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Cui CK, Wong WK, Wong CH, Gill D, Fong MWK. Case Report: Focal, generalized, or both: does generalized network involvement preclude successful epilepsy surgery? FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1425329. [PMID: 39055857 PMCID: PMC11269090 DOI: 10.3389/fnetp.2024.1425329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 06/20/2024] [Indexed: 07/28/2024]
Abstract
We present two cases with focal seizures where scalp electroencephalography (EEG) had prominent features of a developmental and epileptic encephalopathy (DEE): Case 1: a 17-year-old male with complex motor seizures whose EEG demonstrated a slow spike-and-wave pattern and generalized paroxysmal fast activity (GPFA). Case 2: a 12-year-old male with startle-induced asymmetric tonic seizures whose EEG also had a slow spike-and-wave pattern. Both patients had intracranial EEG assessment, and focal cortical resections resulted in long-term seizure freedom and resolution of generalized findings. These cases exemplify patients with focal epilepsy with networks that share similarities to generalized epilepsies, and importantly, these features did not preclude curative epilepsy surgery.
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Affiliation(s)
- Cathy K. Cui
- Westmead Comprehensive Epilepsy Centre, The University of Sydney, Sydney, NSW, Australia
| | - Wui-Kwan Wong
- Westmead Comprehensive Epilepsy Centre, The University of Sydney, Sydney, NSW, Australia
- T. Y. Nelson Department of Neurology and Neurosurgery, The Children’s Hospital at Westmead, Sydney, NSW, Australia
| | - Chong H. Wong
- Westmead Comprehensive Epilepsy Centre, The University of Sydney, Sydney, NSW, Australia
- T. Y. Nelson Department of Neurology and Neurosurgery, The Children’s Hospital at Westmead, Sydney, NSW, Australia
| | - Deepak Gill
- Westmead Comprehensive Epilepsy Centre, The University of Sydney, Sydney, NSW, Australia
- T. Y. Nelson Department of Neurology and Neurosurgery, The Children’s Hospital at Westmead, Sydney, NSW, Australia
| | - Michael W. K. Fong
- Westmead Comprehensive Epilepsy Centre, The University of Sydney, Sydney, NSW, Australia
- T. Y. Nelson Department of Neurology and Neurosurgery, The Children’s Hospital at Westmead, Sydney, NSW, Australia
- Department of Neurology, Comprehensive Epilepsy Center, Yale University School of Medicine, New Haven, CT, United States
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2
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Tait L, Staniaszek LE, Galizia E, Martin-Lopez D, Walker MC, Azeez AAA, Meiklejohn K, Allen D, Price C, Georgiou S, Bagary M, Khalsa S, Manfredonia F, Tittensor P, Lawthom C, Howes BB, Shankar R, Terry JR, Woldman W. Estimating the likelihood of epilepsy from clinically noncontributory electroencephalograms using computational analysis: A retrospective, multisite case-control study. Epilepsia 2024. [PMID: 38780578 DOI: 10.1111/epi.18024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 05/09/2024] [Accepted: 05/09/2024] [Indexed: 05/25/2024]
Abstract
OBJECTIVE This study was undertaken to validate a set of candidate biomarkers of seizure susceptibility in a retrospective, multisite case-control study, and to determine the robustness of these biomarkers derived from routinely collected electroencephalography (EEG) within a large cohort (both epilepsy and common alternative conditions such as nonepileptic attack disorder). METHODS The database consisted of 814 EEG recordings from 648 subjects, collected from eight National Health Service sites across the UK. Clinically noncontributory EEG recordings were identified by an experienced clinical scientist (N = 281; 152 alternative conditions, 129 epilepsy). Eight computational markers (spectral [n = 2], network-based [n = 4], and model-based [n = 2]) were calculated within each recording. Ensemble-based classifiers were developed using a two-tier cross-validation approach. We used standard regression methods to assess whether potential confounding variables (e.g., age, gender, treatment status, comorbidity) impacted model performance. RESULTS We found levels of balanced accuracy of 68% across the cohort with clinically noncontributory normal EEGs (sensitivity =61%, specificity =75%, positive predictive value =55%, negative predictive value =79%, diagnostic odds ratio =4.64, area under receiver operated characteristics curve =.72). Group level analysis found no evidence suggesting any of the potential confounding variables significantly impacted the overall performance. SIGNIFICANCE These results provide evidence that the set of biomarkers could provide additional value to clinical decision-making, providing the foundation for a decision support tool that could reduce diagnostic delay and misdiagnosis rates. Future work should therefore assess the change in diagnostic yield and time to diagnosis when utilizing these biomarkers in carefully designed prospective studies.
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Affiliation(s)
- Luke Tait
- Cardiff University, Cardiff, UK
- University of Birmingham, Birmingham
| | - Lydia E Staniaszek
- University Hospitals Bristol and Weston National Health Service Foundation Trust, Bristol, UK
- Neuronostics, Bristol, UK
| | - Elizabeth Galizia
- St. George's Hospital National Health Service Foundation Trust, London, UK
| | - David Martin-Lopez
- St. George's Hospital National Health Service Foundation Trust, London, UK
- Kingston Hospital National Health Service Foundation Trust, Kingston, UK
| | - Matthew C Walker
- University College London, London, UK
- University College London Hospitals, London, UK
| | | | - Kay Meiklejohn
- Neuronostics, Bristol, UK
- University Hospital Southampton National Health Service Foundation Trust, Southampton, UK
| | - David Allen
- University Hospital Southampton National Health Service Foundation Trust, Southampton, UK
| | - Chris Price
- Royal Devon and Exeter National Health Service Foundation Trust, Exeter, UK
| | - Sophie Georgiou
- Royal Devon and Exeter National Health Service Foundation Trust, Exeter, UK
| | - Manny Bagary
- Birmingham and Solihull Mental Health National Health Service Foundation Trust, Birmingham, UK
| | - Sakh Khalsa
- Birmingham and Solihull Mental Health National Health Service Foundation Trust, Birmingham, UK
| | | | - Phil Tittensor
- Royal Wolverhampton National Health Service Trust, Wolverhampton, UK
- University of Wolverhampton, Wolverhampton, UK
| | | | | | - Rohit Shankar
- University of Plymouth, Plymouth, UK
- Cornwall Partnership National Health Service Foundation Trust, Bodmin, UK
| | - John R Terry
- University of Birmingham, Birmingham
- Neuronostics, Bristol, UK
| | - Wessel Woldman
- University of Birmingham, Birmingham
- Neuronostics, Bristol, UK
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Jiang S, Pei H, Chen J, Li H, Liu Z, Wang Y, Gong J, Wang S, Li Q, Duan M, Calhoun VD, Yao D, Luo C. Striatum- and Cerebellum-Modulated Epileptic Networks Varying Across States with and without Interictal Epileptic Discharges. Int J Neural Syst 2024; 34:2450017. [PMID: 38372049 DOI: 10.1142/s0129065724500175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Idiopathic generalized epilepsy (IGE) is characterized by cryptogenic etiology and the striatum and cerebellum are recognized as modulators of epileptic network. We collected simultaneous electroencephalogram and functional magnetic resonance imaging data from 145 patients with IGE, 34 of whom recorded interictal epileptic discharges (IEDs) during scanning. In states without IEDs, hierarchical connectivity was performed to search core cortical regions which might be potentially modulated by striatum and cerebellum. Node-node and edge-edge moderation models were constructed to depict direct and indirect moderation effects in states with and without IEDs. Patients showed increased hierarchical connectivity with sensorimotor cortices (SMC) and decreased connectivity with regions in the default mode network (DMN). In the state without IEDs, striatum, cerebellum, and thalamus were linked to weaken the interactions of regions in the salience network (SN) with DMN and SMC. In periods with IEDs, overall increased moderation effects on the interaction between regions in SN and DMN, and between regions in DMN and SMC were observed. The thalamus and striatum were implicated in weakening interactions between regions in SN and SMC. The striatum and cerebellum moderated the cortical interaction among DMN, SN, and SMC in alliance with the thalamus, contributing to the dysfunction in states with and without IEDs in IGE. The current work revealed state-specific modulation effects of striatum and cerebellum on thalamocortical circuits and uncovered the potential core cortical targets which might contribute to develop new clinical neuromodulation techniques.
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Affiliation(s)
- Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Haonan Pei
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Junxia Chen
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Zetao Liu
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Yuehan Wang
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Jinnan Gong
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- School of Computer Science, Chengdu University of Information Technology, Chengdu, P. R. China
| | - Sheng Wang
- Department of Neurology, Hainan Medical University, Hainan 571199, P. R. China
| | - Qifu Li
- Department of Neurology, Hainan Medical University, Hainan 571199, P. R. China
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, P. R. China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P. R. China
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Bröhl T, Rings T, Pukropski J, von Wrede R, Lehnertz K. The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1338864. [PMID: 38293249 PMCID: PMC10825060 DOI: 10.3389/fnetp.2023.1338864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024]
Abstract
Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.
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Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Jan Pukropski
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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Bou Assi E, Schindler K, de Bézenac C, Denison T, Desai S, Keller SS, Lemoine É, Rahimi A, Shoaran M, Rummel C. From basic sciences and engineering to epileptology: A translational approach. Epilepsia 2023; 64 Suppl 3:S72-S84. [PMID: 36861368 DOI: 10.1111/epi.17566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 02/23/2023] [Accepted: 02/28/2023] [Indexed: 03/03/2023]
Abstract
Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (ICTALS 2022): (1) novel developments of structural magnetic resonance imaging; (2) latest electroencephalography signal-processing applications; (3) big data for the development of clinical tools; (4) the emerging field of hyperdimensional computing; (5) the new generation of artificial intelligence (AI)-enabled neuroprostheses; and (6) the use of collaborative platforms to facilitate epilepsy research translation. We highlight the promise of AI reported in recent investigations and the need for multicenter data-sharing initiatives.
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Affiliation(s)
- Elie Bou Assi
- Department of Neuroscience, Université de Montréal, Montréal, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Canada
| | - Kaspar Schindler
- Department of Neurology, Inselspital, Sleep-Wake-Epilepsy-Center, Bern University Hospital, Bern University, Bern, Switzerland
| | - Christophe de Bézenac
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
- Department of Engineering Science, University of Oxford, Oxford, UK
| | | | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Émile Lemoine
- Centre de Recherche du CHUM (CRCHUM), Montréal, Canada
- Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Canada
| | | | - Mahsa Shoaran
- Institute of Electrical and Micro Engineering, Neuro-X Institute, EPFL, Lausanne, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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Lemoine É, Toffa D, Pelletier-Mc Duff G, Xu AQ, Jemel M, Tessier JD, Lesage F, Nguyen DK, Bou Assi E. Machine-learning for the prediction of one-year seizure recurrence based on routine electroencephalography. Sci Rep 2023; 13:12650. [PMID: 37542101 PMCID: PMC10403587 DOI: 10.1038/s41598-023-39799-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/31/2023] [Indexed: 08/06/2023] Open
Abstract
Predicting seizure recurrence risk is critical to the diagnosis and management of epilepsy. Routine electroencephalography (EEG) is a cornerstone of the estimation of seizure recurrence risk. However, EEG interpretation relies on the visual identification of interictal epileptiform discharges (IEDs) by neurologists, with limited sensitivity. Automated processing of EEG could increase its diagnostic yield and accessibility. The main objective was to develop a prediction model based on automated EEG processing to predict one-year seizure recurrence in patients undergoing routine EEG. We retrospectively selected a consecutive cohort of 517 patients undergoing routine EEG at our institution (training set) and a separate, temporally shifted cohort of 261 patients (testing set). We developed an automated processing pipeline to extract linear and non-linear features from the EEGs. We trained machine learning algorithms on multichannel EEG segments to predict one-year seizure recurrence. We evaluated the impact of IEDs and clinical confounders on performances and validated the performances on the testing set. The receiver operating characteristic area-under-the-curve for seizure recurrence after EEG in the testing set was 0.63 (95% CI 0.55-0.71). Predictions were still significantly above chance in EEGs with no IEDs. Our findings suggest that there are changes other than IEDs in the EEG signal embodying seizure propensity.
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Affiliation(s)
- Émile Lemoine
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Denahin Toffa
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Geneviève Pelletier-Mc Duff
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - An Qi Xu
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Mezen Jemel
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Jean-Daniel Tessier
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Frédéric Lesage
- Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Qc, Canada
- Centre de Recherche de l'institut de Cardiologie de Montréal, Montréal, Qc, Canada
| | - Dang K Nguyen
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada
| | - Elie Bou Assi
- Department of Neurosciences, Université de Montréal, Montréal, Qc, Canada.
- Centre de Recherche du CHUM (CRCHUM), Montréal, Qc, Canada.
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Li D, Zhang X, Liu R, Long M, Zhou S, Lin J, Zhang L. Kainic acid induced hyperexcitability in thalamic reticular nucleus that initiates an inflammatory response through the HMGB1/TLR4 pathway. Neurotoxicology 2023; 95:94-106. [PMID: 36669621 DOI: 10.1016/j.neuro.2023.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 01/20/2023]
Abstract
OBJECTIVE To explore the relationship between the proinflammatory factor high-mobility group box 1 (HMGB1) and glutamatergic alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors in the development of epilepsy. METHODS Thalamic reticular nucleus (TRN) slices were treated with kainic acid (KA) to simulate seizures. Action potentials and spontaneous inhibitory postsynaptic currents (sIPSCs) were recorded within TRN slices using whole-cell patch clamp techniques. The translocation of HMGB1 was detected by immunofluorescence. The HMGB1/TLR4 signaling pathway and its downstream inflammatory factors (IL-1β and NF-κB) were detected by RTPCR, Western blot and ELISA. RESULTS KA-evoked spikings were observed in TRN slices and blocked by perampanel. sIPSCs in the TRN were enhanced by KA and reduced by perampanel. The translocation of HMGB1 in the TRN was promoted by KA and inhibited by perampanel. The expression of the HMGB1/TLR4 signaling pathway was promoted by KA and suppressed by perampanel. CONCLUSION KA induced hyperexcitability activates the HMGB1/TLR4 pathway, which potentially leading to neuroinflammation in epilepsy.
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Affiliation(s)
- Dongbin Li
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China; First Department of Neurology and Neuroscience Center, Heilongjiang Provincial Hospital, Harbin, China
| | - Xiaosi Zhang
- Metro-Medic Clinic, 1538 sherbrooke Ouest, suite 100, Montreal, QC H3G 1L5, Canada
| | - Ruoshi Liu
- Department of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Meixin Long
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Shanshan Zhou
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jinghan Lin
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Liming Zhang
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
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Au Yong HM, Clough M, Perucca P, Malpas CB, Kwan P, O'Brien TJ, Fielding J. Ocular motility as a measure of cerebral dysfunction in adults with focal epilepsy. Epilepsy Behav 2023; 141:109140. [PMID: 36812874 DOI: 10.1016/j.yebeh.2023.109140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 01/11/2023] [Accepted: 02/05/2023] [Indexed: 02/22/2023]
Abstract
OBJECTIVE Using objective oculomotor measures, we aimed to: (1) compare oculomotor performance in patients with drug-resistant focal epilepsy to healthy controls, and (2) investigate the differential impact of epileptogenic focus laterality and location on oculomotor performance. METHODS We recruited 51 adults with drug-resistant focal epilepsy from the Comprehensive Epilepsy Programs of two tertiary hospitals and 31 healthy controls to perform prosaccade and antisaccade tasks. Oculomotor variables of interest were latency, visuospatial accuracy, and antisaccade error rate. Linear mixed models were performed to compare interactions between groups (epilepsy, control) and oculomotor tasks, and between epilepsy subgroups and oculomotor tasks for each oculomotor variable. RESULTS Compared to healthy controls, patients with drug-resistant focal epilepsy exhibited longer antisaccade latencies (mean difference = 42.8 ms, P = 0.001), poorer spatial accuracy for both prosaccade (mean difference = 0.4°, P = 0.002), and antisaccade tasks (mean difference = 2.1°, P < 0.001), and more antisaccade errors (mean difference = 12.6%, P < 0.001). In the epilepsy subgroup analysis, left-hemispheric epilepsy patients exhibited longer antisaccade latencies compared to controls (mean difference = 52.2 ms, P = 0.003), while right-hemispheric epilepsy was the most spatially inaccurate compared to controls (mean difference = 2.5°, P = 0.003). The temporal lobe epilepsy subgroup displayed longer antisaccade latencies compared to controls (mean difference = 47.6 ms, P = 0.005). SIGNIFICANCE Patients with drug-resistant focal epilepsy exhibit poor inhibitory control as evidenced by a high percentage of antisaccade errors, slower cognitive processing speed, and impaired visuospatial accuracy on oculomotor tasks. Patients with left-hemispheric epilepsy and temporal lobe epilepsy have markedly impaired processing speed. Overall, oculomotor tasks can be a useful tool to objectively quantify cerebral dysfunction in drug-resistant focal epilepsy.
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Affiliation(s)
- Hue Mun Au Yong
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia; Department of Neuroscience, The Central Clinical School, Monash University, Melbourne, Victoria, Australia; Department of Neurology, Royal Melbourne Hospital, Parkville, Victoria, Australia.
| | - Meaghan Clough
- Department of Neuroscience, The Central Clinical School, Monash University, Melbourne, Victoria, Australia.
| | - Piero Perucca
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia; Department of Neuroscience, The Central Clinical School, Monash University, Melbourne, Victoria, Australia; Department of Neurology, Royal Melbourne Hospital, Parkville, Victoria, Australia; Epilepsy Research Centre, Department of Medicine (Austin Health), The University of Melbourne, Heidelberg, Victoria, Australia; Bladin-Berkovic Comprehensive Epilepsy Program, Department of Neurology, Austin Health, Heidelberg, Victoria, Australia.
| | - Charles B Malpas
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia; Department of Neuroscience, The Central Clinical School, Monash University, Melbourne, Victoria, Australia; Department of Neurology, Royal Melbourne Hospital, Parkville, Victoria, Australia; Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia.
| | - Patrick Kwan
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia; Department of Neuroscience, The Central Clinical School, Monash University, Melbourne, Victoria, Australia; Department of Neurology, Royal Melbourne Hospital, Parkville, Victoria, Australia; Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia.
| | - Terence J O'Brien
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia; Department of Neuroscience, The Central Clinical School, Monash University, Melbourne, Victoria, Australia; Department of Neurology, Royal Melbourne Hospital, Parkville, Victoria, Australia; Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia.
| | - Joanne Fielding
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia; Department of Neuroscience, The Central Clinical School, Monash University, Melbourne, Victoria, Australia.
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Lai N, Li Z, Xu C, Wang Y, Chen Z. Diverse nature of interictal oscillations: EEG-based biomarkers in epilepsy. Neurobiol Dis 2023; 177:105999. [PMID: 36638892 DOI: 10.1016/j.nbd.2023.105999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/07/2023] [Accepted: 01/09/2023] [Indexed: 01/11/2023] Open
Abstract
Interictal electroencephalogram (EEG) patterns, including high-frequency oscillations (HFOs), interictal spikes (ISs), and slow wave activities (SWAs), are defined as specific oscillations between seizure events. These interictal oscillations reflect specific dynamic changes in network excitability and play various roles in epilepsy. In this review, we briefly describe the electrographic characteristics of HFOs, ISs, and SWAs in the interictal state, and discuss the underlying cellular and network mechanisms. We also summarize representative evidence from experimental and clinical epilepsy to address their critical roles in ictogenesis and epileptogenesis, indicating their potential as electrophysiological biomarkers of epilepsy. Importantly, we put forwards some perspectives for further research in the field.
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Affiliation(s)
- Nanxi Lai
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zhisheng Li
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Cenglin Xu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yi Wang
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China; Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhong Chen
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China; Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, China; Epilepsy Center, Department of Neurology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
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10
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Portoles O, Qin Y, Hadida J, Woolrich M, Cao M, van Vugt M. Modulations of local synchrony over time lead to resting-state functional connectivity in a parsimonious large-scale brain model. PLoS One 2022; 17:e0275819. [PMID: 36288273 PMCID: PMC9604991 DOI: 10.1371/journal.pone.0275819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 09/24/2022] [Indexed: 11/30/2022] Open
Abstract
Biophysical models of large-scale brain activity are a fundamental tool for understanding the mechanisms underlying the patterns observed with neuroimaging. These models combine a macroscopic description of the within- and between-ensemble dynamics of neurons within a single architecture. A challenge for these models is accounting for modulations of within-ensemble synchrony over time. Such modulations in local synchrony are fundamental for modeling behavioral tasks and resting-state activity. Another challenge comes from the difficulty in parametrizing large scale brain models which hinders researching principles related with between-ensembles differences. Here we derive a parsimonious large scale brain model that can describe fluctuations of local synchrony. Crucially, we do not reduce within-ensemble dynamics to macroscopic variables first, instead we consider within and between-ensemble interactions similarly while preserving their physiological differences. The dynamics of within-ensemble synchrony can be tuned with a parameter which manipulates local connectivity strength. We simulated resting-state static and time-resolved functional connectivity of alpha band envelopes in models with identical and dissimilar local connectivities. We show that functional connectivity emerges when there are high fluctuations of local and global synchrony simultaneously (i.e. metastable dynamics). We also show that for most ensembles, leaning towards local asynchrony or synchrony correlates with the functional connectivity with other ensembles, with the exception of some regions belonging to the default-mode network.
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Affiliation(s)
- Oscar Portoles
- Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands
- * E-mail:
| | - Yuzhen Qin
- Department of Mechanical Engineering, University of California, Riverside, California, United States of America
| | - Jonathan Hadida
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Mark Woolrich
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Ming Cao
- Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands
| | - Marieke van Vugt
- Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands
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11
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Ray J, Wijesekera L, Cirstea S. Machine learning and clinical neurophysiology. J Neurol 2022; 269:6678-6684. [PMID: 35907045 DOI: 10.1007/s00415-022-11283-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/05/2022] [Accepted: 07/09/2022] [Indexed: 11/29/2022]
Abstract
Clinical neurophysiology constructs a wealth of dynamic information pertaining to the integrity and function of both central and peripheral nervous systems. As with many technological fields, there has been an explosion of data in neurophysiology over recent years, and this requires considerable analysis by experts. Computational algorithms and especially advances in machine learning (ML) have the ability to assist with this task and potentially reveal hidden insights. In this update article, we will provide a brief overview where such technology is being applied in clinical neurophysiology and possible future directions.
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Affiliation(s)
- Julian Ray
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK.
| | - Lokesh Wijesekera
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK
| | - Silvia Cirstea
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK
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12
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Varatharajah Y, Joseph B, Brinkmann B, Morita-Sherman M, Fitzgerald Z, Vegh D, Nair D, Burgess R, Cendes F, Jehi L, Worrell G. Quantitative Analysis of Visually Reviewed Normal Scalp EEG Predicts Seizure Freedom Following Anterior Temporal Lobectomy. Epilepsia 2022; 63:1630-1642. [PMID: 35416285 PMCID: PMC9283304 DOI: 10.1111/epi.17257] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 04/10/2022] [Accepted: 04/11/2022] [Indexed: 11/28/2022]
Abstract
Objective Anterior temporal lobectomy (ATL) is a widely performed and successful intervention for drug‐resistant temporal lobe epilepsy (TLE). However, up to one third of patients experience seizure recurrence within 1 year after ATL. Despite the extensive literature on presurgical electroencephalography (EEG) and magnetic resonance imaging (MRI) abnormalities to prognosticate seizure freedom following ATL, the value of quantitative analysis of visually reviewed normal interictal EEG in such prognostication remains unclear. In this retrospective multicenter study, we investigate whether machine learning analysis of normal interictal scalp EEG studies can inform the prediction of postoperative seizure freedom outcomes in patients who have undergone ATL. Methods We analyzed normal presurgical scalp EEG recordings from 41 Mayo Clinic (MC) and 23 Cleveland Clinic (CC) patients. We used an unbiased automated algorithm to extract eyes closed awake epochs from scalp EEG studies that were free of any epileptiform activity and then extracted spectral EEG features representing (a) spectral power and (b) interhemispheric spectral coherence in frequencies between 1 and 25 Hz across several brain regions. We analyzed the differences between the seizure‐free and non–seizure‐free patients and employed a Naïve Bayes classifier using multiple spectral features to predict surgery outcomes. We trained the classifier using a leave‐one‐patient‐out cross‐validation scheme within the MC data set and then tested using the out‐of‐sample CC data set. Finally, we compared the predictive performance of normal scalp EEG‐derived features against MRI abnormalities. Results We found that several spectral power and coherence features showed significant differences correlated with surgical outcomes and that they were most pronounced in the 10–25 Hz range. The Naïve Bayes classification based on those features predicted 1‐year seizure freedom following ATL with area under the curve (AUC) values of 0.78 and 0.76 for the MC and CC data sets, respectively. Subsequent analyses revealed that (a) interhemispheric spectral coherence features in the 10–25 Hz range provided better predictability than other combinations and (b) normal scalp EEG‐derived features provided superior and potentially distinct predictive value when compared with MRI abnormalities (>10% higher F1 score). Significance These results support that quantitative analysis of even a normal presurgical scalp EEG may help prognosticate seizure freedom following ATL in patients with drug‐resistant TLE. Although the mechanism for this result is not known, the scalp EEG spectral and coherence properties predicting seizure freedom may represent activity arising from the neocortex or the networks responsible for temporal lobe seizure generation within vs outside the margins of an ATL.
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Affiliation(s)
- Yogatheesan Varatharajah
- Department of Bioengineering, University of Illinois, Urbana, IL, 61801, USA.,Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
| | - Boney Joseph
- Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
| | - Benjamin Brinkmann
- Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
| | | | | | - Deborah Vegh
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, 44195, USA
| | - Dileep Nair
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, 44195, USA
| | - Richard Burgess
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, 44195, USA
| | - Fernando Cendes
- Department of Neurology, University of Campinas UNICAMP, Campinas, Brazil
| | - Lara Jehi
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, 44195, USA
| | - Gregory Worrell
- Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
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13
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Dharan AL, Bowden SC, Lai A, Peterson ADH, Cheung MWL, Woldman W, D'Souza WJ. Resting-state functional connectivity in the idiopathic generalized epilepsies: A systematic review and meta-analysis of EEG and MEG studies. Epilepsy Behav 2021; 124:108336. [PMID: 34607215 DOI: 10.1016/j.yebeh.2021.108336] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 09/09/2021] [Accepted: 09/12/2021] [Indexed: 11/20/2022]
Abstract
For idiopathic generalized epilepsies (IGE), brain network analysis is emerging as a biomarker for potential use in clinical care. To determine whether people with IGE show alterations in resting-state brain connectivity compared to healthy controls, and to quantify these differences, we conducted a systematic review and meta-analysis of EEG and magnetoencephalography (MEG) functional connectivity and network studies. The review was conducted according to PRISMA guidelines. Twenty-two studies were eligible for inclusion. Outcomes from individual studies supported hypotheses for interictal, resting-state brain connectivity alterations in IGE patients compared to healthy controls. In contrast, meta-analysis from six studies of common network metrics clustering coefficient, path length, mean degree and nodal strength showed no significant differences between IGE and control groups (effect sizes ranged from -0.151 -1.78). The null findings of the meta-analysis and the heterogeneity of the included studies highlights the importance of developing standardized, validated methodologies for future research. Network neuroscience has significant potential as both a diagnostic and prognostic biomarker in epilepsy, though individual variability in network dynamics needs to be better understood and accounted for.
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Affiliation(s)
- Anita L Dharan
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia.
| | - Stephen C Bowden
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia; Department of Clinical Neurosciences, St Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Alan Lai
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
| | - Andre D H Peterson
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
| | - Mike W-L Cheung
- Department of Psychology, National University of Singapore, Singapore
| | - Wessel Woldman
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Edgbaston, United Kingdom
| | - Wendyl J D'Souza
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
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14
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Tait L, Lopes MA, Stothart G, Baker J, Kazanina N, Zhang J, Goodfellow M. A large-scale brain network mechanism for increased seizure propensity in Alzheimer's disease. PLoS Comput Biol 2021; 17:e1009252. [PMID: 34379638 PMCID: PMC8382184 DOI: 10.1371/journal.pcbi.1009252] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 08/23/2021] [Accepted: 07/06/2021] [Indexed: 11/19/2022] Open
Abstract
People with Alzheimer’s disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider hyperexcitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, in the general population of people with epilepsy, large-scale brain network organization additionally plays a role in determining seizure likelihood and phenotype. Here, we propose that alterations to large-scale brain network organization seen in AD may contribute to increased seizure likelihood. To test this hypothesis, we combine computational modelling with electrophysiological data using an approach that has proved informative in clinical epilepsy cohorts without AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions. As cortical tissue excitability was increased in the simulations, AD simulations were more likely to transition into seizures than simulations from healthy controls, suggesting an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with AD pathologies. People with Alzheimer’s disease (AD) are more likely to develop seizures than cognitively healthy people. In this study, we aimed to understand whether whole-brain network structure is related to this increased seizure likelihood. We used electroencephalography (EEG) to estimate brain networks from people with AD and healthy controls. We subsequently inserted these networks into a model brain and simulated disease progression by increasing the excitability of brain tissue. We found the simulated AD brains were more likely to develop seizures than the simulated control brains. No participants had seizures when we collected data, so our results suggest an increased probability of developing seizures at a future time for AD participants. Therefore functional brain network structure may play a role in increased seizure likelihood in AD. We also used the model to examine which brain regions were most important for generating seizures, and found that the seizure-generating regions corresponded to those typically affected in early AD. Our results also provide a potential explanation for why people with AD are more likely to have generalized seizures (i.e. seizures involving the whole brain, as opposed to ‘focal’ seizures which only involve certain areas) than the general population with epilepsy.
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Affiliation(s)
- Luke Tait
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- * E-mail:
| | - Marinho A. Lopes
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - George Stothart
- Department of Psychology, University of Bath, Bath, United Kingdom
| | - John Baker
- Dementia Research Centre, Queen Square Institute of Neurology, UCL, London, United Kingdom
| | - Nina Kazanina
- School of Psychological Science, University of Bristol, Bristol, United Kingdom
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, United Kingdom
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15
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Faiman I, Smith S, Hodsoll J, Young AH, Shotbolt P. Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review. Epilepsy Behav 2021; 121:108047. [PMID: 34091130 DOI: 10.1016/j.yebeh.2021.108047] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 04/28/2021] [Indexed: 12/17/2022]
Abstract
Quantitative markers extracted from resting-state electroencephalogram (EEG) reveal subtle neurophysiological dynamics which may provide useful information to support the diagnosis of seizure disorders. We performed a systematic review to summarize evidence on markers extracted from interictal, visually normal resting-state EEG in adults with idiopathic epilepsy or psychogenic nonepileptic seizures (PNES). Studies were selected from 5 databases and evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2. 26 studies were identified, 19 focusing on people with epilepsy, 6 on people with PNES, and one comparing epilepsy and PNES directly. Results suggest that oscillations along the theta frequency (4-8 Hz) may have a relevant role in idiopathic epilepsy, whereas in PNES there was no evident trend. However, studies were subject to a number of methodological limitations potentially introducing bias. There was often a lack of appropriate reporting and high heterogeneity. Results were not appropriate for quantitative synthesis. We identify and discuss the challenges that must be addressed for valid resting-state EEG markers of epilepsy and PNES to be developed.
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Affiliation(s)
- Irene Faiman
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, Camberwell, London SE5 8AB, United Kingdom.
| | - Stuart Smith
- Department of Neurophysiology, Great Ormond Street Hospital, Great Ormond Street, London WC1N 3JH, United Kingdom.
| | - John Hodsoll
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, Camberwell, London SE5 8AB, United Kingdom.
| | - Allan H Young
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, Camberwell, London SE5 8AB, United Kingdom; South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Monks Orchard Road, Beckenham, Kent BR3 3BX, United Kingdom.
| | - Paul Shotbolt
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, Camberwell, London SE5 8AB, United Kingdom.
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16
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Varatharajah Y, Berry B, Joseph B, Balzekas I, Pal Attia T, Kremen V, Brinkmann B, Iyer R, Worrell G. Characterizing the electrophysiological abnormalities in visually reviewed normal EEGs of drug-resistant focal epilepsy patients. Brain Commun 2021; 3:fcab102. [PMID: 34131643 PMCID: PMC8196245 DOI: 10.1093/braincomms/fcab102] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 03/28/2021] [Accepted: 04/08/2021] [Indexed: 11/13/2022] Open
Abstract
Routine scalp EEG is essential in the clinical diagnosis and management of epilepsy. However, a normal scalp EEG (based on expert visual review) recorded from a patient with epilepsy can cause delays in diagnosis and clinical care delivery. Here, we investigated whether normal EEGs might contain subtle electrophysiological clues of epilepsy. Specifically, we investigated (i) whether there are indicators of abnormal brain electrophysiology in normal EEGs of epilepsy patients, and (ii) whether such abnormalities are modulated by the side of the brain generating seizures in focal epilepsy. We analysed awake scalp EEG recordings of age-matched groups of 144 healthy individuals and 48 individuals with drug-resistant focal epilepsy who had normal scalp EEGs. After preprocessing, using a bipolar montage of eight channels, we extracted the fraction of spectral power in the alpha band (8-13 Hz) relative to a wide band of 0.5-40 Hz within 10-s windows. We analysed the extracted features for (i) the extent to which people with drug-resistant focal epilepsy differed from healthy subjects, and (ii) whether differences within the drug-resistant focal epilepsy patients were related to the hemisphere generating seizures. We then used those differences to classify whether an EEG is likely to have been recorded from a person with drug-resistant focal epilepsy, and if so, the epileptogenic hemisphere. Furthermore, we tested the significance of these differences while controlling for confounders, such as acquisition system, age and medications. We found that the fraction of alpha power is generally reduced (i) in drug-resistant focal epilepsy compared to healthy controls, and (ii) in right-handed drug-resistant focal epilepsy subjects with left hemispheric seizures compared to those with right hemispheric seizures, and that the differences are most prominent in the frontal and temporal regions. The fraction of alpha power yielded area under curve values of 0.83 in distinguishing drug-resistant focal epilepsy from healthy and 0.77 in identifying the epileptic hemisphere in drug-resistant focal epilepsy patients. Furthermore, our results suggest that the differences in alpha power are greater when compared with differences attributable to acquisition system differences, age and medications. Our findings support that EEG-based measures of normal brain function, such as the normalized spectral power of alpha activity, may help identify patients with epilepsy even when an EEG does not contain any epileptiform activity, recorded seizures or other abnormalities. Although alpha abnormalities are unlikely to be disease-specific, we propose that such abnormalities may provide a higher pre-test probability for epilepsy when an individual being screened for epilepsy has a normal EEG on visual assessment.
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Affiliation(s)
- Yogatheesan Varatharajah
- Department of Bioengineering, University of Illinois, Urbana, IL 61801, USA.,Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA.,Electrical and Computer Engineering, University of Illinois, Urbana, IL 61801, USA
| | - Brent Berry
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Boney Joseph
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Irena Balzekas
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Tal Pal Attia
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Vaclav Kremen
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA.,Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 160 00 Prague 6, Czech Republic
| | - Benjamin Brinkmann
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ravishankar Iyer
- Electrical and Computer Engineering, University of Illinois, Urbana, IL 61801, USA
| | - Gregory Worrell
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
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17
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Balestrini S, Lopez SM, Chinthapalli K, Sargsyan N, Demurtas R, Vos S, Altmann A, Suttie M, Hammond P, Sisodiya SM. Increased facial asymmetry in focal epilepsies associated with unilateral lesions. Brain Commun 2021; 3:fcab068. [PMID: 34222868 PMCID: PMC8244637 DOI: 10.1093/braincomms/fcab068] [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: 01/10/2021] [Revised: 02/20/2021] [Accepted: 03/11/2021] [Indexed: 12/22/2022] Open
Abstract
The epilepsies are now conceptualized as network disruptions: focal epilepsies are considered to have network alterations in the hemisphere of seizure onset, whilst generalized epilepsies are considered to have bi-hemispheric network changes. Increasingly, many epilepsies are also considered to be neurodevelopmental disorders, with early changes in the brain underpinning seizure biology. The development of the structure of the face is influenced by complex molecular interactions between surface ectoderm and underlying developing forebrain and neural crest cells. This influence is likely to continue postnatally, given the evidence of facial growth changes over time in humans until at least 18 years of age. In this case-control study, we hypothesized that people with lateralized focal epilepsies (i.e. unilateral network changes) have an increased degree of facial asymmetry, compared with people with generalized epilepsies or controls without epilepsy. We applied three-dimensional stereophotogrammetry and dense surface models to evaluate facial asymmetry in people with epilepsy, aiming to generate new tools to explore pathophysiological mechanisms in epilepsy. We analysed neuroimaging data to explore the correlation between face and brain asymmetry. We consecutively recruited 859 people with epilepsy attending the epilepsy clinics at a tertiary referral centre. We used dense surface modelling of the full face and signature analyses of three-dimensional facial photographs to analyse facial differences between 378 cases and 205 healthy controls. Neuroimaging around the time of the facial photograph was available for 234 cases. We computed the brain asymmetry index between contralateral regions. Cases with focal symptomatic epilepsy associated with unilateral lesions showed greater facial asymmetry compared to controls (P = 0.0001, two-sample t-test). This finding was confirmed by linear regression analysis after controlling for age and gender. We also found a significant correlation between duration of illness and the brain asymmetry index of total average cortical thickness (r = -0.19, P = 0.0075) but not for total average surface area (r = 0.06, P = 0.3968). There was no significant correlation between facial asymmetry and asymmetry of regional cortical thickness or surface area. We propose that the greater facial asymmetry in cases with focal epilepsy caused by unilateral abnormality might be explained by early unilateral network disruption, and that this is independent of underlying brain asymmetry. Three-dimensional stereophotogrammetry and dense surface modelling are a novel powerful phenotyping tool in epilepsy that may permit greater understanding of pathophysiology in epilepsy, and generate further insights into the development of cerebral networks underlying epilepsy, and the genetics of facial and neural development.
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Affiliation(s)
- Simona Balestrini
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London.,Chalfont Centre for Epilepsy, Gerrards Cross, UK
| | - Seymour M Lopez
- Department of Medical Physics, Centre for Medical Image Computing, UCL, London, UK
| | - Krishna Chinthapalli
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London.,Chalfont Centre for Epilepsy, Gerrards Cross, UK
| | - Narek Sargsyan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London.,Chalfont Centre for Epilepsy, Gerrards Cross, UK
| | - Rita Demurtas
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London.,Chalfont Centre for Epilepsy, Gerrards Cross, UK
| | - Sjoerd Vos
- Department of Medical Physics, Centre for Medical Image Computing, UCL, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Andre Altmann
- Department of Medical Physics, Centre for Medical Image Computing, UCL, London, UK
| | - Michael Suttie
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.,Big Data Institute, Old Road Campus, University of Oxford, Oxford, UK
| | - Peter Hammond
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.,Big Data Institute, Old Road Campus, University of Oxford, Oxford, UK
| | - Sanjay M Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London.,Chalfont Centre for Epilepsy, Gerrards Cross, UK
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18
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Woo JH, Honey CJ, Moon JY. Phase and amplitude dynamics of coupled oscillator systems on complex networks. CHAOS (WOODBURY, N.Y.) 2020; 30:121102. [PMID: 33380037 PMCID: PMC7714526 DOI: 10.1063/5.0031031] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 11/05/2020] [Indexed: 06/12/2023]
Abstract
We investigated locking behaviors of coupled limit-cycle oscillators with phase and amplitude dynamics. We focused on how the dynamics are affected by inhomogeneous coupling strength and by angular and radial shifts in coupling functions. We performed mean-field analyses of oscillator systems with inhomogeneous coupling strength, testing Gaussian, power-law, and brain-like degree distributions. Even for oscillators with identical intrinsic frequencies and intrinsic amplitudes, we found that the coupling strength distribution and the coupling function generated a wide repertoire of phase and amplitude dynamics. These included fully and partially locked states in which high-degree or low-degree nodes would phase-lead the network. The mean-field analytical findings were confirmed via numerical simulations. The results suggest that, in oscillator systems in which individual nodes can independently vary their amplitude over time, qualitatively different dynamics can be produced via shifts in the coupling strength distribution and the coupling form. Of particular relevance to information flows in oscillator networks, changes in the non-specific drive to individual nodes can make high-degree nodes phase-lag or phase-lead the rest of the network.
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Affiliation(s)
- Jae Hyung Woo
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Christopher J. Honey
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Joon-Young Moon
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, Maryland 21218, USA
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19
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Junges L, Woldman W, Benjamin OJ, Terry JR. Epilepsy surgery: Evaluating robustness using dynamic network models. CHAOS (WOODBURY, N.Y.) 2020; 30:113106. [PMID: 33261362 DOI: 10.1063/5.0022171] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 10/08/2020] [Indexed: 06/12/2023]
Abstract
Epilepsy is one of the most common neurological conditions affecting over 65 million people worldwide. Over one third of people with epilepsy are considered refractory: they do not respond to drug treatments. For this significant cohort of people, surgery is a potentially transformative treatment. However, only a small minority of people with refractory epilepsy are considered suitable for surgery, and long-term seizure freedom is only achieved in half the cases. Recently, several computational approaches have been proposed to support presurgical planning. Typically, these approaches use a dynamic network model to explore the potential impact of surgical resection in silico. The network component of the model is informed by clinical imaging data and is considered static thereafter. This assumption critically overlooks the plasticity of the brain and, therefore, how continued evolution of the brain network post-surgery may impact upon the success of a resection in the longer term. In this work, we use a simplified dynamic network model, which describes transitions to seizures, to systematically explore how the network structure influences seizure propensity, both before and after virtual resections. We illustrate key results in small networks, before extending our findings to larger networks. We demonstrate how the evolution of brain networks post resection can result in a return to increased seizure propensity. Our results effectively determine the robustness of a given resection to possible network reconfigurations and so provide a potential strategy for optimizing long-term seizure freedom.
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Affiliation(s)
- Leandro Junges
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Wessel Woldman
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Oscar J Benjamin
- Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, United Kingdom
| | - John R Terry
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham B15 2TT, United Kingdom
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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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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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