpable. Craniofacial CT scan and skull anteroposterolateral radiographs revealed the tip of the harpoon to be at the right orbital apex. A hook attached 1 cm from the tip was lodged in the sphenoid sinus. The hook was dismantled from the shaft via a combined external and endoscopic transnasal approach, enabling the shaft to be gently pulled. The hook, together with the tip, were removed endoscopically. The patient's visual acuity improved. He was discharged after 2 days on oral antibiotics with no deficits on follow-up.
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Affiliation(s)
- Jan Alexeis Lacuata
- Department of Otorhinolaryngology, Philippine General Hospital, University of the Philippines Manila, Manila, Philippines
| | - Agnes Tirona-Remulla
- Department of Otorhinolaryngology, Philippine General Hospital, University of the Philippines Manila, Manila, Philippines
| | - Arsenio Claro Cabungcal
- Department of Otorhinolaryngology, Philippine General Hospital, University of the Philippines Manila, Manila, Philippines
| | - Romiena Mae Santos
- Department of Otorhinolaryngology, Philippine General Hospital, University of the Philippines Manila, Manila, Philippines
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14
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Biondi A, Laiou P, Bruno E, Viana PF, Schreuder M, Hart W, Nurse E, Pal DK, Richardson MP. Remote and Long-Term Self-Monitoring of Electroencephalographic and Noninvasive Measurable Variables at Home in Patients With Epilepsy (EEG@HOME): Protocol for an Observational Study. JMIR Res Protoc 2021; 10:e25309. [PMID: 33739290 PMCID: PMC8088854 DOI: 10.2196/25309] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 12/22/2020] [Accepted: 12/23/2020] [Indexed: 01/06/2023] Open
Abstract
Background Epileptic seizures are spontaneous events that severely affect the lives of patients due to their recurrence and unpredictability. The integration of new wearable and mobile technologies to collect electroencephalographic (EEG) and extracerebral signals in a portable system might be the solution to prospectively identify times of seizure occurrence or propensity. The performances of several seizure detection devices have been assessed by validated studies, and patient perspectives on wearables have been explored to better match their needs. Despite this, there is a major gap in the literature on long-term, real-life acceptability and performance of mobile technology essential to managing chronic disorders such as epilepsy. Objective EEG@HOME is an observational, nonrandomized, noninterventional study that aims to develop a new feasible procedure that allows people with epilepsy to independently, continuously, and safely acquire noninvasive variables at home. The data collected will be analyzed to develop a general model to predict periods of increased seizure risk. Methods A total of 12 adults with a diagnosis of pharmaco-resistant epilepsy and at least 20 seizures per year will be recruited at King’s College Hospital, London. Participants will be asked to self-apply an easy and portable EEG recording system (ANT Neuro) to record scalp EEG at home twice daily. From each serial EEG recording, brain network ictogenicity (BNI), a new biomarker of the propensity of the brain to develop seizures, will be extracted. A noninvasive wrist-worn device (Fitbit Charge 3; Fitbit Inc) will be used to collect non-EEG biosignals (heart rate, sleep quality index, and steps), and a smartphone app (Seer app; Seer Medical) will be used to collect data related to seizure occurrence, medication taken, sleep quality, stress, and mood. All data will be collected continuously for 6 months. Standardized questionnaires (the Post-Study System Usability Questionnaire and System Usability Scale) will be completed to assess the acceptability and feasibility of the procedure. BNI, continuous wrist-worn sensor biosignals, and electronic survey data will be correlated with seizure occurrence as reported in the diary to investigate their potential values as biomarkers of seizure risk. Results The EEG@HOME project received funding from Epilepsy Research UK in 2018 and was approved by the Bromley Research Ethics Committee in March 2020. The first participants were enrolled in October 2020, and we expect to publish the first results by the end of 2022. Conclusions With the EEG@HOME study, we aim to take advantage of new advances in remote monitoring technology, including self-applied EEG, to investigate the feasibility of long-term disease self-monitoring. Further, we hope our study will bring new insights into noninvasively collected personalized risk factors of seizure occurrence and seizure propensity that may help to mitigate one of the most difficult aspects of refractory epilepsy: the unpredictability of seizure occurrence. International Registered Report Identifier (IRRID) PRR1-10.2196/25309
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Affiliation(s)
- Andrea Biondi
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Petroula Laiou
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Elisa Bruno
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Pedro F Viana
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.,Faculty of Medicine, University of Lisbon, Hospital de Santa Maria, Lisbon, Portugal
| | | | | | - Ewan Nurse
- Seer Medical Inc, Melbourne, Australia.,Department of Medicine, St. Vincent's Hospital Melbourne, The University of Melbourne, Melbourne, Australia
| | - Deb K Pal
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Mark P Richardson
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
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15
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Adde L, Brown A, van den Broeck C, DeCoen K, Eriksen BH, Fjørtoft T, Groos D, Ihlen EAF, Osland S, Pascal A, Paulsen H, Skog OM, Sivertsen W, Støen R. In-Motion-App for remote General Movement Assessment: a multi-site observational study. BMJ Open 2021; 11:e042147. [PMID: 33664072 PMCID: PMC7934716 DOI: 10.1136/bmjopen-2020-042147] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 01/21/2021] [Accepted: 02/19/2021] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVES To determine whether videos taken by parents of their infants' spontaneous movements were in accordance with required standards in the In-Motion-App, and whether the videos could be remotely scored by a trained General Movement Assessment (GMA) observer. Additionally, to assess the feasibility of using home-based video recordings for automated tracking of spontaneous movements, and to examine parents' perceptions and experiences of taking videos in their homes. DESIGN The study was a multi-centre prospective observational study. SETTING Parents/families of high-risk infants in tertiary care follow-up programmes in Norway, Denmark and Belgium. METHODS Parents/families were asked to video record their baby in accordance with the In-Motion standards which were based on published GMA criteria and criteria covering lighting and stability of smartphone. Videos were evaluated as GMA 'scorable' or 'non-scorable' based on predefined criteria. The accuracy of a 7-point body tracker software was compared with manually annotated body key points. Parents were surveyed about the In-Motion-App information and clarity. PARTICIPANTS The sample comprised 86 parents/families of high-risk infants. RESULTS The 86 parent/families returned 130 videos, and 121 (96%) of them were in accordance with the requirements for GMA assessment. The 7-point body tracker software detected more than 80% of body key point positions correctly. Most families found the instructions for filming their baby easy to follow, and more than 90% reported that they did not become more worried about their child's development through using the instructions. CONCLUSIONS This study reveals that a short instructional video enabled parents to video record their infant's spontaneous movements in compliance with the standards required for remote GMA. Further, an accurate automated body point software detecting infant body landmarks in smartphone videos will facilitate clinical and research use soon. Home-based video recordings could be performed without worrying parents about their child's development. TRIALS REGISTRATION NUMBER NCT03409978.
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Affiliation(s)
- Lars Adde
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Clinical Services, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Annemette Brown
- Department of Health Sciences, Lund University, Lund, Sweden
- Department of Pediatric and Adolescent and Department of Neurology and Physiotherapy, Copenhagen University Hospital, Nordsjællands Hospital, Hillerød, Denmark
| | | | - Kris DeCoen
- Department of Neonatology, University Hospital Ghent, Gent, Belgium
| | - Beate Horsberg Eriksen
- Department of Pediatrics, Møre og Romsdal Hospital Trust, Ålesund Hospital, Ålesund, Norway
| | - Toril Fjørtoft
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Clinical Services, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Daniel Groos
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Espen Alexander F Ihlen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Siril Osland
- Clinic of Clinical Services, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Aurelie Pascal
- Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium
| | - Henriette Paulsen
- Department of Physiotherapy and Rehabilitation, Vestfold Hospital Trust, Tønsberg, Norway
| | - Ole Morten Skog
- Habilitation Center, Vestfold Hospital Trust, Tønsberg, Norway
| | - Wiebke Sivertsen
- Department of Pediatrics, Møre og Romsdal Hospital Trust, Ålesund Hospital, Ålesund, Norway
| | - Ragnhild Støen
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neonatology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
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16
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Lopes MA, Krzemiński D, Hamandi K, Singh KD, Masuda N, Terry JR, Zhang J. A computational biomarker of juvenile myoclonic epilepsy from resting-state MEG. Clin Neurophysiol 2021; 132:922-927. [PMID: 33636607 PMCID: PMC7992031 DOI: 10.1016/j.clinph.2020.12.021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 12/07/2020] [Accepted: 12/18/2020] [Indexed: 11/29/2022]
Abstract
Computational modelling is combined with MEG to differentiate people with juvenile myoclonic epilepsy from healthy controls. Brain network ictogenicity (BNI) was found higher in people with juvenile myoclonic epilepsy relative to healthy controls. BNI’s classification accuracy in our cohort was 73%.
Objective For people with idiopathic generalized epilepsy, functional networks derived from their resting-state scalp electrophysiological recordings have shown an inherent higher propensity to generate seizures than those from healthy controls when assessed using the concept of brain network ictogenicity (BNI). Herein we tested whether the BNI framework is applicable to resting-state magnetoencephalography (MEG) from people with juvenile myoclonic epilepsy (JME). Methods The BNI framework consists in deriving a functional network from apparently normal brain activity, placing a mathematical model of ictogenicity into the network and then computing how often such network generates seizures in silico. We considered data from 26 people with JME and 26 healthy controls. Results We found that resting-state MEG functional networks from people with JME are characterized by a higher propensity to generate seizures (i.e., higher BNI) than those from healthy controls. We found a classification accuracy of 73%. Conclusions The BNI framework is applicable to MEG and was capable of differentiating people with epilepsy from healthy controls. Significance The BNI framework may be applied to resting-state MEG to aid in epilepsy diagnosis.
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Affiliation(s)
- Marinho A Lopes
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, United Kingdom.
| | - Dominik Krzemiński
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, United Kingdom
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, United Kingdom; The Welsh Epilepsy Unit, Department of Neurology, University Hospital of Wales, Cardiff CF14 4XW, United Kingdom
| | - Krish D Singh
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, United Kingdom
| | - Naoki Masuda
- Department of Mathematics, University at Buffalo, State University of New York, USA; Computational and Data-Enabled Science and Engineering Program, University at Buffalo, State University of New York, USA
| | - John R Terry
- EPSRC Centre for Predictive Modelling in Healthcare, University of Birmingham, Birmingham, United Kingdom; Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Edgbaston, United Kingdom; Institute for Metabolism and Systems Research, University of Birmingham, Edgbaston, United Kingdom
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, United Kingdom
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17
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Gerster M, Berner R, Sawicki J, Zakharova A, Škoch A, Hlinka J, Lehnertz K, Schöll E. FitzHugh-Nagumo oscillators on complex networks mimic epileptic-seizure-related synchronization phenomena. CHAOS (WOODBURY, N.Y.) 2020; 30:123130. [PMID: 33380049 DOI: 10.1063/5.0021420] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 11/23/2020] [Indexed: 06/12/2023]
Abstract
We study patterns of partial synchronization in a network of FitzHugh-Nagumo oscillators with empirical structural connectivity measured in human subjects. We report the spontaneous occurrence of synchronization phenomena that closely resemble the ones seen during epileptic seizures in humans. In order to obtain deeper insights into the interplay between dynamics and network topology, we perform long-term simulations of oscillatory dynamics on different paradigmatic network structures: random networks, regular nonlocally coupled ring networks, ring networks with fractal connectivities, and small-world networks with various rewiring probability. Among these networks, a small-world network with intermediate rewiring probability best mimics the findings achieved with the simulations using the empirical structural connectivity. For the other network topologies, either no spontaneously occurring epileptic-seizure-related synchronization phenomena can be observed in the simulated dynamics, or the overall degree of synchronization remains high throughout the simulation. This indicates that a topology with some balance between regularity and randomness favors the self-initiation and self-termination of episodes of seizure-like strong synchronization.
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Affiliation(s)
- Moritz Gerster
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstr. 36, 10623 Berlin, Germany
| | - Rico Berner
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstr. 36, 10623 Berlin, Germany
| | - Jakub Sawicki
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstr. 36, 10623 Berlin, Germany
| | - Anna Zakharova
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstr. 36, 10623 Berlin, Germany
| | - Antonín Škoch
- National Institute of Mental Health, Topolová 748, 250 67 Klecany, Czech Republic
| | - Jaroslav Hlinka
- National Institute of Mental Health, Topolová 748, 250 67 Klecany, Czech Republic
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany
| | - Eckehard Schöll
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstr. 36, 10623 Berlin, Germany
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18
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Junges L, Woldman W, Benjamin OJ, Terry JR. Epilepsy surgery: Evaluating robustness using dynamic network models. CHAOS (WOODBURY, N.Y.) 2020; 30:113106. [PMID: 33261362 DOI: 10.1063/5.0022171] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 10/08/2020] [Indexed: 06/12/2023]
Abstract
Epilepsy is one of the most common neurological conditions affecting over 65 million people worldwide. Over one third of people with epilepsy are considered refractory: they do not respond to drug treatments. For this significant cohort of people, surgery is a potentially transformative treatment. However, only a small minority of people with refractory epilepsy are considered suitable for surgery, and long-term seizure freedom is only achieved in half the cases. Recently, several computational approaches have been proposed to support presurgical planning. Typically, these approaches use a dynamic network model to explore the potential impact of surgical resection in silico. The network component of the model is informed by clinical imaging data and is considered static thereafter. This assumption critically overlooks the plasticity of the brain and, therefore, how continued evolution of the brain network post-surgery may impact upon the success of a resection in the longer term. In this work, we use a simplified dynamic network model, which describes transitions to seizures, to systematically explore how the network structure influences seizure propensity, both before and after virtual resections. We illustrate key results in small networks, before extending our findings to larger networks. We demonstrate how the evolution of brain networks post resection can result in a return to increased seizure propensity. Our results effectively determine the robustness of a given resection to possible network reconfigurations and so provide a potential strategy for optimizing long-term seizure freedom.
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Affiliation(s)
- Leandro Junges
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Wessel Woldman
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Oscar J Benjamin
- Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, United Kingdom
| | - John R Terry
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham B15 2TT, United Kingdom
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19
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Demuru M, Kalitzin S, Zweiphenning W, van Blooijs D, Van't Klooster M, Van Eijsden P, Leijten F, Zijlmans M. The value of intra-operative electrographic biomarkers for tailoring during epilepsy surgery: from group-level to patient-level analysis. Sci Rep 2020; 10:14654. [PMID: 32887896 PMCID: PMC7474097 DOI: 10.1038/s41598-020-71359-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 04/23/2020] [Indexed: 01/08/2023] Open
Abstract
Signal analysis biomarkers, in an intra-operative setting, may be complementary tools to guide and tailor the resection in drug-resistant focal epilepsy patients. Effective assessment of biomarker performances are needed to evaluate their clinical usefulness and translation. We defined a realistic ground-truth scenario and compared the effectiveness of different biomarkers alone and combined to localize epileptogenic tissue during surgery. We investigated the performances of univariate, bivariate and multivariate signal biomarkers applied to 1 min inter-ictal intra-operative electrocorticography to discriminate between epileptogenic and non-epileptogenic locations in 47 drug-resistant people with epilepsy (temporal and extra-temporal) who had been seizure-free one year after the operation. The best result using a single biomarker was obtained using the phase-amplitude coupling measure for which the epileptogenic tissue was localized in 17 out of 47 patients. Combining the whole set of biomarkers provided an improvement of the performances: 27 out of 47 patients. Repeating the analysis only on the temporal-lobe resections we detected the epileptogenic tissue in 29 out of 30 combining all the biomarkers. We suggest that the assessment of biomarker performances on a ground-truth scenario is required to have a proper estimate on how biomarkers translate into clinical use. Phase-amplitude coupling seems the best performing single biomarker and combining biomarkers improves localization of epileptogenic tissue. Performance achieved is not adequate as a tool in the operation theater yet, but it can improve the understanding of pathophysiological process.
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Affiliation(s)
- Matteo Demuru
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands.
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Stiliyan Kalitzin
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Willemiek Zweiphenning
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Dorien van Blooijs
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Maryse Van't Klooster
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Pieter Van Eijsden
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Frans Leijten
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Maeike Zijlmans
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
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20
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Creaser J, Lin C, Ridler T, Brown JT, D’Souza W, Seneviratne U, Cook M, Terry JR, Tsaneva-Atanasova K. Domino-like transient dynamics at seizure onset in epilepsy. PLoS Comput Biol 2020; 16:e1008206. [PMID: 32986695 PMCID: PMC7544071 DOI: 10.1371/journal.pcbi.1008206] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 10/08/2020] [Accepted: 07/29/2020] [Indexed: 12/20/2022] Open
Abstract
The International League Against Epilepsy (ILAE) groups seizures into "focal", "generalized" and "unknown" based on whether the seizure onset is confined to a brain region in one hemisphere, arises in several brain region simultaneously, or is not known, respectively. This separation fails to account for the rich diversity of clinically and experimentally observed spatiotemporal patterns of seizure onset and even less so for the properties of the brain networks generating them. We consider three different patterns of domino-like seizure onset in Idiopathic Generalized Epilepsy (IGE) and present a novel approach to classification of seizures. To understand how these patterns are generated on networks requires understanding of the relationship between intrinsic node dynamics and coupling between nodes in the presence of noise, which currently is unknown. We investigate this interplay here in the framework of domino-like recruitment across a network. In particular, we use a phenomenological model of seizure onset with heterogeneous coupling and node properties, and show that in combination they generate a range of domino-like onset patterns observed in the IGE seizures. We further explore the individual contribution of heterogeneous node dynamics and coupling by interpreting in-vitro experimental data in which the speed of onset can be chemically modulated. This work contributes to a better understanding of possible drivers for the spatiotemporal patterns observed at seizure onset and may ultimately contribute to a more personalized approach to classification of seizure types in clinical practice.
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Affiliation(s)
- Jennifer Creaser
- Department of Mathematics, University of Exeter, Exeter, EX4 4QF, UK
- EPSRC Centre for Predictive modeling in Healthcare, University of Exeter, Exeter, EX4 4QJ, UK
| | - Congping Lin
- Center for Mathematical Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
- Hubei Key Lab of Engineering Modeling and Scientific Computing, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Thomas Ridler
- Institute of Biomedical and Clinical Sciences, College of Medicine and Health, University of Exeter, EX4 4PS, UK
| | - Jonathan T. Brown
- Institute of Biomedical and Clinical Sciences, College of Medicine and Health, University of Exeter, EX4 4PS, UK
| | - Wendyl D’Souza
- Department of Medicine, St. Vincent’s Hospital, University of Melbourne, Melbourne, VIC 3065, Australia
| | - Udaya Seneviratne
- Department of Medicine, St. Vincent’s Hospital, University of Melbourne, Melbourne, VIC 3065, Australia
- Department of Neuroscience, Monash Medical Centre, Melbourne, VIC 3168, Australia
| | - Mark Cook
- Department of Medicine, St. Vincent’s Hospital, University of Melbourne, Melbourne, VIC 3065, Australia
- Graeme Clark Institute, University of Melbourne, Parkville, VIC 3010, Australia
| | - John R. Terry
- EPSRC Centre for Predictive modeling in Healthcare, University of Exeter, Exeter, EX4 4QJ, UK
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, B15 2TT, UK
| | - Krasimira Tsaneva-Atanasova
- Department of Mathematics, University of Exeter, Exeter, EX4 4QF, UK
- EPSRC Centre for Predictive modeling in Healthcare, University of Exeter, Exeter, EX4 4QJ, UK
- Living System Institute, University of Exeter, Exeter, EX4 4QJ, UK
- Institute for Advanced Study, Technical University of Munich, Lichtenbergstrasse 2a, D-85748 Garching, Germany
- Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str, 1113 Sofia, Bulgaria
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21
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Hashemi M, Vattikonda AN, Sip V, Guye M, Bartolomei F, Woodman MM, Jirsa VK. The Bayesian Virtual Epileptic Patient: A probabilistic framework designed to infer the spatial map of epileptogenicity in a personalized large-scale brain model of epilepsy spread. Neuroimage 2020; 217:116839. [PMID: 32387625 DOI: 10.1016/j.neuroimage.2020.116839] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 04/02/2020] [Accepted: 04/07/2020] [Indexed: 12/28/2022] Open
Abstract
Despite the importance and frequent use of Bayesian frameworks in brain network modeling for parameter inference and model prediction, the advanced sampling algorithms implemented in probabilistic programming languages to overcome the inference difficulties have received relatively little attention in this context. In this technical note, we propose a probabilistic framework, namely the Bayesian Virtual Epileptic Patient (BVEP), which relies on the fusion of structural data of individuals to infer the spatial map of epileptogenicity in a personalized large-scale brain model of epilepsy spread. To invert the individualized whole-brain model employed in this study, we use the recently developed algorithms known as No-U-Turn Sampler (NUTS) as well as Automatic Differentiation Variational Inference (ADVI). Our results indicate that NUTS and ADVI accurately estimate the degree of epileptogenicity of brain regions, therefore, the hypothetical brain areas responsible for the seizure initiation and propagation, while the convergence diagnostics and posterior behavior analysis validate the reliability of the estimations. Moreover, we illustrate the efficiency of the transformed non-centered parameters in comparison to centered form of parameterization. The Bayesian framework used in this work proposes an appropriate patient-specific strategy for estimating the epileptogenicity of the brain regions to improve outcome after epilepsy surgery.
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Affiliation(s)
- M Hashemi
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
| | - A N Vattikonda
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - V Sip
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - M Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - F Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France; Epileptology Department, and Clinical Neurophysiology Department, Assistance Publique des Hôpitaux de Marseille, Marseille, France
| | - M M Woodman
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - V K Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
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22
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Lopes MA, Junges L, Woldman W, Goodfellow M, Terry JR. The Role of Excitability and Network Structure in the Emergence of Focal and Generalized Seizures. Front Neurol 2020; 11:74. [PMID: 32117033 PMCID: PMC7027568 DOI: 10.3389/fneur.2020.00074] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 01/21/2020] [Indexed: 01/12/2023] Open
Abstract
Epileptic seizures are generally classified as either focal or generalized. It had been traditionally assumed that focal seizures imply localized brain abnormalities, whereas generalized seizures involve widespread brain pathologies. However, recent evidence suggests that large-scale brain networks are involved in the generation of focal seizures, and generalized seizures can originate in localized brain regions. Herein we study how network structure and tissue heterogeneities underpin the emergence of focal and widespread seizure dynamics. Mathematical modeling of seizure emergence in brain networks enables the clarification of the characteristics responsible for focal and generalized seizures. We consider neural mass network dynamics of seizure generation in exemplar synthetic networks and we measure the variance in ictogenicity across the network. Ictogenicity is defined as the involvement of network nodes in seizure activity, and its variance is used to quantify whether seizure patterns are focal or widespread across the network. We address both the influence of network structure and different excitability distributions across the network on the ictogenic variance. We find that this variance depends on both network structure and excitability distribution. High variance, i.e., localized seizure activity, is observed in networks highly heterogeneous with regard to the distribution of connections or excitabilities. However, networks that are both heterogeneous in their structure and excitability can underlie the emergence of generalized seizures, depending on the interplay between structure and excitability. Thus, our results imply that the emergence of focal and generalized seizures is underpinned by an interplay between network structure and excitability distribution.
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Affiliation(s)
- Marinho A Lopes
- Living Systems Institute, University of Exeter, Exeter, United Kingdom.,Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom.,Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom.,Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Leandro Junges
- Living Systems Institute, University of Exeter, Exeter, United Kingdom.,Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom.,Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom.,Institute for Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom
| | - Wessel Woldman
- Living Systems Institute, University of Exeter, Exeter, United Kingdom.,Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom.,Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom.,Institute for Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, United Kingdom.,Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
| | - John R Terry
- Living Systems Institute, University of Exeter, Exeter, United Kingdom.,Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom.,Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom.,Institute for Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom
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23
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Computational modelling in source space from scalp EEG to inform presurgical evaluation of epilepsy. Clin Neurophysiol 2019; 131:225-234. [PMID: 31812920 PMCID: PMC6941468 DOI: 10.1016/j.clinph.2019.10.027] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 09/26/2019] [Accepted: 10/26/2019] [Indexed: 11/21/2022]
Abstract
OBJECTIVE The effectiveness of intracranial electroencephalography (iEEG) to inform epilepsy surgery depends on where iEEG electrodes are implanted. This decision is informed by noninvasive recording modalities such as scalp EEG. Herein we propose a framework to interrogate scalp EEG and determine epilepsy lateralization to aid in electrode implantation. METHODS We use eLORETA to map source activities from seizure epochs recorded from scalp EEG and consider 15 regions of interest (ROIs). Functional networks are then constructed using the phase-locking value and studied using a mathematical model. By removing different ROIs from the network and simulating their impact on the network's ability to generate seizures in silico, the framework provides predictions of epilepsy lateralization. We consider 15 individuals from the EPILEPSIAE database and study a total of 62 seizures. Results were assessed by taking into account actual intracranial implantations and surgical outcome. RESULTS The framework provided potentially useful information regarding epilepsy lateralization in 12 out of the 15 individuals (p=0.02, binomial test). CONCLUSIONS Our results show promise for the use of this framework to better interrogate scalp EEG to determine epilepsy lateralization. SIGNIFICANCE The framework may aid clinicians in the decision process to define where to implant electrodes for intracranial monitoring.
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24
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Kalitzin S, Petkov G, Suffczynski P, Grigorovsky V, Bardakjian BL, Lopes da Silva F, Carlen PL. Epilepsy as a manifestation of a multistate network of oscillatory systems. Neurobiol Dis 2019; 130:104488. [DOI: 10.1016/j.nbd.2019.104488] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 06/03/2019] [Accepted: 06/04/2019] [Indexed: 12/18/2022] Open
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25
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Laiou P, Avramidis E, Lopes MA, Abela E, Müller M, Akman OE, Richardson MP, Rummel C, Schindler K, Goodfellow M. Quantification and Selection of Ictogenic Zones in Epilepsy Surgery. Front Neurol 2019; 10:1045. [PMID: 31632339 PMCID: PMC6779811 DOI: 10.3389/fneur.2019.01045] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 09/16/2019] [Indexed: 01/23/2023] Open
Abstract
Network models of brain dynamics provide valuable insight into the healthy functioning of the brain and how this breaks down in disease. A pertinent example is the use of network models to understand seizure generation (ictogenesis) in epilepsy. Recently, computational models have emerged to aid our understanding of seizures and to predict the outcome of surgical perturbations to brain networks. Such approaches provide the opportunity to quantify the effect of removing regions of tissue from brain networks and thereby search for the optimal resection strategy. Here, we use computational models to elucidate how sets of nodes contribute to the ictogenicity of networks. In small networks we fully elucidate the ictogenicity of all possible sets of nodes and demonstrate that the distribution of ictogenicity across sets depends on network topology. However, the full elucidation is a combinatorial problem that becomes intractable for large networks. Therefore, we combine computational models with a genetic algorithm to search for minimal sets of nodes that contribute significantly to ictogenesis. We demonstrate the potential applicability of these methods in practice by identifying optimal sets of nodes to resect in networks derived from 20 individuals who underwent resective surgery for epilepsy. We show that they have the potential to aid epilepsy surgery by suggesting alternative resection sites as well as facilitating the avoidance of brain regions that should not be resected.
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Affiliation(s)
- Petroula Laiou
- Living Systems Institute, University of Exeter, Exeter, United Kingdom
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | | | - Marinho A. Lopes
- Living Systems Institute, University of Exeter, Exeter, United Kingdom
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
- Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom
| | - Eugenio Abela
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Michael Müller
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Department of Neurology, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Ozgur E. Akman
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
| | - Mark P. Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Christian Rummel
- Support Center for Advanced Neuroimaging, University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Kaspar Schindler
- Department of Neurology, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, United Kingdom
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
- Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom
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26
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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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27
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Proix T, Aghagolzadeh M, Madsen JR, Cosgrove R, Eskandar E, Hochberg LR, Cash SS, Truccolo W. Intracortical neural activity distal to seizure-onset-areas predicts human focal seizures. PLoS One 2019; 14:e0211847. [PMID: 31329587 PMCID: PMC6645464 DOI: 10.1371/journal.pone.0211847] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 07/04/2019] [Indexed: 01/19/2023] Open
Abstract
The apparent unpredictability of epileptic seizures has a major impact in the quality of life of people with pharmacologically resistant seizures. Here, we present initial results and a proof-of-concept of how focal seizures can be predicted early in advance based on intracortical signals recorded from small neocortical patches away from identified seizure onset areas. We show that machine learning algorithms can discriminate between interictal and preictal periods based on multiunit activity (i.e. thresholded action potential counts) and multi-frequency band local field potentials recorded via 4 X 4 mm2 microelectrode arrays. Microelectrode arrays were implanted in 5 patients undergoing neuromonitoring for resective surgery. Post-implant analysis revealed arrays were outside the seizure onset areas. Preictal periods were defined as the 1-hour period leading to a seizure. A 5-minute gap between the preictal period and the putative seizure onset was enforced to account for potential errors in the determination of actual seizure onset times. We used extreme gradient boosting and long short-term memory networks for prediction. Prediction accuracy based on the area under the receiver operating characteristic curves reached 90% for at least one feature type in each patient. Importantly, successful prediction could be achieved based exclusively on multiunit activity. This result indicates that preictal activity in the recorded neocortical patches involved not only subthreshold postsynaptic potentials, perhaps driven by the distal seizure onset areas, but also neuronal spiking in distal recurrent neocortical networks. Beyond the commonly identified seizure onset areas, our findings point to the engagement of large-scale neuronal networks in the neural dynamics building up toward a seizure. Our initial results obtained on currently available human intracortical microelectrode array recordings warrant new studies on larger datasets, and open new perspectives for seizure prediction and control by emphasizing the contribution of multiscale neural signals in large-scale neuronal networks.
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Affiliation(s)
- Timothée Proix
- Department of Neuroscience, Brown University, Providence, Rhode Island, United States of America
- Carney Institute for Brain Science, Brown University, Providence, Rhode Island, United States of America
- Center for Neurorestoration & Neurotechnology, U.S. Department of Veterans Affairs, Providence, Rhode Island, United States of America
| | - Mehdi Aghagolzadeh
- Department of Neuroscience, Brown University, Providence, Rhode Island, United States of America
- Carney Institute for Brain Science, Brown University, Providence, Rhode Island, United States of America
- Center for Neurorestoration & Neurotechnology, U.S. Department of Veterans Affairs, Providence, Rhode Island, United States of America
| | - Joseph R. Madsen
- Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Rees Cosgrove
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Emad Eskandar
- Department of Neurosurgery and Nayef Al-Rodhan Laboratories for Cellular Neurosurgery and Neurosurgical Technology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Leigh R. Hochberg
- Carney Institute for Brain Science, Brown University, Providence, Rhode Island, United States of America
- Center for Neurorestoration & Neurotechnology, U.S. Department of Veterans Affairs, Providence, Rhode Island, United States of America
- School of Engineering, Brown University, Providence, Rhode Island, United States of America
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Sydney S. Cash
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Wilson Truccolo
- Department of Neuroscience, Brown University, Providence, Rhode Island, United States of America
- Carney Institute for Brain Science, Brown University, Providence, Rhode Island, United States of America
- Center for Neurorestoration & Neurotechnology, U.S. Department of Veterans Affairs, Providence, Rhode Island, United States of America
- * E-mail:
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28
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Lopes MA, Perani S, Yaakub SN, Richardson MP, Goodfellow M, Terry JR. Revealing epilepsy type using a computational analysis of interictal EEG. Sci Rep 2019; 9:10169. [PMID: 31308412 PMCID: PMC6629665 DOI: 10.1038/s41598-019-46633-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 07/02/2019] [Indexed: 01/10/2023] Open
Abstract
Seizure onset in epilepsy can usually be classified as focal or generalized, based on a combination of clinical phenomenology of the seizures, EEG recordings and MRI. This classification may be challenging when seizures and interictal epileptiform discharges are infrequent or discordant, and MRI does not reveal any apparent abnormalities. To address this challenge, we introduce the concept of Ictogenic Spread (IS) as a prediction of how pathological electrical activity associated with seizures will propagate throughout a brain network. This measure is defined using a person-specific computer representation of the functional network of the brain, constructed from interictal EEG, combined with a computer model of the transition from background to seizure-like activity within nodes of a distributed network. Applying this method to a dataset comprising scalp EEG from 38 people with epilepsy (17 with genetic generalized epilepsy (GGE), 21 with mesial temporal lobe epilepsy (mTLE)), we find that people with GGE display a higher IS in comparison to those with mTLE. We propose IS as a candidate computational biomarker to classify focal and generalized epilepsy using interictal EEG.
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Affiliation(s)
- Marinho A Lopes
- Living Systems Institute, University of Exeter, Exeter, EX4 4QD, UK.
- Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, EX4 4QD, UK.
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, EX4 4QD, UK.
| | - Suejen Perani
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Siti N Yaakub
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Mark P Richardson
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, EX4 4QD, UK
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
- King's College Hospital NHS Foundation Trust, London, SE5 9RS, UK
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, EX4 4QD, UK
- Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, EX4 4QD, UK
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, EX4 4QD, UK
| | - John R Terry
- Living Systems Institute, University of Exeter, Exeter, EX4 4QD, UK
- Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, EX4 4QD, UK
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, EX4 4QD, UK
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Tangwiriyasakul C, Perani S, Centeno M, Yaakub SN, Abela E, Carmichael DW, Richardson MP. Dynamic brain network states in human generalized spike-wave discharges. Brain 2019; 141:2981-2994. [PMID: 30169608 PMCID: PMC6158757 DOI: 10.1093/brain/awy223] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2018] [Accepted: 07/15/2018] [Indexed: 12/21/2022] Open
Abstract
Generalized spike-wave discharges in idiopathic generalized epilepsy are conventionally assumed to have abrupt onset and offset. However, in rodent models, discharges emerge during a dynamic evolution of brain network states, extending several seconds before and after the discharge. In human idiopathic generalized epilepsy, simultaneous EEG and functional MRI shows cortical regions may be active before discharges, and network connectivity around discharges may not be normal. Here, in human idiopathic generalized epilepsy, we investigated whether generalized spike-wave discharges emerge during a dynamic evolution of brain network states. Using EEG-functional MRI, we studied 43 patients and 34 healthy control subjects. We obtained 95 discharges from 20 patients. We compared data from patients with discharges with data from patients without discharges and healthy controls. Changes in MRI (blood oxygenation level-dependent) signal amplitude in discharge epochs were observed only at and after EEG onset, involving a sequence of parietal and frontal cortical regions then thalamus (P < 0.01, across all regions and measurement time points). Examining MRI signal phase synchrony as a measure of functional connectivity between each pair of 90 brain regions, we found significant connections (P < 0.01, across all connections and measurement time points) involving frontal, parietal and occipital cortex during discharges, and for 20 s after EEG offset. This network prominent during discharges showed significantly low synchrony (below 99% confidence interval for synchrony in this network in non-discharge epochs in patients) from 16 s to 10 s before discharges, then ramped up steeply to a significantly high level of synchrony 2 s before discharge onset. Significant connections were seen in a sensorimotor network in the minute before discharge onset. This network also showed elevated synchrony in patients without discharges compared to healthy controls (P = 0.004). During 6 s prior to discharges, additional significant connections to this sensorimotor network were observed, involving prefrontal and precuneus regions. In healthy subjects, significant connections involved a posterior cortical network. In patients with discharges, this posterior network showed significantly low synchrony during the minute prior to discharge onset. In patients without discharges, this network showed the same level of synchrony as in healthy controls. Our findings suggest persistently high sensorimotor network synchrony, coupled with transiently (at least 1 min) low posterior network synchrony, may be a state predisposing to generalized spike-wave discharge onset. Our findings also show that EEG onset and associated MRI signal amplitude change is embedded in a considerably longer period of evolving brain network states before and after discharge events.
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Affiliation(s)
- Chayanin Tangwiriyasakul
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
| | - Suejen Perani
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK.,Developmental Imaging and Biophysics Section, Developmental Neurosciences Program, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Maria Centeno
- Developmental Imaging and Biophysics Section, Developmental Neurosciences Program, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Siti Nurbaya Yaakub
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
| | - Eugenio Abela
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
| | - David W Carmichael
- Developmental Imaging and Biophysics Section, Developmental Neurosciences Program, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Mark P Richardson
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK.,King's College Hospital, London, UK
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The role that choice of model plays in predictions for epilepsy surgery. Sci Rep 2019; 9:7351. [PMID: 31089190 PMCID: PMC6517411 DOI: 10.1038/s41598-019-43871-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 05/02/2019] [Indexed: 12/26/2022] Open
Abstract
Mathematical modelling has been widely used to predict the effects of perturbations to brain networks. An important example is epilepsy surgery, where the perturbation in question is the removal of brain tissue in order to render the patient free of seizures. Different dynamical models have been proposed to represent transitions to ictal states in this context. However, our choice of which mathematical model to use to address this question relies on making assumptions regarding the mechanism that defines the transition from background to the seizure state. Since these mechanisms are unknown, it is important to understand how predictions from alternative dynamical descriptions compare. Herein we evaluate to what extent three different dynamical models provide consistent predictions for the effect of removing nodes from networks. We show that for small, directed, connected networks the three considered models provide consistent predictions. For larger networks, predictions are shown to be less consistent. However consistency is higher in networks that have sufficiently large differences in ictogenicity between nodes. We further demonstrate that heterogeneity in ictogenicity across nodes correlates with variability in the number of connections for each node.
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Lopes MA, Goodfellow M, Terry JR. A Model-Based Assessment of the Seizure Onset Zone Predictive Power to Inform the Epileptogenic Zone. Front Comput Neurosci 2019; 13:25. [PMID: 31105545 PMCID: PMC6498870 DOI: 10.3389/fncom.2019.00025] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 04/10/2019] [Indexed: 02/03/2023] Open
Abstract
Epilepsy surgery is a clinical procedure that aims to remove the brain tissue responsible for the emergence of seizures, the epileptogenic zone (EZ). It is preceded by an evaluation to determine the brain tissue that must be resected. The identification of the seizure onset zone (SOZ) from intracranial EEG recordings stands as one of the key proxies for the EZ. In this study we used computational models of epilepsy to assess to what extent the SOZ may or may not represent the EZ. We considered a set of different synthetic networks (e.g., regular, small-world, random, and scale-free networks) to represent large-scale brain networks and a phenomenological network model of seizure generation. In the model, the SOZ was inferred from the seizure likelihood (SL), a measure of the propensity of single nodes to produce epileptiform dynamics, whilst a surgery corresponded to the removal of nodes and connections from the network. We used the concept of node ictogenicity (NI) to quantify the effectiveness of each node removal on reducing the network's propensity to generate seizures. This framework enabled us to systematically compare the SOZ and the seizure control achieved by each considered surgery. Specifically, we compared the distributions of SL and NI across different networks. We found that SL and NI were concordant when all nodes were similarly ictogenic, whereas when there was a small fraction of nodes with high NI, the SL was not specific at identifying these nodes. We further considered networks with heterogeneous node excitabilities, i.e., nodes with different susceptibilities of being engaged in seizure activity, to understand how such heterogeneity may affect the relationship between SL and NI. We found that while SL and NI are concordant when there is a small fraction of hyper-excitable nodes in a network that is otherwise homogeneous, they do diverge if the network is heterogeneous, such as in scale-free networks. We observe that SL is highly dependent on node excitabilities, whilst the effect of surgical resections as revealed by NI is mostly determined by network structure. Together our results suggest that the SOZ is not always a good marker of the EZ.
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Affiliation(s)
- Marinho A Lopes
- Living Systems Institute, University of Exeter, Exeter, United Kingdom.,Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, United Kingdom.,Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
| | - John R Terry
- Living Systems Institute, University of Exeter, Exeter, United Kingdom.,Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
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32
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Tangwiriyasakul C, Perani S, Abela E, Carmichael DW, Richardson MP. Sensorimotor network hypersynchrony as an endophenotype in families with genetic generalized epilepsy: A resting-state functional magnetic resonance imaging study. Epilepsia 2019; 60:e14-e19. [PMID: 30730052 PMCID: PMC6446943 DOI: 10.1111/epi.14663] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 12/24/2018] [Accepted: 01/15/2019] [Indexed: 12/30/2022]
Abstract
Recent evidence suggests that three specific brain networks show state-dependent levels of synchronization before, during, and after episodes of generalized spike-wave discharges (GSW) in patients with genetic generalized epilepsy (GGE). Here, we investigate whether synchronization in these networks differs between patients with GGE (n = 13), their unaffected first-degree relatives (n = 17), and healthy controls (n = 18). All subjects underwent two 10-minute simultaneous electroencephalographic-functional magnetic resonance imaging (fMRI) recordings without GSW. Whole-brain data were divided into 90 regions, and blood oxygen level-dependent (BOLD) phase synchrony in a 0.04-0.07-Hz band was estimated between all pairs of regions. Three networks were defined: (1) the network with highest synchrony during GSW events, (2) a sensorimotor network, and (3) an occipital network. Average synchrony (mean node degree) was inferred across each network over time. Notably, synchrony was significantly higher in the sensorimotor network in patients and in unaffected relatives, compared to controls. There was a trend toward higher synchrony in the GSW network in patients and in unaffected relatives. There was no difference between groups for the occipital network. Our findings provide evidence that elevated fMRI BOLD synchrony in a sensorimotor network is a state-independent endophenotype of GGE, present in patients in the absence of GSW, and present in unaffected relatives.
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Affiliation(s)
- Chayanin Tangwiriyasakul
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry Psychology and NeuroscienceKing's College LondonLondonUK
| | - Suejen Perani
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry Psychology and NeuroscienceKing's College LondonLondonUK
- UCL Great Ormond Street Institute of Child Health, University College LondonLondonUK
| | - Eugenio Abela
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry Psychology and NeuroscienceKing's College LondonLondonUK
| | - David W. Carmichael
- UCL Great Ormond Street Institute of Child Health, University College LondonLondonUK
- School of Imaging and BioengineeringFaculty of Life Sciences and MedicineKing's College LondonLondonUK
| | - Mark P. Richardson
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry Psychology and NeuroscienceKing's College LondonLondonUK
- Centre for EpilepsyKing's College HospitalLondonUK
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Abela E, Pawley AD, Tangwiriyasakul C, Yaakub SN, Chowdhury FA, Elwes RDC, Brunnhuber F, Richardson MP. Slower alpha rhythm associates with poorer seizure control in epilepsy. Ann Clin Transl Neurol 2019; 6:333-343. [PMID: 30847365 PMCID: PMC6389754 DOI: 10.1002/acn3.710] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 11/26/2018] [Indexed: 01/27/2023] Open
Abstract
Objective Slowing and frontal spread of the alpha rhythm have been reported in multiple epilepsy syndromes. We investigated whether these phenomena are associated with seizure control. Methods We prospectively acquired resting-state electroencephalogram (EEG) in 63 patients with focal and idiopathic generalized epilepsy (FE and IGE) and 39 age- and gender-matched healthy subjects (HS). Patients were divided into good and poor (≥4 seizures/12 months) seizure control groups based on self-reports and clinical records. We computed spectral power from 20-sec EEG segments during eyes-closed wakefulness, free of interictal abnormalities, and quantified power in high- and low-alpha bands. Analysis of covariance and post hoc t-tests were used to assess group differences in alpha-power shift across all EEG channels. Permutation-based statistics were used to assess the topography of this shift across the whole scalp. Results Compared to HS, patients showed a statistically significant shift of spectral power from high- to low-alpha frequencies (effect size g = 0.78 [95% confidence interval 0.43, 1.20]). This alpha-power shift was driven by patients with poor seizure control in both FE and IGE (g = 1.14, [0.65, 1.74]), and occurred over midline frontal and bilateral occipital regions. IGE exhibited less alpha power shift compared to FE over bilateral frontal regions (g = -1.16 [-0.68, -1.74]). There was no interaction between syndrome and seizure control. Effects were independent of antiepileptic drug load, time of day, or subgroup definitions. Interpretation Alpha slowing and anteriorization are a robust finding in patients with epilepsy and might represent a generic indicator of seizure liability.
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Affiliation(s)
- Eugenio Abela
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUnited Kingdom
- Department of Clinical NeurophysiologyKing's College Hospital NHS Foundation TrustLondonUnited Kingdom
| | - Adam D. Pawley
- Social, Genetic and Developmental Psychiatry Research CenterInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUnited Kingdom
| | - Chayanin Tangwiriyasakul
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUnited Kingdom
| | - Siti N. Yaakub
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUnited Kingdom
- School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUnited Kingdom
| | - Fahmida A. Chowdhury
- National Hospital for Neurology and NeurosurgeryUCL Hospitals NHS Foundation TrustLondonUnited Kingdom
| | - Robert D. C. Elwes
- Department of Clinical NeurophysiologyKing's College Hospital NHS Foundation TrustLondonUnited Kingdom
| | - Franz Brunnhuber
- Department of Clinical NeurophysiologyKing's College Hospital NHS Foundation TrustLondonUnited Kingdom
| | - Mark P. Richardson
- Department of Basic and Clinical NeuroscienceInstitute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUnited Kingdom
- Department of Clinical NeurophysiologyKing's College Hospital NHS Foundation TrustLondonUnited Kingdom
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34
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Sandroff BM, Motl RW, Bamman M, Cutter GR, Bolding M, Rinker JR, Wylie GR, Genova H, DeLuca J. Rationale and design of a single-blind, randomised controlled trial of exercise training for managing learning and memory impairment in persons with multiple sclerosis. BMJ Open 2018; 8:e023231. [PMID: 30552263 PMCID: PMC6303579 DOI: 10.1136/bmjopen-2018-023231] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
INTRODUCTION This randomised controlled trial (RCT) examines treadmill walking exercise training effects on learning and memory performance, hippocampal volume, and hippocampal resting-state functional connectivity in persons with multiple sclerosis (MS) who have objective impairments in learning new information. METHODS AND ANALYSIS Forty fully ambulatory persons with MS who demonstrate objective learning and memory impairments will be randomly assigned into either the intervention or active control study conditions. The intervention condition involves supervised, progressive treadmill walking exercise training three times per week for a 3-month period. The active control condition involves supervised, progressive low-intensity resistive exercise that will be delivered at the same frequency as the intervention condition. The primary outcome will involve composite performance on neuropsychological learning and memory tests, and the secondary outcomes involve MRI measures of hippocampal volume and resting-state functional connectivity administered before and after the 3-month study period. Outcomes will be administered by treatment-blinded assessors using alternate test forms to minimise practice effects, and MRI data processing will be performed by blinded data analysts. ETHICS AND DISSEMINATION This study has been approved by a university institutional review board. The primary results will be disseminated via peer-reviewed publications and the final data will be made available to third parties in applicable data repositories. If successful, the results from this study will eventually inform subsequent RCTs for developing physical rehabilitation interventions (ie, treadmill walking exercise training) for improving learning and memory and its relationship with hippocampal outcomes in larger samples of cognitively impaired persons with MS. The results from this early-phase RCT will further lay preliminary groundwork for ultimately providing clinicians and patients with guidelines for better using chronic treadmill walking exercise for improving cognition and brain health. This approach is paramount as learning and memory impairment is common, burdensome and poorly managed in MS. TRIAL REGISTRATION NUMBER NCT03319771; Pre-results.
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Affiliation(s)
- Brian M Sandroff
- Department of Physical Therapy, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Robert W Motl
- Department of Physical Therapy, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Marcus Bamman
- Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama, USA
- Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Gary R Cutter
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Mark Bolding
- Department of Radiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - John R Rinker
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Glenn R Wylie
- Kessler Foundation, Neuropsychology and Neuroscience Research, West Orange, New Jersey, USA
| | - Helen Genova
- Kessler Foundation, Neuropsychology and Neuroscience Research, West Orange, New Jersey, USA
| | - John DeLuca
- Kessler Foundation, Neuropsychology and Neuroscience Research, West Orange, New Jersey, USA
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35
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Alarcón G, Jiménez-Jiménez D, Valentín A, Martín-López D. Characterizing EEG Cortical Dynamics and Connectivity with Responses to Single Pulse Electrical Stimulation (SPES). Int J Neural Syst 2018; 28:1750057. [DOI: 10.1142/s0129065717500575] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Objectives: To model cortical connections in order to characterize their oscillatory behavior and role in the generation of spontaneous electroencephalogram (EEG). Methods: We studied averaged responses to single pulse electrical stimulation (SPES) from the non-epileptogenic hemisphere of five patients assessed with intracranial EEG who became seizure free after contralateral temporal lobectomy. Second-order control system equations were modified to characterize the systems generating a given response. SPES responses were modeled as responses to a unit step input. EEG power spectrum was calculated on the 20[Formula: see text]s preceding SPES. Results: 121 channels showed responses to 32 stimulation sites. A single system could model the response in 41.3% and two systems were required in 58.7%. Peaks in the frequency response of the models tended to occur within the frequency range of most activity on the spontaneous EEG. Discrepancies were noted between activity predicted by models and activity recorded in the spontaneous EEG. These discrepancies could be explained by the existence of alpha rhythm or interictal epileptiform discharges. Conclusions: Cortical interactions shown by SPES can be described as control systems which can predict cortical oscillatory behavior. The method is unique as it describes connectivity as well as dynamic interactions.
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Affiliation(s)
- Gonzalo Alarcón
- Comprehensive Epilepsy Center Neuroscience Institute, Academic Health Systems, Hamad Medical Corporation, Doha, Qatar
- Department of Clinical Neuroscience, King’s College London, Institute of Psychiatry, Psychology and Neuroscience London, UK
- Department of Clinical Neurophysiology, King’s College Hospital NHS FT, London, UK
- Weill Cornell Medical College, Doha, Qatar
| | - Diego Jiménez-Jiménez
- Department of Clinical Neuroscience, King’s College London, Institute of Psychiatry, Psychology and Neuroscience London, UK
- Department of Clinical Neurophysiology, King’s College Hospital NHS FT, London, UK
- Universidad San Francisco de Quito, School of Medicine, Quito, Ecuador
| | - Antonio Valentín
- Department of Clinical Neuroscience, King’s College London, Institute of Psychiatry, Psychology and Neuroscience London, UK
- Department of Clinical Neurophysiology, King’s College Hospital NHS FT, London, UK
- Weill Cornell Medical College, Doha, Qatar
| | - David Martín-López
- Department of Clinical Neuroscience, King’s College London, Institute of Psychiatry, Psychology and Neuroscience London, UK
- Weill Cornell Medical College, Doha, Qatar
- Department of Clinical Neurophysiology, Kingston Hospital NHS FT, London, UK
- Department of Clinical Neurophysiology, St George’s University Hospitals NHS FT, London, UK
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36
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Park KM, Lee BI, Shin KJ, Ha SY, Park J, Kim TH, Mun CW, Kim SE. Progressive topological disorganization of brain network in focal epilepsy. Acta Neurol Scand 2018; 137:425-431. [PMID: 29344935 DOI: 10.1111/ane.12899] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/22/2017] [Indexed: 12/21/2022]
Abstract
OBJECTIVE Increasing evidence has suggested that epilepsy is a network disease. Graph theory is a mathematical tool that allows for the analysis and quantification of the brain network. We aimed to evaluate the influences of duration of epilepsy on the topological organization of brain network in focal epilepsy patients with normal MRI using the graph theoretical analysis based on diffusion tenor imaging. METHODS We prospectively enrolled 66 patients with focal epilepsy (18/66 patients were newly diagnosed) and 84 healthy subjects. All of the patients with epilepsy had normal MRI on visual inspection. All of the subjects underwent diffusion tensor imaging that was analyzed using graph theory to obtain network measures. RESULTS The measures of characteristic path length and small-worldness in the patients with focal epilepsy were significantly decreased, even after multiple corrections (P < .01). Moreover, the measures including mean clustering coefficient and global efficiency in the patients with epilepsy had strong tendency to decrease compared to those in healthy subjects (P = .0153 and P = .0138, respectively). When comparing the measures among the patients with newly diagnosed/chronic epilepsy and healthy subjects using ANOVA, the characteristic path length (P = .006), small-worldness (P = .032), and global efficiency (P = .004) were significantly different. In addition, the duration of epilepsy was negatively correlated with global efficiency (r = -.249, P = .0454). CONCLUSIONS We newly found a progressive topological disorganization of the brain network in focal epilepsy. In addition, we demonstrated disrupted topological organization in focal epilepsy, shifting toward a more random state.
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Affiliation(s)
- K. M. Park
- Department of Neurology; Haeundae Paik Hospital; Inje University College of Medicine; Busan Korea
| | - B. I. Lee
- Department of Neurology; Haeundae Paik Hospital; Inje University College of Medicine; Busan Korea
| | - K. J. Shin
- Department of Neurology; Haeundae Paik Hospital; Inje University College of Medicine; Busan Korea
| | - S. Y. Ha
- Department of Neurology; Haeundae Paik Hospital; Inje University College of Medicine; Busan Korea
| | - J. Park
- Department of Neurology; Haeundae Paik Hospital; Inje University College of Medicine; Busan Korea
| | - T. H. Kim
- Department of Health Science and Technology; Inje University; Gimhae Korea
| | - C. W. Mun
- Department of Health Science and Technology; Inje University; Gimhae Korea
- School of Biomedical Engineering/u-HARC; Inje University; Gimhae Korea
| | - S. E. Kim
- Department of Neurology; Haeundae Paik Hospital; Inje University College of Medicine; Busan Korea
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Chouzouris T, Omelchenko I, Zakharova A, Hlinka J, Jiruska P, Schöll E. Chimera states in brain networks: Empirical neural vs. modular fractal connectivity. CHAOS (WOODBURY, N.Y.) 2018; 28:045112. [PMID: 31906648 DOI: 10.1063/1.5009812] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Complex spatiotemporal patterns, called chimera states, consist of coexisting coherent and incoherent domains and can be observed in networks of coupled oscillators. The interplay of synchrony and asynchrony in complex brain networks is an important aspect in studies of both the brain function and disease. We analyse the collective dynamics of FitzHugh-Nagumo neurons in complex networks motivated by its potential application to epileptology and epilepsy surgery. We compare two topologies: an empirical structural neural connectivity derived from diffusion-weighted magnetic resonance imaging and a mathematically constructed network with modular fractal connectivity. We analyse the properties of chimeras and partially synchronized states and obtain regions of their stability in the parameter planes. Furthermore, we qualitatively simulate the dynamics of epileptic seizures and study the influence of the removal of nodes on the network synchronizability, which can be useful for applications to epileptic surgery.
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Affiliation(s)
- Teresa Chouzouris
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstraße 36, 10623 Berlin, Germany
| | - Iryna Omelchenko
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstraße 36, 10623 Berlin, Germany
| | - Anna Zakharova
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstraße 36, 10623 Berlin, Germany
| | - Jaroslav Hlinka
- Institute of Computer Science, Czech Academy of Sciences, Pod Vodarenskou vezi 2, 18207 Prague, Czech Republic
| | - Premysl Jiruska
- Institute of Physiology, Czech Academy of Sciences, Videnska 1083, 14220 Prague, Czech Republic
| | - Eckehard Schöll
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstraße 36, 10623 Berlin, Germany
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38
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Lopes MA, Richardson MP, Abela E, Rummel C, Schindler K, Goodfellow M, Terry JR. Elevated Ictal Brain Network Ictogenicity Enables Prediction of Optimal Seizure Control. Front Neurol 2018; 9:98. [PMID: 29545769 PMCID: PMC5837986 DOI: 10.3389/fneur.2018.00098] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 02/12/2018] [Indexed: 01/30/2023] Open
Abstract
Recent studies have shown that mathematical models can be used to analyze brain networks by quantifying how likely they are to generate seizures. In particular, we have introduced the quantity termed brain network ictogenicity (BNI), which was demonstrated to have the capability of differentiating between functional connectivity (FC) of healthy individuals and those with epilepsy. Furthermore, BNI has also been used to quantify and predict the outcome of epilepsy surgery based on FC extracted from pre-operative ictal intracranial electroencephalography (iEEG). This modeling framework is based on the assumption that the inferred FC provides an appropriate representation of an ictogenic network, i.e., a brain network responsible for the generation of seizures. However, FC networks have been shown to change their topology depending on the state of the brain. For example, topologies during seizure are different to those pre- and post-seizure. We therefore sought to understand how these changes affect BNI. We studied peri-ictal iEEG recordings from a cohort of 16 epilepsy patients who underwent surgery and found that, on average, ictal FC yield higher BNI relative to pre- and post-ictal FC. However, elevated ictal BNI was not observed in every individual, rather it was typically observed in those who had good post-operative seizure control. We therefore hypothesize that elevated ictal BNI is indicative of an ictogenic network being appropriately represented in the FC. We evidence this by demonstrating superior model predictions for post-operative seizure control in patients with elevated ictal BNI.
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Affiliation(s)
- Marinho A Lopes
- Living Systems Institute, University of Exeter, Exeter, United Kingdom.,Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
| | - Mark P Richardson
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Eugenio Abela
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland
| | | | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, United Kingdom.,Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
| | - John R Terry
- Living Systems Institute, University of Exeter, Exeter, United Kingdom.,Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
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39
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Fletcher JM, Wennekers T. From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity. Int J Neural Syst 2018; 28:1750013. [DOI: 10.1142/s0129065717500137] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
It is clear that the topological structure of a neural network somehow determines the activity of the neurons within it. In the present work, we ask to what extent it is possible to examine the structural features of a network and learn something about its activity? Specifically, we consider how the centrality (the importance of a node in a network) of a neuron correlates with its firing rate. To investigate, we apply an array of centrality measures, including In-Degree, Closeness, Betweenness, Eigenvector, Katz, PageRank, Hyperlink-Induced Topic Search (HITS) and NeuronRank to Leaky-Integrate and Fire neural networks with different connectivity schemes. We find that Katz centrality is the best predictor of firing rate given the network structure, with almost perfect correlation in all cases studied, which include purely excitatory and excitatory–inhibitory networks, with either homogeneous connections or a small-world structure. We identify the properties of a network which will cause this correlation to hold. We argue that the reason Katz centrality correlates so highly with neuronal activity compared to other centrality measures is because it nicely captures disinhibition in neural networks. In addition, we argue that these theoretical findings are applicable to neuroscientists who apply centrality measures to functional brain networks, as well as offer a neurophysiological justification to high level cognitive models which use certain centrality measures.
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Affiliation(s)
- Jack McKay Fletcher
- Centre for Robotic and Neural Systems, University of Plymouth, Drake Circus, Plymouth PL48AA, UK
| | - Thomas Wennekers
- Centre for Robotic and Neural Systems, University of Plymouth, Drake Circus, Plymouth PL48AA, UK
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40
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Lopes MA, Richardson MP, Abela E, Rummel C, Schindler K, Goodfellow M, Terry JR. An optimal strategy for epilepsy surgery: Disruption of the rich-club? PLoS Comput Biol 2017; 13:e1005637. [PMID: 28817568 PMCID: PMC5560820 DOI: 10.1371/journal.pcbi.1005637] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 06/20/2017] [Indexed: 01/05/2023] Open
Abstract
Surgery is a therapeutic option for people with epilepsy whose seizures are not controlled by anti-epilepsy drugs. In pre-surgical planning, an array of data modalities, often including intra-cranial EEG, is used in an attempt to map regions of the brain thought to be crucial for the generation of seizures. These regions are then resected with the hope that the individual is rendered seizure free as a consequence. However, post-operative seizure freedom is currently sub-optimal, suggesting that the pre-surgical assessment may be improved by taking advantage of a mechanistic understanding of seizure generation in large brain networks. Herein we use mathematical models to uncover the relative contribution of regions of the brain to seizure generation and consequently which brain regions should be considered for resection. A critical advantage of this modeling approach is that the effect of different surgical strategies can be predicted and quantitatively compared in advance of surgery. Herein we seek to understand seizure generation in networks with different topologies and study how the removal of different nodes in these networks reduces the occurrence of seizures. Since this a computationally demanding problem, a first step for this aim is to facilitate tractability of this approach for large networks. To do this, we demonstrate that predictions arising from a neural mass model are preserved in a lower dimensional, canonical model that is quicker to simulate. We then use this simpler model to study the emergence of seizures in artificial networks with different topologies, and calculate which nodes should be removed to render the network seizure free. We find that for scale-free and rich-club networks there exist specific nodes that are critical for seizure generation and should therefore be removed, whereas for small-world networks the strategy should instead focus on removing sufficient brain tissue. We demonstrate the validity of our approach by analysing intra-cranial EEG recordings from a database comprising 16 patients who have undergone epilepsy surgery, revealing rich-club structures within the obtained functional networks. We show that the postsurgical outcome for these patients was better when a greater proportion of the rich club was removed, in agreement with our theoretical predictions.
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Affiliation(s)
- Marinho A. Lopes
- Living Systems Institute, University of Exeter, Exeter, United Kingdom
- Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
- * E-mail:
| | - Mark P. Richardson
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
- Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, United Kingdom
| | - Eugenio Abela
- Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, United Kingdom
- Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University of Bern, Bern, Switzerland
| | | | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, United Kingdom
- Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
| | - John R. Terry
- Living Systems Institute, University of Exeter, Exeter, United Kingdom
- Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
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41
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Dynamic models of large-scale brain activity. Nat Neurosci 2017; 20:340-352. [PMID: 28230845 DOI: 10.1038/nn.4497] [Citation(s) in RCA: 468] [Impact Index Per Article: 66.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2016] [Accepted: 01/06/2017] [Indexed: 12/14/2022]
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42
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Jedynak M, Pons AJ, Garcia-Ojalvo J, Goodfellow M. Temporally correlated fluctuations drive epileptiform dynamics. Neuroimage 2017; 146:188-196. [PMID: 27865920 PMCID: PMC5353705 DOI: 10.1016/j.neuroimage.2016.11.034] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Revised: 10/17/2016] [Accepted: 11/13/2016] [Indexed: 12/27/2022] Open
Abstract
Macroscopic models of brain networks typically incorporate assumptions regarding the characteristics of afferent noise, which is used to represent input from distal brain regions or ongoing fluctuations in non-modelled parts of the brain. Such inputs are often modelled by Gaussian white noise which has a flat power spectrum. In contrast, macroscopic fluctuations in the brain typically follow a 1/fb spectrum. It is therefore important to understand the effect on brain dynamics of deviations from the assumption of white noise. In particular, we wish to understand the role that noise might play in eliciting aberrant rhythms in the epileptic brain. To address this question we study the response of a neural mass model to driving by stochastic, temporally correlated input. We characterise the model in terms of whether it generates "healthy" or "epileptiform" dynamics and observe which of these dynamics predominate under different choices of temporal correlation and amplitude of an Ornstein-Uhlenbeck process. We find that certain temporal correlations are prone to eliciting epileptiform dynamics, and that these correlations produce noise with maximal power in the δ and θ bands. Crucially, these are rhythms that are found to be enhanced prior to seizures in humans and animal models of epilepsy. In order to understand why these rhythms can generate epileptiform dynamics, we analyse the response of the model to sinusoidal driving and explain how the bifurcation structure of the model gives rise to these findings. Our results provide insight into how ongoing fluctuations in brain dynamics can facilitate the onset and propagation of epileptiform rhythms in brain networks. Furthermore, we highlight the need to combine large-scale models with noise of a variety of different types in order to understand brain (dys-)function.
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Affiliation(s)
- Maciej Jedynak
- Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Terrassa, Spain; Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain.
| | - Antonio J Pons
- Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Terrassa, Spain
| | - Jordi Garcia-Ojalvo
- Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
| | - Marc Goodfellow
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK; Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK; EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK
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43
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Karoly PJ, Nurse ES, Freestone DR, Ung H, Cook MJ, Boston R. Bursts of seizures in long-term recordings of human focal epilepsy. Epilepsia 2017; 58:363-372. [PMID: 28084639 DOI: 10.1111/epi.13636] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/07/2016] [Indexed: 12/14/2022]
Abstract
OBJECTIVE We report on temporally clustered seizures detected from continuous long-term ambulatory human electroencephalographic data. The objective was to investigate short-term seizure clustering, which we have termed bursting, and consider implications for patient care, seizure prediction, and evaluating therapies. METHODS Chronic ambulatory intracranial electroencephalography (EEG) data collected for the purpose of seizure prediction were annotated to identify seizure events. A detection algorithm was used to identify bursts of events. Burst events were compared to nonburst events to evaluate event dispersion, duration and dynamics. RESULTS Bursts of seizures were present in 6 of 15 subjects, and detections were consistent over long-term monitoring (>2 years). Subjects with bursts of seizures had highly overdispersed seizure rates, compared to other subjects. There was a complicated relationship between bursts and clinical seizures, although bursts were associated with multimodal distributions of seizure duration, and poorer predictive outcomes. For three subjects, bursts demonstrated distinctive preictal dynamics compared to clinical seizures. SIGNIFICANCE We have previously hypothesized that there are distinct physiologic pathways underlying short- and long-duration seizures. Herein we show that burst seizures fall almost exclusively within the short population of seizure durations; however, a short duration event was not sufficient to induce or imply bursting. We can therefore conclude that in addition to distinct mechanisms underlying seizure duration, there are separate factors regulating bursts of seizures. We show that bursts were a robust phenomenon in our patient cohort, which were consistent with overdispersed seizure rates, suggesting long-memory dynamics.
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Affiliation(s)
- Philippa J Karoly
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Fitzroy, Victoria, Australia.,Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, Victoria, Australia
| | - Ewan S Nurse
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Fitzroy, Victoria, Australia.,Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, Victoria, Australia
| | - Dean R Freestone
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Fitzroy, Victoria, Australia
| | - Hoameng Ung
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A
| | - Mark J Cook
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Fitzroy, Victoria, Australia
| | - Ray Boston
- Department of Medicine, St. Vincent's Hospital, University of Melbourne, Fitzroy, Victoria, Australia
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Khambhati AN, Bassett DS, Oommen BS, Chen SH, Lucas TH, Davis KA, Litt B. Recurring Functional Interactions Predict Network Architecture of Interictal and Ictal States in Neocortical Epilepsy. eNeuro 2017; 4:ENEURO.0091-16.2017. [PMID: 28303256 PMCID: PMC5343278 DOI: 10.1523/eneuro.0091-16.2017] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Revised: 01/09/2017] [Accepted: 01/10/2017] [Indexed: 01/10/2023] Open
Abstract
Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 h of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (1) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (2) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (3) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that seizures mark a critical shift away from interictal states that is driven by changes in the dynamical expression of strongly interacting components of the epileptic network.
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Affiliation(s)
- Ankit N. Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
| | - Danielle S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104
| | - Brian S. Oommen
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Stephanie H. Chen
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Timothy H. Lucas
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Kathryn A. Davis
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104
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45
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Sinha N, Dauwels J, Kaiser M, Cash SS, Brandon Westover M, Wang Y, Taylor PN. Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling. Brain 2016; 140:319-332. [PMID: 28011454 PMCID: PMC5278304 DOI: 10.1093/brain/aww299] [Citation(s) in RCA: 161] [Impact Index Per Article: 20.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Revised: 10/08/2016] [Accepted: 10/10/2016] [Indexed: 01/03/2023] Open
Abstract
See Eissa and Schevon (doi:10.1093/aww332) for a scientific commentary on this article. Surgery can be a last resort for patients with intractable, medically refractory epilepsy. For many of these patients, however, there is substantial risk that the surgery will be ineffective. The prediction of who is likely to benefit from a surgical approach is crucial for being able to inform patients better, conduct principled prospective clinical trials, and ultimately tailor therapeutic approaches to these patients more effectively. Dynamical computational models, informed with patient data, can be used to make predictions and give mechanistic insight. In this study, we develop patient-specific dynamical network models of epileptogenic cortex. We infer the network connectivity matrix from non-seizure electrographic recordings of patients and use these connectivity matrices as the network structure in our model. The model simulates the dynamics of a bi-stable switch at every node in this network, meaning that every node starts in a background state, but has the ability to transit to a co-existing seizure state. Whether a transition happens in a node is partly determined by the stochastic nature of the input to the node, but also by the input the node receives from other connected nodes in the network. By conducting simulations with such a model, we can detect the average transition time for nodes in a given network, and therefore define nodes with a short transition time as highly epileptogenic. In a retrospective study, we found that in some patients the regions with high epileptogenicity in the model overlap with those identified clinically as the seizure onset zone. Moreover, it was found that the resection of these regions in the model reduces the overall likelihood of a seizure. Following removal of these regions in the model, we predicted surgical outcomes and compared these to actual patient outcomes. Our predictions were found to be 81.3% accurate on a dataset of 16 patients with intractable epilepsy. Intriguingly, in patients with unsuccessful outcomes, the proposed computational approach is able to suggest alternative resection sites. The model presented here gives mechanistic insight as to why surgery may be unsuccessful in some patients. This may aid clinicians in presurgical evaluation by providing a tool to explore various surgical options, offering complementary information to existing clinical techniques.
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Affiliation(s)
- Nishant Sinha
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
| | - Justin Dauwels
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK.,Institute of Neuroscience, Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, UK
| | - Sydney S Cash
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - M Brandon Westover
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK
| | - Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK .,Institute of Neuroscience, Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, UK.,Institute of Neurology, University College London, UK
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46
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Goodfellow M, Rummel C, Abela E, Richardson MP, Schindler K, Terry JR. Estimation of brain network ictogenicity predicts outcome from epilepsy surgery. Sci Rep 2016; 6:29215. [PMID: 27384316 PMCID: PMC4935897 DOI: 10.1038/srep29215] [Citation(s) in RCA: 104] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 06/13/2016] [Indexed: 02/01/2023] Open
Abstract
Surgery is a valuable option for pharmacologically intractable epilepsy. However, significant post-operative improvements are not always attained. This is due in part to our incomplete understanding of the seizure generating (ictogenic) capabilities of brain networks. Here we introduce an in silico, model-based framework to study the effects of surgery within ictogenic brain networks. We find that factors conventionally determining the region of tissue to resect, such as the location of focal brain lesions or the presence of epileptiform rhythms, do not necessarily predict the best resection strategy. We validate our framework by analysing electrocorticogram (ECoG) recordings from patients who have undergone epilepsy surgery. We find that when post-operative outcome is good, model predictions for optimal strategies align better with the actual surgery undertaken than when post-operative outcome is poor. Crucially, this allows the prediction of optimal surgical strategies and the provision of quantitative prognoses for patients undergoing epilepsy surgery.
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Affiliation(s)
- M Goodfellow
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.,Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK
| | - C Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Switzerland
| | - E Abela
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Switzerland
| | - M P Richardson
- EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - K Schindler
- Department of Neurology, University of Bern, Switzerland
| | - J R Terry
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.,Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK
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47
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Ma Z, Zhou W, Zhang Y, Geng S. Epileptogenic zone localization and seizure control in coupled neural mass models. BIOLOGICAL CYBERNETICS 2015; 109:671-683. [PMID: 26585963 DOI: 10.1007/s00422-015-0667-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Accepted: 11/05/2015] [Indexed: 06/05/2023]
Abstract
Exact localization of the epileptogenic zone (EZ) is the first priority for ensuring epilepsy treatments and reducing side effects. The results of traditional visual methods for localizing the origin of seizures are far from satisfactory in some cases. Signal processing methods could extract substantial information that may complement visual inspection of EEG signals. In this study, EZ localization is changed into a driver identification problem, and a nonlinear interdependence measure, the weighted rank interdependence, is proposed and used as a driver indicator because it can detect coupling information, especially directionality, from EEG signals. A proportional integral derivative (PID) controller is then explored, using simulations, to establish its suitability for seizure control. The seizure control we propose rests on identifying the EZ using nonlinear interdependence measures of directed functional connectivity. Two directionally coupled neural mass models are employed for simulation investigation. Two parameters can adjust the sensitivity and completeness of the weighted rank interdependence for different applications, and their effect is discussed in the context of neural mass models. Simulation results demonstrate that use of the weighted rank interdependence for EZ identification can be applied to different EZ types, and the approach achieves an overall identification rate of 98.84 % for several EZ types. Simulations also indicate that PID control can effectively regulate synchronization between neural masses.
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Affiliation(s)
- Zhen Ma
- School of Information Science and Engineering, Shandong University, 27 Shanda Road, Jinan, 250100, People's Republic of China
- Department of Information Engineering, Binzhou University, Binzhou, 256600, People's Republic of China
| | - Weidong Zhou
- School of Information Science and Engineering, Shandong University, 27 Shanda Road, Jinan, 250100, People's Republic of China.
| | - Yanli Zhang
- School of Information Science and Engineering, Shandong University, 27 Shanda Road, Jinan, 250100, People's Republic of China
| | - Shujuan Geng
- School of Information Science and Engineering, Shandong University, 27 Shanda Road, Jinan, 250100, People's Republic of China
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48
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Koepp MJ, Caciagli L, Pressler RM, Lehnertz K, Beniczky S. Reflex seizures, traits, and epilepsies: from physiology to pathology. Lancet Neurol 2015; 15:92-105. [PMID: 26627365 DOI: 10.1016/s1474-4422(15)00219-7] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Revised: 08/11/2015] [Accepted: 08/13/2015] [Indexed: 10/22/2022]
Abstract
Epileptic seizures are generally unpredictable and arise spontaneously. Patients often report non-specific triggers such as stress or sleep deprivation, but only rarely do seizures occur as a reflex event, in which they are objectively and consistently modulated, precipitated, or inhibited by external sensory stimuli or specific cognitive processes. The seizures triggered by such stimuli and processes in susceptible individuals can have different latencies. Once seizure-suppressing mechanisms fail and a critical mass (the so-called tipping point) of cortical activation is reached, reflex seizures stereotypically manifest with common motor features independent of the physiological network involved. The complexity of stimuli increases from simple sensory to complex cognitive-emotional with increasing age of onset. The topography of physiological networks involved follows the posterior-to-anterior trajectory of brain development, reflecting age-related changes in brain excitability. Reflex seizures and traits probably represent the extremes of a continuum, and understanding of their underlying mechanisms might help to elucidate the transition of normal physiological function to paroxysmal epileptic activity.
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Affiliation(s)
- Matthias J Koepp
- Department of Clinical and Experimental Epilepsy, University College London (UCL) Institute of Neurology, London, UK; National Hospital for Neurology and Neurosurgery, Queen Square, UK.
| | - Lorenzo Caciagli
- Department of Clinical and Experimental Epilepsy, University College London (UCL) Institute of Neurology, London, UK; National Hospital for Neurology and Neurosurgery, Queen Square, UK
| | - Ronit M Pressler
- Department of Clinical Neurophysiology, Great Ormond Street Hospital, London, UK; Clinical Neuroscience, UCL Institute of Child Health, London, UK
| | - Klaus Lehnertz
- Department of Epileptology, University Hospital of Bonn, Bonn, Germany
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurophysiology, Aarhus University, Aarhus, Denmark
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49
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O'Muircheartaigh J, Keller SS, Barker GJ, Richardson MP. White Matter Connectivity of the Thalamus Delineates the Functional Architecture of Competing Thalamocortical Systems. Cereb Cortex 2015; 25:4477-89. [PMID: 25899706 PMCID: PMC4816794 DOI: 10.1093/cercor/bhv063] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
There is an increasing awareness of the involvement of thalamic connectivity on higher level cortical functioning in the human brain. This is reflected by the influence of thalamic stimulation on cortical activity and behavior as well as apparently cortical lesion syndromes occurring as a function of small thalamic insults. Here, we attempt to noninvasively test the correspondence of structural and functional connectivity of the human thalamus using diffusion-weighted and resting-state functional MRI. Using a large sample of 102 adults, we apply tensor independent component analysis to diffusion MRI tractography data to blindly parcellate bilateral thalamus according to diffusion tractography-defined structural connectivity. Using resting-state functional MRI collected in the same subjects, we show that the resulting structurally defined thalamic regions map to spatially distinct, and anatomically predictable, whole-brain functional networks in the same subjects. Although there was significant variability in the functional connectivity patterns, the resulting 51 structural and functional patterns could broadly be reduced to a subset of 7 similar core network types. These networks were distinct from typical cortical resting-state networks. Importantly, these networks were distributed across the brain and, in a subset, map extremely well to known thalamocortico-basal-ganglial loops.
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Affiliation(s)
- Jonathan O'Muircheartaigh
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London WC2R 2LS, UK
| | - Simon S Keller
- Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Merseyside L69 3BX, UK Department of Radiology, Walton Centre National Health Service Foundation Trust, Liverpool, UK
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London WC2R 2LS, UK
| | - Mark P Richardson
- Department of Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London WC2R 2LS, UK
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