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Bröhl T, Rings T, Pukropski J, von Wrede R, Lehnertz K. The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1338864. [PMID: 38293249 PMCID: PMC10825060 DOI: 10.3389/fnetp.2023.1338864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024]
Abstract
Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.
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Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Jan Pukropski
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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2
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Nanda P, Richardson RM. Evolution of Stereo-Electroencephalography at Massachusetts General Hospital. Neurosurg Clin N Am 2024; 35:87-94. [PMID: 38000845 DOI: 10.1016/j.nec.2023.09.007] [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] [Indexed: 11/26/2023]
Abstract
The practice of invasive monitoring for presurgical epilepsy workup has evolved at Massachusetts General Hospital (MGH) in parallel to the evolution in the field's understanding of epilepsy as a network disorder. Implantations have shifted from an emphasis on singularly finding single foci for the purpose of resection to a network-hypothesis-driven approach aiming to delineate patients' seizure networks with the goal of developing surgical interventions that disrupt critical nodes of these networks. Here, the authors review all invasive monitoring cases at MGH from April 2016 through June 2023 to describe how this paradigm shift has taken form.
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Affiliation(s)
- Pranav Nanda
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Neurosurgery, Harvard Medical School, Boston, MA 02115, USA.
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Neurosurgery, Harvard Medical School, Boston, MA 02115, USA
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Dallmer-Zerbe I, Jiruska P, Hlinka J. Personalized dynamic network models of the human brain as a future tool for planning and optimizing epilepsy therapy. Epilepsia 2023; 64:2221-2238. [PMID: 37340565 DOI: 10.1111/epi.17690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/17/2023] [Accepted: 06/19/2023] [Indexed: 06/22/2023]
Abstract
Epilepsy is a common neurological disorder, with one third of patients not responding to currently available antiepileptic drugs. The proportion of pharmacoresistant epilepsies has remained unchanged for many decades. To cure epilepsy and control seizures requires a paradigm shift in the development of new approaches to epilepsy diagnosis and treatment. Contemporary medicine has benefited from the exponential growth of computational modeling, and the application of network dynamics theory to understanding and treating human brain disorders. In epilepsy, the introduction of these approaches has led to personalized epileptic network modeling that can explore the patient's seizure genesis and predict the functional impact of resection on its individual network's propensity to seize. The application of the dynamic systems approach to neurostimulation therapy of epilepsy allows designing stimulation strategies that consider the patient's seizure dynamics and long-term fluctuations in the stability of their epileptic networks. In this article, we review, in a nontechnical fashion suitable for a broad neuroscientific audience, recent progress in personalized dynamic brain network modeling that is shaping the future approach to the diagnosis and treatment of epilepsy.
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Affiliation(s)
- Isa Dallmer-Zerbe
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
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Pei H, Ma S, Yan W, Liu Z, Wang Y, Yang Z, Li Q, Yao D, Jiang S, Luo C, Yu L. Functional and structural networks decoupling in generalized tonic-clonic seizures and its reorganization by drugs. Epilepsia Open 2023; 8:1038-1048. [PMID: 37394869 PMCID: PMC10472403 DOI: 10.1002/epi4.12781] [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: 07/13/2022] [Accepted: 06/27/2023] [Indexed: 07/04/2023] Open
Abstract
OBJECTIVE To investigate potential functional and structural large-scale network disturbances in untreated patients with generalized tonic-clonic seizures (GTCS) and the effects of antiseizure drugs. METHODS In this study, 41 patients with GTCS, comprising 21 untreated patients and 20 patients who received antiseizure medications (ASMs), and 29 healthy controls were recruited to construct large-scale brain networks based on resting-state functional magnetic resonance imaging and diffusion tensor imaging. Structural and functional connectivity and network-level weighted correlation probability (NWCP) were further investigated to identify network features that corresponded to response to ASMs. RESULTS Untreated patients showed more extensive enhancement of functional and structural connections than controls. Specifically, we observed abnormally enhanced connections between the default mode network (DMN) and the frontal-parietal network. In addition, treated patients showed similar functional connection strength to that of the control group. However, all patients exhibited similar structural network alterations. Moreover, the NWCP value was lower for connections within the DMN and between the DMN and other networks in the untreated patients; receiving ASMs could reverse this pattern. SIGNIFICANCE Our study identified alterations in structural and functional connectivity in patients with GTCS. The influence of ASMs may be more noticeable within the functional network; moreover, abnormalities in both the functional and structural coupling state may be improved by ASM treatment. Therefore, the coupling state of structural and functional connectivity may be used as an indicator of the efficacy of ASMs.
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Affiliation(s)
- Haonan Pei
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
| | - Shuai Ma
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
- Neurology DepartmentSichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, The Affiliated Hospital of University of Electronic Science and Technology of ChinaChengduChina
| | - Wei Yan
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
| | - Zetao Liu
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
| | - Yuehan Wang
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
| | - Zhihuan Yang
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
| | - Qifu Li
- Department of NeurologyThe First Affiliated Hospital of Hainan Medical UniversityHaikouChina
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
- Department of NeurologyThe First Affiliated Hospital of Hainan Medical UniversityHaikouChina
- High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
- High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science InstituteMOE Key Lab for NeuroinformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Research Unit of NeuroInformation (2019RU035)Chinese Academy of Medical SciencesChengduChina
- High‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Liang Yu
- Neurology DepartmentSichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, The Affiliated Hospital of University of Electronic Science and Technology of ChinaChengduChina
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Rigoni I, Rué Queralt J, Glomb K, Preti MG, Roehri N, Tourbier S, Spinelli L, Seeck M, Van De Ville D, Hagmann P, Vulliémoz S. Structure-function coupling increases during interictal spikes in temporal lobe epilepsy: A graph signal processing study. Clin Neurophysiol 2023; 153:1-10. [PMID: 37364402 DOI: 10.1016/j.clinph.2023.05.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 04/21/2023] [Accepted: 05/18/2023] [Indexed: 06/28/2023]
Abstract
OBJECTIVE Structure-function coupling remains largely unknown in brain disorders. We studied this coupling during interictal epileptic discharges (IEDs), using graph signal processing in temporal lobe epilepsy (TLE). METHODS We decomposed IEDs of 17 patients on spatial maps, i.e. network harmonics, extracted from a structural connectome. Harmonics were split in smooth maps (long-range interactions reflecting integration) and coarse maps (short-range interactions reflecting segregation) and were used to reconstruct the part of the signal coupled (Xc) and decoupled (Xd) from the structure, respectively. We analysed how Xc and Xd embed the IED energy over time, at global and regional level. RESULTS For Xc, the energy was smaller than for Xd before the IED onset (p < .001), but became larger around the first IED peak (p < .05, cluster 2, C2). Locally, the ipsilateral mesial regions were significantly coupled to the structure over the whole epoch. The ipsilateral hippocampus increased its coupling during C2 (p < .01). CONCLUSIONS At whole-brain level, segregation gives way to integrative processes during the IED. Locally, brain regions commonly involved in the TLE epileptogenic network increase their reliance on long-range couplings during IED (C2). SIGNIFICANCE In TLE, integration mechanisms prevail during the IED and are localized in the ipsilateral mesial temporal regions.
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Affiliation(s)
- I Rigoni
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland.
| | - J Rué Queralt
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Switzerland
| | - K Glomb
- Brain Simulation Section, Berlin Institute of Health/Charite, 10098 Berlin, Germany
| | - M G Preti
- Neuro-X Institute, School of Engineering, Ecole polytechnique fédérale de Lausanne, Geneva, Switzerland; Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland; CIBM Center for Biomedical Imaging, Switzerland
| | - N Roehri
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland
| | - S Tourbier
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Switzerland
| | - L Spinelli
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland
| | - M Seeck
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland
| | - D Van De Ville
- Neuro-X Institute, School of Engineering, Ecole polytechnique fédérale de Lausanne, Geneva, Switzerland; Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - P Hagmann
- Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Switzerland
| | - S Vulliémoz
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland
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Yuan Y, Duan Y, Li W, Ren J, Li Z, Yang C. Differences in the Default Mode Network of Temporal Lobe Epilepsy Patients Detected by Hilbert-Huang Transform Based Dynamic Functional Connectivity. Brain Topogr 2023:10.1007/s10548-023-00966-9. [PMID: 37115390 DOI: 10.1007/s10548-023-00966-9] [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: 04/25/2022] [Accepted: 04/15/2023] [Indexed: 04/29/2023]
Abstract
Resting-state functional connectivity, constructed via functional magnetic resonance imaging, has become an essential tool for exploring brain functions. Aside from the methods focusing on the static state, investigating dynamic functional connectivity can better uncover the fundamental properties of brain networks. Hilbert-Huang transform (HHT) is a novel time-frequency technique that can adapt to both non-linear and non-stationary signals, which may be an effective tool for investigating dynamic functional connectivity. To perform the present study, we investigated time-frequency dynamic functional connectivity among 11 brain regions of the default mode network by first projecting the coherence into the time and frequency domains, and subsequently by identifying clusters in the time-frequency domain using k-means clustering. Experiments on 14 temporal lobe epilepsy (TLE) patients and 21 age and sex-matched healthy controls were performed. The results show that functional connections in the brain regions of the hippocampal formation, parahippocampal gyrus, and retrosplenial cortex (Rsp) were reduced in the TLE group. However, the connections in the brain regions of the posterior inferior parietal lobule, ventral medial prefrontal cortex, and the core subsystem could hardly be detected in TLE patients. The findings not only demonstrate the feasibility of utilizing HHT in dynamic functional connectivity for epilepsy research, but also indicate that TLE may cause damage to memory functions, disorders of processing self-related tasks, and impairment of constructing a mental scene.
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Affiliation(s)
- Ye Yuan
- Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, China
- Department of Bioengineering, Imperial College London, London, UK
| | - Ying Duan
- Beijing Universal Medical Imaging Diagnostic Center, Beijing, China
| | - Wan Li
- School of Computer Science and Engineering, Beijing Technology and Business University, Beijing, China
| | - Jiechuan Ren
- Beijing Tian Tan Hospital, Capital Medical University, Beijing, China
| | - Zhimei Li
- Beijing Tian Tan Hospital, Capital Medical University, Beijing, China
| | - Chunlan Yang
- Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, China.
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Koster LK, Zamyadi R, Yan L, Payne ET, McBain KL, Dunkley BT, Hahn CD. Brain network properties of clinical versus subclinical seizures among critically ill children. Clin Neurophysiol 2023; 149:33-41. [PMID: 36878028 DOI: 10.1016/j.clinph.2023.02.160] [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: 07/01/2022] [Revised: 01/16/2023] [Accepted: 02/05/2023] [Indexed: 02/17/2023]
Abstract
OBJECTIVE Electrographic seizures are common among critically ill children, and have been associated with worse outcomes. Despite their often-widespread cortical representation, most of these seizures remain subclinical, a phenomenon which remains poorly understood. We compared the brain network properties of clinical versus subclinical seizures to gain insight into their relative potential deleterious effects. METHODS Functional connectivity (phase lag index) and graph measures (global efficiency and clustering coefficients) were computed for 2178 electrographic seizures recorded during 48-hours of 19-channel continuous EEG monitoring obtained in 20 comatose children. Frequency-specific group differences in clinical versus subclinical seizures were analyzed using a non-parametric ANCOVA, adjusting for age, sex, medication exposure, treatment intensity and seizures per subject. RESULTS Clinical seizures demonstrated greater functional connectivity than subclinical seizures at alpha frequencies, but less connectivity than subclinical seizures at delta frequencies. Clinical seizures also demonstrated significantly higher median global efficiency than subclinical seizures (p < 0.01), and significantly higher median clustering coefficients across all electrodes at alpha frequencies. CONCLUSIONS Clinical expression of seizures correlates with greater alpha synchronization of distributed brain networks. SIGNIFICANCE The stronger global and local alpha-mediated functional connectivity observed during clinical seizures may indicate greater pathological network recruitment. These observations motivate further studies to investigate whether the clinical expression of seizures may influence their potential to cause secondary brain injury.
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Affiliation(s)
- Laura K Koster
- Division of Neurology, The Hospital for Sick Children and Department of Paediatrics, University of Toronto, Toronto, Canada; Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada
| | - Rouzbeh Zamyadi
- Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada
| | - Luowei Yan
- Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada
| | - Eric T Payne
- Department of Pediatrics, Section of Neurology, Alberta Children's Hospital and University of Calgary, Calgary, Canada
| | - Kristin L McBain
- MAP Centre for Urban Health Solutions, Unity Health Toronto, Toronto, Canada
| | - Benjamin T Dunkley
- Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada; Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Canada; Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Cecil D Hahn
- Division of Neurology, The Hospital for Sick Children and Department of Paediatrics, University of Toronto, Toronto, Canada; Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada.
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Matos J, Peralta G, Heyse J, Menetre E, Seeck M, van Mierlo P. Diagnosis of Epilepsy with Functional Connectivity in EEG after a Suspected First Seizure. Bioengineering (Basel) 2022; 9:690. [PMID: 36421091 PMCID: PMC9687589 DOI: 10.3390/bioengineering9110690] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/30/2022] [Accepted: 10/04/2022] [Indexed: 09/29/2023] Open
Abstract
Epilepsy is regarded as a structural and functional network disorder, affecting around 50 million people worldwide. A correct disease diagnosis can lead to quicker medical action, preventing adverse effects. This paper reports the design of a classifier for epilepsy diagnosis in patients after a first ictal episode, using electroencephalogram (EEG) recordings. The dataset consists of resting-state EEG from 629 patients, of which 504 were retained for the study. The patient's cohort exists out of 291 patients with epilepsy and 213 patients with other pathologies. The data were split into two sets: 80% training set and 20% test set. The extracted features from EEG included functional connectivity measures, graph measures, band powers and brain asymmetry ratios. Feature reduction was performed, and the models were trained using Machine Learning (ML) techniques. The models' evaluation was performed with the area under the receiver operating characteristic curve (AUC). When focusing specifically on focal lesional epileptic patients, better results were obtained. This classification task was optimized using a 5-fold cross-validation, where SVM using PCA for feature reduction achieved an AUC of 0.730 ± 0.030. In the test set, the same model achieved 0.649 of AUC. The verified decrease is justified by the considerable diversity of pathologies in the cohort. An analysis of the selected features across tested models shows that functional connectivity and its graph measures have the most considerable predictive power, along with full-spectrum frequency-based features. To conclude, the proposed algorithms, with some refinement, can be of added value for doctors diagnosing epilepsy from EEG recordings after a suspected first seizure.
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Affiliation(s)
- João Matos
- Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Guilherme Peralta
- Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
| | - Jolan Heyse
- Department of Electronics and Information Systems, Ghent University, 9000 Ghent, Belgium
| | - Eric Menetre
- EEG and Epilepsy Unit, University Hospital of Geneva, 1205 Geneva, Switzerland
| | - Margitta Seeck
- EEG and Epilepsy Unit, University Hospital of Geneva, 1205 Geneva, Switzerland
| | - Pieter van Mierlo
- Department of Electronics and Information Systems, Ghent University, 9000 Ghent, Belgium
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Piper RJ, Richardson RM, Worrell G, Carmichael DW, Baldeweg T, Litt B, Denison T, Tisdall MM. Towards network-guided neuromodulation for epilepsy. Brain 2022; 145:3347-3362. [PMID: 35771657 PMCID: PMC9586548 DOI: 10.1093/brain/awac234] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 05/30/2022] [Accepted: 06/16/2022] [Indexed: 11/30/2022] Open
Abstract
Epilepsy is well-recognized as a disorder of brain networks. There is a growing body of research to identify critical nodes within dynamic epileptic networks with the aim to target therapies that halt the onset and propagation of seizures. In parallel, intracranial neuromodulation, including deep brain stimulation and responsive neurostimulation, are well-established and expanding as therapies to reduce seizures in adults with focal-onset epilepsy; and there is emerging evidence for their efficacy in children and generalized-onset seizure disorders. The convergence of these advancing fields is driving an era of 'network-guided neuromodulation' for epilepsy. In this review, we distil the current literature on network mechanisms underlying neurostimulation for epilepsy. We discuss the modulation of key 'propagation points' in the epileptogenic network, focusing primarily on thalamic nuclei targeted in current clinical practice. These include (i) the anterior nucleus of thalamus, now a clinically approved and targeted site for open loop stimulation, and increasingly targeted for responsive neurostimulation; and (ii) the centromedian nucleus of the thalamus, a target for both deep brain stimulation and responsive neurostimulation in generalized-onset epilepsies. We discuss briefly the networks associated with other emerging neuromodulation targets, such as the pulvinar of the thalamus, piriform cortex, septal area, subthalamic nucleus, cerebellum and others. We report synergistic findings garnered from multiple modalities of investigation that have revealed structural and functional networks associated with these propagation points - including scalp and invasive EEG, and diffusion and functional MRI. We also report on intracranial recordings from implanted devices which provide us data on the dynamic networks we are aiming to modulate. Finally, we review the continuing evolution of network-guided neuromodulation for epilepsy to accelerate progress towards two translational goals: (i) to use pre-surgical network analyses to determine patient candidacy for neurostimulation for epilepsy by providing network biomarkers that predict efficacy; and (ii) to deliver precise, personalized and effective antiepileptic stimulation to prevent and arrest seizure propagation through mapping and modulation of each patients' individual epileptogenic networks.
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Affiliation(s)
- Rory J Piper
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | | | | | - Torsten Baldeweg
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Brian Litt
- Department of Neurology and Bioengineering, University of Pennsylvania, Philadelphia, USA
| | | | - Martin M Tisdall
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
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Mercier MR, Dubarry AS, Tadel F, Avanzini P, Axmacher N, Cellier D, Vecchio MD, Hamilton LS, Hermes D, Kahana MJ, Knight RT, Llorens A, Megevand P, Melloni L, Miller KJ, Piai V, Puce A, Ramsey NF, Schwiedrzik CM, Smith SE, Stolk A, Swann NC, Vansteensel MJ, Voytek B, Wang L, Lachaux JP, Oostenveld R. Advances in human intracranial electroencephalography research, guidelines and good practices. Neuroimage 2022; 260:119438. [PMID: 35792291 DOI: 10.1016/j.neuroimage.2022.119438] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022] Open
Abstract
Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
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11
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Gastrointestinal and Autonomic Symptoms—How to Improve the Diagnostic Process in Panayiotopoulos Syndrome? CHILDREN 2022; 9:children9060814. [PMID: 35740751 PMCID: PMC9222198 DOI: 10.3390/children9060814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 05/26/2022] [Accepted: 05/30/2022] [Indexed: 11/29/2022]
Abstract
One of the most common epileptic disorders in the pediatric population is Panayiotopoulos syndrome. Clinical manifestations of this idiopathic illness include predominantly autonomic symptoms and dysfunction of the cardiorespiratory system. Another feature constitutes prolonged seizures that usually occur at sleep. It is crucial to differentiate the aforementioned disease from other forms of epilepsy, especially occipital and structural epilepsy and non-epileptic disorders. The diagnostic process is based on medical history, clinical examination, neuroimaging and electroencephalography—though results of the latter may be unspecific. Patients with Panayiotopoulos syndrome (PS) do not usually require treatment, as the course of the disease is, in most cases, mild, and the prognosis is good. The purpose of this review is to underline the role of central autonomic network dysfunction in the development of Panayiotopoulos syndrome, as well as the possibility of using functional imaging techniques, especially functional magnetic resonance imaging (fMRI), in the diagnostic process. These methods could be crucial for understanding the pathogenesis of PS. More data arerequired to create algorithms that will be able to predict the exposure to various complications of PS. It also concerns the importance of electroencephalography (EEG) as a tool to distinguish Panayiotopoulos syndrome from other childhood epileptic syndromes and non-epileptic disorders.
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Sun Y, Li Y, Sun J, Zhang K, Tang L, Wu C, Gao Y, Liu H, Huang S, Hu Z, Xiang J, Wang X. Functional reorganization of brain regions into a network in childhood absence epilepsy: A magnetoencephalography study. Epilepsy Behav 2021; 122:108117. [PMID: 34246893 DOI: 10.1016/j.yebeh.2021.108117] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 05/24/2021] [Accepted: 05/25/2021] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Epilepsy is considered as a network disorder. However, it is unknown how normal brain activity develops into the highly synchronized discharging activity seen in disordered networks. This study aimed to explore the epilepsy brain network and the significant re-combined brain areas in childhood absence epilepsy (CAE). METHODS Twenty-two children with CAE were recruited to study the neural source activity during ictal-onset and interictal periods at frequency bands of 1-30 Hz and 30-80 Hz with magnetoencephalography (MEG) scanning. Accumulated source imaging (ASI) was used to analyze the locations of neural source activity and peak source strength. RESULTS Most of the participants had more active source activity locations in the ictal-onset period rather than in the interictal period, both at 1-30 Hz and 30-80 Hz. The frontal lobe (FL), the temporo-parietal junction (T-P), and the parietal lobe (PL) became the main active areas of source activity during the ictal period, while the precuneus (PC), cuneus, and thalamus were relatively inactive. CONCLUSIONS Some brain areas become more excited and have increased source activity during seizures. These significant brain regions might be re-combined to form an epilepsy network that regulates the process of absence seizures. SIGNIFICANCE The study confirmed that important brain regions are reorganized in an epilepsy network, which provides a basis for exploring the network mechanism of CAE development. Imaging findings may provide a reference for clinical characteristics.
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Affiliation(s)
- Yulei Sun
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Yihan Li
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Jintao Sun
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Ke Zhang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Lu Tang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Caiyun Wu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Yuan Gao
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Hongxing Liu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Shuyang Huang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Zheng Hu
- Department of Neurology, Nanjing Children's Hospital, Nanjing, Jiangsu 210029, China
| | - Jing Xiang
- Division of Neurology, MEG Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Xiaoshan Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu 210029, China.
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Intracranial EEG seizure onset and termination patterns and their association. Epilepsy Res 2021; 176:106739. [PMID: 34455176 DOI: 10.1016/j.eplepsyres.2021.106739] [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: 04/20/2021] [Revised: 07/19/2021] [Accepted: 08/12/2021] [Indexed: 11/24/2022]
Abstract
OBJECTIVE The study of seizure onset and termination patterns has the potential to enhance our understanding of the underlying mechanisms of seizure generation and cessation. It is largely unclear whether seizures with distinct onset patterns originate from varying network interactions and terminate through different termination pathways. METHODS We investigated the morphology and location of 103 intracranial EEG seizure onset and termination patterns from 20 patients with drug-resistant focal epilepsy. We determined if seizure onset patterns were associated with specific termination patterns. Finally, we looked at network interactions prior to the generation of distinct seizure onset patterns by calculating directed functional connectivity matrices. RESULTS We identified nine seizure onset and six seizure termination patterns. The most common onset pattern was Low-Voltage Fast Activity (36 %), and the most frequent termination pattern was Burst Suppression (44 %). All seizures with fast (>13 Hz) termination patterns had a fast (>13 Hz) onset pattern type. Almost any onset pattern could terminate with the Burst Suppression and rhythmic Spike/PolySpike and Wave (rSW/rPSW) termination patterns. Seizures with a fast activity onset had higher inflow to the seizure onset zone from other regions in the gamma and high gamma frequency ranges prior to their generation compared to seizures with a slow onset. SIGNIFICANCE Our observations suggest that different mechanisms underlie the generation of different seizure onset patterns although seizure onset patterns can share a common termination pattern. Possible mechanisms underlying these patterns are discussed.
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14
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Hijacking of hippocampal-cortical oscillatory coupling during sleep in temporal lobe epilepsy. Epilepsy Behav 2021; 121:106608. [PMID: 31740330 DOI: 10.1016/j.yebeh.2019.106608] [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: 09/05/2019] [Revised: 10/11/2019] [Accepted: 10/11/2019] [Indexed: 11/21/2022]
Abstract
Memory impairment is the most common cognitive deficit in patients with temporal lobe epilepsy (TLE). This type of epilepsy is currently regarded as a network disease because of its brain-wide alterations in functional connectivity between temporal and extra-temporal regions. In patients with TLE, network dysfunctions can be observed during ictal states, but are also described interictally during rest or sleep. Here, we examined the available literature supporting the hypothesis that hippocampal-cortical coupling during sleep is hijacked in TLE. First, we look at studies showing that the coordination between hippocampal sharp-wave ripples (100-200 Hz), corticothalamic spindles (9-16 Hz), and cortical delta waves (1-4 Hz) during nonrapid eye movement (NREM) sleep is critical for spatial memory consolidation. Then, we reviewed studies showing that animal models of TLE display precise coordination between hippocampal interictal epileptiform discharges (IEDs) and spindle oscillations in the prefrontal cortex. This aberrant oscillatory coupling seems to surpass the physiological ripple-delta-spindle coordination, which could underlie memory consolidation impairments. We also discuss the role of rapid eye movement (REM) sleep for local synaptic plasticity and memory. Sleep episodes of REM provide windows of opportunity for reactivation of expression of immediate early genes (i.e., zif-268 and Arc). Besides, hippocampal theta oscillations during REM sleep seem to be critical for memory consolidation of novel object place recognition task. However, it is still unclear which extend this particular phase of sleep is affected in TLE. In this context, we show some preliminary results from our group, suggesting that hippocampal theta-gamma phase-amplitude coupling is exacerbated during REM in a model of basolateral amygdala fast kindling. In conclusion, there is an increasing body of evidence suggesting that circuits responsible for memory consolidation during sleep seem to be gradually coopted and degraded in TLE. This article is part of the Special Issue "NEWroscience 2018".
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15
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Dai XJ, Yang Y, Wang N, Tao W, Fan J, Wang Y. Reliability and availability of granger causality density in localization of Rolandic focus in BECTS. Brain Imaging Behav 2021; 15:1542-1552. [PMID: 32737823 DOI: 10.1007/s11682-020-00352-0] [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] [Indexed: 11/26/2022]
Abstract
A new method, called granger causality density (GCD), could reflect the directed information flow of the epileptiform activity, which is much closely match with excitatory and inhibitory imbalance theory of epilepsy. Here, we investigated if GCD could effectively localize the Rolandic focus in 50 patients with benign childhood epilepsy with central-temporal spikes (BECTS) from 27 normal children. The BECTS were classified into ictal epileptiform discharges (IEDs; 12 females, 15 males;age, 8.15 ± 1.68 years) and non-IEDs (10 females, 13 males; age, 9.09 ± 1.98 years) subgroups depending on the presence of central-temporal spikes. Multiple correlation-modality analyses (Pearson, across-voxel and across-subject correlations) were used to calculate the couplings between the GCD maps and IEDs-related brain activation map. The individual lateralization coefficient of localize IEDs and multiple regression analysis were used to identify the reliability of the GCD method in localizing the Rolandic focus. In this study, multiple correlation-modality analyses showed that the IEDs-related brain activation map and the GCD maps had highly temporal (coefficient ׀r\= 0.56 ~ 0.65) and spatial (\r\=0.53~0.91) (r\=~ couplings. The proposed GCD method and multiple regression analyses showed consistent findings with the clinical EEG recordings in lateralization of Rolandic focus. Furthermore, the GCD method could reflect the epilepsy-related brain activity during non-IEDs substate. Therefore, the proposed GCD method has the potential to be served as an effective and reliable neuroimaging biomarker to localize the Rolandic focus of BECTS. These findings are critical for clinical early diagnosis, and may promote the progression of treatment and management of pediatric epilepsy.
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Affiliation(s)
- Xi-Jian Dai
- Shenzhen Mental Health Centre, Shenzhen Kangning Hospital, Shenzhen, 518003, China.
| | - Yang Yang
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, 563000, China
| | - Na Wang
- Shenzhen Mental Health Centre, Shenzhen Kangning Hospital, Shenzhen, 518003, China
| | - Weiqun Tao
- Shenzhen Mental Health Centre, Shenzhen Kangning Hospital, Shenzhen, 518003, China
| | - Jingyi Fan
- Shenzhen Mental Health Centre, Shenzhen Kangning Hospital, Shenzhen, 518003, China
| | - Yongjun Wang
- Shenzhen Mental Health Centre, Shenzhen Kangning Hospital, Shenzhen, 518003, China.
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16
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Jaseja H. Pedunculopontine Nucleus--Rapid Eye Movement Sleep--Electroencephalogram--Desynchronization (PRED) Axis in the Evolution of Epilepsy: A Novel Concept. J Epilepsy Res 2021; 11:1-5. [PMID: 34395217 PMCID: PMC8357554 DOI: 10.14581/jer.21001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 02/28/2021] [Accepted: 03/07/2021] [Indexed: 11/03/2022] Open
Abstract
Epilepsy is one of the commonest and oldest neurological diseases in the history of mankind, the exact pathophysiology of the evolution of which still remains elusive. The intimate and intriguing relation between epilepsy and sleep has been known for a long time. Rapid eye movement sleep (REMS) is well documented to exert potent antiepileptic action in human epilepsies and the underlying mechanism of which is largely based on its property to induce widespread electroencephalogram (EEG)-desynchronization. The pedunculopontine nucleus (PPN) owing to its property to enhance REMS has recently been under study for its potential role in intractable epilepsy (IE) and has been proposed as a novel deep brain stimulation target in IE. This brief paper unfolds the existing role of PPN, REMS, and EEG-desynchronization (PRED) in the evolution of epilepsy in an axial manner, the realization and comprehension of which is likely to open new avenues for further understanding of epileptogenesis, improved treatment of epilepsy and reducing the risk of IE.
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17
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Altered spontaneous brain activity in patients with childhood absence epilepsy: associations with treatment effects. Neuroreport 2021; 31:613-618. [PMID: 32366812 DOI: 10.1097/wnr.0000000000001447] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The study aims to detect resting-state functional MRI (RS-fMRI) changes and their relationships with the clinical treatment effects of anti-epileptic drugs (AEDs) for patients with childhood absence epilepsy (CAE) using the fractional amplitude of low-frequency fluctuation (fALFF). RS-fMRI data from 30 CAE patients were collected and compared with findings from 30 healthy controls (HCs) with matched sex and age. Patients were treated with first-line AEDs for 46.2 months before undergoing a second RS-fMRI scan. fALFF data were processed using DPABI and SPM12 software. Compared with the HCs, CAE patients at baseline showed increased fALFF in anterior cingulate cortex, inferior parietal lobule, inferior frontal lobule, supplementary motor area and reduced fALFF in putamen and thalamus. At follow-up, the fALFF showed a clear rebound which indicated a normalization of spontaneous brain activities in these regions. In addition, the fALFF changes within thalamus showed significant positive correlation with the seizure frequency improvements. Our results suggest that specific cortical and subcortical regions are involved in seizure generation and the neurological impairments found in CAE children and might shed new light about the AEDs effects on CAE patients.
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18
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Bosl WJ, Leviton A, Loddenkemper T. Prediction of Seizure Recurrence. A Note of Caution. Front Neurol 2021; 12:675728. [PMID: 34054713 PMCID: PMC8155381 DOI: 10.3389/fneur.2021.675728] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 04/20/2021] [Indexed: 12/31/2022] Open
Abstract
Great strides have been made recently in documenting that machine-learning programs can predict seizure occurrence in people who have epilepsy. Along with this progress have come claims that appear to us to be a bit premature. We anticipate that many people will benefit from seizure prediction. We also doubt that all will benefit. Although machine learning is a useful tool for aiding discovery, we believe that the greatest progress will come from deeper understanding of seizures, epilepsy, and the EEG features that enable seizure prediction. In this essay, we lay out reasons for optimism and skepticism.
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Affiliation(s)
- William J Bosl
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States.,Health Informatics Program, University of San Francisco, San Francisco, CA, United States
| | - Alan Leviton
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Tobias Loddenkemper
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
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19
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Jiang S, Li H, Pei H, Liu L, Li Z, Chen Y, Li X, Li Q, Yao D, Luo C. Connective profiles and antagonism between dynamic and static connectivity underlying generalized epilepsy. Brain Struct Funct 2021; 226:1423-1435. [PMID: 33730218 DOI: 10.1007/s00429-021-02248-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 02/27/2021] [Indexed: 11/28/2022]
Abstract
This study aims to characterize the connective profiles and the coupling relationship between dynamic and static functional connectivity (dFC and sFC) in large-scale brain networks in patients with generalized epilepsy (GE). Functional, structural and diffuse MRI data were collected from 83 patients with GE and 106 matched healthy controls (HC). Resting-state BOLD time course was deconvolved to neural time course using a blind hemodynamic deconvolution method. Then, five connective profiles, including the structural connectivity (SC) and BOLD/neural time course-derived sFC/dFC networks, were constructed based on the proposed whole brain atlas. Network-level weighted correlation probability (NWCP) were proposed to evaluate the association between dFC and sFC. Both the BOLD signal and neural time course showed highly concordant findings and the present study emphasized the consistent findings between two functional approaches. The patients with GE showed hypervariability and enhancement of FC, and notably decreased SC in the subcortical network. Besides, increased dFC, weaker anatomic links, and complex alterations of sFC were observed in the default mode network of GE. Moreover, significantly increased SC and predominantly increased sFC were found in the frontoparietal network. Remarkably, antagonism between dFC and sFC was observed in large-scale networks in HC, while patients with GE showed significantly decreased antagonism in core epileptic networks. In sum, our study revealed distinct connective profiles in different epileptic networks and provided new clues to the brain network mechanism of epilepsy from the perspective of antagonism between dynamic and static functional connectivity.
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Affiliation(s)
- Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Hechun Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Haonan Pei
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Linli Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Zhiliang Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Yan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Xiangkui Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China
| | - Qifu Li
- Department of Neurology, First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China.,Department of Neurology, First Affiliated Hospital of Hainan Medical University, Haikou, China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, 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, People's Republic of China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Qingshuihe Campus: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, People's Republic of China. .,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, 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, People's Republic of China.
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20
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Zhang Y, Huang G, Liu M, Li M, Wang Z, Wang R, Yang D. Functional and structural connective disturbance of the primary and default network in patients with generalized tonic-clonic seizures. Epilepsy Res 2021; 174:106595. [PMID: 33993017 DOI: 10.1016/j.eplepsyres.2021.106595] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 02/20/2021] [Accepted: 02/23/2021] [Indexed: 01/27/2023]
Abstract
OBJECTIVE The present study aims to investigate the disturbance of functional and structural profiles of patients with generalized tonic-clonic seizures (GTCS). METHODS Resting-state fMRI and diffusion tensor imaging (DTI) data was collected from fifty-six patients and sixty-two healthy controls. Degree centrality (DC) of functional connectivity was first calculated and compared between groups using a two-sample t-test. Furthermore, the regions with significant alteration of DC in patients with GTCS were used as nodes to construct the brain network. Functional connectivity (FC) network was constructed using the Person's correlation analysis and structural connectivity (SC) network was obtained using deterministic tractography technology. Gray matter volume (GMV) and cortical thickness (CT) were computed and correlated with connective profiles. RESULTS The patients with GTCS showed increased DC in the primary network (PN), including bilateral precentral gyrus, supplementary motor areas (SMA), and visual cortex, and decreased DC in core regions of default mode network (DMN), bilateral anterior insular, and supramarginal gyrus. In the present study, 14 regions were identified to construct networks. In patients, the FC and SC were increased within the sensorimotor network (mainly linking with SMA) and decreased within DMN (mainly linking with the posterior cingulate cortex (PCC)). Except for the decreased FC and SC between cerebellum and SMA, patients demonstrated increased connectivity between DMN and PN. Besides, the insula demonstrated decreased FC with DMN and increased FC with PN, without significant SC alterations in patients with GTCS. Decreased GMV in bilateral thalamus and increased GMV in frontoparietal regions were found in patients. The decreased GMV of thalamus and increased GMV of SMA positively and negatively correlated with the FC between PCC and left superior frontal cortex, the FC between SMA and left precuneus respectively. CONCLUSION Hyper-connectivity within PN helps to understand the disturbance of primary functions, especially the motor abnormality in GTCS. The hypo-connectivity within DMN suggested abnormal network organization possibly related to epileptogenesis. Moreover, over-interaction between DMN and PN and unbalanced connectivity between them and insula provided potential evidence reflecting abnormal interactions between primary and high-order function systems.
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Affiliation(s)
- Yaodan Zhang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, PR China; Chengdu University of Traditional Chinese Medicine Affiliated Fifth People's Hospital, Chengdu, PR China
| | - Gengzhen Huang
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, PR China
| | - Meijun Liu
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, PR China
| | - Mao Li
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, PR China
| | - Zhiqiang Wang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, PR China
| | - Rongyu Wang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, PR China
| | - Dongdong Yang
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, PR China.
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Jiang S, Pei H, Huang Y, Chen Y, Liu L, Li J, He H, Yao D, Luo C. Dynamic Temporospatial Patterns of Functional Connectivity and Alterations in Idiopathic Generalized Epilepsy. Int J Neural Syst 2020; 30:2050065. [PMID: 33161788 DOI: 10.1142/s0129065720500653] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The dynamic profile of brain function has received much attention in recent years and is also a focus in the study of epilepsy. The present study aims to integrate the dynamics of temporal and spatial characteristics to provide comprehensive and novel understanding of epileptic dynamics. Resting state fMRI data were collected from eighty-three patients with idiopathic generalized epilepsy (IGE) and 87 healthy controls (HC). Specifically, we explored the temporal and spatial variation of functional connectivity density (tvFCD and svFCD) in the whole brain. Using a sliding-window approach, for a given region, the standard variation of the FCD series was calculated as the tvFCD and the variation of voxel-wise spatial distribution was calculated as the svFCD. We found primary, high-level, and sub-cortical networks demonstrated distinct tvFCD and svFCD patterns in HC. In general, the high-level networks showed the highest variation, the subcortical and primary networks showed moderate variation, and the limbic system showed the lowest variation. Relative to HC, the patients with IGE showed weaken temporal and enhanced spatial variation in the default mode network and weaken temporospatial variation in the subcortical network. Besides, enhanced temporospatial variation in sensorimotor and high-level networks was also observed in patients. The hyper-synchronization of specific brain networks was inferred to be associated with the phenomenon responsible for the intrinsic propensity of generation and propagation of epileptic activities. The disrupted dynamic characteristics of sensorimotor and high-level networks might potentially contribute to the driven motion and cognition phenotypes in patients. In all, presently provided evidence from the temporospatial variation of functional interaction shed light on the dynamics underlying neuropathological profiles of epilepsy.
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Affiliation(s)
- Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Haonan Pei
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Yang Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Yan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Linli Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Jianfu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu P. R. China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu P. R. China
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22
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Iannotti GR, Preti MG, Grouiller F, Carboni M, De Stefano P, Pittau F, Momjian S, Carmichael D, Centeno M, Seeck M, Korff CM, Schaller K, De Ville DV, Vulliemoz S. Modulation of epileptic networks by transient interictal epileptic activity: A dynamic approach to simultaneous EEG-fMRI. NEUROIMAGE-CLINICAL 2020; 28:102467. [PMID: 33395963 PMCID: PMC7645285 DOI: 10.1016/j.nicl.2020.102467] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 09/15/2020] [Accepted: 10/09/2020] [Indexed: 12/27/2022]
Abstract
EEG-fMRI has been instrumental in characterizing brain networks in epilepsy. Its value is documented in the pre-surgical assessment of drug-resistant epilepsy. The delineation of brain areas to resect is fundamental for the post-surgical outcome. Standard EEG-fMRI in epilepsy assesses static functional connectivity of the network. EEG-fMRI dynamic connectivity identifies transitory features of specific connections. We integrate dynamic fMRI connectivity and dynamic patterns of simultaneous scalp EEG. This allows to better characterize the spatiotemporal aspects of epileptic networks. This may help in more efficiently target the surgical intervention.
Epileptic networks, defined as brain regions involved in epileptic brain activity, have been mapped by functional connectivity in simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recordings. This technique allows to define brain hemodynamic changes, measured by the Blood Oxygen Level Dependent (BOLD) signal, associated to the interictal epileptic discharges (IED), which together with ictal events constitute a signature of epileptic disease. Given the highly time-varying nature of epileptic activity, a dynamic functional connectivity (dFC) analysis of EEG-fMRI data appears particularly suitable, having the potential to identify transitory features of specific connections in epileptic networks. In the present study, we propose a novel method, defined dFC-EEG, that integrates dFC assessed by fMRI with the information recorded by simultaneous scalp EEG, in order to identify the connections characterised by a dynamic profile correlated with the occurrence of IED, forming the dynamic epileptic subnetwork. Ten patients with drug-resistant focal epilepsy were included, with different aetiology and showing a widespread (or multilobar) BOLD activation, defined as involving at least two distinct clusters, located in two different lobes and/or extended to the hemisphere contralateral to the epileptic focus. The epileptic focus was defined from the IED-related BOLD map. Regions involved in the occurrence of interictal epileptic activity; i.e., forming the epileptic network, were identified by a general linear model considering the timecourse of the fMRI-defined focus as main regressor. dFC between these regions was assessed with a sliding-window approach. dFC timecourses were then correlated with the sliding-window variance of the IED signal (VarIED), to identify connections whose dynamics related to the epileptic activity; i.e., the dynamic epileptic subnetwork. As expected, given the very different clinical picture of each individual, the extent of this subnetwork was highly variable across patients, but was but was reduced of at least 30% with respect to the initially identified epileptic network in 9/10 patients. The connections of the dynamic subnetwork were most commonly close to the epileptic focus, as reflected by the laterality index of the subnetwork connections, reported higher than the one within the original epileptic network. Moreover, the correlation between dFC timecourses and VarIED was predominantly positive, suggesting a strengthening of the dynamic subnetwork associated to the occurrence of IED. The integration of dFC and scalp IED offers a more specific description of the epileptic network, identifying connections strongly influenced by IED. These findings could be relevant in the pre-surgical evaluation for the resection or disconnection of the epileptogenic zone and help in reaching a better post-surgical outcome. This would be particularly important for patients characterised by a widespread pathological brain activity which challenges the surgical intervention.
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Affiliation(s)
- G R Iannotti
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland; Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Switzerland; Neurosurgery, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland.
| | - M G Preti
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - F Grouiller
- Swiss Center for Affective Sciences, University of Geneva, Switzerland; Laboratory of Behavioral Neurology and Imaging of Cognition, Department of Fundamental Neurosciences, University of Geneva, Switzerland
| | - M Carboni
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland; Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Switzerland
| | - P De Stefano
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
| | - F Pittau
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland; Epilepsy Unit, Institution de Lavigny, Switzerland
| | - S Momjian
- Neurosurgery, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
| | - D Carmichael
- Biomedical Engineering Department, Kings College London, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, United Kingdom
| | - M Centeno
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, United Kingdom; Epilepsy Unit, Neurology Department, Clinica Universidad de Pamplona, Navarra, Spain
| | - M Seeck
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
| | - C M Korff
- Pediatric Neurology Unit, University Hospitals of Geneva, Geneva, Switzerland
| | - K Schaller
- Neurosurgery, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
| | - D Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - S Vulliemoz
- EEG and Epilepsy, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Switzerland
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23
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Kowalczyk MA, Omidvarnia A, Dhollander T, Jackson GD. Dynamic analysis of fMRI activation during epileptic spikes can help identify the seizure origin. Epilepsia 2020; 61:2558-2571. [PMID: 32954506 DOI: 10.1111/epi.16695] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 08/25/2020] [Accepted: 08/25/2020] [Indexed: 11/29/2022]
Abstract
OBJECTIVE We use the dynamic electroencephalography-functional magnetic resonance imaging (EEG-fMRI) method to incorporate variability in the amplitude and field of the interictal epileptic discharges (IEDs) into the fMRI analysis. We ask whether IED variability analysis can (a) identify additional activated brain regions during the course of IEDs, not seen in standard analysis; and (b) demonstrate the origin and spread of epileptic activity. We explore whether these functional changes recapitulate the structural connections and propagation of epileptic activity during seizures. METHODS Seventeen patients with focal epilepsy and at least 30 IEDs of a single type during simultaneous EEG-fMRI were studied. IED variability and EEG source imaging (ESI) analysis extracted time-varying dynamic changes. General linear modeling (GLM) generated static functional maps. Dynamic maps were compared to static functional maps. The dynamic sequence from IED variability was compared to the ESI results. In a subset of patients, we investigated structural connections between active brain regions using diffusion-based fiber tractography. RESULTS IED variability distinguished the origin of epileptic activity from its propagation in 15 of 17 (88%) patients. This included two cases where no result was obtained from the standard GLM analysis. In both of these cases, IED variability revealed activation in line with the presumed epileptic focus. Two cases showed no result from either method. Both had very high spike rates associated with dysplasia in the postcentral gyrus. In all 15 cases with dynamic activation, the observed dynamics were concordant with ESI. Fiber tractography identified specific white matter pathways between brain regions that were active at IED onset and propagation. SIGNIFICANCE Dynamic techniques involving IED variability can provide additional power for EEG-fMRI analysis, compared to standard analysis, revealing additional biologically plausible information in cases with no result from the standard analysis and gives insight into the origin and spread of IEDs.
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Affiliation(s)
- Magdalena A Kowalczyk
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne Vic., Australia
| | - Amir Omidvarnia
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne Vic., Australia.,Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Campus Biotech, Geneva, Switzerland.,Department of Radiology and Medical Informatics, Campus Biotech, University of Geneva, Geneva, Switzerland
| | - Thijs Dhollander
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne Vic., Australia.,Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Vic., Australia
| | - Graeme D Jackson
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne Vic., Australia.,Department of Neurology, Austin Health, Heidelberg, Vic., Australia
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24
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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: 21] [Impact Index Per Article: 5.3] [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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25
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Lapinlampi N, Andrade P, Paananen T, Hämäläinen E, Ekolle Ndode-Ekane X, Puhakka N, Pitkänen A. Postinjury weight rather than cognitive or behavioral impairment predicts development of posttraumatic epilepsy after lateral fluid-percussion injury in rats. Epilepsia 2020; 61:2035-2052. [PMID: 32786029 DOI: 10.1111/epi.16632] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 07/06/2020] [Accepted: 07/06/2020] [Indexed: 12/11/2022]
Abstract
OBJECTIVE To identify postinjury physiologic, behavioral, and cognitive biomarkers for posttraumatic epilepsy to enrich study populations for long-term antiepileptogenesis studies. METHODS The EPITARGET cohort with behavioral follow-up and 1-month 24/7 video-electroencephalography (vEEG) monitoring included 115 adult male Sprague-Dawley rats with lateral fluid-percussion-induced traumatic brain injury (TBI), 23 sham-operated controls, and 13 naive rats. Animals underwent assessment of somatomotor performance (composite neuroscore), anxiety-like behavior (elevated plus maze, open field), spatial memory (Morris water maze), and depression-like behavior (Porsolt forced swim, sucrose preference). Impact force, postimpact apnea time, postimpact seizure-like behavior, and body weight were monitored. RESULTS TBI rats were impaired in the composite neuroscore (P < .001) on days (D) 2-14 and in the spatial memory test (P < .001) on D35-39 post-TBI. Differences in the elevated plus-maze (D28 and D126) and in the open field (D29 and D127) between TBI rats and controls were meager. No differences were observed in the Porsolt forced swim and sucrose preference tests as compared with sham-operated controls. Epilepsy developed in 27% of rats by the end of the sixth month. None of the behavioral or cognitive outcome measures discriminated rats with or without epilepsy. The receiver-operating characteristic analysis indicated that a decrease in body weight between D0 and D4 differentiated TBI rats with epilepsy from TBI rats without epilepsy (48% sensitivity, 83% specificity, area under the curve [AUC] 0.679, confidence interval [CI] 95% 0.56-0.80, P < .01). A 16% body weight decrease during D0-D4 could be used as a biomarker to enrich the study population from 27% (observed) to 50%. SIGNIFICANCE Single behavioral and cognitive outcome measures showed no power as prognostic/diagnostic biomarkers for posttraumatic epilepsy. A reduction in body weight during the first postinjury week showed some prognostic value for posttraumatic epileptogenesis and could serve as a subacute measure for selectively enriching the study population for long-term preclinical biomarker and therapy discovery studies of posttraumatic epileptogenesis.
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Affiliation(s)
- Niina Lapinlampi
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Pedro Andrade
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Tomi Paananen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Elina Hämäläinen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | | | - Noora Puhakka
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Asla Pitkänen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
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26
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Carboni M, De Stefano P, Vorderwülbecke BJ, Tourbier S, Mullier E, Rubega M, Momjian S, Schaller K, Hagmann P, Seeck M, Michel CM, van Mierlo P, Vulliemoz S. Abnormal directed connectivity of resting state networks in focal epilepsy. NEUROIMAGE-CLINICAL 2020; 27:102336. [PMID: 32679553 PMCID: PMC7363703 DOI: 10.1016/j.nicl.2020.102336] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 06/30/2020] [Accepted: 07/01/2020] [Indexed: 11/03/2022]
Abstract
OBJECTIVE Epilepsy diagnosis can be difficult in the absence of interictal epileptic discharges (IED) on scalp EEG. We used high-density EEG to measure connectivity in large-scale functional networks of patients with focal epilepsy (Temporal and Extratemporal Lobe Epilepsy, TLE and ETLE) and tested for network alterations during resting wakefulness without IEDs, compared to healthy controls. We measured global efficiency as a marker of integration within networks. METHODS We analysed 49 adult patients with focal epilepsy and 16 healthy subjects who underwent high-density-EEG and structural MRI. We estimated cortical activity using electric source analysis in 82 atlas-based cortical regions based on the individual MRI. We applied directed connectivity analysis (Partial Directed Coherence) on these sources and performed graph analysis: we computed the Global Efficiency on the whole brain and on each resting state network. We tested these features in different group of patients. RESULTS Compared to controls, efficiency was increased in both TLE and ETLE (p < 0.05). The somato-motor-network, the ventral-attention-network and the default-mode-network had a significantly increased efficiency (p < 0.05) in both TLE and ETLE as well as TLE with hippocampal sclerosis. SIGNIFICANCE During interictal scalp EEG epochs without IED, patients with focal epilepsy show brain functional connectivity alterations in the whole brain and in specific resting-state-networks. This higher integration reflects a chronic effect of pathological activity within these structures and complement previous work on altered information outflow. These findings could increase the diagnostic sensitivity of scalp EEG to identify epileptic activity in the absence of IED.
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Affiliation(s)
- Margherita Carboni
- EEG and Epilepsy Unit, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland; Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland.
| | - Pia De Stefano
- EEG and Epilepsy Unit, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Bernd J Vorderwülbecke
- EEG and Epilepsy Unit, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland; Epilepsy-Center Berlin-Brandenburg, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sebastien Tourbier
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne, Lausanne, Switzerland
| | - Emeline Mullier
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne, Lausanne, Switzerland
| | - Maria Rubega
- Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland; Department of Neurosciences, University of Padova, Padova, Italy
| | - Shahan Momjian
- Department of Neurosurgery, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Karl Schaller
- Department of Neurosurgery, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Patric Hagmann
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne, Lausanne, Switzerland
| | - Margitta Seeck
- EEG and Epilepsy Unit, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Christoph M Michel
- Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - Pieter van Mierlo
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, Clinical Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
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27
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Lopes MA, Junges L, Woldman W, Goodfellow M, Terry JR. The Role of Excitability and Network Structure in the Emergence of Focal and Generalized Seizures. Front Neurol 2020; 11:74. [PMID: 32117033 PMCID: PMC7027568 DOI: 10.3389/fneur.2020.00074] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 01/21/2020] [Indexed: 01/12/2023] Open
Abstract
Epileptic seizures are generally classified as either focal or generalized. It had been traditionally assumed that focal seizures imply localized brain abnormalities, whereas generalized seizures involve widespread brain pathologies. However, recent evidence suggests that large-scale brain networks are involved in the generation of focal seizures, and generalized seizures can originate in localized brain regions. Herein we study how network structure and tissue heterogeneities underpin the emergence of focal and widespread seizure dynamics. Mathematical modeling of seizure emergence in brain networks enables the clarification of the characteristics responsible for focal and generalized seizures. We consider neural mass network dynamics of seizure generation in exemplar synthetic networks and we measure the variance in ictogenicity across the network. Ictogenicity is defined as the involvement of network nodes in seizure activity, and its variance is used to quantify whether seizure patterns are focal or widespread across the network. We address both the influence of network structure and different excitability distributions across the network on the ictogenic variance. We find that this variance depends on both network structure and excitability distribution. High variance, i.e., localized seizure activity, is observed in networks highly heterogeneous with regard to the distribution of connections or excitabilities. However, networks that are both heterogeneous in their structure and excitability can underlie the emergence of generalized seizures, depending on the interplay between structure and excitability. Thus, our results imply that the emergence of focal and generalized seizures is underpinned by an interplay between network structure and excitability distribution.
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Affiliation(s)
- Marinho A Lopes
- Living Systems Institute, University of Exeter, Exeter, United Kingdom.,Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom.,Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom.,Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Leandro Junges
- Living Systems Institute, University of Exeter, Exeter, United Kingdom.,Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom.,Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom.,Institute for Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom
| | - Wessel Woldman
- Living Systems Institute, University of Exeter, Exeter, United Kingdom.,Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom.,Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom.,Institute for Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, United Kingdom.,Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom
| | - John R Terry
- Living Systems Institute, University of Exeter, Exeter, United Kingdom.,Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom.,EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom.,Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom.,Institute for Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom
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28
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Nair S, Szaflarski JP. Neuroimaging of memory in frontal lobe epilepsy. Epilepsy Behav 2020; 103:106857. [PMID: 31937510 DOI: 10.1016/j.yebeh.2019.106857] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 12/04/2019] [Accepted: 12/13/2019] [Indexed: 10/25/2022]
Abstract
In a large percentage of epilepsies, seizures have focal onset. These epilepsies are associated with a wide range of behavioral and cognitive deficits sometimes limited to the functions encompassed within the ictal onset zone but, more frequently, expanding beyond it. The presence of impairments associated with neuroanatomical areas outside of the ictal onset zone suggests distal propagation of epileptic activity via brain networks and interconnected whole-brain neural circuitry. In patients with frontal lobe epilepsy (FLE), using functional magnetic resonance imaging (fMRI) to identify deficits in working, semantic, and episodic memory may provide a lens through which to understand typical and atypical network organization. A network approach to focal epilepsy is relevant in these patients because of the frequently noted early age of seizure onset. Early seizure-related disruption in healthy brain development may result in a significant brain reorganization, development of compensation-related mechanisms of dealing with function abnormalities and disruptions, and the propagation of epileptic activity from the focus to widespread brain areas (functional deficit zones). Benefits of a network approach in the study of focal epilepsy are discussed along with considerations for future neuroimaging studies of patients with FLE.
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Affiliation(s)
- Sangeeta Nair
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA.
| | - Jerzy P Szaflarski
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA; Department of Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA; Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL, USA
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29
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Zhang Z, Zhou X, Liu J, Qin L, Ye W, Zheng J. Aberrant executive control networks and default mode network in patients with right-sided temporal lobe epilepsy: a functional and effective connectivity study. Int J Neurosci 2019; 130:683-693. [PMID: 31851554 DOI: 10.1080/00207454.2019.1702545] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Objective: We aimed to explore functional connectivity (FC) and effective connectivity (EC) of the executive control networks (ECNs) and the default mode network (DMN) in patients with right-sided TLE (rTLE) by applying independent component analysis (ICA) and Granger causal analysis (GCA).Methods: Twenty-seven patients with rTLE and 20 healthy controls (HCs) matched for age, gender underwent resting-state functional magnetic resonance imaging and Attention Network Test (ANT).Results: The FC analysis showed compared to HCs, patients with rTLE demonstrated reduced FC strength in the right inferior parietal gyrus (IPG) and the right middle temporal gyrus (MTG). The left superior temporal gyrus (STG) displayed reduced FC values whereas the left thalamus revealed increased FC values in rTLE. ROI-wise GCA revealed that patients with rTLE displayed increased EC from the left thalamus to the left STG, and as well as enhanced EC from the right IPG to the right MTG compared to HCs. Voxel-wise GCA showed positive EC from the left thalamus to the left insula while the right middle occipital gyrus (MOG) exhibited increased EC to the right MTG in patients. The ANT results demonstrated executive dysfunction in patients compared to HCs. The increased FC in the left thalamus showed a negative association with ECF in patients.Conclusion: We speculated that recurrent seizures take effect on disruption among the brain networks, and self-modulation occurs simultaneously to compensate for cognitive decline. Our findings revealed new insights on the neuropathophysiological mechanisms of rTLE.
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Affiliation(s)
- Zhao Zhang
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xia Zhou
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jinping Liu
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Lu Qin
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Wei Ye
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jinou Zheng
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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30
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Kowalczyk MA, Omidvarnia A, Abbott DF, Tailby C, Vaughan DN, Jackson GD. Clinical benefit of presurgical EEG‐fMRI in difficult‐to‐localize focal epilepsy: A single‐institution retrospective review. Epilepsia 2019; 61:49-60. [DOI: 10.1111/epi.16399] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 11/05/2019] [Accepted: 11/06/2019] [Indexed: 01/18/2023]
Affiliation(s)
- Magdalena A. Kowalczyk
- The Florey Institute of Neuroscience and Mental Health Heidelberg Australia
- The Florey Department of Neuroscience and Mental Health Faculty of Medicine Dentistry and Health Sciences University of Melbourne Parkville Australia
| | - Amir Omidvarnia
- The Florey Institute of Neuroscience and Mental Health Heidelberg Australia
- The Florey Department of Neuroscience and Mental Health Faculty of Medicine Dentistry and Health Sciences University of Melbourne Parkville Australia
| | - David F. Abbott
- The Florey Institute of Neuroscience and Mental Health Heidelberg Australia
- The Florey Department of Neuroscience and Mental Health Faculty of Medicine Dentistry and Health Sciences University of Melbourne Parkville Australia
| | - Chris Tailby
- The Florey Institute of Neuroscience and Mental Health Heidelberg Australia
| | - David N. Vaughan
- The Florey Institute of Neuroscience and Mental Health Heidelberg Australia
- Department of Neurology Austin Health Heidelberg Australia
| | - Graeme D. Jackson
- The Florey Institute of Neuroscience and Mental Health Heidelberg Australia
- The Florey Department of Neuroscience and Mental Health Faculty of Medicine Dentistry and Health Sciences University of Melbourne Parkville Australia
- Department of Neurology Austin Health Heidelberg Australia
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31
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Dai XJ, Xu Q, Hu J, Zhang Q, Xu Y, Zhang Z, Lu G. BECTS Substate Classification by Granger Causality Density Based Support Vector Machine Model. Front Neurol 2019; 10:1201. [PMID: 31798523 PMCID: PMC6868120 DOI: 10.3389/fneur.2019.01201] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 10/28/2019] [Indexed: 12/30/2022] Open
Abstract
Objectives: To investigate the performance of substate classification of children with benign epilepsy with centrotemporal spikes (BECTS) by granger causality density (GCD) based support vector machine (SVM) model. Methods: Forty-two children with BECTS (21 females, 21 males; mean age, 8.6 ± 1.96 years) were classified into interictal epileptic discharges (IEDs; 11 females, 10 males) and non-IEDs (10 females, 11 males) substates depending on presence of central-temporal spikes or not. GCD was calculated on four metrics, including inflow, outflow, total-flow (inflow + outflow) and int-flow (inflow – outflow) connectivity. SVM classifier was applied to discriminate the two substates. Results: The Rolandic area, caudate, dorsal attention network, visual cortex, language networks, and cerebellum had discriminative effect on distinguishing the two substates. Relative to each of the four GCD metrics, using combined metrics could reach up the classification performance (best value; AUC, 0.928; accuracy rate, 90.83%; sensitivity, 90%; specificity, 95%), especially for the combinations with more than three GCD metrics. Specially, combined the inflow, outflow and int-flow metric received the best classification performance with the highest AUC value, classification accuracy and specificity. Furthermore, the GCD-SVM model received good and stable classification performance across 14 dimension reduced data sets. Conclusions: The GCD-SVM model could be used for BECTS substate classification, which might have the potential to provide a promising model for IEDs detection. This may help assist clinicians for administer drugs and prognosis evaluation.
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Affiliation(s)
- Xi-Jian Dai
- Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.,Shenzhen Mental Health Centre, Shenzhen Kangning Hospital, Shenzhen, China.,Sleep Assessment Unit, Department of Psychiatry, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Qiang Xu
- Shenzhen Mental Health Centre, Shenzhen Kangning Hospital, Shenzhen, China
| | - Jianping Hu
- Shenzhen Mental Health Centre, Shenzhen Kangning Hospital, Shenzhen, China
| | - QiRui Zhang
- Shenzhen Mental Health Centre, Shenzhen Kangning Hospital, Shenzhen, China
| | - Yin Xu
- Shenzhen Mental Health Centre, Shenzhen Kangning Hospital, Shenzhen, China
| | - Zhiqiang Zhang
- Shenzhen Mental Health Centre, Shenzhen Kangning Hospital, Shenzhen, China
| | - Guangming Lu
- Shenzhen Mental Health Centre, Shenzhen Kangning Hospital, Shenzhen, China
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Precursors of seizures due to specific spatial-temporal modifications of evolving large-scale epileptic brain networks. Sci Rep 2019; 9:10623. [PMID: 31337840 PMCID: PMC6650408 DOI: 10.1038/s41598-019-47092-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 07/10/2019] [Indexed: 12/25/2022] Open
Abstract
Knowing when, where, and how seizures are initiated in large-scale epileptic brain networks remains a widely unsolved problem. Seizure precursors – changes in brain dynamics predictive of an impending seizure – can now be identified well ahead of clinical manifestations, but either the seizure onset zone or remote brain areas are reported as network nodes from which seizure precursors emerge. We aimed to shed more light on the role of constituents of evolving epileptic networks that recurrently transit into and out of seizures. We constructed such networks from more than 3200 hours of continuous intracranial electroencephalograms recorded in 38 patients with medication refractory epilepsy. We succeeded in singling out predictive edges and predictive nodes. Their particular characteristics, namely edge weight respectively node centrality (a fundamental concept of network theory), from the pre-ictal periods of 78 out of 97 seizures differed significantly from the characteristics seen during inter-ictal periods. The vast majority of predictive nodes were connected by most of the predictive edges, but these nodes never played a central role in the evolving epileptic networks. Interestingly, predictive nodes were entirely associated with brain regions deemed unaffected by the focal epileptic process. We propose a network mechanism for a transition into the pre-seizure state, which puts into perspective the role of the seizure onset zone in this transition and highlights the necessity to reassess current concepts for seizure generation and seizure prevention.
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Abstract
Epilepsy is a chronic neurological condition, following some trigger, transforming a normal brain to one that produces recurrent unprovoked seizures. In the search for the mechanisms that best explain the epileptogenic process, there is a growing body of evidence suggesting that the epilepsies are network level disorders. In this review, we briefly describe the concept of neuronal networks and highlight 2 methods used to analyse such networks. The first method, graph theory, is used to describe general characteristics of a network to facilitate comparison between normal and abnormal networks. The second, dynamic causal modelling, is useful in the analysis of the pathways of seizure spread. We concluded that the end results of the epileptogenic process are best understood as abnormalities of neuronal circuitry and not simply as molecular or cellular abnormalities. The network approach promises to generate new understanding and more targeted treatment of epilepsy.
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Affiliation(s)
- Aminu T Abdullahi
- Department of Psychiatry, Aminu Kano Teaching Hospital, Kano, Nigeria
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Zangiabadi N, Ladino LD, Sina F, Orozco-Hernández JP, Carter A, Téllez-Zenteno JF. Deep Brain Stimulation and Drug-Resistant Epilepsy: A Review of the Literature. Front Neurol 2019; 10:601. [PMID: 31244761 PMCID: PMC6563690 DOI: 10.3389/fneur.2019.00601] [Citation(s) in RCA: 97] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Accepted: 05/21/2019] [Indexed: 01/08/2023] Open
Abstract
Introduction: Deep brain stimulation is a safe and effective neurointerventional technique for the treatment of movement disorders. Electrical stimulation of subcortical structures may exert a control on seizure generators initiating epileptic activities. The aim of this review is to present the targets of the deep brain stimulation for the treatment of drug-resistant epilepsy. Methods: We performed a structured review of the literature from 1980 to 2018 using Medline and PubMed. Articles assessing the impact of deep brain stimulation on seizure frequency in patients with DRE were selected. Meta-analyses, randomized controlled trials, and observational studies were included. Results: To date, deep brain stimulation of various neural targets has been investigated in animal experiments and humans. This article presents the use of stimulation of the anterior and centromedian nucleus of the thalamus, hippocampus, basal ganglia, cerebellum and hypothalamus. Anterior thalamic stimulation has demonstrated efficacy and there is evidence to recommend it as the target of choice. Conclusion: Deep brain stimulation for seizures may be an option in patients with drug-resistant epilepsy. Anterior thalamic nucleus stimulation could be recommended over other targets.
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Affiliation(s)
- Nasser Zangiabadi
- Shefa Neuroscience Research Center, Khatam Alanbia Hospital, Tehran, Iran
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
| | - Lady Diana Ladino
- Epilepsy Program, Hospital Pablo Tobón Uribe, Neuroclinica, University of Antioquia, Medellín, Colombia
| | - Farzad Sina
- Department of Neurology, Rasool Akram Hospital, IUMS, Tehran, Iran
| | - Juan Pablo Orozco-Hernández
- Departamento de Investigación Clínica, Facultad de Ciencias de la Salud, Universidad Tecnológica de Pereira-Clínica Comfamiliar, Pereira, Colombia
| | - Alexandra Carter
- Saskatchewan Epilepsy Program, Department of Medicine, University of Saskatchewan, Saskatoon, SK, Canada
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Atefi SR, Serano P, Poulsen C, Angelone LM, Bonmassar G. Numerical and Experimental Analysis of Radiofrequency-Induced Heating Versus Lead Conductivity During EEG-MRI at 3 T. IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY 2019; 61:852-859. [PMID: 31210669 PMCID: PMC6579539 DOI: 10.1109/temc.2018.2840050] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
This study investigates radiofrequency (RF)-induced heating in a head model with a 256-channel electroencephalogram (EEG) cap during magnetic resonance imaging (MRI). Nine computational models were implemented each with different EEG lead electrical conductivity, ranging from 1 to 5.8 × 107 S/m. The peak values of specific absorption rate (SAR) averaged over different volumes were calculated for each lead conductivity. Experimental measurements were also performed at 3-T MRI with a Gracilaria Lichenoides (GL) phantom with and without a low-conductive EEG lead cap ("InkNet"). The simulation results showed that SAR was a nonlinear function of the EEG lead conductivity. The experimental results were in line with the numerical simulations. Specifically, there was a ΔT of 1.7 °C in the GL phantom without leads compared to ΔT of 1.8 °C calculated with the simulations. Additionally, there was a ΔT of 1.5 °C in the GL phantom with the InkNet compared to a ΔT of 1.7 °C in the simulations with a cap of similar conductivity. The results showed that SAR is affected by specific location, number of electrodes, and the volume of tissue considered. As such, SAR averaged over the whole head, or even SAR averaged over volumes of 1 or 0.1 g, may conceal significant heating effects and local analysis of RF heating (in terms of peak SAR and temperature) is needed.
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Affiliation(s)
- Seyed Reza Atefi
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129 USA, and also with the University of Boras 50190, Boras Sweden
| | - Peter Serano
- Division of Biomedical Physics, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, U.S. Food and Drug Administration, Silver Spring, MD 11401 USA
| | | | - Leonardo M Angelone
- Division of Biomedical Physics, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, U.S. Food and Drug Administration, Silver Spring, MD 11401 USA
| | - Giorgio Bonmassar
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129 USA
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Coito A, Biethahn S, Tepperberg J, Carboni M, Roelcke U, Seeck M, van Mierlo P, Gschwind M, Vulliemoz S. Interictal epileptogenic zone localization in patients with focal epilepsy using electric source imaging and directed functional connectivity from low-density EEG. Epilepsia Open 2019; 4:281-292. [PMID: 31168495 PMCID: PMC6546067 DOI: 10.1002/epi4.12318] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 02/25/2019] [Accepted: 03/15/2019] [Indexed: 01/23/2023] Open
Abstract
OBJECTIVE Electrical source imaging (ESI) is used increasingly to estimate the epileptogenic zone (EZ) in patients with epilepsy. Directed functional connectivity (DFC) coupled to ESI helps to better characterize epileptic networks, but studies on interictal activity have relied on high-density recordings. We investigated the accuracy of ESI and DFC for localizing the EZ, based on low-density clinical electroencephalography (EEG). METHODS We selected patients with the following: (a) focal epilepsy, (b) interictal spikes on standard EEG, (c) either a focal structural lesion concordant with the electroclinical semiology or good postoperative outcome. In 34 patients (20 temporal lobe epilepsy [TLE], 14 extra-TLE [ETLE]), we marked interictal spikes and estimated the cortical activity during each spike in 82 cortical regions using a patient-specific head model and distributed linear inverse solution. DFC between brain regions was computed using Granger-causal modeling followed by network topologic measures. The concordance with the presumed EZ at the sublobar level was computed using the epileptogenic lesion or the resected area in postoperative seizure-free patients. RESULTS ESI, summed outflow, and efficiency were concordant with the presumed EZ in 76% of the patients, whereas the clustering coefficient and betweenness centrality were concordant in 70% of patients. There was no significant difference between ESI and connectivity measures. In all measures, patients with TLE had a significantly higher (P < 0.05) concordance with the presumed EZ than patients with with ETLE. The brain volume accepted for concordance was significantly larger in TLE. SIGNIFICANCE ESI and DFC derived from low-density EEG can reliably estimate the EZ from interictal spikes. Connectivity measures were not superior to ESI for EZ localization during interictal spikes, but the current validation of the localization of connectivity measure is promising for other applications.
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Affiliation(s)
- Ana Coito
- Department of NeurologyCantonal Hospital AarauAarauSwitzerland
| | - Silke Biethahn
- Department of NeurologyCantonal Hospital AarauAarauSwitzerland
| | | | | | - Ulrich Roelcke
- Department of Neurology and Brain Tumor CenterCantonal Hospital AarauAarauSwitzerland
| | - Margitta Seeck
- Department of NeurologyUniversity Hospital GenevaGenevaSwitzerland
| | - Pieter van Mierlo
- Department of Electronics and Information SystemsGhent UniversityGhentBelgium
| | - Markus Gschwind
- Department of NeurologyCantonal Hospital AarauAarauSwitzerland
| | - Serge Vulliemoz
- Department of NeurologyUniversity Hospital GenevaGenevaSwitzerland
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Gharaylou Z, Shafaghi L, Oghabian MA, Yoonessi A, Tafakhori A, Shahsavand Ananloo E, Hadjighassem M. Longitudinal Effects of Bumetanide on Neuro-Cognitive Functioning in Drug-Resistant Epilepsy. Front Neurol 2019; 10:483. [PMID: 31133976 PMCID: PMC6517515 DOI: 10.3389/fneur.2019.00483] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 04/23/2019] [Indexed: 12/25/2022] Open
Abstract
Antiepileptic drugs (AEDs) have repeatedly shown inconsistent and almost contradictory effects on the neurocognitive system, from substantial impairments in processing speed to the noticeable improvement in working memory and executive functioning. Previous studies have provided a novel insight into the cognitive improvement by bumetanide as a potential antiepileptic drug. Through the current investigation, we evaluated the longitudinal effects of bumetanide, an NKCC1 co-transporter antagonist, on the brain microstructural organization as a probable underlying component for cognitive performance. Microstructure assessment was completed using SPM for the whole brain assay and Freesurfer/TRACULA for the automatic probabilistic tractography analysis. Primary cognitive operations including selective attention and processing speed, working memory capacity and spatial memory were evaluated in 12 patients with a confirmed diagnosis of refractory epilepsy. Participants treated with bumetanide (2 mg/ day) in two divided doses as an adjuvant therapy to their regular AEDs for 6 months, which followed by the re-assessment of their cognitive functions and microstructural organizations. Seizure frequency reduced in eight patients which accompanied by white matter reconstruction; fractional anisotropy (FA) increased in the cingulum-cingulate gyrus (CCG), anterior thalamic radiation (ATR), and temporal part of the superior longitudinal fasciculus (SLFt) in correlation with the clinical response. The voxel-based analysis in responder patients revealed increased FA in the left hippocampus, right cerebellum, and right medial temporal lobe, while mean diffusivity (MD) values reduced in the right occipital lobe and cerebellum. Microstructural changes in SLFt and ATR accompanied by a reduction in the error rate in the spatial memory test. These primary results have provided preliminary evidence for the effect of bumetanide on cognitive functioning through microstructural changes in patients with drug-resistant epilepsy.
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Affiliation(s)
- Zeinab Gharaylou
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Lida Shafaghi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Oghabian
- Neuroimaging and Analysis Group, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Yoonessi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Abbas Tafakhori
- Imam Khomeini Hospital, Iranian Center of Neurological Research, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Mahmoudreza Hadjighassem
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
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Xiong X, Fu Y, Chen J, Liu L, Zhang X. Single-Trial Recognition of Imagined Forces and Speeds of Hand Clenching Based on Brain Topography and Brain Network. Brain Topogr 2019; 32:240-254. [PMID: 30599076 PMCID: PMC6373301 DOI: 10.1007/s10548-018-00696-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 12/20/2018] [Indexed: 01/16/2023]
Abstract
To provide optional force and speed control parameters for brain-computer interfaces (BCIs), an effective feature extraction method of imagined force and speed of hand clenching based on electroencephalography (EEG) was explored. Twenty subjects were recruited to participate in the experiment. They were instructed to perform three different actual/imagined hand clenching force tasks (4 kg, 10 kg, and 16 kg) and three different hand clenching speed tasks (0.5 Hz, 1 Hz, and 2 Hz). Topographical maps parameters and brain network parameters of EEG were calculated as new features of imagined force and speed of hand clenching, which were classified by three classifiers: linear discrimination analysis, extreme learning machines and support vector machines. Topographical maps parameters were better for recognition of the hand clenching force task, while brain network parameters were better for recognition of the hand clenching speed task. After a combination of five types of features (energy, power spectrum of the autoregressive model, wavelet packet coefficients, topographical maps parameters and brain network parameters), the recognition rate of the hand clenching force task was 97%, and that of the hand clenching speed task was as high as 100%. The brain topographical and the brain network parameters are expected to improve the accuracy of decoding the EEG signal of imagined force and speed of hand clenching. A more efficient brain network may facilitate the recognition of force/speed of hand clenching. Combined features could significantly improve the single-trial recognition rate of imagined forces and speeds of hand clenching. The current study provides a new idea for the imagined force and speed of hand clenching that offers more control intention instructions for BCIs.
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Affiliation(s)
- Xin Xiong
- School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming, 650500, People's Republic of China
| | - Yunfa Fu
- School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming, 650500, People's Republic of China.
| | - Jian Chen
- School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming, 650500, People's Republic of China
| | - Lijun Liu
- School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming, 650500, People's Republic of China
| | - Xiabing Zhang
- School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming, 650500, People's Republic of China
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Abstract
PURPOSE OF REVIEW MRI has a crucial position in the diagnostic routine of epilepsy patients. It relevantly contributes to etiological diagnostics and is indispensable in presurgical evaluation. As modern MRI research has been a boon to clinical neuroscience in general, it also holds the promise of enhancing diagnostics of epilepsy patients; i.e. increasing the diagnostic yield while decreasing the number of MRI-negative patients. Its rapid progress, however, has caused uncertainty about which of its latest developments already are of clinical interest and which still are of academic interest. It is the purpose of this review to clarify what, to the authors' mind, good practice of MRI in epilepsy patient care is today and what it might be tomorrow. RECENT FINDINGS Progress of diagnostic MRI in epilepsy patients is driven by development of scanner hardware, scanner sequence and data postprocessing. Ultra high-field MRI and elaborate sequences provide datasets of novel quality which can be fed into postprocessing programs extracting pathognomonic features of structural or functional anatomy. The integration of these features by means of computerized classifiers yield previously unsurpassed diagnostic validity. Enthusiasm about Diffusion Tensor Imaging and functional MRI in the evaluation before epilepsy surgery is quelled. SUMMARY The application of an epilepsy tailored MRI protocol at 3 Tesla followed by meticulous expert evaluation early after the onset of epilepsy is most crucial. It is hoped that future research will result in MRI workups more standardized than today and widely used postprocessing routines analyzing co-registered three-dimensional volumes from different modalities.
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Olmi S, Petkoski S, Guye M, Bartolomei F, Jirsa V. Controlling seizure propagation in large-scale brain networks. PLoS Comput Biol 2019; 15:e1006805. [PMID: 30802239 PMCID: PMC6405161 DOI: 10.1371/journal.pcbi.1006805] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 03/07/2019] [Accepted: 01/18/2019] [Indexed: 01/26/2023] Open
Abstract
Information transmission in the human brain is a fundamentally dynamic network process. In partial epilepsy, this process is perturbed and highly synchronous seizures originate in a local network, the so-called epileptogenic zone (EZ), before recruiting other close or distant brain regions. We studied patient-specific brain network models of 15 drug-resistant epilepsy patients with implanted stereotactic electroencephalography (SEEG) electrodes. Each personalized brain model was derived from structural data of magnetic resonance imaging (MRI) and diffusion tensor weighted imaging (DTI), comprising 88 nodes equipped with region specific neural mass models capable of demonstrating a range of epileptiform discharges. Each patient's virtual brain was further personalized through the integration of the clinically hypothesized EZ. Subsequent simulations and connectivity modulations were performed and uncovered a finite repertoire of seizure propagation patterns. Across patients, we found that (i) patient-specific network connectivity is predictive for the subsequent seizure propagation pattern; (ii) seizure propagation is characterized by a systematic sequence of brain states; (iii) propagation can be controlled by an optimal intervention on the connectivity matrix; (iv) the degree of invasiveness can be significantly reduced via the proposed seizure control as compared to traditional resective surgery. To stop seizures, neurosurgeons typically resect the EZ completely. We showed that stability analysis of the network dynamics, employing structural and dynamical information, estimates reliably the spatiotemporal properties of seizure propagation. This suggests novel less invasive paradigms of surgical interventions to treat and manage partial epilepsy.
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Affiliation(s)
- Simona Olmi
- Inria Sophia Antipolis Méditerranée Research Centre, MathNeuro Team, 2004 route des Lucioles-Boîte Postale 93 06902 Sophia Antipolis, Cedex, France
- CNR - Consiglio Nazionale delle Ricerche - Istituto dei Sistemi Complessi, 50019, Sesto Fiorentino, Italy
| | - Spase Petkoski
- Aix Marseille Université, Inserm, Institut de Neurosciences des Systèmes, UMR_S 1106, 13005, Marseille, France
| | - 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, 13005, Marseille, France
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Pôle d’Imagerie, CHU, 13005, Marseille, France
| | - Fabrice Bartolomei
- Assistance Publique - Hôpitaux de Marseille, Hôpital de la Timone, Service de Neurophysiologie Clinique, CHU, 13005 Marseille, France
| | - Viktor Jirsa
- Aix Marseille Université, Inserm, Institut de Neurosciences des Systèmes, UMR_S 1106, 13005, Marseille, France
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Abreu R, Leal A, Figueiredo P. Identification of epileptic brain states by dynamic functional connectivity analysis of simultaneous EEG-fMRI: a dictionary learning approach. Sci Rep 2019; 9:638. [PMID: 30679773 PMCID: PMC6345787 DOI: 10.1038/s41598-018-36976-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Accepted: 11/30/2018] [Indexed: 12/12/2022] Open
Abstract
Most fMRI studies of the brain's intrinsic functional connectivity (FC) have assumed that this is static; however, it is now clear that it changes over time. This is particularly relevant in epilepsy, which is characterized by a continuous interchange between epileptic and normal brain states associated with the occurrence of epileptic activity. Interestingly, recurrent states of dynamic FC (dFC) have been found in fMRI data using unsupervised learning techniques, assuming either their sparse or non-sparse combination. Here, we propose an l1-norm regularized dictionary learning (l1-DL) approach for dFC state estimation, which allows an intermediate and flexible degree of sparsity in time, and demonstrate its application in the identification of epilepsy-related dFC states using simultaneous EEG-fMRI data. With this l1-DL approach, we aim to accommodate a potentially varying degree of sparsity upon the interchange between epileptic and non-epileptic dFC states. The simultaneous recording of the EEG is used to extract time courses representative of epileptic activity, which are incorporated into the fMRI dFC state analysis to inform the selection of epilepsy-related dFC states. We found that the proposed l1-DL method performed best at identifying epilepsy-related dFC states, when compared with two alternative methods of extreme sparsity (k-means clustering, maximum; and principal component analysis, minimum), as well as an l0-norm regularization framework (l0-DL), with a fixed amount of temporal sparsity. We further showed that epilepsy-related dFC states provide novel insights into the dynamics of epileptic networks, which go beyond the information provided by more conventional EEG-correlated fMRI analysis, and which were concordant with the clinical profile of each patient. In addition to its application in epilepsy, our study provides a new dFC state identification method of potential relevance for studying brain functional connectivity dynamics in general.
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Affiliation(s)
- Rodolfo Abreu
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
| | - Alberto Leal
- Department of Neurophysiology, Centro Hospitalar Psiquiátrico de Lisboa, Lisbon, Portugal
| | - Patrícia Figueiredo
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
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Multifocal epilepsy in children is associated with increased long-distance functional connectivity: An explorative EEG-fMRI study. Eur J Paediatr Neurol 2018; 22:1054-1065. [PMID: 30017619 DOI: 10.1016/j.ejpn.2018.07.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 06/12/2018] [Accepted: 07/01/2018] [Indexed: 01/22/2023]
Abstract
OBJECTIVE Multifocal epileptic activity is an unfavourable feature of a number of epileptic syndromes (Lennox-Gastaut syndrome, West syndrome, severe focal epilepsies) which suggests an overall vulnerability of the brain to pathological synchronization. However, the mechanisms of multifocal activity are insufficiently understood. This explorative study investigates whether pathological connectivity within brain areas of the default mode network as well as thalamus, brainstem and retrosplenial cortex may predispose individuals to multifocal epileptic activity. METHODS 33 children suffering from multifocal and monofocal (control group) epilepsies were investigated using EEG-fMRI recordings during sleep. The blood oxygenated level dependent (BOLD) signal of 15 regions of interest was extracted and temporally correlated (resting-state functional connectivity). RESULTS Patients with monofocal epilepsies were characterized by strong correlations between the corresponding interhemispheric homotopic regions. This pattern of correlations with pronounced short-distance and weak long-distance functional connectivity resembles the connectivity pattern described for healthy children. Patients with multifocal epileptic activity, however, demonstrated significantly stronger correlations between a large number of regions of the default mode network as well as thalamus and brainstem, with a significant increase in long-distance connectivity compared to children with monofocal epileptic activity. In the group of patients with multifocal epilepsies there were no differences in functional connectivity between patients with or without Lennox-Gastaut syndrome. CONCLUSION This explorative study shows that multifocal activity is associated with generally increased long-distance functional connectivity in the brain. It can be suggested that this pronounced connectivity may represent either a risk to pathological over-synchronization or a consequence of the multifocal epileptic activity.
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Markoula S, Chaudhary UJ, Perani S, De Ciantis A, Yadee T, Duncan JS, Diehl B, McEvoy AW, Lemieux L. The impact of mapping interictal discharges using EEG-fMRI on the epilepsy presurgical clinical decision making process: A prospective study. Seizure 2018; 61:30-37. [PMID: 30059825 DOI: 10.1016/j.seizure.2018.07.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Revised: 03/27/2018] [Accepted: 07/20/2018] [Indexed: 10/28/2022] Open
Abstract
PURPOSE We set out to establish the clinical utility of EEG-correlated fMRI as part of the presurgical evaluation, by measuring prospectively its effects on the clinical decision. METHODS Patients with refractory extra-temporal focal epilepsy, referred for presurgical evaluation were recruited in a period of 18 months. The EEG-fMRI based localization was presented during a multi-disciplinary meeting after the team had defined the presumed RESULTS: Sixteen patients (six women), with a median age of 28 years, were recruited. Interpretable EEG-fMRI results were available in 13: interictal epileptic discharges (IEDs) were recorded in eleven patients and seizures were recorded in two patients. In three patients, no epileptic activity was captured during EEG-fMRI acquisition and in two of those an IED topographic map correlation was performed (between EEG recorded inside the scanner and long-term video EEG monitoring). EEG-fMRI results presentation had no impact on the initial clinical decision in three patients (23%) of the thirteen and resulted in a modification of the initial surgical plan in ten patients (77%) of the thirteen finally presented in MDT; in eight patients the impact was on the planned placement of invasive electrodes and in two patients the EEG-fMRI led to additional non-invasive tests before proceeding further with surgery. CONCLUSION The study is a prospective observational cohort study specifically designed to assess the impact of EEG-fMRI on the clinical decision making process, suggesting a significant influence of EEG-fMRI on epilepsy surgery planning.
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Affiliation(s)
- Sofia Markoula
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK; MRI Unit, Epilepsy Society, Chalfont St. Peter, Buckinghamshire, UK; Neurology Department, University Hospital of Ioannina, Ioannina, Greece.
| | - Umair J Chaudhary
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK; MRI Unit, Epilepsy Society, Chalfont St. Peter, Buckinghamshire, UK; Department of Clinical Neuroscience, Western General Hospital, Edinburgh, UK
| | - Suejen Perani
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK
| | - Alessio De Ciantis
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK; MRI Unit, Epilepsy Society, Chalfont St. Peter, Buckinghamshire, UK
| | - Tinonkorn Yadee
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK; MRI Unit, Epilepsy Society, Chalfont St. Peter, Buckinghamshire, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK; MRI Unit, Epilepsy Society, Chalfont St. Peter, Buckinghamshire, UK
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK
| | - Andrew W McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK
| | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK; MRI Unit, Epilepsy Society, Chalfont St. Peter, Buckinghamshire, UK
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Hassan M, Mheich A, Biraben A, Merlet I, Wendling F. Identification of epileptogenic networks from dense EEG: A model-based study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2015:5610-3. [PMID: 26737564 DOI: 10.1109/embc.2015.7319664] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Epilepsy is a network disease. Identifying the epileptogenic networks from noninvasive recordings is a challenging issue. In this context, M/EEG source connectivity is a promising tool to identify brain networks with high temporal and spatial resolution. In this paper, we analyze the impact of the two main factors that intervene in EEG source connectivity processing: i) the algorithm used to solve the EEG inverse problem and ii) the method used to measure the functional connectivity. We evaluate three inverse solutions algorithms (dSPM, wMNE and cMEM) and three connectivity measures (r(2), h(2) and MI) on data simulated from a combined biophysical/physiological model able to generate realistic interictal epileptic spikes reflected in scalp EEG. The performance criterion used here is the similarity between the network identified by each of the inverse/connectivity combination and the original network used in the source model. Results show that the choice of the combination has a high impact on the identified network. Results suggest also that nonlinear methods (nonlinear correlation coefficient and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The dSPM as inverse solution shows the lowest performance compared to cMEM and wMNE.
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Dynamic functional disturbances of brain network in seizure-related cognitive outcomes. Epilepsy Res 2018; 140:15-21. [DOI: 10.1016/j.eplepsyres.2017.12.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 11/13/2017] [Accepted: 12/02/2017] [Indexed: 11/23/2022]
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Sahib AK, Erb M, Marquetand J, Martin P, Elshahabi A, Klamer S, Vulliemoz S, Scheffler K, Ethofer T, Focke NK. Evaluating the impact of fast-fMRI on dynamic functional connectivity in an event-based paradigm. PLoS One 2018; 13:e0190480. [PMID: 29357371 PMCID: PMC5777653 DOI: 10.1371/journal.pone.0190480] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Accepted: 12/15/2017] [Indexed: 01/08/2023] Open
Abstract
The human brain is known to contain several functional networks that interact dynamically. Therefore, it is desirable to analyze the temporal features of these networks by dynamic functional connectivity (dFC). A sliding window approach was used in an event-related fMRI (visual stimulation using checkerboards) to assess the impact of repetition time (TR) and window size on the temporal features of BOLD dFC. In addition, we also examined the spatial distribution of dFC and tested the feasibility of this approach for the analysis of interictal epileptiforme discharges. 15 healthy controls (visual stimulation paradigm) and three patients with epilepsy (EEG-fMRI) were measured with EPI-fMRI. We calculated the functional connectivity degree (FCD) by determining the total number of connections of a given voxel above a predefined threshold based on Pearson correlation. FCD could capture hemodynamic changes relative to stimulus onset in controls. A significant effect of TR and window size was observed on FCD estimates. At a conventional TR of 2.6 s, FCD values were marginal compared to FCD values using sub-seconds TRs achievable with multiband (MB) fMRI. Concerning window sizes, a specific maximum of FCD values (inverted u-shape behavior) was found for each TR, indicating a limit to the possible gain in FCD for increasing window size. In patients, a dynamic FCD change was found relative to the onset of epileptiform EEG patterns, which was compatible with their clinical semiology. Our findings indicate that dynamic FCD transients are better detectable with sub-second TR than conventional TR. This approach was capable of capturing neuronal connectivity across various regions of the brain, indicating a potential to study the temporal characteristics of interictal epileptiform discharges and seizures in epilepsy patients or other brain diseases with brief events.
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Affiliation(s)
- Ashish Kaul Sahib
- Werner Reichardt Centre for Integrative Neuroscience, Tuebingen, Germany
- Department of Biomedical Magnetic Resonance, University Hospital Tuebingen, Tuebingen, Germany
- Department of Neurology/Epileptology, University Hospital Tuebingen and Hertie Institute of Clinical Brain Research, Tuebingen, Germany
- Graduate School of Neural and Behavioural Sciences/International Max Planck Research School, University of Tuebingen, Tuebingen, Germany
| | - Michael Erb
- Department of Biomedical Magnetic Resonance, University Hospital Tuebingen, Tuebingen, Germany
| | - Justus Marquetand
- Department of Neurology/Epileptology, University Hospital Tuebingen and Hertie Institute of Clinical Brain Research, Tuebingen, Germany
| | - Pascal Martin
- Department of Neurology/Epileptology, University Hospital Tuebingen and Hertie Institute of Clinical Brain Research, Tuebingen, Germany
| | - Adham Elshahabi
- Department of Neurology/Epileptology, University Hospital Tuebingen and Hertie Institute of Clinical Brain Research, Tuebingen, Germany
- MEG-Center, University of Tuebingen, Tuebingen, Germany
| | - Silke Klamer
- Department of Neurology/Epileptology, University Hospital Tuebingen and Hertie Institute of Clinical Brain Research, Tuebingen, Germany
| | - Serge Vulliemoz
- Department of Neurology, University Hospital of Geneva, Geneva, Switzerland
| | - Klaus Scheffler
- Department of Biomedical Magnetic Resonance, University Hospital Tuebingen, Tuebingen, Germany
- Max-Planck-Institute for Biological Cybernetics, Tuebingen, Germany
| | - Thomas Ethofer
- Werner Reichardt Centre for Integrative Neuroscience, Tuebingen, Germany
- Department of Biomedical Magnetic Resonance, University Hospital Tuebingen, Tuebingen, Germany
| | - Niels K. Focke
- Werner Reichardt Centre for Integrative Neuroscience, Tuebingen, Germany
- Department of Neurology/Epileptology, University Hospital Tuebingen and Hertie Institute of Clinical Brain Research, Tuebingen, Germany
- Clinical Neurophysiology, University Medicine, Goettingen, Germany
- * E-mail:
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Tung JK, Shiu FH, Ding K, Gross RE. Chemically activated luminopsins allow optogenetic inhibition of distributed nodes in an epileptic network for non-invasive and multi-site suppression of seizure activity. Neurobiol Dis 2018; 109:1-10. [PMID: 28923596 PMCID: PMC5696076 DOI: 10.1016/j.nbd.2017.09.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2017] [Revised: 09/05/2017] [Accepted: 09/14/2017] [Indexed: 01/06/2023] Open
Abstract
Although optogenetic techniques have proven to be invaluable for manipulating and understanding complex neural dynamics over the past decade, they still face practical and translational challenges in targeting networks involving multiple, large, or difficult-to-illuminate areas of the brain. We utilized inhibitory luminopsins to simultaneously inhibit the dentate gyrus and anterior nucleus of the thalamus of the rat brain in a hardware-independent and cell-type specific manner. This approach was more effective at suppressing behavioral seizures than inhibition of the individual structures in a rat model of epilepsy. In addition to elucidating mechanisms of seizure suppression never directly demonstrated before, this work also illustrates how precise multi-focal control of pathological circuits can be advantageous for the treatment and understanding of disorders involving broad neural circuits such as epilepsy.
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Affiliation(s)
- Jack K Tung
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States; Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, United States
| | - Fu Hung Shiu
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, United States
| | - Kevin Ding
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, United States
| | - Robert E Gross
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States; Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, United States.
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Rosenow F, van Alphen N, Becker A, Chiocchetti A, Deichmann R, Deller T, Freiman T, Freitag CM, Gehrig J, Hermsen AM, Jedlicka P, Kell C, Klein KM, Knake S, Kullmann DM, Liebner S, Norwood BA, Omigie D, Plate K, Reif A, Reif PS, Reiss Y, Roeper J, Ronellenfitsch MW, Schorge S, Schratt G, Schwarzacher SW, Steinbach JP, Strzelczyk A, Triesch J, Wagner M, Walker MC, von Wegner F, Bauer S. Personalized translational epilepsy research - Novel approaches and future perspectives: Part I: Clinical and network analysis approaches. Epilepsy Behav 2017; 76:13-18. [PMID: 28917501 DOI: 10.1016/j.yebeh.2017.06.041] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 06/05/2017] [Indexed: 01/01/2023]
Abstract
Despite the availability of more than 15 new "antiepileptic drugs", the proportion of patients with pharmacoresistant epilepsy has remained constant at about 20-30%. Furthermore, no disease-modifying treatments shown to prevent the development of epilepsy following an initial precipitating brain injury or to reverse established epilepsy have been identified to date. This is likely in part due to the polyetiologic nature of epilepsy, which in turn requires personalized medicine approaches. Recent advances in imaging, pathology, genetics and epigenetics have led to new pathophysiological concepts and the identification of monogenic causes of epilepsy. In the context of these advances, the First International Symposium on Personalized Translational Epilepsy Research (1st ISymPTER) was held in Frankfurt on September 8, 2016, to discuss novel approaches and future perspectives for personalized translational research. These included new developments and ideas in a range of experimental and clinical areas such as deep phenotyping, quantitative brain imaging, EEG/MEG-based analysis of network dysfunction, tissue-based translational studies, innate immunity mechanisms, microRNA as treatment targets, functional characterization of genetic variants in human cell models and rodent organotypic slice cultures, personalized treatment approaches for monogenic epilepsies, blood-brain barrier dysfunction, therapeutic focal tissue modification, computational modeling for target and biomarker identification, and cost analysis in (monogenic) disease and its treatment. This report on the meeting proceedings is aimed at stimulating much needed investments of time and resources in personalized translational epilepsy research. Part I includes the clinical phenotyping and diagnostic methods, EEG network-analysis, biomarkers, and personalized treatment approaches. In Part II, experimental and translational approaches will be discussed (Bauer et al., 2017) [1].
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Affiliation(s)
- Felix Rosenow
- Epilepsy Center Frankfurt Rhine-Main, Department of Neurology, Center of Neurology and Neurosurgery, Goethe University Frankfurt, 60528 Frankfurt, Germany; Epilepsy Center Marburg, Department of Neurology, Philipps-University Marburg, 35043 Marburg, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1).
| | - Natascha van Alphen
- Epilepsy Center Frankfurt Rhine-Main, Department of Neurology, Center of Neurology and Neurosurgery, Goethe University Frankfurt, 60528 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Albert Becker
- Institute for Neuropathology, University Bonn, 53105 Bonn, Germany
| | - Andreas Chiocchetti
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Goethe University Frankfurt, 60528 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Ralf Deichmann
- Brain Imaging Center (BIC) Frankfurt, Department of Neurology, Center of Neurology and Neurosurgery, Goethe University Frankfurt, 60528 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Thomas Deller
- Institute of Clinical Neuroanatomy, Dr. Senckenberg Anatomy, Neuroscience Center, Goethe University Frankfurt, 60590 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Thomas Freiman
- Epilepsy Center Frankfurt Rhine-Main, Department of Neurology, Center of Neurology and Neurosurgery, Goethe University Frankfurt, 60528 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Christine M Freitag
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Goethe University Frankfurt, 60528 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Johannes Gehrig
- Emmy-Noether Group Kell, Department of Neurology, Center of Neurology and Neurosurgery, Goethe University Frankfurt, 60528 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Anke M Hermsen
- Epilepsy Center Frankfurt Rhine-Main, Department of Neurology, Center of Neurology and Neurosurgery, Goethe University Frankfurt, 60528 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Peter Jedlicka
- Institute of Clinical Neuroanatomy, Dr. Senckenberg Anatomy, Neuroscience Center, Goethe University Frankfurt, 60590 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Christian Kell
- Emmy-Noether Group Kell, Department of Neurology, Center of Neurology and Neurosurgery, Goethe University Frankfurt, 60528 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Karl Martin Klein
- Epilepsy Center Frankfurt Rhine-Main, Department of Neurology, Center of Neurology and Neurosurgery, Goethe University Frankfurt, 60528 Frankfurt, Germany; Epilepsy Center Marburg, Department of Neurology, Philipps-University Marburg, 35043 Marburg, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Susanne Knake
- Epilepsy Center Marburg, Department of Neurology, Philipps-University Marburg, 35043 Marburg, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Dimitri M Kullmann
- Institute of Neurology, University College London (UCL), London WC1E 6BT, United Kingdom
| | - Stefan Liebner
- Edinger-Institute Frankfurt, Neuroscience Center, Goethe University Frankfurt, 60590 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Braxton A Norwood
- Epilepsy Center Marburg, Department of Neurology, Philipps-University Marburg, 35043 Marburg, Germany
| | - Diana Omigie
- Max-Planck-Institute for Empirical Aesthetics, 60322 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Karlheinz Plate
- Edinger-Institute Frankfurt, Neuroscience Center, Goethe University Frankfurt, 60590 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Andreas Reif
- Department of Psychiatry, Psychosomatics and Psychotherapy, Goethe University Frankfurt, 60528 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Philipp S Reif
- Epilepsy Center Frankfurt Rhine-Main, Department of Neurology, Center of Neurology and Neurosurgery, Goethe University Frankfurt, 60528 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Yvonne Reiss
- Edinger-Institute Frankfurt, Neuroscience Center, Goethe University Frankfurt, 60590 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Jochen Roeper
- Institute of Neurophysiology, Neuroscience Center, Goethe-University Frankfurt, 60590 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Michael W Ronellenfitsch
- Dr. Senckenberg Institute for Neurooncology, Center of Neurology and Neurosurgery, Goethe University Frankfurt, 60528 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Stephanie Schorge
- Institute of Neurology, University College London (UCL), London WC1E 6BT, United Kingdom
| | - Gerhard Schratt
- Institute of Physiological Chemistry, Philipps-University Marburg, 35043 Marburg, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Stephan W Schwarzacher
- Institute of Clinical Neuroanatomy, Dr. Senckenberg Anatomy, Neuroscience Center, Goethe University Frankfurt, 60590 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Joachim P Steinbach
- Dr. Senckenberg Institute for Neurooncology, Center of Neurology and Neurosurgery, Goethe University Frankfurt, 60528 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Adam Strzelczyk
- Epilepsy Center Frankfurt Rhine-Main, Department of Neurology, Center of Neurology and Neurosurgery, Goethe University Frankfurt, 60528 Frankfurt, Germany; Epilepsy Center Marburg, Department of Neurology, Philipps-University Marburg, 35043 Marburg, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Jochen Triesch
- Frankfurt Institute for Advanced Studies (FIAS), 60438 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Marlies Wagner
- Institute of Neuroradiology, Center of Neurology and Neurosurgery, Goethe University Frankfurt, 60528 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Matthew C Walker
- Institute of Neurology, University College London (UCL), London WC1E 6BT, United Kingdom
| | - Frederic von Wegner
- Epilepsy Center Frankfurt Rhine-Main, Department of Neurology, Center of Neurology and Neurosurgery, Goethe University Frankfurt, 60528 Frankfurt, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
| | - Sebastian Bauer
- Epilepsy Center Frankfurt Rhine-Main, Department of Neurology, Center of Neurology and Neurosurgery, Goethe University Frankfurt, 60528 Frankfurt, Germany; Epilepsy Center Marburg, Department of Neurology, Philipps-University Marburg, 35043 Marburg, Germany; Center for Personalized Translational Epilepsy Research (CePTER), 60528 Frankfurt, Germany(1)
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Verhoeven T, Coito A, Plomp G, Thomschewski A, Pittau F, Trinka E, Wiest R, Schaller K, Michel C, Seeck M, Dambre J, Vulliemoz S, van Mierlo P. Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes. NEUROIMAGE-CLINICAL 2017. [PMID: 29527470 PMCID: PMC5842753 DOI: 10.1016/j.nicl.2017.09.021] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Objective To diagnose and lateralise temporal lobe epilepsy (TLE) by building a classification system that uses directed functional connectivity patterns estimated during EEG periods without visible pathological activity. Methods Resting-state high-density EEG recording data from 20 left TLE patients, 20 right TLE patients and 35 healthy controls was used. Epochs without interictal spikes were selected. The cortical source activity was obtained for 82 regions of interest and whole-brain directed functional connectivity was estimated in the theta, alpha and beta frequency bands. These connectivity values were then used to build a classification system based on two two-class Random Forests classifiers: TLE vs healthy controls and left vs right TLE. Feature selection and classifier training were done in a leave-one-out procedure to compute the mean classification accuracy. Results The diagnosis and lateralization classifiers achieved a high accuracy (90.7% and 90.0% respectively), sensitivity (95.0% and 90.0% respectively) and specificity (85.7% and 90.0% respectively). The most important features for diagnosis were the outflows from left and right medial temporal lobe, and for lateralization the right anterior cingulate cortex. The interaction between features was important to achieve correct classification. Significance This is the first study to automatically diagnose and lateralise TLE based on EEG. The high accuracy achieved demonstrates the potential of directed functional connectivity estimated from EEG periods without visible pathological activity for helping in the diagnosis and lateralization of TLE. Both classifiers for TLE diagnosis and lateralization achieved high accuracy (90%). Outflow from left and right hippocampus was the most important for diagnosis. Outflow from the right ACC was the most important for lateralization. The interaction between features is important for a correct classification.
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Affiliation(s)
- Thibault Verhoeven
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium.
| | - Ana Coito
- Functional Brain Mapping Lab, Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
| | - Gijs Plomp
- Perceptual Networks Group, Department of Psychology, University of Fribourg, Fribourg, Switzerland
| | - Aljoscha Thomschewski
- Department of Neurology, Paracelsus Medical University and Center for Cognitive Neuroscience, Salzburg, Austria
| | - Francesca Pittau
- Epilepsy Unit, Neurology Clinic, University Hospital of Geneva, Geneva, Switzerland
| | - Eugen Trinka
- Department of Neurology, Paracelsus Medical University and Center for Cognitive Neuroscience, Salzburg, Austria
| | - Roland Wiest
- Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Karl Schaller
- Neurosurgery Clinic, University Hospital Geneva, Geneva, Switzerland
| | - Christoph Michel
- Functional Brain Mapping Lab, Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
| | - Margitta Seeck
- Epilepsy Unit, Neurology Clinic, University Hospital of Geneva, Geneva, Switzerland
| | - Joni Dambre
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Serge Vulliemoz
- Epilepsy Unit, Neurology Clinic, University Hospital of Geneva, Geneva, Switzerland
| | - Pieter van Mierlo
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium; Functional Brain Mapping Lab, Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
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