51
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Brain simulation as a cloud service: The Virtual Brain on EBRAINS. Neuroimage 2022; 251:118973. [DOI: 10.1016/j.neuroimage.2022.118973] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/02/2022] [Accepted: 02/03/2022] [Indexed: 12/18/2022] Open
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52
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van Albada SJ, Morales-Gregorio A, Dickscheid T, Goulas A, Bakker R, Bludau S, Palm G, Hilgetag CC, Diesmann M. Bringing Anatomical Information into Neuronal Network Models. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:201-234. [DOI: 10.1007/978-3-030-89439-9_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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53
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Alim-Marvasti A, Romagnoli G, Dahele K, Modarres H, Pérez-García F, Sparks R, Ourselin S, Clarkson MJ, Chowdhury F, Diehl B, Duncan JS. Probabilistic landscape of seizure semiology localizing values. Brain Commun 2022; 4:fcac130. [PMID: 35663381 PMCID: PMC9156627 DOI: 10.1093/braincomms/fcac130] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 02/19/2022] [Accepted: 05/16/2022] [Indexed: 11/13/2022] Open
Abstract
Semiology describes the evolution of symptoms and signs during epileptic seizures and contributes to the evaluation of individuals with focal drug-resistant epilepsy for curative resection. Semiology varies in complexity from elementary sensorimotor seizures arising from primary cortex to complex behaviours and automatisms emerging from distributed cerebral networks. Detailed semiology interpreted by expert epileptologists may point towards the likely site of seizure onset, but this process is subjective. No study has captured the variances in semiological localizing values in a data-driven manner to allow objective and probabilistic determinations of implicated networks and nodes. We curated an open data set from the epilepsy literature, in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, linking semiology to hierarchical brain localizations. A total of 11 230 data points were collected from 4643 patients across 309 articles, labelled using ground truths (postoperative seizure-freedom, concordance of imaging and neurophysiology, and/or invasive EEG) and a designation method that distinguished between semiologies arising from a predefined cortical region and descriptions of neuroanatomical localizations responsible for generating a particular semiology. This allowed us to mitigate temporal lobe publication bias by filtering studies that preselected patients based on prior knowledge of their seizure foci. Using this data set, we describe the probabilistic landscape of semiological localizing values as forest plots at the resolution of seven major brain regions: temporal, frontal, cingulate, parietal, occipital, insula, and hypothalamus, and five temporal subregions. We evaluated the intrinsic value of any one semiology over all other ictal manifestations. For example, epigastric auras implicated the temporal lobe with 83% probability when not accounting for the publication bias that favoured temporal lobe epilepsies. Unbiased results for a prior distribution of cortical localizations revised the prevalence of temporal lobe epilepsies from 66% to 44%. Therefore, knowledge about the presence of epigastric auras updates localization to the temporal lobe with an odds ratio (OR) of 2.4 [CI95% (1.9, 2.9); and specifically, mesial temporal structures OR: 2.8 (2.3, 2.9)], attesting the value of epigastric auras. As a further example, although head version is thought to implicate the frontal lobes, it did not add localizing value compared with the prior distribution of cortical localizations [OR: 0.9 (0.7, 1.2)]. Objectification of the localizing values of the 12 most common semiologies provides a complementary view of brain dysfunction to that of lesion-deficit mappings, as instead of linking brain regions to phenotypic-deficits, semiological phenotypes are linked back to brain sources. This work enables coupling of seizure propagation with ictal manifestations, and clinical support algorithms for localizing seizure phenotypes.
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Affiliation(s)
- Ali Alim-Marvasti
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, UCL, London, UK.,Department of Medical Physics and Biomedical Engineering, UCL, London, UK.,Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), London, UK.,National Hospital for Neurology and Neurosurgery, London, UK
| | - Gloria Romagnoli
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, UCL, London, UK.,National Hospital for Neurology and Neurosurgery, London, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Karan Dahele
- University College London Medical School, London, UK
| | - Hadi Modarres
- Faculty of Engineering, University of Cambridge, Cambridge, UK
| | - Fernando Pérez-García
- Department of Medical Physics and Biomedical Engineering, UCL, London, UK.,Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), London, UK.,School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Rachel Sparks
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Sébastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Matthew J Clarkson
- Department of Medical Physics and Biomedical Engineering, UCL, London, UK.,Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), London, UK
| | - Fahmida Chowdhury
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, UCL, London, UK.,National Hospital for Neurology and Neurosurgery, London, UK
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, UCL, London, UK.,National Hospital for Neurology and Neurosurgery, London, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, UCL, London, UK.,National Hospital for Neurology and Neurosurgery, London, UK
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54
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An S, Fousek J, Kiss ZHT, Cortese F, van der Wijk G, McAusland LB, Ramasubbu R, Jirsa VK, Protzner AB. High-resolution Virtual Brain Modeling Personalizes Deep Brain Stimulation for Treatment-Resistant Depression: Spatiotemporal Response Characteristics Following Stimulation of Neural Fiber Pathways. Neuroimage 2021; 249:118848. [PMID: 34954330 DOI: 10.1016/j.neuroimage.2021.118848] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 11/25/2021] [Accepted: 12/21/2021] [Indexed: 02/07/2023] Open
Abstract
Over the past 15 years, deep brain stimulation (DBS) has been actively investigated as a groundbreaking therapy for patients with treatment-resistant depression (TRD); nevertheless, outcomes have varied from patient to patient, with an average response rate of ∼50%. The engagement of specific fiber tracts at the stimulation site has been hypothesized to be an important factor in determining outcomes, however, the resulting individual network effects at the whole-brain scale remain largely unknown. Here we provide a computational framework that can explore each individual's brain response characteristics elicited by selective stimulation of fiber tracts. We use a novel personalized in-silico approach, the Virtual Big Brain, which makes use of high-resolution virtual brain models at a mm-scale and explicitly reconstructs more than 100 000 fiber tracts for each individual. Each fiber tract is active and can be selectively stimulated. Simulation results demonstrate distinct stimulus-induced event-related potentials as a function of stimulation location, parametrized by the contact positions of the electrodes implanted in each patient, even though validation against empirical patient data reveals some limitations (i.e., the need for individual parameter adjustment, and differential accuracy across stimulation locations). This study provides evidence for the capacity of personalized high-resolution virtual brain models to investigate individual network effects in DBS for patients with TRD and opens up novel avenues in the personalized optimization of brain stimulation.
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Affiliation(s)
- Sora An
- Department of Communication Disorders, Ewha Womans University, 03760, Seoul, Republic of Korea.
| | - Jan Fousek
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, 13005, Marseille, France
| | - Zelma H T Kiss
- Hotchkiss Brain Institute, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Mathison Centre for Mental Health, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Department of Clinical Neurosciences and Psychiatry, Cumming School of Medicine, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
| | - Filomeno Cortese
- Hotchkiss Brain Institute, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Seaman Family MR Centre, Foothills Medical Centre, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
| | - Gwen van der Wijk
- Department of Psychology, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
| | - Laina Beth McAusland
- Department of Clinical Neurosciences and Psychiatry, Cumming School of Medicine, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
| | - Rajamannar Ramasubbu
- Hotchkiss Brain Institute, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Mathison Centre for Mental Health, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Department of Clinical Neurosciences and Psychiatry, Cumming School of Medicine, University of Calgary, T2N 1N4, Calgary, Alberta, Canada
| | - Viktor K Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, 13005, Marseille, France.
| | - Andrea B Protzner
- Hotchkiss Brain Institute, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Mathison Centre for Mental Health, University of Calgary, T2N 1N4, Calgary, Alberta, Canada; Department of Psychology, University of Calgary, T2N 1N4, Calgary, Alberta, Canada.
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55
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Lemaréchal JD, Jedynak M, Trebaul L, Boyer A, Tadel F, Bhattacharjee M, Deman P, Tuyisenge V, Ayoubian L, Hugues E, Chanteloup-Forêt B, Saubat C, Zouglech R, Reyes Mejia GC, Tourbier S, Hagmann P, Adam C, Barba C, Bartolomei F, Blauwblomme T, Curot J, Dubeau F, Francione S, Garcés M, Hirsch E, Landré E, Liu S, Maillard L, Metsähonkala EL, Mindruta I, Nica A, Pail M, Petrescu AM, Rheims S, Rocamora R, Schulze-Bonhage A, Szurhaj W, Taussig D, Valentin A, Wang H, Kahane P, George N, David O. A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials. Brain 2021; 145:1653-1667. [PMID: 35416942 PMCID: PMC9166555 DOI: 10.1093/brain/awab362] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 08/03/2021] [Accepted: 08/14/2021] [Indexed: 11/16/2022] Open
Abstract
Epilepsy presurgical investigation may include focal intracortical single-pulse electrical stimulations with depth electrodes, which induce cortico-cortical evoked potentials at distant sites because of white matter connectivity. Cortico-cortical evoked potentials provide a unique window on functional brain networks because they contain sufficient information to infer dynamical properties of large-scale brain connectivity, such as preferred directionality and propagation latencies. Here, we developed a biologically informed modelling approach to estimate the neural physiological parameters of brain functional networks from the cortico-cortical evoked potentials recorded in a large multicentric database. Specifically, we considered each cortico-cortical evoked potential as the output of a transient stimulus entering the stimulated region, which directly propagated to the recording region. Both regions were modelled as coupled neural mass models, the parameters of which were estimated from the first cortico-cortical evoked potential component, occurring before 80 ms, using dynamic causal modelling and Bayesian model inversion. This methodology was applied to the data of 780 patients with epilepsy from the F-TRACT database, providing a total of 34 354 bipolar stimulations and 774 445 cortico-cortical evoked potentials. The cortical mapping of the local excitatory and inhibitory synaptic time constants and of the axonal conduction delays between cortical regions was obtained at the population level using anatomy-based averaging procedures, based on the Lausanne2008 and the HCP-MMP1 parcellation schemes, containing 130 and 360 parcels, respectively. To rule out brain maturation effects, a separate analysis was performed for older (>15 years) and younger patients (<15 years). In the group of older subjects, we found that the cortico-cortical axonal conduction delays between parcels were globally short (median = 10.2 ms) and only 16% were larger than 20 ms. This was associated to a median velocity of 3.9 m/s. Although a general lengthening of these delays with the distance between the stimulating and recording contacts was observed across the cortex, some regions were less affected by this rule, such as the insula for which almost all efferent and afferent connections were faster than 10 ms. Synaptic time constants were found to be shorter in the sensorimotor, medial occipital and latero-temporal regions, than in other cortical areas. Finally, we found that axonal conduction delays were significantly larger in the group of subjects younger than 15 years, which corroborates that brain maturation increases the speed of brain dynamics. To our knowledge, this study is the first to provide a local estimation of axonal conduction delays and synaptic time constants across the whole human cortex in vivo, based on intracerebral electrophysiological recordings.
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Affiliation(s)
- Jean-Didier Lemaréchal
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Inserm, CNRS, Centre MEG-EEG and Experimental Neurosurgery Team, F-75013 Paris, France.,Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France.,Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Maciej Jedynak
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Lena Trebaul
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Anthony Boyer
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - François Tadel
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Manik Bhattacharjee
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Pierre Deman
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Viateur Tuyisenge
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Leila Ayoubian
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Etienne Hugues
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | | | - Carole Saubat
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Raouf Zouglech
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | | | - Sébastien Tourbier
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Claude Adam
- Department of Neurology, Epilepsy Unit, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013 Paris, France
| | - Carmen Barba
- Neuroscience Department, Children's Hospital Meyer-University of Florence, Florence, Italy
| | - Fabrice Bartolomei
- Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.,Service de Neurophysiologie Clinique, APHM, Hôpitaux de la Timone, Marseille, France
| | - Thomas Blauwblomme
- Department of Pediatric Neurosurgery, Hôpital Necker-Enfants Malades, Université Paris V Descartes, Sorbonne Paris Cité, Paris, France
| | - Jonathan Curot
- Department of Neurophysiological Explorations, CerCo, CNRS, UMR5549, Centre Hospitalier Universitaire de Toulouse and University of Toulouse, Toulouse, France
| | - François Dubeau
- Montreal Neurological Institute and Hospital, Montreal, Canada
| | - Stefano Francione
- 'Claudio Munari' Centre for Epilepsy Surgery; Neuroscience Department, GOM, Niguarda, Milano, Italy
| | - Mercedes Garcés
- Multidisciplinary Epilepsy Unit, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Edouard Hirsch
- University Hospital, Department of Neurology, Strasbourg, France
| | | | - Sinclair Liu
- Canton Sanjiu Brain Hospital Epilepsy Center, Jinan University, Guangzhou, China
| | - Louis Maillard
- Centre Hospitalier Universitaire de Nancy, Nancy, France
| | | | - Ioana Mindruta
- Neurology Department, University Emergency Hospital, Bucharest, Romania
| | - Anca Nica
- Neurology Department, CIC 1414, Rennes University Hospital; LTSI, INSERM U 1099, F-35000 Rennes, France
| | - Martin Pail
- Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital and Medical Faculty of Masaryk University, Brno, Czech Republic
| | | | - Sylvain Rheims
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon and Lyon's Neurosciences Research Center, INSERM U1028/CNRS UMR5292/Lyon 1 University, Lyon, France
| | - Rodrigo Rocamora
- Epilepsy Monitoring Unit, Department of Neurology, Hospital del Mar-IMIM, Barcelona, Spain
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - William Szurhaj
- Epilepsy Unit, Department of Clinical Neurophysiology, Lille University Medical Center, Lille, France
| | - Delphine Taussig
- Neurophysiology and Epilepsy Unit, Bicêtre Hospital, France.,Service de Neurochirurgie Pédiatrique, Fondation Rothschild, Paris, France
| | - Antonio Valentin
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), London, UK
| | - Haixiang Wang
- Yuquan Hospital Epilepsy Center, Tsinghua University, Beijing, China
| | - Philippe Kahane
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France.,Neurology Department, CHU Grenoble Alpes, Grenoble, France
| | - Nathalie George
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Inserm, CNRS, Centre MEG-EEG and Experimental Neurosurgery Team, F-75013 Paris, France
| | - Olivier David
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France.,Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
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56
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Parasuram H, Gopinath S, Pillai A, Diwakar S, Kumar A. Quantification of Epileptogenic Network From Stereo EEG Recordings Using Epileptogenicity Ranking Method. Front Neurol 2021; 12:738111. [PMID: 34803883 PMCID: PMC8595106 DOI: 10.3389/fneur.2021.738111] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 08/31/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: Precise localization of the epileptogenic zone is very essential for the success of epilepsy surgery. Epileptogenicity index (EI) computationally estimates epileptogenicity of brain structures based on the temporal domain parameters and magnitude of ictal discharges. This method works well in cases of mesial temporal lobe epilepsy but it showed reduced accuracy in neocortical epilepsy. To overcome this scenario, in this study, we propose Epileptogenicity Rank (ER), a modified method of EI for quantifying epileptogenicity, that is based on spatio-temporal properties of Stereo EEG (SEEG). Methods: Energy ratio during ictal discharges, the time of involvement and Euclidean distance between brain structures were used to compute the ER. Retrospectively, we localized the EZ for 33 patients (9 for mesial-temporal lobe epilepsy and 24 for neocortical epilepsy) using post op MRI and Engel 1 surgical outcome at a mean of 40.9 months and then optimized the ER in this group. Results: Epileptic network estimation based on ER successfully differentiated brain regions involved in the seizure onset from the propagation network. ER was calculated at multiple thresholds leading to an optimum value that differentiated the seizure onset from the propagation network. We observed that ER < 7.1 could localize the EZ in neocortical epilepsy with a sensitivity of 94.6% and specificity of 98.3% and ER < 7.3 in mesial temporal lobe epilepsy with a sensitivity of 95% and specificity of 98%. In non-seizure-free patients, the EZ localization based on ER pointed to brain area beyond the cortical resections. Significance: Methods like ER can improve the accuracy of EZ localization for brain resection and increase the precision of minimally invasive surgery techniques (radio-frequency or laser ablation) by identifying the epileptic hubs where the lesion is extensive or in nonlesional cases. For inclusivity with other clinical applications, this ER method has to be studied in more patients.
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Affiliation(s)
- Harilal Parasuram
- Amrita Advanced Centre for Epilepsy (AACE), Amrita Institute of Medical Sciences, Amrita Vishwa Vidyapeetham, Kochi, India.,Department of Neurology, Amrita Institute of Medical Sciences, Amrita Vishwa Vidyapeetham, Kochi, India.,Amrita Mind Brain Center, Amrita Vishwa Vidyapeetham, Kollam, India
| | - Siby Gopinath
- Amrita Advanced Centre for Epilepsy (AACE), Amrita Institute of Medical Sciences, Amrita Vishwa Vidyapeetham, Kochi, India.,Department of Neurology, Amrita Institute of Medical Sciences, Amrita Vishwa Vidyapeetham, Kochi, India.,Amrita Mind Brain Center, Amrita Vishwa Vidyapeetham, Kollam, India
| | - Ashok Pillai
- Amrita Advanced Centre for Epilepsy (AACE), Amrita Institute of Medical Sciences, Amrita Vishwa Vidyapeetham, Kochi, India.,Department of Neurosurgery, Amrita Institute of Medical Sciences, Amrita Vishwa Vidyapeetham, Kochi, India
| | - Shyam Diwakar
- Amrita Mind Brain Center, Amrita Vishwa Vidyapeetham, Kollam, India
| | - Anand Kumar
- Department of Neurology, Amrita Institute of Medical Sciences, Amrita Vishwa Vidyapeetham, Kochi, India.,Amrita Mind Brain Center, Amrita Vishwa Vidyapeetham, Kollam, India
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57
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Brogin JAF, Faber J, Bueno DD. Burster Reconstruction Considering Unmeasurable Variables in the Epileptor Model. Neural Comput 2021; 33:3288-3333. [PMID: 34710900 DOI: 10.1162/neco_a_01443] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 06/14/2021] [Indexed: 11/04/2022]
Abstract
Epilepsy is one of the most common brain disorders worldwide, affecting millions of people every year. Although significant effort has been put into better understanding it and mitigating its effects, the conventional treatments are not fully effective. Advances in computational neuroscience, using mathematical dynamic models that represent brain activities at different scales, have enabled addressing epilepsy from a more theoretical standpoint. In particular, the recently proposed Epileptor model stands out among these models, because it represents well the main features of seizures, and the results from its simulations have been consistent with experimental observations. In addition, there has been an increasing interest in designing control techniques for Epileptor that might lead to possible realistic feedback controllers in the future. However, such approaches rely on knowing all of the states of the model, which is not the case in practice. The work explored in this letter aims to develop a state observer to estimate Epileptor's unmeasurable variables, as well as reconstruct the respective so-called bursters. Furthermore, an alternative modeling is presented for enhancing the convergence speed of an observer. The results show that the proposed approach is efficient under two main conditions: when the brain is undergoing a seizure and when a transition from the healthy to the epileptiform activity occurs.
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Affiliation(s)
- João Angelo Ferres Brogin
- Department of Mechanical Engineering, School of Engineering of Ilha Solteira, São Paulo State University, Ilha Solteira, São Paulo, 15385-000, Brazil
| | - Jean Faber
- Department of Neurology and Neurosurgery, Federal University of São Paulo, São Paulo, 04039-032, Brazil
| | - Douglas Domingues Bueno
- Department of Mathematics, São Paulo State University, School of Engineering of Ilha Solteira, São Paulo, 15385-000, Brazil
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58
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Naskar A, Vattikonda A, Deco G, Roy D, Banerjee A. Multiscale dynamic mean field (MDMF) model relates resting-state brain dynamics with local cortical excitatory-inhibitory neurotransmitter homeostasis. Netw Neurosci 2021; 5:757-782. [PMID: 34746626 PMCID: PMC8567829 DOI: 10.1162/netn_a_00197] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 04/19/2021] [Indexed: 11/24/2022] Open
Abstract
Previous computational models have related spontaneous resting-state brain activity with local excitatory–inhibitory balance in neuronal populations. However, how underlying neurotransmitter kinetics associated with E–I balance govern resting-state spontaneous brain dynamics remains unknown. Understanding the mechanisms by virtue of which fluctuations in neurotransmitter concentrations, a hallmark of a variety of clinical conditions, relate to functional brain activity is of critical importance. We propose a multiscale dynamic mean field (MDMF) model—a system of coupled differential equations for capturing the synaptic gating dynamics in excitatory and inhibitory neural populations as a function of neurotransmitter kinetics. Individual brain regions are modeled as population of MDMF and are connected by realistic connection topologies estimated from diffusion tensor imaging data. First, MDMF successfully predicts resting-state functional connectivity. Second, our results show that optimal range of glutamate and GABA neurotransmitter concentrations subserve as the dynamic working point of the brain, that is, the state of heightened metastability observed in empirical blood-oxygen-level-dependent signals. Third, for predictive validity the network measures of segregation (modularity and clustering coefficient) and integration (global efficiency and characteristic path length) from existing healthy and pathological brain network studies could be captured by simulated functional connectivity from an MDMF model. How changes in neurotransmitter kinetics impact the organization of large-scale neurocognitive networks is an open question in neuroscience. Here, we propose a multiscale dynamic mean field (MDMF) model that incorporates biophysically realistic kinetic parameters of receptor binding in a dynamic mean field model and captures brain dynamics from the “whole brain.” MDMF could reliably reproduce the resting-state brain functional connectivity patterns. Further employing graph theoretic methods, MDMF could qualitatively explain the idiosyncrasies of network integration and segregation measures reported by previous clinical studies.
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Affiliation(s)
- Amit Naskar
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, Gurgaon, India
| | - Anirudh Vattikonda
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, Gurgaon, India
| | - Gustavo Deco
- Computational Neuroscience Research Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, Gurgaon, India
| | - Arpan Banerjee
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, Gurgaon, India
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59
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Vattikonda AN, Hashemi M, Sip V, Woodman MM, Bartolomei F, Jirsa VK. Identifying spatio-temporal seizure propagation patterns in epilepsy using Bayesian inference. Commun Biol 2021; 4:1244. [PMID: 34725441 PMCID: PMC8560929 DOI: 10.1038/s42003-021-02751-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 10/04/2021] [Indexed: 01/24/2023] Open
Abstract
Focal drug resistant epilepsy is a neurological disorder characterized by seizures caused by abnormal activity originating in one or more regions together called as epileptogenic zone. Treatment for such patients involves surgical resection of affected regions. Epileptogenic zone is typically identified using stereotactic EEG recordings from the electrodes implanted into the patient's brain. Identifying the epileptogenic zone is a challenging problem due to the spatial sparsity of electrode implantation. We propose a probabilistic hierarchical model of seizure propagation patterns, based on a phenomenological model of seizure dynamics called Epileptor. Using Bayesian inference, the Epileptor model is optimized to build patient specific virtual models that best fit to the log power of intracranial recordings. First, accuracy of the model predictions and identifiability of the model are investigated using synthetic data. Then, model predictions are evaluated against a retrospective patient cohort of 25 patients with varying surgical outcomes. In the patients who are seizure free after surgery, model predictions showed good match with the clinical hypothesis. In patients where surgery failed to achieve seizure freedom model predictions showed a strong mismatch. Our results demonstrate that proposed probabilistic model could be a valuable tool to aid the clinicians in identifying the seizure focus.
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Affiliation(s)
- Anirudh N Vattikonda
- Aix Marseille Univ, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Meysam Hashemi
- Aix Marseille Univ, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Viktor Sip
- Aix Marseille Univ, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Marmaduke M Woodman
- Aix Marseille Univ, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
- Epileptology Department and Clinical Neurophysiology Department, Assistance publique des Hopitaux de Marseille, Marseille, France
| | - Viktor K Jirsa
- Aix Marseille Univ, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France.
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60
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Computational modeling of seizure spread on a cortical surface. J Comput Neurosci 2021; 50:17-31. [PMID: 34686937 PMCID: PMC8818012 DOI: 10.1007/s10827-021-00802-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 06/16/2021] [Accepted: 09/24/2021] [Indexed: 10/26/2022]
Abstract
In the field of computational epilepsy, neural field models helped to understand some large-scale features of seizure dynamics. These insights however remain on general levels, without translation to the clinical settings via personalization of the model with the patient-specific structure. In particular, a link was suggested between epileptic seizures spreading across the cortical surface and the so-called theta-alpha activity (TAA) pattern seen on intracranial electrographic signals, yet this link was not demonstrated on a patient-specific level. Here we present a single patient computational study linking the seizure spreading across the patient-specific cortical surface with a specific instance of the TAA pattern recorded in the patient. Using the realistic geometry of the cortical surface we perform the simulations of seizure dynamics in The Virtual Brain platform, and we show that the simulated electrographic signals qualitatively agree with the recorded signals. Furthermore, the comparison with the simulations performed on surrogate surfaces reveals that the best quantitative fit is obtained for the real surface. The work illustrates how the patient-specific cortical geometry can be utilized in The Virtual Brain for personalized model building, and the importance of such approach.
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61
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Nissen IA, Millán AP, Stam CJ, van Straaten ECW, Douw L, Pouwels PJW, Idema S, Baayen JC, Velis D, Van Mieghem P, Hillebrand A. Optimization of epilepsy surgery through virtual resections on individual structural brain networks. Sci Rep 2021; 11:19025. [PMID: 34561483 PMCID: PMC8463605 DOI: 10.1038/s41598-021-98046-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 08/13/2021] [Indexed: 11/10/2022] Open
Abstract
The success of epilepsy surgery in patients with refractory epilepsy depends upon correct identification of the epileptogenic zone (EZ) and an optimal choice of the resection area. In this study we developed individualized computational models based upon structural brain networks to explore the impact of different virtual resections on the propagation of seizures. The propagation of seizures was modelled as an epidemic process [susceptible-infected-recovered (SIR) model] on individual structural networks derived from presurgical diffusion tensor imaging in 19 patients. The candidate connections for the virtual resection were all connections from the clinically hypothesized EZ, from which the seizures were modelled to start, to other brain areas. As a computationally feasible surrogate for the SIR model, we also removed the connections that maximally reduced the eigenvector centrality (EC) (large values indicate network hubs) of the hypothesized EZ, with a large reduction meaning a large effect. The optimal combination of connections to be removed for a maximal effect were found using simulated annealing. For comparison, the same number of connections were removed randomly, or based on measures that quantify the importance of a node or connection within the network. We found that 90% of the effect (defined as reduction of EC of the hypothesized EZ) could already be obtained by removing substantially less than 90% of the connections. Thus, a smaller, optimized, virtual resection achieved almost the same effect as the actual surgery yet at a considerably smaller cost, sparing on average 27.49% (standard deviation: 4.65%) of the connections. Furthermore, the maximally effective connections linked the hypothesized EZ to hubs. Finally, the optimized resection was equally or more effective than removal based on structural network characteristics both regarding reducing the EC of the hypothesized EZ and seizure spreading. The approach of using reduced EC as a surrogate for simulating seizure propagation can suggest more restrictive resection strategies, whilst obtaining an almost optimal effect on reducing seizure propagation, by taking into account the unique topology of individual structural brain networks of patients.
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Affiliation(s)
- Ida A Nissen
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Ana P Millán
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Elisabeth C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Linda Douw
- Department of Anatomy and Neuroscience, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Petra J W Pouwels
- Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Sander Idema
- Department of Neurosurgery, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Johannes C Baayen
- Department of Neurosurgery, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Demetrios Velis
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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62
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Nguyen RD, Smyth MD, Zhu L, Pao LP, Swisher SK, Kennady EH, Mitra A, Patel RP, Lankford JE, Von Allmen G, Watkins MW, Funke ME, Shah MN. A comparison of machine learning classifiers for pediatric epilepsy using resting-state functional MRI latency data. Biomed Rep 2021; 15:77. [PMID: 34405049 PMCID: PMC8330002 DOI: 10.3892/br.2021.1453] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 07/09/2021] [Indexed: 01/03/2023] Open
Abstract
Epilepsy affects 1 in 150 children under the age of 10 and is the most common chronic pediatric neurological condition; poor seizure control can irreversibly disrupt normal brain development. The present study compared the ability of different machine learning algorithms trained with resting-state functional MRI (rfMRI) latency data to detect epilepsy. Preoperative rfMRI and anatomical MRI scans were obtained for 63 patients with epilepsy and 259 healthy controls. The normal distribution of latency z-scores from the epilepsy and healthy control cohorts were analyzed for overlap in 36 seed regions. In these seed regions, overlap between the study cohorts ranged from 0.44-0.58. Machine learning features were extracted from latency z-score maps using principal component analysis. Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forest algorithms were trained with these features. Area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity and F1-scores were used to evaluate model performance. The XGBoost model outperformed all other models with a test AUC of 0.79, accuracy of 74%, specificity of 73%, and a sensitivity of 77%. The Random Forest model performed comparably to XGBoost across multiple metrics, but it had a test sensitivity of 31%. The SVM model did not perform >70% in any of the test metrics. The XGBoost model had the highest sensitivity and accuracy for the detection of epilepsy. Development of machine learning algorithms trained with rfMRI latency data could provide an adjunctive method for the diagnosis and evaluation of epilepsy with the goal of enabling timely and appropriate care for patients.
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Affiliation(s)
- Ryan D. Nguyen
- Division of Pediatric Neurosurgery, McGovern Medical School at UTHealth, Houston, TX 77030, USA
| | - Matthew D. Smyth
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Liang Zhu
- Biostatistics and Epidemiology Research Design Core, Institute for Clinical and Translational Sciences, McGovern Medical School at UTHealth, Houston, TX 77030, USA
| | - Ludovic P. Pao
- Division of Pediatric Neurosurgery, McGovern Medical School at UTHealth, Houston, TX 77030, USA
| | - Shannon K. Swisher
- Division of Pediatric Neurosurgery, McGovern Medical School at UTHealth, Houston, TX 77030, USA
| | - Emmett H. Kennady
- Division of Pediatric Neurosurgery, McGovern Medical School at UTHealth, Houston, TX 77030, USA
| | - Anish Mitra
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Rajan P. Patel
- Department of Diagnostic and Interventional Imaging, McGovern Medical School at UTHealth, Houston, TX 77030, USA
| | - Jeremy E. Lankford
- Department of Pediatric Neurology, McGovern Medical School at UTHealth, Houston, TX 77030, USA
| | - Gretchen Von Allmen
- Department of Pediatric Neurology, McGovern Medical School at UTHealth, Houston, TX 77030, USA
| | - Michael W. Watkins
- Department of Pediatric Neurology, McGovern Medical School at UTHealth, Houston, TX 77030, USA
| | - Michael E. Funke
- Department of Pediatric Neurology, McGovern Medical School at UTHealth, Houston, TX 77030, USA
| | - Manish N. Shah
- Division of Pediatric Neurosurgery, McGovern Medical School at UTHealth, Houston, TX 77030, USA
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63
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Gerster M, Taher H, Škoch A, Hlinka J, Guye M, Bartolomei F, Jirsa V, Zakharova A, Olmi S. Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation. Front Syst Neurosci 2021; 15:675272. [PMID: 34539355 PMCID: PMC8440880 DOI: 10.3389/fnsys.2021.675272] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 07/07/2021] [Indexed: 11/13/2022] Open
Abstract
Dynamics underlying epileptic seizures span multiple scales in space and time, therefore, understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. In this view, mathematical models have been developed, ranging from single neuron to neural population. In this study, we consider a neural mass model able to exactly reproduce the dynamics of heterogeneous spiking neural networks. We combine mathematical modeling with structural information from non invasive brain imaging, thus building large-scale brain network models to explore emergent dynamics and test the clinical hypothesis. We provide a comprehensive study on the effect of external drives on neuronal networks exhibiting multistability, in order to investigate the role played by the neuroanatomical connectivity matrices in shaping the emergent dynamics. In particular, we systematically investigate the conditions under which the network displays a transition from a low activity regime to a high activity state, which we identify with a seizure-like event. This approach allows us to study the biophysical parameters and variables leading to multiple recruitment events at the network level. We further exploit topological network measures in order to explain the differences and the analogies among the subjects and their brain regions, in showing recruitment events at different parameter values. We demonstrate, along with the example of diffusion-weighted magnetic resonance imaging (dMRI) connectomes of 20 healthy subjects and 15 epileptic patients, that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics, as observed in network-based brain disorders. In particular, for epileptic patients, by means of the integration of the clinical hypotheses on the epileptogenic zone (EZ), i.e., the local network where highly synchronous seizures originate, we have identified the sequence of recruitment events and discussed their links with the topological properties of the specific connectomes. The predictions made on the basis of the implemented set of exact mean-field equations turn out to be in line with the clinical pre-surgical evaluation on recruited secondary networks.
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Affiliation(s)
- Moritz Gerster
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
| | - Halgurd Taher
- Inria Sophia Antipolis Méditerranée Research Centre, MathNeuro Team, Valbonne, France
| | - Antonín Škoch
- National Institute of Mental Health, Klecany, Czechia
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czechia
| | - Jaroslav Hlinka
- National Institute of Mental Health, Klecany, Czechia
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czechia
| | - Maxime Guye
- Faculté de Médecine de la Timone, Centre de Résonance Magnétique et Biologique et Médicale (CRMBM, UMR CNRS-AMU 7339), Medical School of Marseille, Aix-Marseille Université, Marseille, France
- Assistance Publique -Hôpitaux de Marseille, Hôpital de la Timone, Pôle d'Imagerie, Marseille, France
| | - Fabrice Bartolomei
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Université, Inserm, Institut de Neurosciences des Systèmes, UMRS 1106, Marseille, France
| | - Anna Zakharova
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
| | - Simona Olmi
- Inria Sophia Antipolis Méditerranée Research Centre, MathNeuro Team, Valbonne, France
- Consiglio Nazionale delle Ricerche, Istituto dei Sistemi Complessi, Sesto Fiorentino, Italy
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64
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Deep characterization of individual brain-phenotype relations using a multilevel atlas. Curr Opin Behav Sci 2021. [DOI: 10.1016/j.cobeha.2021.04.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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65
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Virtual Connectomic Datasets in Alzheimer's Disease and Aging Using Whole-Brain Network Dynamics Modelling. eNeuro 2021; 8:ENEURO.0475-20.2021. [PMID: 34045210 PMCID: PMC8260273 DOI: 10.1523/eneuro.0475-20.2021] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/08/2021] [Accepted: 04/12/2021] [Indexed: 12/18/2022] Open
Abstract
Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g., lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring “virtual FC” from empirical SC or “virtual SC” from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.
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66
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Osborne H, Lai YM, Lepperød ME, Sichau D, Deutz L, de Kamps M. MIIND : A Model-Agnostic Simulator of Neural Populations. Front Neuroinform 2021; 15:614881. [PMID: 34295233 PMCID: PMC8291130 DOI: 10.3389/fninf.2021.614881] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 05/24/2021] [Indexed: 11/13/2022] Open
Abstract
MIIND is a software platform for easily and efficiently simulating the behaviour of interacting populations of point neurons governed by any 1D or 2D dynamical system. The simulator is entirely agnostic to the underlying neuron model of each population and provides an intuitive method for controlling the amount of noise which can significantly affect the overall behaviour. A network of populations can be set up quickly and easily using MIIND's XML-style simulation file format describing simulation parameters such as how populations interact, transmission delays, post-synaptic potentials, and what output to record. During simulation, a visual display of each population's state is provided for immediate feedback of the behaviour and population activity can be output to a file or passed to a Python script for further processing. The Python support also means that MIIND can be integrated into other software such as The Virtual Brain. MIIND's population density technique is a geometric and visual method for describing the activity of each neuron population which encourages a deep consideration of the dynamics of the neuron model and provides insight into how the behaviour of each population is affected by the behaviour of its neighbours in the network. For 1D neuron models, MIIND performs far better than direct simulation solutions for large populations. For 2D models, performance comparison is more nuanced but the population density approach still confers certain advantages over direct simulation. MIIND can be used to build neural systems that bridge the scales between an individual neuron model and a population network. This allows researchers to maintain a plausible path back from mesoscopic to microscopic scales while minimising the complexity of managing large numbers of interconnected neurons. In this paper, we introduce the MIIND system, its usage, and provide implementation details where appropriate.
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Affiliation(s)
- Hugh Osborne
- Institute for Artificial Intelligence and Biological Computation, School of Computing, University of Leeds, Leeds, United Kingdom
| | - Yi Ming Lai
- School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | | | - David Sichau
- Department of Computer Science, Eidgenössische Technische Hochschule Zurich, Zurich, Switzerland
| | - Lukas Deutz
- Institute for Artificial Intelligence and Biological Computation, School of Computing, University of Leeds, Leeds, United Kingdom
| | - Marc de Kamps
- School of Computing and Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom
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67
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Hashemi M, Vattikonda AN, Sip V, Diaz-Pier S, Peyser A, Wang H, Guye M, Bartolomei F, Woodman MM, Jirsa VK. On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread. PLoS Comput Biol 2021; 17:e1009129. [PMID: 34260596 PMCID: PMC8312957 DOI: 10.1371/journal.pcbi.1009129] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 07/26/2021] [Accepted: 05/29/2021] [Indexed: 11/18/2022] Open
Abstract
Individualized anatomical information has been used as prior knowledge in Bayesian inference paradigms of whole-brain network models. However, the actual sensitivity to such personalized information in priors is still unknown. In this study, we introduce the use of fully Bayesian information criteria and leave-one-out cross-validation technique on the subject-specific information to assess different epileptogenicity hypotheses regarding the location of pathological brain areas based on a priori knowledge from dynamical system properties. The Bayesian Virtual Epileptic Patient (BVEP) model, which relies on the fusion of structural data of individuals, a generative model of epileptiform discharges, and a self-tuning Monte Carlo sampling algorithm, is used to infer the spatial map of epileptogenicity across different brain areas. Our results indicate that measuring the out-of-sample prediction accuracy of the BVEP model with informative priors enables reliable and efficient evaluation of potential hypotheses regarding the degree of epileptogenicity across different brain regions. In contrast, while using uninformative priors, the information criteria are unable to provide strong evidence about the epileptogenicity of brain areas. We also show that the fully Bayesian criteria correctly assess different hypotheses about both structural and functional components of whole-brain models that differ across individuals. The fully Bayesian information-theory based approach used in this study suggests a patient-specific strategy for epileptogenicity hypothesis testing in generative brain network models of epilepsy to improve surgical outcomes.
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Affiliation(s)
- Meysam Hashemi
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | | | - Viktor Sip
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Sandra Diaz-Pier
- SimLab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Alexander Peyser
- SimLab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
- Google, München, Germany
| | - Huifang Wang
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Maxime Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - Fabrice Bartolomei
- Epileptology Department, and Clinical Neurophysiology Department, Assistance Publique des Hôpitaux de Marseille, Marseille, France
| | | | - Viktor K. Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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68
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Rigney G, Lennon M, Holderrieth P. The use of computational models in the management and prognosis of refractory epilepsy: A critical evaluation. Seizure 2021; 91:132-140. [PMID: 34153898 DOI: 10.1016/j.seizure.2021.06.006] [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: 03/20/2021] [Revised: 05/05/2021] [Accepted: 06/06/2021] [Indexed: 11/17/2022] Open
Abstract
PURPOSE Drug resistant epilepsy (DRE) affects approximately 30 percent of individuals with epilepsy worldwide. Surgery remains the most effective treatment for individuals with DRE, but referral to surgery is low and only about 60 percent of individuals who undergo surgery experience seizure control postoperatively. The present paper evaluates the evidence for using computational models in the prediction of surgical resection sites and surgical outcomes for patients with DRE. METHODS We conducted a search in the Medline data base using the terms "refractory epilepsy", "drug-resistant epilepsy", "surgery", "computational model", and "artificial intelligence". Inclusion: original articles in English and case reports from 2000 to 2020. Reviews were excluded. RESULTS Clinical applications of computational models may lead to increased utilisation of surgical services through improving our ability to predict outcomes and by improving surgical outcomes outright. The identification and optimisation of nodes that are crucial for the genesis and propagation of epileptiform activity offers the most promising clinical applications of computational models discussed herein. CONCLUSION Advances in computational models may in the future significantly increase the application and efficacy of surgery for patients with DRE by optimising the site and amount of cortex to resect, but more research is needed before it achieves therapeutic utility.
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Affiliation(s)
- Grant Rigney
- The University of Oxford Department of Psychiatry, Warneford Hospital, Warneford Ln, Headington, Oxford OX3 7JX, United Kingdom.
| | - Matthew Lennon
- Department of Physiology, Anatomy and Genetics, Sherrington Building, University of Oxford, United Kingdom; Faculty of Medicine, University of New South Wales, NSW, Australia.
| | - Peter Holderrieth
- Department of Physiology, Anatomy and Genetics, Sherrington Building, University of Oxford, United Kingdom.
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69
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Khateb M, Bosak N, Herskovitz M. The Effect of Anti-seizure Medications on the Propagation of Epileptic Activity: A Review. Front Neurol 2021; 12:674182. [PMID: 34122318 PMCID: PMC8191738 DOI: 10.3389/fneur.2021.674182] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 04/09/2021] [Indexed: 11/13/2022] Open
Abstract
The propagation of epileptiform events is a highly interesting phenomenon from the pathophysiological point of view, as it involves several mechanisms of recruitment of neural networks. Extensive in vivo and in vitro research has been performed, suggesting that multiple networks as well as cellular candidate mechanisms govern this process, including the co-existence of wave propagation, coupled oscillator dynamics, and more. The clinical importance of seizure propagation stems mainly from the fact that the epileptic manifestations cannot be attributed solely to the activity in the seizure focus itself, but rather to the propagation of epileptic activity to other brain structures. Propagation, especially when causing secondary generalizations, poses a risk to patients due to recurrent falls, traumatic injuries, and poor neurological outcome. Anti-seizure medications (ASMs) affect propagation in diverse ways and with different potencies. Importantly, for drug-resistant patients, targeting seizure propagation may improve the quality of life even without a major reduction in simple focal events. Motivated by the extensive impact of this phenomenon, we sought to review the literature regarding the propagation of epileptic activity and specifically the effect of commonly used ASMs on it. Based on this body of knowledge, we propose a novel classification of ASMs into three main categories: major, minor, and intermediate efficacy in reducing the propagation of epileptiform activity.
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Affiliation(s)
- Mohamed Khateb
- Department of Neurology, Rambam Health Care Campus, Haifa, Israel
| | - Noam Bosak
- Department of Neurology, Rambam Health Care Campus, Haifa, Israel
| | - Moshe Herskovitz
- Department of Neurology, Rambam Health Care Campus, Haifa, Israel.,The Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel
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70
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Mechanisms of Drug Resistance in the Pathogenesis of Epilepsy: Role of Neuroinflammation. A Literature Review. Brain Sci 2021; 11:brainsci11050663. [PMID: 34069567 PMCID: PMC8161227 DOI: 10.3390/brainsci11050663] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 05/13/2021] [Accepted: 05/17/2021] [Indexed: 12/16/2022] Open
Abstract
Epilepsy is a chronic neurological disorder characterized by recurring spontaneous seizures. Drug resistance appears in 30% of patients and it can lead to premature death, brain damage or a reduced quality of life. The purpose of the study was to analyze the drug resistance mechanisms, especially neuroinflammation, in the epileptogenesis. The information bases of biomedical literature Scopus, PubMed, Google Scholar and SciVerse were used. To obtain full-text documents, electronic resources of PubMed Central and Research Gate were used. The article examines the recent research of the mechanisms of drug resistance in epilepsy and discusses the hypotheses of drug resistance development (genetic, epigenetic, target hypothesis, etc.). Drug-resistant epilepsy is associated with neuroinflammatory, autoimmune and neurodegenerative processes. Neuroinflammation causes immune, pathophysiological, biochemical and psychological consequences. Focal or systemic unregulated inflammatory processes lead to the formation of aberrant neural connections and hyperexcitable neural networks. Inflammatory mediators affect the endothelium of cerebral vessels, destroy contacts between endothelial cells and induce abnormal angiogenesis (the formation of “leaky” vessels), thereby affecting the blood–brain barrier permeability. Thus, the analysis of pro-inflammatory and other components of epileptogenesis can contribute to the further development of the therapeutic treatment of drug-resistant epilepsy.
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71
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Peng GCY, Alber M, Tepole AB, Cannon WR, De S, Dura-Bernal S, Garikipati K, Karniadakis G, Lytton WW, Perdikaris P, Petzold L, Kuhl E. Multiscale modeling meets machine learning: What can we learn? ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2021; 28:1017-1037. [PMID: 34093005 PMCID: PMC8172124 DOI: 10.1007/s11831-020-09405-5] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 02/09/2020] [Indexed: 05/10/2023]
Abstract
Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.
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Affiliation(s)
| | - Mark Alber
- University of California, Riverside, USA
| | | | - William R Cannon
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Suvranu De
- Rensselaer Polytechnic Institute, Troy, New York, USA
| | | | | | | | | | | | - Linda Petzold
- University of California, Santa Barbara, California, USA
| | - Ellen Kuhl
- Stanford University, Stanford, California, USA
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72
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Rapidly spreading seizures arise from large-scale functional brain networks in focal epilepsy. Neuroimage 2021; 237:118104. [PMID: 33933597 DOI: 10.1016/j.neuroimage.2021.118104] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 04/22/2021] [Accepted: 04/25/2021] [Indexed: 11/21/2022] Open
Abstract
It remains unclear whether epileptogenic networks in focal epilepsy develop on physiological networks. This work aimed to explore the association between the rapid spread of ictal fast activity (IFA), a proposed biomarker for epileptogenic networks, and the functional connectivity or networks of healthy subjects. We reviewed 45 patients with focal epilepsy who underwent electrocorticographic (ECoG) recordings to identify the patients showing the rapid spread of IFA. IFA power was quantified as normalized beta-gamma band power. Using published resting-state functional magnetic resonance imaging databases, we estimated resting-state functional connectivity of healthy subjects (RSFC-HS) and resting-state networks of healthy subjects (RSNs-HS) at the locations corresponding to the patients' electrodes. We predicted the IFA power of each electrode based on RSFC-HS between electrode locations (RSFC-HS-based prediction) using a recently developed method, termed activity flow mapping. RSNs-HS were identified using seed-based and atlas-based methods. We compared IFA power with RSFC-HS-based prediction or RSNs-HS using non-parametric correlation coefficients. RSFC and seed-based RSNs of each patient (RSFC-PT and seed-based RSNs-PT) were also estimated using interictal ECoG data and compared with IFA power in the same way as RSFC-HS and seed-based RSNs-HS. Spatial autocorrelation-preserving randomization tests were performed for significance testing. Nine patients met the inclusion criteria. None of the patients had reflex seizures. Six patients showed pathological evidence of a structural etiology. In total, we analyzed 49 seizures (2-13 seizures per patient). We observed significant correlations between IFA power and RSFC-HS-based prediction, seed-based RSNs-HS, or atlas-based RSNs-HS in 28 (57.1%), 21 (42.9%), and 28 (57.1%) seizures, respectively. Thirty-two (65.3%) seizures showed a significant correlation with either seed-based or atlas-based RSNs-HS, but this ratio varied across patients: 27 (93.1%) of 29 seizures in six patients correlated with either of them. Among atlas-based RSNs-HS, correlated RSNs-HS with IFA power included the default mode, control, dorsal attention, somatomotor, and temporal-parietal networks. We could not obtain RSFC-PT and RSNs-PT in one patient due to frequent interictal epileptiform discharges. In the remaining eight patients, most of the seizures showed significant correlations between IFA power and RSFC-PT-based prediction or seed-based RSNs-PT. Our study provides evidence that the rapid spread of IFA in focal epilepsy can arise from physiological RSNs. This finding suggests an overlap between epileptogenic and functional networks, which may explain why functional networks in patients with focal epilepsy frequently disrupt.
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73
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Park HJ, Kang J. A Computational Framework for Controlling the Self-Restorative Brain Based on the Free Energy and Degeneracy Principles. Front Comput Neurosci 2021; 15:590019. [PMID: 33935674 PMCID: PMC8079648 DOI: 10.3389/fncom.2021.590019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 03/18/2021] [Indexed: 11/13/2022] Open
Abstract
The brain is a non-linear dynamical system with a self-restoration process, which protects itself from external damage but is often a bottleneck for clinical treatment. To treat the brain to induce the desired functionality, formulation of a self-restoration process is necessary for optimal brain control. This study proposes a computational model for the brain's self-restoration process following the free-energy and degeneracy principles. Based on this model, a computational framework for brain control is established. We posited that the pre-treatment brain circuit has long been configured in response to the environmental (the other neural populations') demands on the circuit. Since the demands persist even after treatment, the treated circuit's response to the demand may gradually approximate the pre-treatment functionality. In this framework, an energy landscape of regional activities, estimated from resting-state endogenous activities by a pairwise maximum entropy model, is used to represent the pre-treatment functionality. The approximation of the pre-treatment functionality occurs via reconfiguration of interactions among neural populations within the treated circuit. To establish the current framework's construct validity, we conducted various simulations. The simulations suggested that brain control should include the self-restoration process, without which the treatment was not optimal. We also presented simulations for optimizing repetitive treatments and optimal timing of the treatment. These results suggest a plausibility of the current framework in controlling the non-linear dynamical brain with a self-restoration process.
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Affiliation(s)
- Hae-Jeong Park
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea.,Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Department of Psychiatry, Yonsei University College of Medicine, Seoul, South Korea.,Brain Korea 21 Project, Graduate School of Medical Science, Yonsei University College of Medicine, Seoul, South Korea.,Department of Cognitive Science, Yonsei University, Seoul, South Korea
| | - Jiyoung Kang
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea.,Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea
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74
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Stefanovski L, Meier JM, Pai RK, Triebkorn P, Lett T, Martin L, Bülau K, Hofmann-Apitius M, Solodkin A, McIntosh AR, Ritter P. Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain. Front Neuroinform 2021; 15:630172. [PMID: 33867964 PMCID: PMC8047422 DOI: 10.3389/fninf.2021.630172] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 03/08/2021] [Indexed: 12/18/2022] Open
Abstract
Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.
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Affiliation(s)
- Leon Stefanovski
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Jil Mona Meier
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Roopa Kalsank Pai
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Paul Triebkorn
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | - Tristram Lett
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Leon Martin
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Konstantin Bülau
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany
| | - Ana Solodkin
- Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, United States
| | | | - Petra Ritter
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
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75
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Pérez-Cervera A, Hlinka J. Perturbations both trigger and delay seizures due to generic properties of slow-fast relaxation oscillators. PLoS Comput Biol 2021; 17:e1008521. [PMID: 33780437 PMCID: PMC8032201 DOI: 10.1371/journal.pcbi.1008521] [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: 11/19/2020] [Revised: 04/08/2021] [Accepted: 02/22/2021] [Indexed: 01/24/2023] Open
Abstract
The mechanisms underlying the emergence of seizures are one of the most important unresolved issues in epilepsy research. In this paper, we study how perturbations, exogenous or endogenous, may promote or delay seizure emergence. To this aim, due to the increasingly adopted view of epileptic dynamics in terms of slow-fast systems, we perform a theoretical analysis of the phase response of a generic relaxation oscillator. As relaxation oscillators are effectively bistable systems at the fast time scale, it is intuitive that perturbations of the non-seizing state with a suitable direction and amplitude may cause an immediate transition to seizure. By contrast, and perhaps less intuitively, smaller amplitude perturbations have been found to delay the spontaneous seizure initiation. By studying the isochrons of relaxation oscillators, we show that this is a generic phenomenon, with the size of such delay depending on the slow flow component. Therefore, depending on perturbation amplitudes, frequency and timing, a train of perturbations causes an occurrence increase, decrease or complete suppression of seizures. This dependence lends itself to analysis and mechanistic understanding through methods outlined in this paper. We illustrate this methodology by computing the isochrons, phase response curves and the response to perturbations in several epileptic models possessing different slow vector fields. While our theoretical results are applicable to any planar relaxation oscillator, in the motivating context of epilepsy they elucidate mechanisms of triggering and abating seizures, thus suggesting stimulation strategies with effects ranging from mere delaying to full suppression of seizures. Despite its simplicity, the modelling of epileptic dynamics as a slow-fast transition between low and high activity states mediated by some slow feedback variable is a relatively novel albeit fruitful approach. This study is the first, to our knowledge, characterizing the response of such slow-fast models of epileptic brain to perturbations by computing its isochrons. Besides its numerical computation, we theoretically determine which factors shape the geometry of isochrons for planar slow-fast oscillators. As a consequence, we introduce a theoretical approach providing a clear understanding of the response of perturbations of slow-fast oscillators. Within the epilepsy context, this elucidates the origin of the contradictory role of interictal epileptiform discharges in the transition to seizure, manifested by both pro-convulsive and anti-convulsive effect depending on the amplitude, frequency and timing. More generally, this paper provides theoretical framework highlighting the role of the slow flow component on the response to perturbations in relaxation oscillators, pointing to the general phenomena in such slow-fast oscillators ubiquitous in biological systems.
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Affiliation(s)
- Alberto Pérez-Cervera
- Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
- Center for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
- * E-mail: (AP); (JH)
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
- * E-mail: (AP); (JH)
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76
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Glomb K, Cabral J, Cattani A, Mazzoni A, Raj A, Franceschiello B. Computational Models in Electroencephalography. Brain Topogr 2021; 35:142-161. [PMID: 33779888 PMCID: PMC8813814 DOI: 10.1007/s10548-021-00828-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 02/19/2021] [Indexed: 12/17/2022]
Abstract
Computational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test in reality. Yet, what is meant by “computational model” is understood in many different ways by researchers in different fields of neuroscience and psychology, hindering communication and collaboration. In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level models, and behavior. On the one hand, computational models serve to investigate the mechanisms that generate brain activity, for example measured with EEG, such as the transient emergence of oscillations at different frequency bands and/or with different spatial topographies. On the other hand, computational models serve to design experiments and test hypotheses in silico. The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. This is crucial for an accurate interpretation of EEG measurements that may ultimately serve in the development of novel clinical applications.
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Affiliation(s)
- Katharina Glomb
- Connectomics Lab, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland.
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal
| | - Anna Cattani
- Department of Psychiatry, University of Wisconsin-Madison, Madison, USA.,Department of Biomedical and Clinical Sciences 'Luigi Sacco', University of Milan, Milan, Italy
| | - Alberto Mazzoni
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Ashish Raj
- School of Medicine, UCSF, San Francisco, USA
| | - Benedetta Franceschiello
- Department of Ophthalmology, Hopital Ophthalmic Jules Gonin, FAA, Lausanne, Switzerland.,CIBM Centre for Biomedical Imaging, EEG Section CHUV-UNIL, Lausanne, Switzerland.,Laboratory for Investigative Neurophysiology, Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
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77
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Abstract
Epilepsy is characterized by specific alterations in network organization. The main parameters at the basis of epileptogenic network formation are alterations of cortical thickness, development of pathologic hubs, modification of hub distribution, and white matter alterations. The effect is a reinforcement of brain connectivity in both the epileptogenic zone and the propagation zone. Moreover, the epileptogenic network is characterized by some specific neurophysiologic biomarkers that evidence the tendency of the network itself to shift from an interictal state to an ictal one. The recognition of these features is crucial in planning epilepsy surgery.
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78
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Turkheimer FE, Rosas FE, Dipasquale O, Martins D, Fagerholm ED, Expert P, Váša F, Lord LD, Leech R. A Complex Systems Perspective on Neuroimaging Studies of Behavior and Its Disorders. Neuroscientist 2021; 28:382-399. [PMID: 33593120 PMCID: PMC9344570 DOI: 10.1177/1073858421994784] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The study of complex systems deals with emergent behavior that arises as
a result of nonlinear spatiotemporal interactions between a large
number of components both within the system, as well as between the
system and its environment. There is a strong case to be made that
neural systems as well as their emergent behavior and disorders can be
studied within the framework of complexity science. In particular, the
field of neuroimaging has begun to apply both theoretical and
experimental procedures originating in complexity science—usually in
parallel with traditional methodologies. Here, we illustrate the basic
properties that characterize complex systems and evaluate how they
relate to what we have learned about brain structure and function from
neuroimaging experiments. We then argue in favor of adopting a complex
systems-based methodology in the study of neuroimaging, alongside
appropriate experimental paradigms, and with minimal influences from
noncomplex system approaches. Our exposition includes a review of the
fundamental mathematical concepts, combined with practical examples
and a compilation of results from the literature.
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Affiliation(s)
- Federico E Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Fernando E Rosas
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK.,Data Science Institute, Imperial College London, London, UK.,Centre for Complexity Science, Imperial College London, London, UK
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Daniel Martins
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Erik D Fagerholm
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Paul Expert
- Global Digital Health Unit, School of Public Health, Imperial College London, London, UK
| | - František Váša
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Robert Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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79
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Sip V, Scholly J, Guye M, Bartolomei F, Jirsa V. Evidence for spreading seizure as a cause of theta-alpha activity electrographic pattern in stereo-EEG seizure recordings. PLoS Comput Biol 2021; 17:e1008731. [PMID: 33635864 PMCID: PMC7946361 DOI: 10.1371/journal.pcbi.1008731] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 03/10/2021] [Accepted: 01/21/2021] [Indexed: 02/07/2023] Open
Abstract
Intracranial electroencephalography is a standard tool in clinical evaluation of patients with focal epilepsy. Various early electrographic seizure patterns differing in frequency, amplitude, and waveform of the oscillations are observed. The pattern most common in the areas of seizure propagation is the so-called theta-alpha activity (TAA), whose defining features are oscillations in the θ - α range and gradually increasing amplitude. A deeper understanding of the mechanism underlying the generation of the TAA pattern is however lacking. In this work we evaluate the hypothesis that the TAA patterns are caused by seizures spreading across the cortex. To do so, we perform simulations of seizure dynamics on detailed patient-derived cortical surfaces using the spreading seizure model as well as reference models with one or two homogeneous sources. We then detect the occurrences of the TAA patterns both in the simulated stereo-electroencephalographic signals and in the signals of recorded epileptic seizures from a cohort of fifty patients, and we compare the features of the groups of detected TAA patterns to assess the plausibility of the different models. Our results show that spreading seizure hypothesis is qualitatively consistent with the evidence available in the seizure recordings, and it can explain the features of the detected TAA groups best among the examined models.
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Affiliation(s)
- Viktor Sip
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Julia Scholly
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, CEMEREM, Pôle d’Imagerie Médicale, CHU, Marseille, France
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, Marseille, France
| | - Maxime Guye
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, CEMEREM, Pôle d’Imagerie Médicale, CHU, Marseille, France
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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80
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Sip V, Hashemi M, Vattikonda AN, Woodman MM, Wang H, Scholly J, Medina Villalon S, Guye M, Bartolomei F, Jirsa VK. Data-driven method to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography. PLoS Comput Biol 2021; 17:e1008689. [PMID: 33596194 PMCID: PMC7920393 DOI: 10.1371/journal.pcbi.1008689] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 03/01/2021] [Accepted: 01/10/2021] [Indexed: 02/07/2023] Open
Abstract
Surgical interventions in epileptic patients aimed at the removal of the epileptogenic zone have success rates at only 60-70%. This failure can be partly attributed to the insufficient spatial sampling by the implanted intracranial electrodes during the clinical evaluation, leading to an incomplete picture of spatio-temporal seizure organization in the regions that are not directly observed. Utilizing the partial observations of the seizure spreading through the brain network, complemented by the assumption that the epileptic seizures spread along the structural connections, we infer if and when are the unobserved regions recruited in the seizure. To this end we introduce a data-driven model of seizure recruitment and propagation across a weighted network, which we invert using the Bayesian inference framework. Using a leave-one-out cross-validation scheme on a cohort of 45 patients we demonstrate that the method can improve the predictions of the states of the unobserved regions compared to an empirical estimate that does not use the structural information, yet it is on the same level as the estimate that takes the structure into account. Furthermore, a comparison with the performed surgical resection and the surgery outcome indicates a link between the inferred excitable regions and the actual epileptogenic zone. The results emphasize the importance of the structural connectome in the large-scale spatio-temporal organization of epileptic seizures and introduce a novel way to integrate the patient-specific connectome and intracranial seizure recordings in a whole-brain computational model of seizure spread.
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Affiliation(s)
- Viktor Sip
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Meysam Hashemi
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | | | | | - Huifang Wang
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Julia Scholly
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, CEMEREM, Pôle d’Imagerie Médicale, CHU, Marseille, France
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, Marseille, France
| | - Samuel Medina Villalon
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, Marseille, France
| | - Maxime Guye
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, CEMEREM, Pôle d’Imagerie Médicale, CHU, Marseille, France
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, Marseille, France
| | - Viktor K. Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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81
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A computational framework for optimal control of a self-adjustive neural system with activity-dependent and homeostatic plasticity. Neuroimage 2021; 230:117805. [PMID: 33524581 DOI: 10.1016/j.neuroimage.2021.117805] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 01/20/2021] [Accepted: 01/24/2021] [Indexed: 12/13/2022] Open
Abstract
The control of the brain system has received increasing attention in the domain of brain science. Most brain control studies have been conducted to explore the brain network's graph-theoretic properties or to produce the desired state based on neural state dynamics, regarding the brain as a passively responding system. However, the self-adjusting nature of neural system after treatment has not been fully considered in the brain control. In the present study, we propose a computational framework for optimal control of the brain with a self-adjustment process in the effective connectivity after treatment. The neural system is modeled to adjust its outgoing effective connectivity as activity-dependent plasticity after treatment, followed by synaptic rescaling of incoming effective connectivity. To control this neural system to induce the desired function, the system's self-adjustment parameter is first estimated, based on which the treatment is optimized. Utilizing this framework, we conducted simulations of optimal control over a functional hippocampal circuitry, estimated using dynamic causal modeling of voltage-sensitive dye imaging from the wild type and mutant mice, responding to consecutive electrical stimuli. Simulation results for optimal control of the abnormal circuit toward a healthy circuit using a single node treatment, neural-type specific treatment as an analogy of medication, and combined treatments of medication and nodal treatment suggest the plausibility of the current framework in controlling the self-adjusting neural system within a restricted treatment setting. We believe the proposed computational framework of the self-adjustment system would help optimal control of the dynamic brain after treatment.
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82
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Tao Y, Rapp B. Investigating the network consequences of focal brain lesions through comparisons of real and simulated lesions. Sci Rep 2021; 11:2213. [PMID: 33500494 PMCID: PMC7838400 DOI: 10.1038/s41598-021-81107-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 01/04/2021] [Indexed: 11/12/2022] Open
Abstract
Given the increased interest in the functional human connectome, a number of computer simulation studies have sought to develop a better quantitative understanding of the effects of focal lesions on the brain’s functional network organization. However, there has been little work evaluating the predictions of this simulation work vis a vis real lesioned connectomes. One of the few relevant studies reported findings from real chronic focal lesions that only partially confirmed simulation predictions. We hypothesize that these discrepancies arose because although the effects of focal lesions likely consist of two components: short-term node subtraction and long-term network re-organization, previous simulation studies have primarily modeled only the short-term consequences of the subtraction of lesioned nodes and their connections. To evaluate this hypothesis, we compared network properties (modularity, participation coefficient, within-module degree) between real functional connectomes obtained from chronic stroke participants and “pseudo-lesioned” functional connectomes generated by subtracting the same sets of lesioned nodes/connections from healthy control connectomes. We found that, as we hypothesized, the network properties of real-lesioned connectomes in chronic stroke differed from those of the pseudo-lesioned connectomes which instantiated only the short-term consequences of node subtraction. Reflecting the long-term consequences of focal lesions, we found re-organization of the neurotopography of global and local hubs in the real but not the pseudo-lesioned connectomes. We conclude that the long-term network re-organization that occurs in response to focal lesions involves changes in functional connectivity within the remaining intact neural tissue that go well beyond the short-term consequences of node subtraction.
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Affiliation(s)
- Yuan Tao
- Department of Cognitive Science, Johns Hopkins University, Baltimore, USA.
| | - Brenda Rapp
- Department of Cognitive Science, Johns Hopkins University, Baltimore, USA
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Convolutional Neural Networks for Pediatric Refractory Epilepsy Classification Using Resting-State Functional Magnetic Resonance Imaging. World Neurosurg 2021; 149:e1112-e1122. [PMID: 33418117 DOI: 10.1016/j.wneu.2020.12.131] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 12/23/2020] [Accepted: 12/24/2020] [Indexed: 11/21/2022]
Abstract
OBJECTIVE This study aims to evaluate the performance of convolutional neural networks (CNNs) trained with resting-state functional magnetic resonance imaging (rfMRI) latency data in the classification of patients with pediatric epilepsy from healthy controls. METHODS Preoperative rfMRI and anatomic magnetic resonance imaging scans were obtained from 63 pediatric patients with refractory epilepsy and 259 pediatric healthy controls. Latency maps of the temporal difference between rfMRI and the global mean signal were calculated using voxel-wise cross-covariance. Healthy control and epilepsy latency z score maps were pseudorandomized and partitioned into training data (60%), validation data (20%), and test data (20%). Healthy control individuals and patients with epilepsy were labeled as negative and positive, respectively. CNN models were then trained with the designated training data. Model hyperparameters were evaluated with a grid-search method. The model with the highest sensitivity was evaluated using unseen test data. Accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve were used to evaluate the ability of the model to classify epilepsy in the test data set. RESULTS The model with the highest validation sensitivity correctly classified 74% of unseen test patients with 85% sensitivity, 71% specificity, F1 score of 0.56, and an area under the receiver operating characteristic curve of 0.86. CONCLUSIONS Using rfMRI latency data, we trained a CNN model to classify patients with pediatric epilepsy from healthy controls with good performance. CNN could serve as an adjunct in the diagnosis of pediatric epilepsy. Identification of pediatric epilepsy earlier in the disease course could decrease time to referral to specialized epilepsy centers and thus improve prognosis in this population.
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84
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Carvalho VR, Moraes MFD, Cash SS, Mendes EMAM. Active probing to highlight approaching transitions to ictal states in coupled neural mass models. PLoS Comput Biol 2021; 17:e1008377. [PMID: 33493165 PMCID: PMC7861539 DOI: 10.1371/journal.pcbi.1008377] [Citation(s) in RCA: 3] [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: 10/01/2020] [Revised: 02/04/2021] [Accepted: 12/02/2020] [Indexed: 01/07/2023] Open
Abstract
The extraction of electrophysiological features that reliably forecast the occurrence of seizures is one of the most challenging goals in epilepsy research. Among possible approaches to tackle this problem is the use of active probing paradigms in which responses to stimuli are used to detect underlying system changes leading up to seizures. This work evaluates the theoretical and mechanistic underpinnings of this strategy using two coupled populations of the well-studied Wendling neural mass model. Different model settings are evaluated, shifting parameters (excitability, slow inhibition, or inter-population coupling gains) from normal towards ictal states while probing stimuli are applied every 2 seconds to the input of either one or both populations. The correlation between the extracted features and the ictogenic parameter shifting indicates if the impending transition to the ictal state may be identified in advance. Results show that not only can the response to the probing stimuli forecast seizures but this is true regardless of the altered ictogenic parameter. That is, similar feature changes are highlighted by probing stimuli responses in advance of the seizure including: increased response variance and lag-1 autocorrelation, decreased skewness, and increased mutual information between the outputs of both model subsets. These changes were mostly restricted to the stimulated population, showing a local effect of this perturbational approach. The transition latencies from normal activity to sustained discharges of spikes were not affected, suggesting that stimuli had no pro-ictal effects. However, stimuli were found to elicit interictal-like spikes just before the transition to the ictal state. Furthermore, the observed feature changes highlighted by probing the neuronal populations may reflect the phenomenon of critical slowing down, where increased recovery times from perturbations may signal the loss of a systems' resilience and are common hallmarks of an impending critical transition. These results provide more evidence that active probing approaches highlight information about underlying system changes involved in ictogenesis and may be able to play a role in assisting seizure forecasting methods which can be incorporated into early-warning systems that ultimately enable closing the loop for targeted seizure-controlling interventions.
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Affiliation(s)
- Vinícius Rezende Carvalho
- Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Núcleo de Neurociências, Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Márcio Flávio Dutra Moraes
- Núcleo de Neurociências, Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Centro de Tecnologia e Pesquisa em Magneto-Ressonância, Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Sydney S. Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Eduardo Mazoni Andrade Marçal Mendes
- Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Núcleo de Neurociências, Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Centro de Tecnologia e Pesquisa em Magneto-Ressonância, Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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85
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Davis KA, Morgan VL. Network Analyses in Epilepsy: Are Nodes and Edges Ready for Clinical Translation? Neurology 2020; 96:195-196. [PMID: 33361264 DOI: 10.1212/wnl.0000000000011316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Kathryn A Davis
- From the Department of Neurology (K.A.D.) and Center for Neuroengineering and Therapeutics (K.A.D.), University of Pennsylvania, Philadelphia; Vanderbilt University Institute of Imaging Science (V.L.M.), Department of Radiology and Radiological Sciences, Department of Neurological Surgery (V.L.M.), and Department of Neurology, Vanderbilt University Medical Center; and Department of Biomedical Engineering, Vanderbilt University, Nashville.
| | - Victoria L Morgan
- From the Department of Neurology (K.A.D.) and Center for Neuroengineering and Therapeutics (K.A.D.), University of Pennsylvania, Philadelphia; Vanderbilt University Institute of Imaging Science (V.L.M.), Department of Radiology and Radiological Sciences, Department of Neurological Surgery (V.L.M.), and Department of Neurology, Vanderbilt University Medical Center; and Department of Biomedical Engineering, Vanderbilt University, Nashville
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Sinha N, Wang Y, Moreira da Silva N, Miserocchi A, McEvoy AW, de Tisi J, Vos SB, Winston GP, Duncan JS, Taylor PN. Structural Brain Network Abnormalities and the Probability of Seizure Recurrence After Epilepsy Surgery. Neurology 2020; 96:e758-e771. [PMID: 33361262 PMCID: PMC7884990 DOI: 10.1212/wnl.0000000000011315] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 09/24/2020] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE We assessed preoperative structural brain networks and clinical characteristics of patients with drug-resistant temporal lobe epilepsy (TLE) to identify correlates of postsurgical seizure recurrences. METHODS We examined data from 51 patients with TLE who underwent anterior temporal lobe resection (ATLR) and 29 healthy controls. For each patient, using the preoperative structural, diffusion, and postoperative structural MRI, we generated 2 networks: presurgery network and surgically spared network. Standardizing these networks with respect to controls, we determined the number of abnormal nodes before surgery and expected to be spared by surgery. We incorporated these 2 abnormality measures and 13 commonly acquired clinical data from each patient into a robust machine learning framework to estimate patient-specific chances of seizures persisting after surgery. RESULTS Patients with more abnormal nodes had a lower chance of complete seizure freedom at 1 year and, even if seizure-free at 1 year, were more likely to relapse within 5 years. The number of abnormal nodes was greater and their locations more widespread in the surgically spared networks of patients with poor outcome than in patients with good outcome. We achieved an area under the curve of 0.84 ± 0.06 and specificity of 0.89 ± 0.09 in predicting unsuccessful seizure outcomes (International League Against Epilepsy [ILAE] 3-5) as opposed to complete seizure freedom (ILAE 1) at 1 year. Moreover, the model-predicted likelihood of seizure relapse was significantly correlated with the grade of surgical outcome at year 1 and associated with relapses up to 5 years after surgery. CONCLUSION Node abnormality offers a personalized, noninvasive marker that can be combined with clinical data to better estimate the chances of seizure freedom at 1 year and subsequent relapse up to 5 years after ATLR. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that node abnormality predicts postsurgical seizure recurrence.
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Affiliation(s)
- Nishant Sinha
- From the Translational and Clinical Research Institute (N.S.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (N.S., Y.W., N.M.d.S., P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne; NIHR University College London Hospitals Biomedical Research Centre (Y.W., A.M., A.W.M., J.d.T., S.B.V., G.P.W., J.S.D., P.N.T.), UCL Institute of Neurology, Queen Square; Centre for Medical Image Computing (S.B.V.), University College London; Epilepsy Society MRI Unit (S.B.V., G.P.W., J.S.D), Chalfont St Peter, UK; and Department of Medicine (G.P.W.,), Division of Neurology, Queen's University, Kingston, Ontario, Canada.
| | - Yujiang Wang
- From the Translational and Clinical Research Institute (N.S.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (N.S., Y.W., N.M.d.S., P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne; NIHR University College London Hospitals Biomedical Research Centre (Y.W., A.M., A.W.M., J.d.T., S.B.V., G.P.W., J.S.D., P.N.T.), UCL Institute of Neurology, Queen Square; Centre for Medical Image Computing (S.B.V.), University College London; Epilepsy Society MRI Unit (S.B.V., G.P.W., J.S.D), Chalfont St Peter, UK; and Department of Medicine (G.P.W.,), Division of Neurology, Queen's University, Kingston, Ontario, Canada
| | - Nádia Moreira da Silva
- From the Translational and Clinical Research Institute (N.S.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (N.S., Y.W., N.M.d.S., P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne; NIHR University College London Hospitals Biomedical Research Centre (Y.W., A.M., A.W.M., J.d.T., S.B.V., G.P.W., J.S.D., P.N.T.), UCL Institute of Neurology, Queen Square; Centre for Medical Image Computing (S.B.V.), University College London; Epilepsy Society MRI Unit (S.B.V., G.P.W., J.S.D), Chalfont St Peter, UK; and Department of Medicine (G.P.W.,), Division of Neurology, Queen's University, Kingston, Ontario, Canada
| | - Anna Miserocchi
- From the Translational and Clinical Research Institute (N.S.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (N.S., Y.W., N.M.d.S., P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne; NIHR University College London Hospitals Biomedical Research Centre (Y.W., A.M., A.W.M., J.d.T., S.B.V., G.P.W., J.S.D., P.N.T.), UCL Institute of Neurology, Queen Square; Centre for Medical Image Computing (S.B.V.), University College London; Epilepsy Society MRI Unit (S.B.V., G.P.W., J.S.D), Chalfont St Peter, UK; and Department of Medicine (G.P.W.,), Division of Neurology, Queen's University, Kingston, Ontario, Canada
| | - Andrew W McEvoy
- From the Translational and Clinical Research Institute (N.S.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (N.S., Y.W., N.M.d.S., P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne; NIHR University College London Hospitals Biomedical Research Centre (Y.W., A.M., A.W.M., J.d.T., S.B.V., G.P.W., J.S.D., P.N.T.), UCL Institute of Neurology, Queen Square; Centre for Medical Image Computing (S.B.V.), University College London; Epilepsy Society MRI Unit (S.B.V., G.P.W., J.S.D), Chalfont St Peter, UK; and Department of Medicine (G.P.W.,), Division of Neurology, Queen's University, Kingston, Ontario, Canada
| | - Jane de Tisi
- From the Translational and Clinical Research Institute (N.S.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (N.S., Y.W., N.M.d.S., P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne; NIHR University College London Hospitals Biomedical Research Centre (Y.W., A.M., A.W.M., J.d.T., S.B.V., G.P.W., J.S.D., P.N.T.), UCL Institute of Neurology, Queen Square; Centre for Medical Image Computing (S.B.V.), University College London; Epilepsy Society MRI Unit (S.B.V., G.P.W., J.S.D), Chalfont St Peter, UK; and Department of Medicine (G.P.W.,), Division of Neurology, Queen's University, Kingston, Ontario, Canada
| | - Sjoerd B Vos
- From the Translational and Clinical Research Institute (N.S.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (N.S., Y.W., N.M.d.S., P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne; NIHR University College London Hospitals Biomedical Research Centre (Y.W., A.M., A.W.M., J.d.T., S.B.V., G.P.W., J.S.D., P.N.T.), UCL Institute of Neurology, Queen Square; Centre for Medical Image Computing (S.B.V.), University College London; Epilepsy Society MRI Unit (S.B.V., G.P.W., J.S.D), Chalfont St Peter, UK; and Department of Medicine (G.P.W.,), Division of Neurology, Queen's University, Kingston, Ontario, Canada
| | - Gavin P Winston
- From the Translational and Clinical Research Institute (N.S.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (N.S., Y.W., N.M.d.S., P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne; NIHR University College London Hospitals Biomedical Research Centre (Y.W., A.M., A.W.M., J.d.T., S.B.V., G.P.W., J.S.D., P.N.T.), UCL Institute of Neurology, Queen Square; Centre for Medical Image Computing (S.B.V.), University College London; Epilepsy Society MRI Unit (S.B.V., G.P.W., J.S.D), Chalfont St Peter, UK; and Department of Medicine (G.P.W.,), Division of Neurology, Queen's University, Kingston, Ontario, Canada
| | - John S Duncan
- From the Translational and Clinical Research Institute (N.S.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (N.S., Y.W., N.M.d.S., P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne; NIHR University College London Hospitals Biomedical Research Centre (Y.W., A.M., A.W.M., J.d.T., S.B.V., G.P.W., J.S.D., P.N.T.), UCL Institute of Neurology, Queen Square; Centre for Medical Image Computing (S.B.V.), University College London; Epilepsy Society MRI Unit (S.B.V., G.P.W., J.S.D), Chalfont St Peter, UK; and Department of Medicine (G.P.W.,), Division of Neurology, Queen's University, Kingston, Ontario, Canada
| | - Peter N Taylor
- From the Translational and Clinical Research Institute (N.S.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (N.S., Y.W., N.M.d.S., P.N.T.), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne; NIHR University College London Hospitals Biomedical Research Centre (Y.W., A.M., A.W.M., J.d.T., S.B.V., G.P.W., J.S.D., P.N.T.), UCL Institute of Neurology, Queen Square; Centre for Medical Image Computing (S.B.V.), University College London; Epilepsy Society MRI Unit (S.B.V., G.P.W., J.S.D), Chalfont St Peter, UK; and Department of Medicine (G.P.W.,), Division of Neurology, Queen's University, Kingston, Ontario, Canada
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87
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Larivière S, Bernasconi A, Bernasconi N, Bernhardt BC. Connectome biomarkers of drug-resistant epilepsy. Epilepsia 2020; 62:6-24. [PMID: 33236784 DOI: 10.1111/epi.16753] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/29/2020] [Accepted: 10/16/2020] [Indexed: 02/06/2023]
Abstract
Drug-resistant epilepsy (DRE) considerably affects patient health, cognition, and well-being, and disproportionally contributes to the overall burden of epilepsy. The most common DRE syndromes are temporal lobe epilepsy related to mesiotemporal sclerosis and extratemporal epilepsy related to cortical malformations. Both syndromes have been traditionally considered as "focal," and most patients benefit from brain surgery for long-term seizure control. However, increasing evidence indicates that many DRE patients also present with widespread structural and functional network disruptions. These anomalies have been suggested to relate to cognitive impairment and prognosis, highlighting their importance for patient management. The advent of multimodal neuroimaging and formal methods to quantify complex systems has offered unprecedented ability to profile structural and functional brain networks in DRE patients. Here, we performed a systematic review on existing DRE network biomarker candidates and their contribution to three key application areas: (1) modeling of cognitive impairments, (2) localization of the surgical target, and (3) prediction of clinical and cognitive outcomes after surgery. Although network biomarkers hold promise for a range of clinical applications, translation of neuroimaging biomarkers to the patient's bedside has been challenged by a lack of clinical and prospective studies. We therefore close by highlighting conceptual and methodological strategies to improve the evaluation and accessibility of network biomarkers, and ultimately guide clinically actionable decisions.
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Affiliation(s)
- Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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88
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Wang HE, Scholly J, Triebkorn P, Sip V, Medina Villalon S, Woodman MM, Le Troter A, Guye M, Bartolomei F, Jirsa V. VEP atlas: An anatomic and functional human brain atlas dedicated to epilepsy patients. J Neurosci Methods 2020; 348:108983. [PMID: 33121983 DOI: 10.1016/j.jneumeth.2020.108983] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 09/27/2020] [Accepted: 10/18/2020] [Indexed: 01/15/2023]
Abstract
BACKGROUND Several automated parcellation atlases of the human brain have been developed over the past decades, based on various criteria, and have been applied in basic and clinical research. NEW METHOD Here we present the Virtual Epileptic Patient (VEP) atlas that offers a new automated brain region parcellation and labeling, which has been developed for the specific use in the domains of epileptology and functional neurosurgery and is able to apply at individual patient's level. RESULTS It comprises 162 brain regions, including 73 cortical and 8 subcortical regions per hemisphere. We demonstrate the successful application of the VEP atlas in a cohort of 50 retrospective patients. The structural organization is complemented by the functional variation of stereotactic intracerebral EEG (SEEG) signal data features establishing brain region-specific 3d-maps. COMPARISON WITH EXISTING METHODS The VEP atlas integrates both anatomical and functional definitions in the same atlas, adapted to applications for epilepsy patients and individualizable. CONCLUSION The covariation of structural and functional organization is the basis for current efforts of patient-specific large-scale brain network modeling exploiting virtual brain technologies for the identification of the epileptogenic regions in an ongoing prospective clinical trial EPINOV.
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Affiliation(s)
- Huifang E Wang
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
| | - Julia Scholly
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France; Epileptology Department, and Clinical Neurophysiology Department, Assistance Publique des Hôpitaux de Marseille, Marseille, France
| | - Paul Triebkorn
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Viktor Sip
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Samuel Medina Villalon
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France; Epileptology Department, and Clinical Neurophysiology Department, Assistance Publique des Hôpitaux de Marseille, Marseille, France
| | | | | | - Maxime Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - Fabrice 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
| | - Viktor Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
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89
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Long-range phase synchronization of high-frequency oscillations in human cortex. Nat Commun 2020; 11:5363. [PMID: 33097714 PMCID: PMC7584610 DOI: 10.1038/s41467-020-18975-8] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 09/03/2020] [Indexed: 02/06/2023] Open
Abstract
Inter-areal synchronization of neuronal oscillations at frequencies below ~100 Hz is a pervasive feature of neuronal activity and is thought to regulate communication in neuronal circuits. In contrast, faster activities and oscillations have been considered to be largely local-circuit-level phenomena without large-scale synchronization between brain regions. We show, using human intracerebral recordings, that 100–400 Hz high-frequency oscillations (HFOs) may be synchronized between widely distributed brain regions. HFO synchronization expresses individual frequency peaks and exhibits reliable connectivity patterns that show stable community structuring. HFO synchronization is also characterized by a laminar profile opposite to that of lower frequencies. Importantly, HFO synchronization is both transiently enhanced and suppressed in separate frequency bands during a response-inhibition task. These findings show that HFO synchronization constitutes a functionally significant form of neuronal spike-timing relationships in brain activity and thus a mesoscopic indication of neuronal communication per se. High-frequency oscillations (HFOs) are common in mammalian brains and have been assumed to be strictly local. Using human intracerebral recordings, the authors find that HFOs can be phase synchronized across long distances between active cortical sites during resting and task states, which may reflect neuronal communication.
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90
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Ramaraju S, Wang Y, Sinha N, McEvoy AW, Miserocchi A, de Tisi J, Duncan JS, Rugg-Gunn F, Taylor PN. Removal of Interictal MEG-Derived Network Hubs Is Associated With Postoperative Seizure Freedom. Front Neurol 2020; 11:563847. [PMID: 33071948 PMCID: PMC7543719 DOI: 10.3389/fneur.2020.563847] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 08/20/2020] [Indexed: 01/21/2023] Open
Abstract
Objective: To investigate whether MEG network connectivity was associated with epilepsy duration, to identify functional brain network hubs in patients with refractory focal epilepsy, and assess if their surgical removal was associated with post-operative seizure freedom. Methods: We studied 31 patients with drug refractory focal epilepsy who underwent resting state magnetoencephalography (MEG), and structural magnetic resonance imaging (MRI) as part of pre-surgical evaluation. Using the structural MRI, we generated 114 cortical regions of interest, performed surface reconstruction and MEG source localization. Representative source localized signals for each region were correlated with each other to generate a functional brain network. We repeated this procedure across three randomly chosen one-minute epochs. Network hubs were defined as those with the highest intra-hemispheric mean correlations. Post-operative MRI identified regions that were surgically removed. Results: Greater mean MEG network connectivity was associated with a longer duration of epilepsy. Patients who were seizure free after surgery had more hubs surgically removed than patients who were not seizure free (AUC = 0.76, p = 0.01) consistently across three randomly chosen time segments. Conclusion: Our results support a growing literature implicating network hub involvement in focal epilepsy, the removal of which by surgery is associated with greater chance of post-operative seizure freedom.
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Affiliation(s)
- Sriharsha Ramaraju
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.,Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom.,Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Nishant Sinha
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.,Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Andrew W McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Fergus Rugg-Gunn
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems Group, CNNP Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.,Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom.,Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, United Kingdom
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91
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Zhuang Y, Zhang Z, Tivarus M, Qiu X, Zhong J, Schifitto G. Whole-brain computational modeling reveals disruption of microscale brain dynamics in HIV infected individuals. Hum Brain Mapp 2020; 42:95-109. [PMID: 32941693 PMCID: PMC7721235 DOI: 10.1002/hbm.25207] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 08/13/2020] [Accepted: 08/30/2020] [Indexed: 01/07/2023] Open
Abstract
MRI‐based neuroimaging techniques have been used to investigate brain injury associated with HIV‐infection. Whole‐brain cortical mean‐field dynamic modeling provides a way to integrate structural and functional imaging outcomes, allowing investigation of microscale brain dynamics. In this study, we adopted the relaxed mean‐field dynamic modeling to investigate structural and functional connectivity in 42 HIV‐infected subjects before and after 12‐week of combination antiretroviral therapy (cART) and compared them with 46 age‐matched healthy subjects. Microscale brain dynamics were modeled by a set of parameters including two region‐specific microscale brain properties, recurrent connection strengths, and subcortical inputs. We also analyzed the relationship between the model parameters (i.e., the recurrent connection and subcortical inputs) and functional network topological characterizations, including smallworldness, clustering coefficient, and network efficiency. The results show that untreated HIV‐infected individuals have disrupted local brain dynamics that in part correlate with network topological measurements. Notably, after 12 weeks of cART, both the microscale brain dynamics and the network topological measurements improved and were closer to those in the healthy brain. This was also associated with improved cognitive performance, suggesting that improvement in local brain dynamics translates into clinical improvement.
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Affiliation(s)
- Yuchuan Zhuang
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York, USA
| | - Zhengwu Zhang
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA.,Department of Neuroscience, University of Rochester Medical Center, Rochester, New York, USA
| | - Madalina Tivarus
- Department of Neuroscience, University of Rochester Medical Center, Rochester, New York, USA.,Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, USA
| | - Xing Qiu
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA
| | - Jianhui Zhong
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, USA.,Department of Biomedical Engineering, University of Rochester, Rochester, New York, USA
| | - Giovanni Schifitto
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
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92
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Brogin JAF, Faber J, Bueno DD. An Efficient Approach to Define the Input Stimuli to Suppress Epileptic Seizures Described by the Epileptor Model. Int J Neural Syst 2020; 30:2050062. [PMID: 32938259 DOI: 10.1142/s0129065720500628] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Epilepsy affects about 70 million people in the world. Every year, approximately 2.4 million people are diagnosed with epilepsy, two-thirds of them will not know the etiology of their disease, and 1% of these individuals will decease as a consequence of it. Due to the inherent complexity of predicting and explaining it, the mathematical model Epileptor was recently developed to reproduce seizure-like events, also providing insights to improve the understanding of the neural dynamics in the interictal and ictal periods, although the physics behind each parameter and variable of the model is not fully established in the literature. This paper introduces an approach to design a feedback-based controller for suppressing epileptic seizures described by Epileptor. Our work establishes how the nonlinear dynamics of this disorder can be written in terms of a combination of linear sub-models employing an exact solution. Additionally, we show how a feedback control gain can be computed to suppress seizures, as well as how specific shapes applied as input stimuli for this purpose can be obtained. The practical application of the approach is discussed and the results show that the proposed technique is promising for developing controllers in this field.
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Affiliation(s)
- João Angelo Ferres Brogin
- Department of Mechanical Engineering, São Paulo State University (UNESP), School of Engineering of Ilha Solteira, 56 Brasil Avenue, Ilha Solteira, São Paulo 15385-000, Brazil
| | - Jean Faber
- Department of Neurology and Neurosurgery, Federal University of São Paulo (UNIFESP), 667 Pedro de Toledo Street, São Paulo 04039-032, Brazil
| | - Douglas Domingues Bueno
- Department of Mathematics, São Paulo State University (UNESP), School of Engineering of Ilha Solteira, 56 Brasil Avenue, Ilha Solteira, São Paulo 15385-000, Brazil
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93
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Guo ZH, Zhao BT, Toprani S, Hu WH, Zhang C, Wang X, Sang L, Ma YS, Shao XQ, Razavi B, Parvizi J, Fisher R, Zhang JG, Zhang K. Epileptogenic network of focal epilepsies mapped with cortico-cortical evoked potentials. Clin Neurophysiol 2020; 131:2657-2666. [PMID: 32957038 DOI: 10.1016/j.clinph.2020.08.012] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 06/23/2020] [Accepted: 08/05/2020] [Indexed: 12/26/2022]
Abstract
OBJECTIVE The goal of this study was to investigate the spatial extent and functional organization of the epileptogenic network through cortico-cortical evoked potentials (CCEPs) in patients being evaluated with intracranial stereoelectroencephalography. METHODS We retrospectively included 25 patients. We divided the recorded sites into three regions: epileptogenic zone (EZ); propagation zone (PZ); and noninvolved zone (NIZ). The root mean square of the amplitudes was calculated to reconstruct effective connectivity network. We also analyzed the N1/N2 amplitudes to explore the responsiveness influenced by epileptogenicity. Prognostic analysis was performed by comparing intra-region and inter-region connectivity between seizure-free and non-seizure-free groups. RESULTS Our results confirmed that stimulation of the EZ caused the strongest responses on other sites within and outside the EZ. Moreover, we found a hierarchical connectivity pattern showing the highest connectivity strength within EZ, and decreasing connectivity gradient from EZ, PZ to NIZ. Prognostic analysis indicated a stronger intra-EZ connection in the seizure-free group. CONCLUSION The EZ showed highest excitability and dominantly influenced other regions. Quantitative CCEPs can be useful in mapping epileptic networks and predicting surgical outcome. SIGNIFICANCE The generated computational connectivity model may enhance our understanding of epileptogenic networks and provide useful information for surgical planning and prognosis prediction.
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Affiliation(s)
- Zhi-Hao Guo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Bao-Tian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Sheela Toprani
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Wen-Han Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lin Sang
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Yan-Shan Ma
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Xiao-Qiu Shao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Babak Razavi
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Josef Parvizi
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Robert Fisher
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, CA, USA.
| | - Jian-Guo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Beijing Key Laboratory of Neurostimulation, Beijing, China.
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Beijing Key Laboratory of Neurostimulation, Beijing, China.
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94
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Sokolov AA, Zeidman P, Razi A, Erb M, Ryvlin P, Pavlova MA, Friston KJ. Asymmetric high-order anatomical brain connectivity sculpts effective connectivity. Netw Neurosci 2020; 4:871-890. [PMID: 33615094 PMCID: PMC7888488 DOI: 10.1162/netn_a_00150] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 05/18/2020] [Indexed: 12/12/2022] Open
Abstract
Bridging the gap between symmetric, direct white matter brain connectivity and neural dynamics that are often asymmetric and polysynaptic may offer insights into brain architecture, but this remains an unresolved challenge in neuroscience. Here, we used the graph Laplacian matrix to simulate symmetric and asymmetric high-order diffusion processes akin to particles spreading through white matter pathways. The simulated indirect structural connectivity outperformed direct as well as absent anatomical information in sculpting effective connectivity, a measure of causal and directed brain dynamics. Crucially, an asymmetric diffusion process determined by the sensitivity of the network nodes to their afferents best predicted effective connectivity. The outcome is consistent with brain regions adapting to maintain their sensitivity to inputs within a dynamic range. Asymmetric network communication models offer a promising perspective for understanding the relationship between structural and functional brain connectomes, both in normalcy and neuropsychiatric conditions.
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Affiliation(s)
- Arseny A. Sokolov
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
- Department of Neurology, University Neurorehabilitation, University Hospital Inselspital, University of Bern, Bern, Switzerland
- Service de Neurologie and Neuroscape@NeuroTech Platform, Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Neuroscape Center, Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Peter Zeidman
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
| | - Adeel Razi
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
- Monash Institute of Cognitive and Clinical Neurosciences & Monash Biomedical Imaging, Monash University, Clayton, Australia
- Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
| | - Michael Erb
- Department of Biomedical Magnetic Resonance, University of Tübingen Medical School, Tübingen, Germany
| | - Philippe Ryvlin
- Service de Neurologie and Neuroscape@NeuroTech Platform, Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Marina A. Pavlova
- Department of Psychiatry and Psychotherapy, University of Tübingen Medical School, Tübingen, Germany
| | - Karl J. Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
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95
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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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96
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Amunts K, Mohlberg H, Bludau S, Zilles K. Julich-Brain: A 3D probabilistic atlas of the human brain’s
cytoarchitecture. Science 2020; 369:988-992. [DOI: 10.1126/science.abb4588] [Citation(s) in RCA: 117] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 06/24/2020] [Indexed: 12/13/2022]
Abstract
Cytoarchitecture is a basic principle of microstructural brain parcellation.
We introduce Julich-Brain, a three-dimensional atlas containing cytoarchitectonic
maps of cortical areas and subcortical nuclei. The atlas is probabilistic, which
enables it to account for variations between individual brains. Building such an
atlas was highly data- and labor-intensive and required the development of nested,
interdependent workflows for detecting borders between brain areas, data
processing, provenance tracking, and flexible execution of processing chains to
handle large amounts of data at different spatial scales. Full cortical coverage
was achieved by the inclusion of gap maps to complement cortical maps. The atlas
is dynamic and will be adapted as mapping progresses; it is openly available to
support neuroimaging studies as well as modeling and simulation; and it is
interoperable, enabling connection to other atlases and resources.
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Affiliation(s)
- Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- C. and O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Hartmut Mohlberg
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Sebastian Bludau
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Karl Zilles
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
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97
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Saggio ML, Crisp D, Scott JM, Karoly P, Kuhlmann L, Nakatani M, Murai T, Dümpelmann M, Schulze-Bonhage A, Ikeda A, Cook M, Gliske SV, Lin J, Bernard C, Jirsa V, Stacey WC. A taxonomy of seizure dynamotypes. eLife 2020; 9:55632. [PMID: 32691734 PMCID: PMC7375810 DOI: 10.7554/elife.55632] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 06/12/2020] [Indexed: 01/02/2023] Open
Abstract
Seizures are a disruption of normal brain activity present across a vast range of species and conditions. We introduce an organizing principle that leads to the first objective Taxonomy of Seizure Dynamics (TSD) based on bifurcation theory. The ‘dynamotype’ of a seizure is the dynamic composition that defines its observable characteristics, including how it starts, evolves and ends. Analyzing over 2000 focal-onset seizures from multiple centers, we find evidence of all 16 dynamotypes predicted in TSD. We demonstrate that patients’ dynamotypes evolve during their lifetime and display complex but systematic variations including hierarchy (certain types are more common), non-bijectivity (a patient may display multiple types) and pairing preference (multiple types may occur during one seizure). TSD provides a way to stratify patients in complement to present clinical classifications, a language to describe the most critical features of seizure dynamics, and a framework to guide future research focused on dynamical properties. Epileptic seizures have been recognized for centuries. But it was only in the 1930s that it was realized that seizures are the result of out-of-control electrical activity in the brain. By placing electrodes on the scalp, doctors can identify when and where in the brain a seizure begins. But they cannot tell much about how the seizure behaves, that is, how it starts, stops or spreads to other areas. This makes it difficult to control and prevent seizures. It also helps explain why almost a third of patients with epilepsy continue to have seizures despite being on medication. Saggio, Crisp et al. have now approached this problem from a new angle using methods adapted from physics and engineering. In these fields, “dynamics research” has been used with great success to predict and control the behavior of complex systems like electrical power grids. Saggio, Crisp et al. reasoned that applying the same approach to the brain would reveal the dynamics of seizures and that such information could then be used to categorize seizures into groups with similar properties. This would in effect create for seizures what the periodic table is for the elements. Applying the dynamics research method to seizure data from more than a hundred patients from across the world revealed 16 types of seizure dynamics. These “dynamotypes” had distinct characteristics. Some were more common than others, and some tended to occur together. Individual patients showed different dynamotypes over time. By constructing a way to classify seizures based on the relationships between the dynamotypes, Saggio, Crisp et al. provide a new tool for clinicians and researchers studying epilepsy. Previous clinical tools have focused on the physical symptoms of a seizure (referred to as the phenotype) or its potential genetic causes (genotype). The current approach complements these tools by adding the dynamotype: how seizures start, spread and stop in the brain. This approach has the potential to lead to new branches of research and better understanding and treatment of seizures.
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Affiliation(s)
- Maria Luisa Saggio
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Dakota Crisp
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States
| | - Jared M Scott
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States
| | - Philippa Karoly
- Graeme Clark Institute, The University of Melbourne, Melbourne, Australia
| | - Levin Kuhlmann
- Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Faculty of Information Technology, Monash University, Clayton, Australia
| | - Mitsuyoshi Nakatani
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Tomohiko Murai
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Matthias Dümpelmann
- Epilepsy Center, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.,Center for Basics in NeuroModulation (NeuroModul Basics), Epilepsy Center, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany
| | - Akio Ikeda
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Mark Cook
- Graeme Clark Institute, The University of Melbourne, Melbourne, Australia.,Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Melbourne, Australia
| | - Stephen V Gliske
- Department of Neurology, University of Michigan, Ann Arbor, United States
| | - Jack Lin
- Department of Neurology, University of Michigan, Ann Arbor, United States
| | - Christophe Bernard
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France, Marseille, France
| | - William C Stacey
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, United States.,Department of Neurology, University of Michigan, Ann Arbor, United States
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98
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Jobst BC, Bartolomei F, Diehl B, Frauscher B, Kahane P, Minotti L, Sharan A, Tardy N, Worrell G, Gotman J. Intracranial EEG in the 21st Century. Epilepsy Curr 2020; 20:180-188. [PMID: 32677484 PMCID: PMC7427159 DOI: 10.1177/1535759720934852] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Intracranial electroencephalography (iEEG) has been the mainstay of identifying the seizure onset zone (SOZ), a key diagnostic procedure in addition to neuroimaging when considering epilepsy surgery. In many patients, iEEG has been the basis for resective epilepsy surgery, to date still the most successful treatment for drug-resistant epilepsy. Intracranial EEG determines the location and resectability of the SOZ. Advances in recording and implantation of iEEG provide multiple options in the 21st century. This not only includes the choice between subdural electrodes (SDE) and stereoelectroencephalography (SEEG) but also includes the implantation and recordings from microelectrodes. Before iEEG implantation, especially in magnetic resonance imaging -negative epilepsy, a clear hypothesis for seizure generation and propagation should be based on noninvasive methods. Intracranial EEG implantation should be planned by a multidisciplinary team considering epileptic networks. Recordings from SDE and SEEG have both their advantages and disadvantages. Stereo-EEG seems to have a lower rate of complications that are clinically significant, but has limitations in spatial sampling of the cortical surface. Stereo-EEG can sample deeper areas of the brain including deep sulci and hard to reach areas such as the insula. To determine the epileptogenic zone, interictal and ictal information should be taken into consideration. Interictal spiking, low frequency slowing, as well as high frequency oscillations may inform about the epileptogenic zone. Ictally, high frequency onsets in the beta/gamma range are usually associated with the SOZ, but specialized recordings with combined macro and microelectrodes may in the future educate us about onset in higher frequency bands. Stimulation of intracranial electrodes triggering habitual seizures can assist in identifying the SOZ. Advanced computational methods such as determining the epileptogenicity index and similar measures may enhance standard clinical interpretation. Improved techniques to record and interpret iEEG may in the future lead to a greater proportion of patients being seizure free after epilepsy surgery.
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Affiliation(s)
- Barbara C Jobst
- Geisel School of Medicine at Dartmouth and Dartmouth-Hitchcock Medical Center, Hanover, NH, USA
| | - Fabrice Bartolomei
- Aix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France.,APHM, Timone hospital, Epileptology department, Marseille, France
| | - Beate Diehl
- National Hospital for Neurology and Neurosurgery, University College London, London, United Kingdom
| | - Birgit Frauscher
- Montreal Neurological Institute & Hospital, McGill University, Montreal, Quebec, Canada
| | - Philippe Kahane
- Neurology Department & INSERM U1216, Grenoble-Alpes University and Hospital, Grenoble, France
| | - Lorella Minotti
- Neurology Department & INSERM U1216, Grenoble-Alpes University and Hospital, Grenoble, France
| | - Ashwini Sharan
- National Hospital for Neurology and Neurosurgery, Jefferson University, Philadelphia, PA, USA
| | - Nastasia Tardy
- Neurology Department & INSERM U1216, Grenoble-Alpes University and Hospital, Grenoble, France
| | | | - Jean Gotman
- Montreal Neurological Institute & Hospital, McGill University, Montreal, Quebec, Canada
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99
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100
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An S, Kang C, Lee HW. Artificial Intelligence and Computational Approaches for Epilepsy. J Epilepsy Res 2020; 10:8-17. [PMID: 32983950 PMCID: PMC7494883 DOI: 10.14581/jer.20003] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 06/18/2020] [Accepted: 07/14/2020] [Indexed: 12/30/2022] Open
Abstract
Studies on treatment of epilepsy have been actively conducted in multiple avenues, but there are limitations in improving its efficacy due to between-subject variability in which treatment outcomes vary from patient to patient. Accordingly, there is a growing interest in precision medicine that provides accurate diagnosis for seizure types and optimal treatment for an individual epilepsy patient. Among these approaches, computational studies making this feasible are rapidly progressing in particular and have been widely applied in epilepsy. These computational studies are being conducted in two main streams: 1) artificial intelligence-based studies implementing computational machines with specific functions, such as automatic diagnosis and prognosis prediction for an individual patient, using machine learning techniques based on large amounts of data obtained from multiple patients and 2) patient-specific modeling-based studies implementing biophysical in-silico platforms to understand pathological mechanisms and derive the optimal treatment for each patient by reproducing the brain network dynamics of the particular patient per se based on individual patient's data. These computational approaches are important as it can integrate multiple types of data acquired from patients and analysis results into a single platform. If these kinds of methods are efficiently operated, it would suggest a novel paradigm for precision medicine.
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Affiliation(s)
- Sora An
- Department of Neurology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea.,Department of Medical Science, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea
| | - Chaewon Kang
- Department of Neurology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea.,Department of Computational Medicine, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea
| | - Hyang Woon Lee
- Department of Neurology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea.,Department of Medical Science, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea.,Department of Computational Medicine, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, Korea
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