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Shi LJ, Li CC, Zhang XT, Lin YC, Wang YP, Zhang JC. Application of HFO and scaling analysis of neuronal oscillations in the presurgical evaluation of focal epilepsy. Brain Res Bull 2024; 215:111018. [PMID: 38908759 DOI: 10.1016/j.brainresbull.2024.111018] [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: 12/18/2023] [Revised: 03/07/2024] [Accepted: 06/19/2024] [Indexed: 06/24/2024]
Abstract
PURPOSE To explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy. METHODS MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results. RESULTS Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p<0.01). Moreover, these two LRCT indices across five distinct frequency bands correlated with EZ. CONCLUSION HFO and source-level LRTCs are correlated with EZ. Integrating HFO and LRTCs may be an effective approach for presurgical evaluation of epilepsy.
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Affiliation(s)
- Li-Juan Shi
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Can-Cheng Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Xia-Ting Zhang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing 100053, China
| | - Yi-Cong Lin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing 100053, China
| | - Yu-Ping Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing 100053, China.
| | - Ji-Cong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China; Hefei Innovation Research Institute, Beihang University, Hefei, Anhui, China.
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Murray NWG, Kneebone AC, Graham PL, Wong CH, Savage G, Gillinder L, Fong MWK. The network is more important than the node: stereo-EEG evidence of neurocognitive networks in epilepsy. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1424004. [PMID: 39114571 PMCID: PMC11303167 DOI: 10.3389/fnetp.2024.1424004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Accepted: 07/01/2024] [Indexed: 08/10/2024]
Abstract
Introduction Neuropsychological assessment forms an integral part of the presurgical evaluation for patients with medically refractory focal epilepsy. Our understanding of cognitive impairment in epilepsy is based on seminal lesional studies that have demonstrated important structure-function relationships within the brain. However, a growing body of literature demonstrating heterogeneity in the cognitive profiles of patients with focal epilepsy (e.g., temporal lobe epilepsy; TLE) has led researchers to speculate that cognition may be impacted by regions outside the seizure onset zone, such as those involved in the interictal or "irritative" network. Methods Neuropsychological data from 48 patients who underwent stereoelectroencephalography (SEEG) monitoring between 2012 and 2023 were reviewed. Patients were categorized based on the site of seizure onset, as well as their irritative network, to determine the impact of wider network activity on cognition. Neuropsychological data were compared with normative standards (i.e., z = 0), and between groups. Results There were very few distinguishing cognitive features between patients when categorized based purely on the seizure onset zone (i.e., frontal lobe vs. temporal lobe epilepsy). In contrast, patients with localized irritative networks (i.e., frontal or temporal interictal epileptiform discharges [IEDs]) demonstrated more circumscribed profiles of impairment compared with those demonstrating wider irritative networks (i.e., frontotemporal IEDs). Furthermore, the directionality of propagation within the irritative network was found to influence the manifestations of cognitive impairment. Discussion The findings suggest that neuropsychological assessment is sensitive to network activity beyond the site of seizure onset. As such, an overly focal interpretation may not accurately reflect the distribution of the underlying pathology. This has important implications for presurgical work-up in epilepsy, as well as subsequent surgical outcomes.
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Affiliation(s)
- Nicholas W. G. Murray
- School of Psychological Sciences, Macquarie University, Sydney, Australia
- Westmead Comprehensive Epilepsy Centre, The University of Sydney, Sydney, Australia
| | - Anthony C. Kneebone
- School of Psychology, University of Queensland, Brisbane, Australia
- Department of Neurology and Stroke, Flinders Medical Centre, Adelaide, Australia
| | - Petra L. Graham
- School of Mathematical and Physical Sciences, Macquarie University, Sydney, Australia
| | - Chong H. Wong
- Westmead Comprehensive Epilepsy Centre, The University of Sydney, Sydney, Australia
| | - Greg Savage
- School of Psychological Sciences, Macquarie University, Sydney, Australia
| | - Lisa Gillinder
- Advanced Epilepsy Unit, The Mater Hospital, Brisbane, Australia
| | - Michael W. K. Fong
- Westmead Comprehensive Epilepsy Centre, The University of Sydney, Sydney, Australia
- Comprehensive Epilepsy Center, Department of Neurology, Yale University School of Medicine, New Haven, CT, United States
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Dmytriw AA, Hadjinicolaou A, Ntolkeras G, Tamilia E, Pesce M, Berto LF, Grant PE, Pang E, Ahtam B. Magnetoencephalography for the pediatric population, indications, acquisition and interpretation for the clinician. Neuroradiol J 2024:19714009241260801. [PMID: 38864180 DOI: 10.1177/19714009241260801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2024] Open
Abstract
Magnetoencephalography (MEG) is an imaging technique that enables the assessment of cortical activity via direct measures of neurophysiology. It is a non-invasive and passive technique that is completely painless. MEG has gained increasing prominence in the field of pediatric neuroimaging. This dedicated review article for the pediatric population summarizes the fundamental technical and clinical aspects of MEG for the clinician. We discuss methods tailored for children to improve data quality, including child-friendly MEG facility environments and strategies to mitigate motion artifacts. We provide an in-depth overview on accurate localization of neural sources and different analysis methods, as well as data interpretation. The contemporary platforms and approaches of two quaternary pediatric referral centers are illustrated, shedding light on practical implementations in clinical settings. Finally, we describe the expanding clinical applications of MEG, including its pivotal role in presurgical evaluation of epilepsy patients, presurgical mapping of eloquent cortices (somatosensory and motor cortices, visual and auditory cortices, lateralization of language), its emerging relevance in autism spectrum disorder research and potential future clinical applications, and its utility in assessing mild traumatic brain injury. In conclusion, this review serves as a comprehensive resource of clinicians as well as researchers, offering insights into the evolving landscape of pediatric MEG. It discusses the importance of technical advancements, data acquisition strategies, and expanding clinical applications in harnessing the full potential of MEG to study neurological conditions in the pediatric population.
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Affiliation(s)
- Adam A Dmytriw
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA
- Division of Neuroradiology, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Aristides Hadjinicolaou
- Division of Neurology, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
- Department of Neurology, Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Boston, MA, USA
| | - Georgios Ntolkeras
- Department of Pediatrics, Division of Newborn Medicine, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - Eleonora Tamilia
- Department of Pediatrics, Division of Newborn Medicine, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - Matthew Pesce
- Department of Pediatrics, Division of Newborn Medicine, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - Laura F Berto
- Department of Pediatrics, Division of Newborn Medicine, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - P Ellen Grant
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Division of Newborn Medicine, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - Elizabeth Pang
- Division of Neurology, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Banu Ahtam
- Department of Pediatrics, Division of Newborn Medicine, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
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Li Y, Cao D, Qu J, Wang W, Xu X, Kong L, Liao J, Hu W, Zhang K, Wang J, Li C, Yang X, Zhang X. Automatic Detection of Scalp High-Frequency Oscillations Based on Deep Learning. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1627-1636. [PMID: 38625771 DOI: 10.1109/tnsre.2024.3389010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2024]
Abstract
Scalp high-frequency oscillations (sHFOs) are a promising non-invasive biomarker of epilepsy. However, the visual marking of sHFOs is a time-consuming and subjective process, existing automatic detectors based on single-dimensional analysis have difficulty with accurately eliminating artifacts and thus do not provide sufficient reliability to meet clinical needs. Therefore, we propose a high-performance sHFOs detector based on a deep learning algorithm. An initial detection module was designed to extract candidate high-frequency oscillations. Then, one-dimensional (1D) and two-dimensional (2D) deep learning models were designed, respectively. Finally, the weighted voting method is used to combine the outputs of the two model. In experiments, the precision, recall, specificity and F1-score were 83.44%, 83.60%, 96.61% and 83.42%, respectively, on average and the kappa coefficient was 80.02%. In addition, the proposed detector showed a stable performance on multi-centre datasets. Our sHFOs detector demonstrated high robustness and generalisation ability, which indicates its potential applicability as a clinical assistance tool. The proposed sHFOs detector achieves an accurate and robust method via deep learning algorithm.
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Kerr WT, McFarlane KN. Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist. Curr Neurol Neurosci Rep 2023; 23:869-879. [PMID: 38060133 DOI: 10.1007/s11910-023-01318-7] [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] [Accepted: 10/24/2023] [Indexed: 12/08/2023]
Abstract
PURPOSE OF REVIEW Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice. RECENT FINDINGS ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.
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Affiliation(s)
- Wesley T Kerr
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Biomedical Informatics, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Neurology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Katherine N McFarlane
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA
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Owen TW, Janiukstyte V, Hall GR, Chowdhury FA, Diehl B, McEvoy A, Miserocchi A, de Tisi J, Duncan JS, Rugg-Gunn F, Wang Y, Taylor PN. Interictal magnetoencephalography abnormalities to guide intracranial electrode implantation and predict surgical outcome. Brain Commun 2023; 5:fcad292. [PMID: 37953844 PMCID: PMC10636564 DOI: 10.1093/braincomms/fcad292] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 08/24/2023] [Accepted: 10/24/2023] [Indexed: 11/14/2023] Open
Abstract
Intracranial EEG is the gold standard technique for epileptogenic zone localization but requires a preconceived hypothesis of the location of the epileptogenic tissue. This placement is guided by qualitative interpretations of seizure semiology, MRI, EEG and other imaging modalities, such as magnetoencephalography. Quantitative abnormality mapping using magnetoencephalography has recently been shown to have potential clinical value. We hypothesized that if quantifiable magnetoencephalography abnormalities were sampled by intracranial EEG, then patients' post-resection seizure outcome may be better. Thirty-two individuals with refractory neocortical epilepsy underwent magnetoencephalography and subsequent intracranial EEG recordings as part of presurgical evaluation. Eyes-closed resting-state interictal magnetoencephalography band power abnormality maps were derived from 70 healthy controls as a normative baseline. Magnetoencephalography abnormality maps were compared to intracranial EEG electrode implantation, with the spatial overlap of intracranial EEG electrode placement and cerebral magnetoencephalography abnormalities recorded. Finally, we assessed if the implantation of electrodes in abnormal tissue and subsequent resection of the strongest abnormalities determined by magnetoencephalography and intracranial EEG corresponded to surgical success. We used the area under the receiver operating characteristic curve as a measure of effect size. Intracranial electrodes were implanted in brain tissue with the most abnormal magnetoencephalography findings-in individuals that were seizure-free postoperatively (T = 3.9, P = 0.001) but not in those who did not become seizure-free. The overlap between magnetoencephalography abnormalities and electrode placement distinguished surgical outcome groups moderately well (area under the receiver operating characteristic curve = 0.68). In isolation, the resection of the strongest abnormalities as defined by magnetoencephalography and intracranial EEG separated surgical outcome groups well, area under the receiver operating characteristic curve = 0.71 and area under the receiver operating characteristic curve = 0.74, respectively. A model incorporating all three features separated surgical outcome groups best (area under the receiver operating characteristic curve = 0.80). Intracranial EEG is a key tool to delineate the epileptogenic zone and help render individuals seizure-free postoperatively. We showed that data-driven abnormality maps derived from resting-state magnetoencephalography recordings demonstrate clinical value and may help guide electrode placement in individuals with neocortical epilepsy. Additionally, our predictive model of postoperative seizure freedom, which leverages both magnetoencephalography and intracranial EEG recordings, could aid patient counselling of expected outcome.
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Affiliation(s)
- Thomas W Owen
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Vytene Janiukstyte
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Gerard R Hall
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Fahmida A Chowdhury
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
| | - Beate Diehl
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
| | - Andrew McEvoy
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
| | - Anna Miserocchi
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
| | - Jane de Tisi
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
- NIHR University College London Hospitals Biomedical Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - John S Duncan
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
- NIHR University College London Hospitals Biomedical Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Fergus Rugg-Gunn
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
| | - Yujiang Wang
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Peter N Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- National Hospital for Neurology & Neurosurgery, London WC1N 3BG, UK
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
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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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Vasilica AM, Litvak V, Cao C, Walker M, Vivekananda U. Detection of pathological high-frequency oscillations in refractory epilepsy patients undergoing simultaneous stereo-electroencephalography and magnetoencephalography. Seizure 2023; 107:81-90. [PMID: 36996757 DOI: 10.1016/j.seizure.2023.03.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 03/22/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Stereo-electroencephalography (SEEG) and magnetoencephalography (MEG) have generally been used independently as part of the pre-surgical evaluation of drug-resistant epilepsy (DRE) patients. However, the possibility of simultaneously employing these recording techniques to determine whether MEG has the potential of offering the same information as SEEG less invasively, or whether it could offer a greater spatial indication of the epileptogenic zone (EZ) to aid surgical planning, has not been previously evaluated. METHODS Data from 24 paediatric and adult DRE patients, undergoing simultaneous SEEG and MEG as part of their pre-surgical evaluation, was analysed employing manual and automated high-frequency oscillations (HFOs) detection, and spectral and source localisation analyses. RESULTS Twelve patients (50%) were included in the analysis (4 males; mean age=25.08 years) and showed interictal SEEG and MEG HFOs. HFOs detection was concordant between the two recording modalities, but SEEG displayed higher ability of differentiating between deep and superficial epileptogenic sources. Automated HFO detector in MEG recordings was validated against the manual MEG detection method. Spectral analysis revealed that SEEG and MEG detect distinct epileptic events. The EZ was well correlated with the simultaneously recorded data in 50% patients, while 25% patients displayed poor correlation or discordance. CONCLUSION MEG recordings can detect HFOs, and simultaneous use of SEEG and MEG HFO identification facilitates EZ localisation during the presurgical planning stage for DRE patients. Further studies are necessary to validate these findings and support the translation of automated HFO detectors into routine clinical practice.
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Affiliation(s)
| | - Vladimir Litvak
- Wellcome Centre for Human Neuroimaging, UCL, Queen Square, London, WC1N 3AR, United Kingdom
| | - Chunyan Cao
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Matthew Walker
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Umesh Vivekananda
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
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Wang L, Zhu W, Wang R, Li W, Liang G, Ji Z, Dong X, Shi X. Suppressing interferences of EIT on synchronous recording EEG based on comb filter for seizure detection. Front Neurol 2022; 13:1070124. [DOI: 10.3389/fneur.2022.1070124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 11/18/2022] [Indexed: 12/03/2022] Open
Abstract
Background and objectiveThe purpose of this study was to eliminate the interferences of electrical impedance tomography (EIT) on synchronous recording electroencephalography (EEG) for seizure detection.MethodsThe simulated EIT signal generated by COMSOL Multiphysics was superimposed on the clinical EEG signal obtained from the CHB-MIT Scalp EEG Database, and then the spectrum features of superimposed mixed signals were analyzed. According to the spectrum analysis, in addition to high-frequency interference at 51.2 kHz related to the drive current, there was also low-frequency interference caused by switching of electrode pairs, which were used to inject drive current. A low pass filter and a comb filter were used to suppress the high-frequency interference and low-frequency interference, respectively. Simulation results suggested the low-pass filter and comb filter working together effectively filtered out the interference of EIT on EEG in the process of synchronous monitoring.ResultsAs a result, the normal EEG and epileptic EEG could be recognized effectively. Pearson correlation analysis further confirmed the interference of EIT on EEG was effectively suppressed.ConclusionsThis study provides a simple and effective interference suppression method for the synchronous monitoring of EIT and EEG, which could be served as a reference for the synchronous monitoring of EEG and other medical electromagnetic devices.
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Zhao X, Peng X, Niu K, Li H, He L, Yang F, Wu T, Chen D, Zhang Q, Ouyang M, Guo J, Pan Y. A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy. Front Neuroinform 2022; 16:771965. [PMID: 36156983 PMCID: PMC9500293 DOI: 10.3389/fninf.2022.771965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 08/03/2022] [Indexed: 12/03/2022] Open
Abstract
Magnetoencephalography is a noninvasive neuromagnetic technology to record epileptic activities for the pre-operative localization of epileptogenic zones, which has received increasing attention in the diagnosis and surgery of epilepsy. As reported by recent studies, pathological high frequency oscillations (HFOs), when utilized as a biomarker to localize the epileptogenic zones, result in a significant reduction in seizure frequency, even seizure elimination in around 80% of cases. Thus, objective, rapid, and automatic detection and recommendation of HFOs are highly desirable for clinicians to alleviate the burden of reviewing a large amount of MEG data from a given patient. Despite the advantage, the performance of existing HFOs rarely satisfies the clinical requirement. Consequently, no HFOs have been successfully applied to real clinical applications so far. In this work, we propose a multi-head self-attention-based detector for recommendation, termed MSADR, to detect and recommend HFO signals. Taking advantage of the state-of-the-art multi-head self-attention mechanism in deep learning, the proposed MSADR achieves a more superior accuracy of 88.6% than peer machine learning models in both detection and recommendation tasks. In addition, the robustness of MSADR is also extensively assessed with various ablation tests, results of which further demonstrate the effectiveness and generalizability of the proposed approach.
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Affiliation(s)
- Xiangyu Zhao
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Xueping Peng
- Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia
- *Correspondence: Xueping Peng
| | - Ke Niu
- Computer School, Beijing Information Science and Technology University, Beijing, China
| | - Hailong Li
- Department of Radiology, Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Lili He
- Department of Radiology, Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Feng Yang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Ting Wu
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
- Department of Magnetoencephalography, Nanjing Brain Hospital, Affiliated to Nanjing Medical University, Nanjing, China
- Ting Wu
| | - Duo Chen
- School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China
| | - Qiusi Zhang
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Menglin Ouyang
- The Affiliated Hospital of Medical School, Ningbo University, Ningbo, China
| | - Jiayang Guo
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
- Department of Hematology, School of Medicine, Xiamen University, Xiamen, China
- Jiayang Guo
| | - Yijie Pan
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
- Ningbo Institute of Information Technology Application, Chinese Academy of Sciences, Ningbo, China
- Yijie Pan
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Mo J, Zhang J, Hu W, Shao X, Sang L, Zheng Z, Zhang C, Wang Y, Wang X, Liu C, Zhao B, Zhang K. Neuroimaging gradient alterations and epileptogenic prediction in focal cortical dysplasia Ⅲa. J Neural Eng 2022; 19. [PMID: 35405671 DOI: 10.1088/1741-2552/ac6628] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 04/10/2022] [Indexed: 11/12/2022]
Abstract
INTRODUCTION Focal cortical dysplasia Type Ⅲa (FCD Ⅲa) is a highly prevalent temporal lobe epilepsy but the seizure outcomes are not satisfactory after epilepsy surgery. Hence, quantitative neuroimaging, epileptogenic alterations, as well as their values in guiding surgery are worth exploring. METHODS We examined 69 patients with pathologically verified FCD Ⅲa using multimodal neuroimaging and stereoelectroencephalography (SEEG). Among them, 18 received postoperative imaging which showed the extent of surgical resection and 9 underwent SEEG implantation. We also explored neuroimaging gradient alterations along with the distance to the temporal pole. Subsequently, the machine learning regression model was employed to predict whole-brain epileptogenicity. Lastly, the correlation between neuroimaging or epileptogenicity and surgical cavities was assessed. RESULTS FCD Ⅲa displayed neuroimaging gradient alterations on the temporal neocortex, morphology-signal intensity decoupling, low similarity of intra-morphological features and high similarity of intra-signal intensity features. The support vector regression model was successfully applied at the whole-brain level to calculate the continuous epileptogenic value at each vertex (mean-squared error = 13.8 ± 9.8). CONCLUSION Our study investigated the neuroimaging gradient alterations and epileptogenicity of FCD Ⅲa, along with their potential values in guiding suitable resection range and in predicting postoperative seizure outcomes. The conclusions from this study may facilitate an accurate presurgical examination of FCD Ⅲa. However, further investigation including a larger cohort is necessary to confirm the results.
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Affiliation(s)
- Jiajie Mo
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
| | - Jianguo Zhang
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
| | - Wenhan Hu
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
| | - Xiaoqiu Shao
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
| | - Lin Sang
- Peking University First Hospital Fengtai Hospital, No. 99 South 4th Fengtai Road, Fengtai District, Beijing, 100070, CHINA
| | - Zhong Zheng
- Peking University First Hospital Fengtai Hospital, No. 99 South 4th Fengtai Road, Fengtai District, Beijing, 100070, CHINA
| | - Chao Zhang
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
| | - Yao Wang
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
| | - Xiu Wang
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
| | - Chang Liu
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
| | - Baotian Zhao
- Beijing Tiantan Hospital, , Beijing, 100070, CHINA
| | - Kai Zhang
- Beijing Tiantan Hospital, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, CHINA
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12
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Jiang W, Sun J, Xiang J, Sun Y, Tang L, Zhang K, Chen Q, Wang X. Altered Neuromagnetic Activity in Persistent Postural-Perceptual Dizziness: A Multifrequency Magnetoencephalography Study. Front Hum Neurosci 2022; 16:759103. [PMID: 35350444 PMCID: PMC8957837 DOI: 10.3389/fnhum.2022.759103] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 01/10/2022] [Indexed: 12/12/2022] Open
Abstract
Objective The aim of our study was to investigate abnormal changes in brain activity in patients with persistent postural-perceptual dizziness (PPPD) using magnetoencephalography (MEG). Methods Magnetoencephalography recordings from 18 PPPD patients and 18 healthy controls were analyzed to determine the source of brain activity in seven frequency ranges using accumulated source imaging (ASI). Results Our study showed that significant changes in the patterns of localization in the temporal-parietal junction (TPJ) were observed at 1–4, 4–8, and 12–30 Hz in PPPD patients compared with healthy controls, and changes in the frontal cortex were found at 1–4, 80–250, and 250–500 Hz in PPPD patients compared with controls. The neuromagnetic activity in TPJ was observed increased significantly in 1–4 and 4–8 Hz, while the neuromagnetic activity in frontal cortex was found increased significantly in 1–4 Hz. In addition, the localized source strength in TPJ in 1–4 Hz was positively correlated with DHI score (r = 0.7085, p < 0.05), while the localized source strength in frontal cortex in 1–4 Hz was positively correlated with HAMA score (r = 0.5542, p < 0.05). Conclusion Our results demonstrated that alterations in the TPJ and frontal cortex may play a critical role in the pathophysiological mechanism of PPPD. The neuromagnetic activity in TPJ may be related to dizziness symptom of PPPD patients, while the neuromagnetic activity in frontal lobe may be related to emotional symptoms of PPPD patients. In addition, frequency-dependent changes in neuromagnetic activity, especially neuromagnetic activity in low frequency bands, were involved in the pathophysiology of PPPD.
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Affiliation(s)
- Weiwei Jiang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Jintao Sun
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Jing Xiang
- Division of Neurology, MEG Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Yulei Sun
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Lu Tang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Ke Zhang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Qiqi Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Xiaoshan Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
- *Correspondence: Xiaoshan Wang,
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13
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Fujiwara H, Olbrecht V, Tenney J. MEG Pharmacology: Sedation and Optimal MEG Acquisition. Clin Neurophysiol 2022; 138:143-147. [DOI: 10.1016/j.clinph.2022.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 02/28/2022] [Accepted: 03/20/2022] [Indexed: 11/03/2022]
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14
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Papadelis C, Conrad SE, Song Y, Shandley S, Hansen D, Bosemani M, Malik S, Keator C, Perry MS. Case Report: Laser Ablation Guided by State of the Art Source Imaging Ends an Adolescent's 16-Year Quest for Seizure Freedom. Front Hum Neurosci 2022; 16:826139. [PMID: 35145387 PMCID: PMC8821813 DOI: 10.3389/fnhum.2022.826139] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/03/2022] [Indexed: 01/14/2023] Open
Abstract
Epilepsy surgery is the most effective therapeutic approach for children with drug resistant epilepsy (DRE). Recent advances in neurosurgery, such as the Laser Interstitial Thermal Therapy (LITT), improved the safety and non-invasiveness of this method. Electric and magnetic source imaging (ESI/MSI) plays critical role in the delineation of the epileptogenic focus during the presurgical evaluation of children with DRE. Yet, they are currently underutilized even in tertiary epilepsy centers. Here, we present a case of an adolescent who suffered from DRE for 16 years and underwent surgery at Cook Children's Medical Center (CCMC). The patient was previously evaluated in a level 4 epilepsy center and treated with multiple antiseizure medications for several years. Presurgical evaluation at CCMC included long-term video electroencephalography (EEG), magnetoencephalography (MEG) with simultaneous conventional EEG (19 channels) and high-density EEG (256 channels) in two consecutive sessions, MRI, and fluorodeoxyglucose - positron emission tomography (FDG-PET). Video long-term EEG captured nine focal-onset clinical seizures with a maximal evolution over the right frontal/frontal midline areas. MRI was initially interpreted as non-lesional. FDG-PET revealed a small region of hypometabolism at the anterior right superior temporal gyrus. ESI and MSI performed with dipole clustering showed a tight cluster of dipoles in the right anterior insula. The patient underwent intracranial EEG which indicated the right anterior insular as seizure onset zone. Eventually LITT rendered the patient seizure free (Engel 1; 12 months after surgery). Retrospective analysis of ESI and MSI clustered dipoles found a mean distance of dipoles from the ablated volume ranging from 10 to 25 mm. Our findings highlight the importance of recent technological advances in the presurgical evaluation and surgical treatment of children with DRE, and the underutilization of epilepsy surgery in children with DRE.
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Affiliation(s)
- Christos Papadelis
- Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, TX, United States
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
- School of Medicine, Texas Christian University, University of North Texas Health Science Center, Fort Worth, TX, United States
- *Correspondence: Christos Papadelis orcid.org/0000-0001-6125-9217
| | - Shannon E. Conrad
- Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, TX, United States
| | - Yanlong Song
- Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, TX, United States
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
| | - Sabrina Shandley
- Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, TX, United States
| | - Daniel Hansen
- Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, TX, United States
| | - Madhan Bosemani
- Department of Radiology, Cook Children's Medical Center, Fort Worth, TX, United States
| | - Saleem Malik
- Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, TX, United States
| | - Cynthia Keator
- Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, TX, United States
| | - M. Scott Perry
- Jane and John Justin Neuroscience Center, Cook Children's Health Care System, Fort Worth, TX, United States
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15
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Jiang X, Ye S, Sohrabpour A, Bagić A, He B. Imaging the extent and location of spatiotemporally distributed epileptiform sources from MEG measurements. Neuroimage Clin 2021; 33:102903. [PMID: 34864288 PMCID: PMC8648830 DOI: 10.1016/j.nicl.2021.102903] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/26/2021] [Accepted: 11/27/2021] [Indexed: 11/23/2022]
Abstract
Non-invasive MEG/EEG source imaging provides valuable information about the epileptogenic brain areas which can be used to aid presurgical planning in focal epilepsy patients suffering from drug-resistant seizures. However, the source extent estimation for electrophysiological source imaging remains to be a challenge and is usually largely dependent on subjective choice. Our recently developed algorithm, fast spatiotemporal iteratively reweighted edge sparsity minimization (FAST-IRES) strategy, has been shown to objectively estimate extended sources from EEG recording, while it has not been applied to MEG recordings. In this work, through extensive numerical experiments and real data analysis in a group of focal drug-resistant epilepsy patients' interictal spikes, we demonstrated the ability of FAST-IRES algorithm to image the location and extent of underlying epilepsy sources from MEG measurements. Our results indicate the merits of FAST-IRES in imaging the location and extent of epilepsy sources for pre-surgical evaluation from MEG measurements.
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Affiliation(s)
- Xiyuan Jiang
- Department of Biomedical Engineering, Carnegie Mellon University, USA
| | - Shuai Ye
- Department of Biomedical Engineering, Carnegie Mellon University, USA
| | - Abbas Sohrabpour
- Department of Biomedical Engineering, Carnegie Mellon University, USA
| | - Anto Bagić
- University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), University of Pittsburgh Medical School, USA
| | - Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, USA.
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16
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Sun J, Li Y, Zhang K, Sun Y, Wang Y, Miao A, Xiang J, Wang X. Frequency-Dependent Dynamics of Functional Connectivity Networks During Seizure Termination in Childhood Absence Epilepsy: A Magnetoencephalography Study. Front Neurol 2021; 12:744749. [PMID: 34759883 PMCID: PMC8573389 DOI: 10.3389/fneur.2021.744749] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 09/21/2021] [Indexed: 12/04/2022] Open
Abstract
Objective: Our aim was to investigate the dynamics of functional connectivity (FC) networks during seizure termination in patients with childhood absence epilepsy (CAE) using magnetoencephalography (MEG) and graph theory (GT) analysis. Methods: MEG data were recorded from 22 drug-naïve patients diagnosed with CAE. FC analysis was performed to evaluate the FC networks in seven frequency bands of the MEG data. GT analysis was used to assess the topological properties of FC networks in different frequency bands. Results: The patterns of FC networks involving the frontal cortex were altered significantly during seizure termination compared with those during the ictal period. Changes in the topological parameters of FC networks were observed in specific frequency bands during seizure termination compared with those in the ictal period. In addition, the connectivity strength at 250–500 Hz during the ictal period was negatively correlated with seizure frequency. Conclusions: FC networks associated with the frontal cortex were involved in the termination of absence seizures. The topological properties of FC networks in different frequency bands could be used as new biomarkers to characterize the dynamics of FC networks related to seizure termination.
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Affiliation(s)
- Jintao Sun
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Yihan Li
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Ke Zhang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Yulei Sun
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Yingfan Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Ailiang Miao
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, 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, China
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17
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Matarrese MAG, Loppini A, Jahromi S, Tamilia E, Fabbri L, Madsen JR, Pearl PL, Filippi S, Papadelis C. Electric Source Imaging on Intracranial EEG Localizes Spatiotemporal Propagation of Interictal Spikes in Children with Epilepsy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2668-2671. [PMID: 34891801 PMCID: PMC8928574 DOI: 10.1109/embc46164.2021.9630246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Interictal epileptiform discharges (IEDs) serve as sensitive but not specific biomarkers of epilepsy that can delineate the epileptogenic zone (EZ) in patients with drug resistant epilepsy (DRE) undergoing surgery. Intracranial EEG (icEEG) studies have shown that IEDs propagate in time across large areas of the brain. The onset of this propagation is regarded as a more specific biomarker of epilepsy than areas of spread. Yet, the limited spatial resolution of icEEG does not allow to identify the onset of this activity with high precision. Here, we propose a new method of mapping the spatiotemporal propagation of IEDs (and identify its onset) by using Electrical Source Imaging (ESI) on icEEG bypassing the spatial limitations of icEEG. We validated our method on icEEG recordings from 8 children with DRE who underwent surgery with good outcome (Engel score =1). On each icEEG channel, we detected IEDs and identified the propagation onset using an automated algorithm. We localized the propagation of IEDs with dynamic Statistical Parametric Mapping (dSPM) using a time-sliding window approach. We defined two brain regions: the ESI-onset and ESI-spread zone. We estimated the overlap of these regions with resection volume (in percentage), which served as the gold-standard of the EZ. We also estimated the mean distance of these regions from resection and clinically defined seizure onset zone (SOZ). We observed spatio-temporal propagation of IEDs in all patients across several channels (98 [85-102]) with a mean duration of 155 ms [96-186 ms]. A higher overlap with resection was seen for the ESI-onset zone compared to spread (73.3 % [ 47.4-100 %], 36.5 % [20.3-59.9 %], p = 0.008). The distance of the ESI-onset from resection was shorter compared to the ESI-spread zone (4.3 mm [3.4-5.5 mm], 7.4 mm [6.0-20.6 mm], p = 0.008) and the same trend was observed for the distance from the SOZ (11.9 mm [7.2-15.1 mm], 20.6 mm [15.4-27.2 mm], p = 0.02). These findings show that our method can map the spatiotemporal propagation of IEDs and de-lineate its onset, which is a reliable and focal biomarker of the EZ in children with DRE.Clinical Relevance - ESI on icEEG recordings of children with DRE can localize the spikes propagation phenomenon and help in the delineation of the EZ.
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18
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Corona L, Tamilia E, Madsen JR, Stufflebeam SM, Pearl PL, Papadelis C. Mapping Functional Connectivity of Epileptogenic Networks through Virtual Implantation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:408-411. [PMID: 34891320 PMCID: PMC8893022 DOI: 10.1109/embc46164.2021.9629686] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Children with medically refractory epilepsy (MRE) require resective neurosurgery to achieve seizure freedom, whose success depends on accurate delineation of the epileptogenic zone (EZ). Functional connectivity (FC) can assess the extent of epileptic brain networks since intracranial EEG (icEEG) studies have shown its link to the EZ and predictive value for surgical outcome in these patients. Here, we propose a new noninvasive method based on magnetoencephalography (MEG) and high-density (HD-EEG) data that estimates FC metrics at the source level through an "implantation" of virtual sensors (VSs). We analyzed MEG, HD-EEG, and icEEG data from eight children with MRE who underwent surgery having good outcome and performed source localization (beamformer) on noninvasive data to build VSs at the icEEG electrode locations. We analyzed data with and without Interictal Epileptiform Discharges (IEDs) in different frequency bands, and computed the following FC matrices: Amplitude Envelope Correlation (AEC), Correlation (CORR), and Phase Locking Value (PLV). Each matrix was used to generate a graph using Minimum Spanning Tree (MST), and for each node (i.e., each sensor) we computed four centrality measures: betweenness, closeness, degree, and eigenvector. We tested the reliability of VSs measures with respect to icEEG (regarded as benchmark) via linear correlation, and compared FC values inside vs. outside resection. We observed higher FC inside than outside resection (p<0.05) for AEC [alpha (8-12 Hz), beta (12-30 Hz), and broadband (1-50 Hz)] on data with IEDs and AEC theta (4-8 Hz) on data without IEDs for icEEG, AEC broadband (1-50 Hz) on data without IEDs for MEG-VSs, as well as for all centrality measures of icEEG and MEG/HD-EEG-VSs. Additionally, icEEG and VSs metrics presented high correlation (0.6-0.9, p<0.05). Our data support the notion that the proposed method can potentially replicate the icEEG ability to map the epileptogenic network in children with MRE.Clinical Relevance - The estimation of FC with noninvasive techniques, such as MEG and HD-EEG, via VSs is a promising tool that would help the presurgical evaluation by delineating the EZ without waiting for a seizure to occur, and potentially improve the surgical outcome of patients with MRE undergoing surgery.
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19
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Jahromi S, Matarrese MA, Tamilia E, Perry MS, Madsen JR, Pearl PL, Papadelis C. Mapping Propagation of Interictal Spikes, Ripples, and Fast Ripples in Intracranial EEG of Children with Refractory Epilepsy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:194-197. [PMID: 34891270 PMCID: PMC8896264 DOI: 10.1109/embc46164.2021.9630250] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Studies on intracranial electroencephalography (icEEG) recordings of patients with drug resistant epilepsy (DRE) show that epilepsy biomarkers propagate in time across brain areas. Here, we propose a novel method that estimates critical features of these propagations for different epilepsy biomarkers (spikes, ripples, and fast ripples), and assess their common onset as a reliable biomarker of the epileptogenic zone (EZ). For each biomarker, an automatic algorithm ranked the icEEG electrodes according to their timing occurrence in propagations and finally dichotomized them as onset or spread. We validated our algorithm on icEEG recordings of 8 children with DRE having a good surgical outcome (Engel score = 1). We estimated the overlap of the onset, spread, and entire zone of propagation with the resection (RZ) and the seizure onset zone (SOZ). Spike and ripple propagations were seen in all patients, whereas fast ripple propagations were seen in 6 patients. Spike, ripple, and fast ripple propagations had a mean duration of 28.3 ± 24.3 ms, 38.7 ± 37 ms, and 25 ± 14 ms respectively. Onset electrodes predicted the RZ and SOZ with higher specificity compared to the entire zone for all three biomarkers (p<0.05). Overlap of spike and ripple onsets presented a higher specificity than each separate biomarker onset: for the SOZ, the onsets overlap was more specific (97.89 ± 2.36) than the ripple onset (p=0.031); for the RZ, the onsets overlap was more specific (98.48 ± 1.5) than the spike onset (p=0.016). Yet, the entire zone for spike and ripple propagations predicted the RZ with higher sensitivity compared to each biomarker's onset (or spread) (p<0.05). We present, for the first time, preliminary evidence from icEEG data that fast ripples propagate in time across large areas of the brain. The onsets overlap of spike and ripple propagations constitutes an extremely specific (but not sensitive) biomarker of the EZ, compared to areas of spread (and entire areas) in propagation.
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Affiliation(s)
- Saeed Jahromi
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, USA
- Jane and John Justin Neurosciences Center, Cook Children's Health Care System, Fort Worth, TX, USA
| | - Margherita A.G. Matarrese
- Jane and John Justin Neurosciences Center, Cook Children's Health Care System, Fort Worth, TX, USA
- Unit of Non-Linear Physics and Mathematical Modeling, Engineering Department, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Eleonora Tamilia
- Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - M Scott Perry
- Jane and John Justin Neurosciences Center, Cook Children's Health Care System, Fort Worth, TX, USA
| | - Joseph R Madsen
- Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Phillip L. Pearl
- Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Christos Papadelis
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, USA
- Jane and John Justin Neurosciences Center, Cook Children's Health Care System, Fort Worth, TX, USA
- Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
- School of Medicine, Texas Christian University and University of North Texas Health Science Center, Fort Worth, TX, USA
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20
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Cheng L, Xing Y, Zhang H, Liu R, Lai H, Piao Y, Wang W, Yan X, Li X, Wang J, Li D, Loh HH, Yu T, Zhang G, Yang X. Mechanistic Analysis of Micro-Neurocircuits Underlying the Epileptogenic Zone in Focal Cortical Dysplasia Patients. Cereb Cortex 2021; 32:2216-2230. [PMID: 34664065 DOI: 10.1093/cercor/bhab350] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 08/22/2021] [Accepted: 08/23/2021] [Indexed: 11/13/2022] Open
Abstract
We aim to explore the microscopic neurophysiology of focal cortical dysplasia (FCD) induced epileptogenesis in specific macroscopic brain regions, therefore mapping a micro-macro neuronal network that potentially indicates the epileptogenic mechanism. Epileptic and relatively non-epileptic temporal neocortex specimens were resected from FCD and mesial temporal lobe epilepsy (mTLE) patients, respectively. Whole-cell patch-clamping was performed on cells from the seizure onset zone (SOZ) and non-SOZ inside the epileptogenic zone (EZ) of FCD patients, as well as the non-epileptic neocortex of mTLE patients. Microscopic data were recorded, including membrane characteristics, spontaneous synaptic activities, and evoked action potentials. Immunohistochemistry was also performed on parvalbumin-positive (PV+) interneurons. We found that SOZ interneurons exhibited abnormal neuronal expression and distribution as well as reduced overall function compared with non-SOZ and mTLE interneurons. The SOZ pyramidal cells experienced higher excitation but lower inhibition than the mTLE controls, whereas the non-SOZ pyramidal cells exhibited intermediate excitability. Action potential properties of both types of neurons also suggested more synchronized neuronal activity inside the EZ, particularly inside the SOZ. Together, our research provides evidence for a potential neurocircuit underlying SOZ epileptogenesis and non-SOZ seizure susceptibility. Further investigation of this microscopic network may promote understanding of the mechanism of FCD-induced epileptogenesis.
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Affiliation(s)
- Lipeng Cheng
- Center of Epilepsy, Beijing Institute of Brain Disorders, Capital Medical University, Beijing 100069, China.,Fundamental Research Department, Guangzhou Laboratory, Guangzhou 510700, China.,Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Yue Xing
- Center of Epilepsy, Beijing Institute of Brain Disorders, Capital Medical University, Beijing 100069, China.,Fundamental Research Department, Guangzhou Laboratory, Guangzhou 510700, China.,Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Herui Zhang
- Fundamental Research Department, Guangzhou Laboratory, Guangzhou 510700, China
| | - Ru Liu
- Center of Epilepsy, Beijing Institute of Brain Disorders, Capital Medical University, Beijing 100069, China.,Fundamental Research Department, Guangzhou Laboratory, Guangzhou 510700, China.,Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Huanling Lai
- Fundamental Research Department, Guangzhou Laboratory, Guangzhou 510700, China
| | - Yueshan Piao
- Department of Pathology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Wei Wang
- Center of Epilepsy, Beijing Institute of Brain Disorders, Capital Medical University, Beijing 100069, China.,Fundamental Research Department, Guangzhou Laboratory, Guangzhou 510700, China.,Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Xiaoming Yan
- Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Xiaonan Li
- Center of Epilepsy, Beijing Institute of Brain Disorders, Capital Medical University, Beijing 100069, China.,Fundamental Research Department, Guangzhou Laboratory, Guangzhou 510700, China.,Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Jiaoyang Wang
- Center of Epilepsy, Beijing Institute of Brain Disorders, Capital Medical University, Beijing 100069, China.,Fundamental Research Department, Guangzhou Laboratory, Guangzhou 510700, China.,Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Donghong Li
- Center of Epilepsy, Beijing Institute of Brain Disorders, Capital Medical University, Beijing 100069, China.,Fundamental Research Department, Guangzhou Laboratory, Guangzhou 510700, China.,Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing 100053, China.,Department of Neurology, The Third Affiliated Hospital of Sun Yat-sen University, Guangdong 510635, China
| | - Horace H Loh
- Fundamental Research Department, Guangzhou Laboratory, Guangzhou 510700, China
| | - Tao Yu
- Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Guojun Zhang
- Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China.,Functional Neurosurgery Department, Beijing Children's Hospital, Capital Medical University, Beijing 100045, China
| | - Xiaofeng Yang
- Center of Epilepsy, Beijing Institute of Brain Disorders, Capital Medical University, Beijing 100069, China.,Fundamental Research Department, Guangzhou Laboratory, Guangzhou 510700, China.,Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
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21
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Papadelis C, Perry MS. Localizing the Epileptogenic Zone with Novel Biomarkers. Semin Pediatr Neurol 2021; 39:100919. [PMID: 34620466 PMCID: PMC8501232 DOI: 10.1016/j.spen.2021.100919] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/09/2021] [Accepted: 08/11/2021] [Indexed: 01/01/2023]
Abstract
Several noninvasive methods, such as high-density EEG or magnetoencephalography, are currently used to delineate the epileptogenic zone (EZ) during the presurgical evaluation of patients with drug resistant epilepsy (DRE). Yet, none of these methods can reliably identify the EZ by their own. In most cases a multimodal approach is needed. Challenging cases often require the implantation of intracranial electrodes, either through stereo-taxic EEG or electro-corticography. Recently, a growing body of literature introduces novel biomarkers of epilepsy that can be used for analyzing both invasive as well as noninvasive electrophysiological data. Some of these biomarkers are able to delineate the EZ with high precision, augment the presurgical evaluation, and predict the surgical outcome of patients with DRE undergoing surgery. However, the use of these epilepsy biomarkers in clinical practice is limited. Here, we summarize and discuss the latest technological advances in the presurgical neurophysiological evaluation of children with DRE with emphasis on electric and magnetic source imaging, high frequency oscillations, and functional connectivity.
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Affiliation(s)
- Christos Papadelis
- Jane and John Justin Neurosciences Center, Cook Children's Health Care System, Fort Worth, TX; School of Medicine, Texas Christian University and University of North Texas Health Science Center, Fort Worth, TX; Department of Bioengineering, University of Texas at Arlington, Arlington, TX; Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA.
| | - M Scott Perry
- Jane and John Justin Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
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22
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Xu N, Shan W, Qi J, Wu J, Wang Q. Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity. Front Hum Neurosci 2021; 15:649074. [PMID: 34276321 PMCID: PMC8283278 DOI: 10.3389/fnhum.2021.649074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Accepted: 06/07/2021] [Indexed: 11/21/2022] Open
Abstract
Epilepsy is caused by abnormal electrical discharges (clinically identified by electrophysiological recording) in a specific part of the brain [originating in only one part of the brain, namely, the epileptogenic zone (EZ)]. Epilepsy is now defined as an archetypical hyperexcited neural network disorder. It can be investigated through the network analysis of interictal discharges, ictal discharges, and resting-state functional connectivity. Currently, there is an increasing interest in embedding resting-state connectivity analysis into the preoperative evaluation of epilepsy. Among the various neuroimaging technologies employed to achieve brain functional networks, magnetoencephalography (MEG) with the excellent temporal resolution is an ideal tool for estimating the resting-state connectivity between brain regions, which can reveal network abnormalities in epilepsy. What value does MEG resting-state functional connectivity offer for epileptic presurgical evaluation? Regarding this topic, this paper introduced the origin of MEG and the workflow of constructing source-space functional connectivity based on MEG signals. Resting-state functional connectivity abnormalities correlate with epileptogenic networks, which are defined by the brain regions involved in the production and propagation of epileptic activities. This paper reviewed the evidence of altered epileptic connectivity based on low- or high-frequency oscillations (HFOs) and the evidence of the advantage of using simultaneous MEG and intracranial electroencephalography (iEEG) recordings. More importantly, this review highlighted that MEG-based resting-state functional connectivity has the potential to predict postsurgical outcomes. In conclusion, resting-state MEG functional connectivity has made a substantial progress toward serving as a candidate biomarker included in epileptic presurgical evaluations.
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Affiliation(s)
- Na Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wei Shan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jing Qi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianping Wu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Qun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
- Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neuromodulation, Beijing, China
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23
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Shi LJ, Wei BX, Xu L, Lin YC, Wang YP, Zhang JC. Magnetoencephalography for epileptic focus localization based on Tucker decomposition with ripple window. CNS Neurosci Ther 2021; 27:820-830. [PMID: 33942534 PMCID: PMC8193700 DOI: 10.1111/cns.13643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 03/24/2021] [Indexed: 11/30/2022] Open
Abstract
AIMS To improve the Magnetoencephalography (MEG) spatial localization precision of focal epileptic. METHODS 306-channel simulated or real clinical MEG is estimated as a lower-dimensional tensor by Tucker decomposition based on Higher-order orthogonal iteration (HOOI) before the inverse problem using linearly constraint minimum variance (LCMV). For simulated MEG data, the proposed method is compared with dynamic imaging of coherent sources (DICS), multiple signal classification (MUSIC), and LCMV. For clinical real MEG of 31 epileptic patients, the ripples (80-250 Hz) were detected to compare the source location precision with spikes using the proposed method or the dipole-fitting method. RESULTS The experimental results showed that the positional accuracy of the proposed method was higher than that of LCMV, DICS, and MUSIC for simulation data. For clinical real MEG data, the positional accuracy of the proposed method was higher than that of dipole-fitting regardless of whether the time window was ripple window or spike window. Also, the positional accuracy of the ripple window was higher than that of the spike window regardless of whether the source location method was the proposed method or the dipole-fitting method. For both shallow and deep sources, the proposed method provided effective performance. CONCLUSION Tucker estimation of MEG for source imaging by ripple window is a promising approach toward the presurgical evaluation of epileptics.
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Affiliation(s)
- Li-Juan Shi
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Bo-Xuan Wei
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.,Hefei Innovation Research Institute, Beihang University, Hefei, Anhui, China
| | - Lu Xu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Yi-Cong Lin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing, China
| | - Yu-Ping Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing, China
| | - Ji-Cong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.,Hefei Innovation Research Institute, Beihang University, Hefei, Anhui, China
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24
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Xiang J, Maue E, Tong H, Mangano FT, Greiner H, Tenney J. Neuromagnetic high frequency spikes are a new and noninvasive biomarker for localization of epileptogenic zones. Seizure 2021; 89:30-37. [PMID: 33975080 DOI: 10.1016/j.seizure.2021.04.024] [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: 03/25/2021] [Accepted: 04/26/2021] [Indexed: 10/21/2022] Open
Abstract
OBJECTIVE One barrier hindering high frequency brain signals (HFBS, >80 Hz) from wide clinical applications is that the brain generates both pathological and physiological HFBS. This study was to find specific biomarkers for localizing epileptogenic zones (EZs). METHODS Twenty three children with drug-resistant epilepsy and age/sex matched healthy controls were studied with magnetoencephalography (MEG). High frequency oscillations (HFOs, > 4 oscillatory waveforms) and high frequency spikes (HFSs, > 1 spiky or sharp waveforms) in 80-250 Hz and 250-600 Hz bands were blindly detected with an artificial intelligence method and validated with visual inspection. The magnitude of HFOs and HFSs were quantified with spectral analyses. Sources of HFSs and HFOs were localized and compared with clinical EZs determined by invasive recordings and surgical outcomes. RESULTS HFOs in 80-250 Hz and 250-600 Hz were identified in both epilepsy patients (18/23, 12/23, respectively) and healthy controls (6/23, 4/23, respectively). HFSs in 80-250 Hz and 250-600 Hz were detected in patients (16/23, 11/23, respectively) but not in healthy controls. A combination of HFOs and HFSs localized EZs for 22 (22/23, 96%) patients. CONCLUSIONS The results indicate, for the first time, that HFSs are a newer and more specific biomarker than HFOs for localizing EZs because HFOs appeared in both epilepsy patients and healthy controls while HFSs appeared only in epilepsy patients.
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Affiliation(s)
- Jing Xiang
- MEG Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
| | - Ellen Maue
- MEG Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Han Tong
- MEG Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Neuroscience Graduate Program, University of Cincinnati, Cincinnati, OH, United States
| | - Francesco T Mangano
- Division of Neurosurgery, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Hansel Greiner
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Jeffrey Tenney
- MEG Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
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25
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Tamilia E, Matarrese MAG, Ntolkeras G, Grant PE, Madsen JR, Stufflebeam SM, Pearl PL, Papadelis C. Noninvasive Mapping of Ripple Onset Predicts Outcome in Epilepsy Surgery. Ann Neurol 2021; 89:911-925. [PMID: 33710676 PMCID: PMC8229023 DOI: 10.1002/ana.26066] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 03/09/2021] [Accepted: 03/10/2021] [Indexed: 01/07/2023]
Abstract
OBJECTIVE Intracranial electroencephalographic (icEEG) studies show that interictal ripples propagate across the brain of children with medically refractory epilepsy (MRE), and the onset of this propagation (ripple onset zone [ROZ]) estimates the epileptogenic zone. It is still unknown whether we can map this propagation noninvasively. The goal of this study is to map ripples (ripple zone [RZ]) and their propagation onset (ROZ) using high-density EEG (HD-EEG) and magnetoencephalography (MEG), and to estimate their prognostic value in pediatric epilepsy surgery. METHODS We retrospectively analyzed simultaneous HD-EEG and MEG data from 28 children with MRE who underwent icEEG and epilepsy surgery. Using electric and magnetic source imaging, we estimated virtual sensors (VSs) at brain locations that matched the icEEG implantation. We detected ripples on VSs, defined the virtual RZ and virtual ROZ, and estimated their distance from icEEG. We assessed the predictive value of resecting virtual RZ and virtual ROZ for postsurgical outcome. Interictal spike localization on HD-EEG and MEG was also performed and compared with ripples. RESULTS We mapped ripple propagation in all patients with HD-EEG and in 27 (96%) patients with MEG. The distance from icEEG did not differ between HD-EEG and MEG when mapping the RZ (26-27mm, p = 0.6) or ROZ (22-24mm, p = 0.4). Resecting the virtual ROZ, but not virtual RZ or the sources of spikes, was associated with good outcome for HD-EEG (p = 0.016) and MEG (p = 0.047). INTERPRETATION HD-EEG and MEG can map interictal ripples and their propagation onset (virtual ROZ). Noninvasively mapping the ripple onset may augment epilepsy surgery planning and improve surgical outcome of children with MRE. ANN NEUROL 2021;89:911-925.
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Affiliation(s)
- Eleonora Tamilia
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of MedicineBoston Children's Hospital, Harvard Medical SchoolBostonMA
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterBoston Children's Hospital, Harvard Medical SchoolBostonMA
| | - Margherita A. G. Matarrese
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of MedicineBoston Children's Hospital, Harvard Medical SchoolBostonMA
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of EngineeringUniversity Bio‐Medico Campus of RomeRomeItaly
| | - Georgios Ntolkeras
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of MedicineBoston Children's Hospital, Harvard Medical SchoolBostonMA
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterBoston Children's Hospital, Harvard Medical SchoolBostonMA
| | - P. Ellen Grant
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterBoston Children's Hospital, Harvard Medical SchoolBostonMA
| | - Joseph R. Madsen
- Epilepsy Surgery Program, Department of NeurosurgeryBoston Children's Hospital, Harvard Medical SchoolBostonMA
| | - Steve M. Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolBostonMA
| | - Phillip L. Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of NeurologyBoston Children's Hospital, Harvard Medical SchoolBostonMA
| | - Christos Papadelis
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of MedicineBoston Children's Hospital, Harvard Medical SchoolBostonMA
- Jane and John Justin Neurosciences CenterCook Children's Health Care SystemFort WorthTX
- School of Medicine, Texas Christian University and University of North Texas Health Science CenterFort WorthTX
- Department of BioengineeringUniversity of Texas at ArlingtonArlingtonTX
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26
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Mo J, Wei W, Liu Z, Zhang J, Ma Y, Sang L, Hu W, Zhang C, Wang Y, Wang X, Liu C, Zhao B, Gao D, Tian J, Zhang K. Neuroimaging Phenotyping and Assessment of Structural-Metabolic-Electrophysiological Alterations in the Temporal Neocortex of Focal Cortical Dysplasia IIIa. J Magn Reson Imaging 2021; 54:925-935. [PMID: 33891371 DOI: 10.1002/jmri.27615] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/09/2021] [Accepted: 03/11/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Focal cortical dysplasia IIIa (FCD IIIa) is a common histopathological finding in temporal lobe epilepsy. However, subtle alterations in the temporal neocortex of FCD IIIa renders presurgical diagnosis and definition of the resective range challenging. PURPOSE To explore neuroimaging phenotyping and structural-metabolic-electrophysiological alterations in FCD IIIa. STUDY TYPE Retrospective. SUBJECTS One hundred and sixty-seven subjects aged 4-39 years, including 64 FCD IIIa patients, 89 healthy controls and 14 FCD I patients as disease controls. FIELD STRENGTH/SEQUENCE 3 T, fast-spin-echo T2 -weighted fluid-attenuated inversion recovery (FLAIR), synthetic T1 -weighted magnetization prepared rapid acquisition gradient echo (MPRAGE). ASSESSMENT Surface-based linear model was applied to reveal neuroimaging phenotyping in FCD IIIa and assess its relationship with clinical variables. Logistic regression was implemented to identify FCD IIIa patients. Epileptogenicity mapping (EM) was conducted to explore the structural-metabolic-electrophysiological alterations in temporal neocortex of FCD IIIa. STATISTICAL TESTS Student's t-test was applied to determine the significance of paired differences. Calibration curves were plotted to assess the goodness-of-fit (GOF) of the models, combined with the Hosmer-Lemeshow test. RESULTS FCD IIIa exhibited widespread hyperintensities in temporal neocortex, and these alterations correlated with disease duration (Puncorrected < 0.01). Machine learning model accurately identified 84.4% of FCD IIIa patients, 92.1% of healthy controls and 92.9% of FCD I patients. Cross-modality analysis showed a significant negative correlation between FLAIR hyperintensity and positron emission tomography hypometabolism P < 0.01). Furthermore, epileptogenic cortices were located predominantly in brain regions with FLAIR hyperintensity and hypometabolism. DATA CONCLUSION FCD IIIa exhibited widespread temporal neocortex FLAIR hyperintensity. Automated machine learning of neuroimaging patterns is conducive for accurate identification of FCD IIIa. The degree and distribution of morphological alterations related to the extent of metabolic and epileptogenic abnormalities, lending support to its potential value for reduction of the radiative and invasive approaches during presurgical workup. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Wei Wei
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yanshan Ma
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Lin Sang
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yao Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Chang Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Dongmei Gao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
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27
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Ayub MA, Soman S. Editorial for "Neuroimaging Phenotyping and Structural-Metabolic-Epileptogenic Correlations in the Temporal Neocortex of Focal Cortical Dysplasia IIIa". J Magn Reson Imaging 2021; 54:936-937. [PMID: 33890322 DOI: 10.1002/jmri.27644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 03/31/2021] [Indexed: 11/09/2022] Open
Affiliation(s)
- Muhammad A Ayub
- Department of Radiology, Beth Israel Deaconess Medical Center Harvard Medical School, Boston, Massachusetts, USA
| | - Salil Soman
- Department of Radiology, Beth Israel Deaconess Medical Center Harvard Medical School, Boston, Massachusetts, USA
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28
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Guo J, Li H, Sun X, Qi L, Qiao H, Pan Y, Xiang J, Ji R. Detecting High Frequency Oscillations for Stereoelectroencephalography in Epilepsy via Hypergraph Learning. IEEE Trans Neural Syst Rehabil Eng 2021; 29:587-596. [PMID: 33534708 DOI: 10.1109/tnsre.2021.3056685] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Successful epilepsy surgeries depend highly on pre-operative localization of epileptogenic zones. Stereoelectroencephalography (SEEG) records interictal and ictal activities of the epilepsy in order to precisely find and localize epileptogenic zones in clinical practice. While it is difficult to find distinct ictal onset patterns generated the seizure onset zone from SEEG recordings in a confined region, high frequency oscillations are commonly considered as putative biomarkers for the identification of epileptogenic zones. Therefore, automatic and accurate detection of high frequency oscillations in SEEG signals is crucial for timely clinical evaluation. This work formulates the detection of high frequency oscillations as a signal segment classification problem and develops a hypergraph-based detector to automatically detect high frequency oscillations such that human experts can visually review SEEG signals. We evaluated our method on 4,000 signal segments from clinical SEEG recordings that contain both ictal and interictal data obtained from 19 patients who suffer from refractory focal epilepsy. The experimental results demonstrate the effectiveness of the proposed detector that can successfully localize interictal high frequency oscillations and outperforms multiple peer machine learning methods. In particular, the proposed detector achieved 90.7% in accuracy, 80.9% in sensitivity, and 96.9% in specificity.
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29
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Brændholt M, Jensen M. Evidence From Meta-Analysis Supports Ictal Magnetoencephalographic Source Imaging as an Accurate Method in Presurgery Evaluation of Patients With Drug-Resistant Epilepsy. Clin EEG Neurosci 2020; 51:403-411. [PMID: 32437218 DOI: 10.1177/1550059420921534] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND. Successful epilepsy surgery relies on localization and removal of the brain area responsible for initializing the seizures called the epileptogenic zone (EZ). Intracranial EEG (icEEG) is gold standard of this localization but has several limitations like invasiveness and limited covered area. A noninvasive method with accurate localization precision is therefore desirable. The aim of this article is to investigate the following hypotheses: (1) Ictal onset zone as localized by magnetic source imaging (iMSI) can reliably localize the EZ in focal epilepsy and (2) this localization is as good as that of icEEG. METHODS. Six original studies and a total of 59 unique patients were included in a meta-analysis. RESULTS. Sensitivity and specificity of iMSI based on surgery outcome were 77% (95% CI 60%-90%) and 75% (95% CI 53%-90%), respectively. Specificity of iMSI was statistically higher than that of icEEG. There was no significant difference between sensitivity of iMSI and icEEG. CONCLUSION. The meta-analysis supports that iMSI is an accurate method, achieving similar sensitivity and higher specificity than icEEG. However, at present the use of the method is limited by short recording times. A limitation that might be overcome in the future using technical advances.
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Affiliation(s)
- Malthe Brændholt
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.,Embodied Computation Group, Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
| | - Mads Jensen
- NedComm Lab-Laboratory of NeuroDynamics of Human Communication and Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
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30
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Sun J, Gao Y, Miao A, Yu C, Tang L, Huang S, Wu C, Shi Q, Zhang T, Li Y, Sun Y, Wang X. Multifrequency Dynamics of Cortical Neuromagnetic Activity Underlying Seizure Termination in Absence Epilepsy. Front Hum Neurosci 2020; 14:221. [PMID: 32670039 PMCID: PMC7332835 DOI: 10.3389/fnhum.2020.00221] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 05/15/2020] [Indexed: 12/23/2022] Open
Abstract
Purpose This study aimed to investigate the spectral and spatial signatures of neuromagnetic activity underlying the termination of absence seizures. Methods Magnetoencephalography (MEG) data were recorded from 18 drug-naive patients with childhood absence epilepsy (CAE). Accumulated source imaging (ASI) was used to analyze MEG data at the source level in seven frequency ranges: delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), gamma (30–80 Hz), ripple (80–250 Hz), and fast ripple (250–500 Hz). Result In the 1–4, 4–8, and 8–12 Hz ranges, the magnetic source during seizure termination appeared to be consistent over the ictal period and was mainly localized in the frontal cortex (FC) and parieto-occipito-temporal junction (POT). In the 12–30 and 30–80 Hz ranges, a significant reduction in source activity was observed in the frontal lobe during seizure termination as well as a decrease in peak source strength. The ictal peak source strength in the 1–4 Hz range was negatively correlated with the ictal duration of the seizure, whereas in the 30–80 Hz range, it was positively correlated with the course of epilepsy. Conclusion The termination of absence seizures is associated with a dynamic neuromagnetic process. Frequency-dependent changes in the FC were observed during seizure termination, which may be involved in the process of neural network interaction. Neuromagnetic activity in different frequency bands may play different roles in the pathophysiological mechanism during absence seizures.
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Affiliation(s)
- Jintao Sun
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Yuan Gao
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Ailiang Miao
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Chuanyong Yu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Lu Tang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Shuyang Huang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Caiyun Wu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Qi Shi
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Tingting Zhang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Yihan Li
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Yulei Sun
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
| | - Xiaoshan Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China
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Wei PH, Fan XT, Wang YH, Lu C, Ou SQ, Meng F, Li MY, Zhang HQ, Chen SC, An Y, Yang YF, Ren LK, Shan YZ, Zhao GG. Stereo-crossed ablation guided by stereoelectroencephalography for epilepsy: comprehensive coagulations via a network of multi-electrodes. Ther Adv Neurol Disord 2020; 13:1756286420928657. [PMID: 32565913 PMCID: PMC7285934 DOI: 10.1177/1756286420928657] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 04/28/2020] [Indexed: 11/21/2022] Open
Abstract
Background: Introducing multiple different stereoelectroencephalography electrodes in a three-dimensional (3D) network to create a 3D-lesioning field or stereo-crossed radiofrequency thermocoagulation (scRF-TC) might create larger lesioning size; however, this has not been quantified to date. This study aimed to quantify the configurations essential for scRF-TC. Methods: By using polyacrylamide gel (PAG), we investigated the effect of electrode conformation (angled/parallel/multiple edges) and electrode distance of creating an electrode network. Volume, time, and temperature were analyzed quantitatively with magnetic resonance imaging, video analysis, and machine learning. A network of electrodes to the pathological left area 47 was created in a patient; the seizure outcome and coverage range were further observed. Results: After the compatibility test between the PAG and brain tissue, the sufficient distance of contacts (from different electrodes) for confluent lesioning was 7 mm with the PAG. Connection to the lesioning field could be achieved even with a different arrangement of electrodes. One contact could achieve at least six connections with different peripheral contacts. Coagulation with a network of electrodes can create more significant lesioning sizes, 1.81–2.12 times those of the classic approaches. The confluent lesioning field created by scRF-TC had a volume of 38.7 cm3; the low metabolic area was adequately covered. The representative patient was free of seizures throughout the 12-month follow up. Conclusion: Lesioning with electrodes in a network manner is practical for adequate 3D coverage. A secondary craniotomy could be potentially prevented by combining both monitoring and a large volume of lesions.
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Affiliation(s)
- Peng-Hu Wei
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xiao-Tong Fan
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yi-He Wang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Chao Lu
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Si-Qi Ou
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Fei Meng
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Mu-Yang Li
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Hua-Qiang Zhang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Si-Chang Chen
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yang An
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yan-Feng Yang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Lian-Kun Ren
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yong-Zhi Shan
- Department of Neurosurgery, Xuan Wu Hospital, Capital Medical University, No. 45 Changchun Street, Xuanwu District, Beijing 100053, China
| | - Guo-Guang Zhao
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
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Migliorelli C, Bachiller A, Alonso JF, Romero S, Aparicio J, Jacobs-Le Van J, Mañanas MA, San Antonio-Arce V. SGM: a novel time-frequency algorithm based on unsupervised learning improves high-frequency oscillation detection in epilepsy. J Neural Eng 2020; 17:026032. [PMID: 32213672 DOI: 10.1088/1741-2552/ab8345] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
OBJECTIVE We propose a novel automated method called the S-Transform Gaussian Mixture detection algorithm (SGM) to detect high-frequency oscillations (HFO) combining the strengths of different families of previously published detectors. APPROACH This algorithm does not depend on parameter tuning on a subject (or database) basis, uses time-frequency characteristics, and relies on non-supervised classification to determine if the events standing out from the baseline activity are HFO or not. SGM consists of three steps: the first stage computes the signal baseline using the entropy of the autocorrelation; the second uses the S-Transform to obtain several time-frequency features (area, entropy, and time and frequency widths); and in the third stage Gaussian mixture models cluster time-frequency features to decide if events correspond to HFO-like activity. To validate the SGM algorithm we tested its performance in simulated and real environments. MAIN RESULTS We assessed the algorithm on a publicly available simulated stereoelectroencephalographic (SEEG) database with varying signal-to-noise ratios (SNR), obtaining very good results for medium and high SNR signals. We further tested the SGM algorithm on real signals from patients with focal epilepsy, in which HFO detection was performed visually by experts, yielding a high agreement between experts and SGM. SIGNIFICANCE The SGM algorithm displayed proper performance in simulated and real environments and therefore can be used for non-supervised detection of HFO. This non-supervised algorithm does not require previous labelling by experts or parameter adjustment depending on the subject or database considered. SGM is not a computationally intensive algorithm, making it suitable to detect and characterize HFO in long-term SEEG recordings.
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Affiliation(s)
- Carolina Migliorelli
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain. Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain. Institut de Recerca Pediàtrica Hospital Sant Joan de Déu, Barcelona, Spain
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Xiang J, Maue E, Fan Y, Qi L, Mangano FT, Greiner H, Tenney J. Kurtosis and skewness of high-frequency brain signals are altered in paediatric epilepsy. Brain Commun 2020; 2:fcaa036. [PMID: 32954294 PMCID: PMC7425348 DOI: 10.1093/braincomms/fcaa036] [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: 10/22/2019] [Revised: 02/19/2020] [Accepted: 03/02/2020] [Indexed: 01/15/2023] Open
Abstract
Intracranial studies provide solid evidence that high-frequency brain signals are a new biomarker for epilepsy. Unfortunately, epileptic (pathological) high-frequency signals can be intermingled with physiological high-frequency signals making these signals difficult to differentiate. Recent success in non-invasive detection of high-frequency brain signals opens a new avenue for distinguishing pathological from physiological high-frequency signals. The objective of the present study is to characterize pathological and physiological high-frequency signals at source levels by using kurtosis and skewness analyses. Twenty-three children with medically intractable epilepsy and age-/gender-matched healthy controls were studied using magnetoencephalography. Magnetoencephalographic data in three frequency bands, which included 2–80 Hz (the conventional low-frequency signals), 80–250 Hz (ripples) and 250–600 Hz (fast ripples), were analysed. The kurtosis and skewness of virtual electrode signals in eight brain regions, which included left/right frontal, temporal, parietal and occipital cortices, were calculated and analysed. Differences between epilepsy and controls were quantitatively compared for each cerebral lobe in each frequency band in terms of kurtosis and skewness measurements. Virtual electrode signals from clinical epileptogenic zones and brain areas outside of the epileptogenic zones were also compared with kurtosis and skewness analyses. Compared to controls, patients with epilepsy showed significant elevation in kurtosis and skewness of virtual electrode signals. The spatial and frequency patterns of the kurtosis and skewness of virtual electrode signals among the eight cerebral lobes in three frequency bands were also significantly different from that of the controls (2–80 Hz, P < 0.001; 80–250 Hz, P < 0.00001; 250–600 Hz, P < 0.0001). Compared to signals from non-epileptogenic zones, virtual electrode signals from epileptogenic zones showed significantly altered kurtosis and skewness (P < 0.001). Compared to normative data from the control group, aberrant virtual electrode signals were, for each patient, more pronounced in the epileptogenic lobes than in other lobes(kurtosis analysis of virtual electrode signals in 250–600 Hz; odds ratio = 27.9; P < 0.0001). The kurtosis values of virtual electrode signals in 80–250 and 250–600 Hz showed the highest sensitivity (88.23%) and specificity (89.09%) for revealing epileptogenic lobe, respectively. The combination of virtual electrode and kurtosis/skewness measurements provides a new quantitative approach to distinguishing pathological from physiological high-frequency signals for paediatric epilepsy. Non-invasive identification of pathological high-frequency signals may provide novel important information to guide clinical invasive recordings and direct surgical treatment of epilepsy.
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Affiliation(s)
- Jing Xiang
- MEG Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.,Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Ellen Maue
- MEG Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.,Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Yuyin Fan
- MEG Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.,Department of Pediatric Neurology, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Lei Qi
- MEG Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.,Department of Neurosurgery, Beijing Fengtai Hospital, Beijing 100071, China
| | - Francesco T Mangano
- Division of Neurosurgery, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Hansel Greiner
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Jeffrey Tenney
- MEG Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.,Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
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Dirodi M, Tamilia E, Grant PE, Madsen JR, Stufflebeam SM, Pearl PL, Papadelis C. Noninvasive Localization of High-Frequency Oscillations in Children with Epilepsy: Validation against Intracranial Gold-Standard. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:1555-1558. [PMID: 31946191 DOI: 10.1109/embc.2019.8857793] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
INTRODUCTION Patients with medically refractory epilepsy (MRE) need surgical resection of the epileptogenic zone (EZ) to gain seizure-freedom. High-frequency oscillations (HFOs, > 80 Hz) are promising biomarkers of the EZ that are typically localized using intracranial electroencephalography (icEEG). The goal of this study was to localize the cortical generators of HFOs non-invasively using high-density (HD) EEG and magnetoencephalography (MEG) and validate the localization against the gold-standard given by the icEEGdefined HFO-zone. METHODS We analyzed simultaneous HDEEG and MEG data from six children with MRE who underwent icEEG and surgery. We detected interictal HFOs (80-160 Hz) on HD-EEG and MEG separately, using an inhouse automatic detector followed by visual human review, and distinguished between HFOs with and without spikes. We localized the cortical generators of each HFO on HD-EEG or MEG using the wavelet Maximum Entropy on the Mean (wMEM). For the HFOs localized in the brain area covered by icEEG, we estimated the localization error (Eloc) with respect to the gold-standard, and classified them as either concordant (Eloc≤15mm) or not. RESULTS We found that: (i) HD-EEG presented a higher rate of HFOs than MEG (1 vs 0.5 HFOs/min, p=0.031); (ii) HFOs without spikes were more likely to be localized outside the brain regions of interest (i.e. covered by icEEG) than HFOs with spikes; and (iii) both HD-EEG and MEG showed high precision to the gold-standard (92% and 96%). CONCLUSION We reported quantitative evidence that HDEEG and MEG can localize the HFO cortical generators with high precision to the icEEG gold-standard in children with MRE, suggesting that they may possibly limit the need for icEEG prior to surgery. We also showed that HFOs with spikes on HD-EEG/MEG are more likely to be epileptogenic than those independent from spikes, which may represent physiological events from normal brain.
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Tamilia E, Dirodi M, Alhilani M, Grant PE, Madsen JR, Stufflebeam SM, Pearl PL, Papadelis C. Scalp ripples as prognostic biomarkers of epileptogenicity in pediatric surgery. Ann Clin Transl Neurol 2020; 7:329-342. [PMID: 32096612 PMCID: PMC7086004 DOI: 10.1002/acn3.50994] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 01/29/2020] [Accepted: 01/30/2020] [Indexed: 12/11/2022] Open
Abstract
Objective To assess the ability of high‐density Electroencephalography (HD‐EEG) and magnetoencephalography (MEG) to localize interictal ripples, distinguish between ripples co‐occurring with spikes (ripples‐on‐spike) and independent from spikes (ripples‐alone), and evaluate their localizing value as biomarkers of epileptogenicity in children with medically refractory epilepsy. Methods We retrospectively studied 20 children who underwent epilepsy surgery. We identified ripples on HD‐EEG and MEG data, localized their generators, and compared them with intracranial EEG (icEEG) ripples. When ripples and spikes co‐occurred, we performed source imaging distinctly on the data above 80 Hz (to localize ripples) and below 70 Hz (to localize spikes). We assessed whether missed resection of ripple sources predicted poor outcome, separately for ripples‐on‐spikes and ripples‐alone. Similarly, predictive value of spikes was calculated. Results We observed scalp ripples in 16 patients (10 good outcome). Ripple sources were highly concordant to the icEEG ripples (HD‐EEG concordance: 79%; MEG: 83%). When ripples and spikes co‐occurred, their sources were spatially distinct in 83‐84% of the cases. Removing the sources of ripples‐on‐spikes predicted good outcome with 90% accuracy for HD‐EEG (P = 0.008) and 86% for MEG (P = 0.044). Conversely, removing ripples‐alone did not predict outcome. Resection of spike sources (generated at the same time as ripples) predicted good outcome for HD‐EEG (P = 0.036; accuracy = 87%), while did not reach significance for MEG (P = 0.1; accuracy = 80%). Interpretation HD‐EEG and MEG localize interictal ripples with high precision in children with refractory epilepsy. Scalp ripples‐on‐spikes are prognostic, noninvasive biomarkers of epileptogenicity, since removing their cortical generators predicts good outcome. Conversely, scalp ripples‐alone are most likely generated by non‐epileptogenic areas.
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Affiliation(s)
- Eleonora Tamilia
- Laboratory of Children’s Brain DynamicsDivision of Newborn MedicineDepartment of MedicineBoston Children's HospitalHarvard Medical SchoolBostonMassachusetts
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterDivision of Newborn MedicineDepartment of MedicineBoston Children’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - Matilde Dirodi
- G. Tec Medical Engineering GmbHGuger Technologies OGGrazAustria
| | - Michel Alhilani
- Laboratory of Children’s Brain DynamicsDivision of Newborn MedicineDepartment of MedicineBoston Children's HospitalHarvard Medical SchoolBostonMassachusetts
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterDivision of Newborn MedicineDepartment of MedicineBoston Children’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - P. Ellen Grant
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterDivision of Newborn MedicineDepartment of MedicineBoston Children’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - Joseph R. Madsen
- Division of Epilepsy SurgeryDepartment of NeurosurgeryBoston Children’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - Steven M. Stufflebeam
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalHarvard Medical SchoolBostonMassachusetts
| | - Phillip L. Pearl
- Division of Epilepsy and Clinical NeurophysiologyDepartment of NeurologyBoston Children’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - Christos Papadelis
- Laboratory of Children’s Brain DynamicsDivision of Newborn MedicineDepartment of MedicineBoston Children's HospitalHarvard Medical SchoolBostonMassachusetts
- Jane and John Justin Neurosciences CenterCook Children's Health Care SystemFort WorthTexas
- School of MedicineTexas Christian University and University of North Texas Health Science CenterFort WorthTexas
- Department of BioengineeringUniversity of Texas at ArlingtonArlingtonTexas
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Swami P, Bhatia M, Tripathi M, Chandra PS, Panigrahi BK, Gandhi TK. Selection of optimum frequency bands for detection of epileptiform patterns. Healthc Technol Lett 2019; 6:126-131. [PMID: 31839968 PMCID: PMC6849498 DOI: 10.1049/htl.2018.5051] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 04/15/2019] [Accepted: 04/25/2019] [Indexed: 01/03/2023] Open
Abstract
The significant research effort in the domain of epilepsy has been directed toward the development of an automated seizure detection system. In their usage of the electrophysiological recordings, most of the proposals thus far have followed the conventional practise of employing all frequency bands following signal decomposition as input features for a classifier. Although seemingly powerful, this approach may prove counterproductive since some frequency bins may not carry relevant information about seizure episodes and may, instead, add noise to the classification process thus degrading performance. A key thesis of the work described here is that the selection of frequency subsets may enhance seizure classification rates. Additionally, the authors explore whether a conservative selection of frequency bins can reduce the amount of training data needed for achieving good classification performance. They have found compelling evidence that using spectral components with <25 Hz frequency in scalp electroencephalograms can yield state-of-the-art classification accuracy while reducing training data requirements to just a tenth of those employed by current approaches.
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Affiliation(s)
- Piyush Swami
- Centre for Biomedical Engineering, Indian Institute of Technology - Delhi, New Delhi 110 016, India.,Department of Electrical Engineering, Indian Institute of Technology - Delhi, New Delhi 110 016, India
| | - Manvir Bhatia
- Department of Neurosciences, Fortis Escorts Hospital, New Delhi 110 025, India.,Neurology and Sleep Centre, New Delhi 110 016, India
| | - Manjari Tripathi
- Department of Neurology, All India Institute of Medical Sciences, New Delhi 110 029, India
| | - Poodipedi Sarat Chandra
- Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi 110 029, India
| | - Bijaya K Panigrahi
- Department of Electrical Engineering, Indian Institute of Technology - Delhi, New Delhi 110 016, India
| | - Tapan K Gandhi
- Department of Electrical Engineering, Indian Institute of Technology - Delhi, New Delhi 110 016, India
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Boran E, Sarnthein J, Krayenbühl N, Ramantani G, Fedele T. High-frequency oscillations in scalp EEG mirror seizure frequency in pediatric focal epilepsy. Sci Rep 2019; 9:16560. [PMID: 31719543 PMCID: PMC6851354 DOI: 10.1038/s41598-019-52700-w] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 10/17/2019] [Indexed: 11/10/2022] Open
Abstract
High-frequency oscillations (HFO) are promising EEG biomarkers of epileptogenicity. While the evidence supporting their significance derives mainly from invasive recordings, recent studies have extended these observations to HFO recorded in the widely accessible scalp EEG. Here, we investigated whether scalp HFO in drug-resistant focal epilepsy correspond to epilepsy severity and how they are affected by surgical therapy. In eleven children with drug-resistant focal epilepsy that underwent epilepsy surgery, we prospectively recorded pre- and postsurgical scalp EEG with a custom-made low-noise amplifier (LNA). In four of these children, we also recorded intraoperative electrocorticography (ECoG). To detect clinically relevant HFO, we applied a previously validated automated detector. Scalp HFO rates showed a significant positive correlation with seizure frequency (R2 = 0.80, p < 0.001). Overall, scalp HFO rates were higher in patients with active epilepsy (19 recordings, p = 0.0066, PPV = 86%, NPV = 80%, accuracy = 84% CI [62% 94%]) and decreased following successful epilepsy surgery. The location of the highest HFO rates in scalp EEG matched the location of the highest HFO rates in ECoG. This study is the first step towards using non-invasively recorded scalp HFO to monitor disease severity in patients affected by epilepsy.
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Affiliation(s)
- Ece Boran
- Klinik für Neurochirurgie, UniversitätsSpital & Universität Zürich, Zürich, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, UniversitätsSpital & Universität Zürich, Zürich, Switzerland.,Zentrum für Neurowissenschaften Zürich, ETH Zürich, Zürich, Switzerland
| | - Niklaus Krayenbühl
- Klinik für Neurochirurgie, UniversitätsSpital & Universität Zürich, Zürich, Switzerland.,Pädiatrische Neurochirurgie, Universitäts-Kinderspital Zürich, Zürich, Switzerland
| | - Georgia Ramantani
- Neuropädiatrie, Universitäts-Kinderspital Zürich, Zürich, Switzerland
| | - Tommaso Fedele
- Institute of Cognitive Neuroscience, Higher School of Economics - National Research University, Moscow, Russian Federation.
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Höller P, Trinka E, Höller Y. MEEGIPS-A Modular EEG Investigation and Processing System for Visual and Automated Detection of High Frequency Oscillations. Front Neuroinform 2019; 13:20. [PMID: 31024284 PMCID: PMC6460903 DOI: 10.3389/fninf.2019.00020] [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: 07/07/2018] [Accepted: 03/11/2019] [Indexed: 11/21/2022] Open
Abstract
High frequency oscillations (HFOs) are electroencephalographic correlates of brain activity detectable in a frequency range above 80 Hz. They co-occur with physiological processes such as saccades, movement execution, and memory formation, but are also related to pathological processes in patients with epilepsy. Localization of the seizure onset zone, and, more specifically, of the to-be resected area in patients with refractory epilepsy seems to be supported by the detection of HFOs. The visual identification of HFOs is very time consuming with approximately 8 h for 10 min and 20 channels. Therefore, automated detection of HFOs is highly warranted. So far, no software for visual marking or automated detection of HFOs meets the needs of everyday clinical practice and research. In the context of the currently available tools and for the purpose of related local HFO study activities we aimed at converging the advantages of clinical and experimental systems by designing and developing a comprehensive and extensible software framework for HFO analysis that, on the one hand, focuses on the requirements of clinical application and, on the other hand, facilitates the integration of experimental code and algorithms. The development project included the definition of use cases, specification of requirements, software design, implementation, and integration. The work comprised the engineering of component-specific requirements, component design, as well as component- and integration-tests. A functional and tested software package is the deliverable of this activity. The project MEEGIPS, a Modular EEG Investigation and Processing System for visual and automated detection of HFOs, introduces a highly user friendly software that includes five of the most prominent automated detection algorithms. Future evaluation of these, as well as implementation of further algorithms is facilitated by the modular software architecture.
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Affiliation(s)
- Peter Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University, Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University, Salzburg, Austria
| | - Yvonne Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,Department of Psychology, University of Akureyri, Akureyri, Iceland,*Correspondence: Yvonne Höller
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Zuo R, Wei J, Li X, Li C, Zhao C, Ren Z, Liang Y, Geng X, Jiang C, Yang X, Zhang X. Automated Detection of High-Frequency Oscillations in Epilepsy Based on a Convolutional Neural Network. Front Comput Neurosci 2019; 13:6. [PMID: 30809142 PMCID: PMC6379273 DOI: 10.3389/fncom.2019.00006] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 01/18/2019] [Indexed: 11/17/2022] Open
Abstract
Epilepsy is one of the most common chronic neurological diseases. High-frequency oscillations (HFOs) have emerged as promising biomarkers for the epileptogenic zone. However, visual marking of HFOs is a time-consuming and laborious process. Several automated techniques have been proposed to detect HFOs, yet these are still far from being suitable for application in a clinical setting. Here, ripples and fast ripples from intracranial electroencephalograms were detected in six patients with intractable epilepsy using a convolutional neural network (CNN) method. This approach proved more accurate than using four other HFO detectors integrated in RIPPLELAB, providing a higher sensitivity (77.04% for ripples and 83.23% for fast ripples) and specificity (72.27% for ripples and 79.36% for fast ripples) for HFO detection. Furthermore, for one patient, the Cohen's kappa coefficients comparing automated detection and visual analysis results were 0.541 for ripples and 0.777 for fast ripples. Hence, our automated detector was capable of reliable estimates of ripples and fast ripples with higher sensitivity and specificity than four other HFO detectors. Our detector may be used to assist clinicians in locating epileptogenic zone in the future.
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Affiliation(s)
- Rui Zuo
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Jing Wei
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Xiaonan Li
- Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing, China.,Center of Epilepsy, Beijing Institute for Brain Disorders, Beijing, China
| | - Chunlin Li
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Cui Zhao
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Zhaohui Ren
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Ying Liang
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Xinling Geng
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Chenxi Jiang
- Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing, China.,Center of Epilepsy, Beijing Institute for Brain Disorders, Beijing, China
| | - Xiaofeng Yang
- Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing, China.,Center of Epilepsy, Beijing Institute for Brain Disorders, Beijing, China
| | - Xu Zhang
- School of Biomedical Engineering, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
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Tamilia E, AlHilani M, Tanaka N, Tsuboyama M, Peters JM, Grant PE, Madsen JR, Stufflebeam SM, Pearl PL, Papadelis C. Assessing the localization accuracy and clinical utility of electric and magnetic source imaging in children with epilepsy. Clin Neurophysiol 2019; 130:491-504. [PMID: 30771726 DOI: 10.1016/j.clinph.2019.01.009] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 12/07/2018] [Accepted: 01/08/2019] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To evaluate the accuracy and clinical utility of conventional 21-channel EEG (conv-EEG), 72-channel high-density EEG (HD-EEG) and 306-channel MEG in localizing interictal epileptiform discharges (IEDs). METHODS Twenty-four children who underwent epilepsy surgery were studied. IEDs on conv-EEG, HD-EEG, MEG and intracranial EEG (iEEG) were localized using equivalent current dipoles and dynamical statistical parametric mapping (dSPM). We compared the localization error (ELoc) with respect to the ground-truth Irritative Zone (IZ), defined by iEEG sources, between non-invasive modalities and the distance from resection (Dres) between good- (Engel 1) and poor-outcomes. For each patient, we estimated the resection percentage of IED sources and tested whether it predicted outcome. RESULTS MEG presented lower ELoc than HD-EEG and conv-EEG. For all modalities, Dres was shorter in good-outcome than poor-outcome patients, but only the resection percentage of the ground-truth IZ and MEG-IZ predicted surgical outcome. CONCLUSIONS MEG localizes the IZ more accurately than conv-EEG and HD-EEG. MSI may help the presurgical evaluation in terms of patient's outcome prediction. The promising clinical value of ESI for both conv-EEG and HD-EEG prompts the use of higher-density EEG-systems to possibly achieve MEG performance. SIGNIFICANCE Localizing the IZ non-invasively with MSI/ESI facilitates presurgical evaluation and surgical prognosis assessment.
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Affiliation(s)
- Eleonora Tamilia
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Fetal-Neonatal Neuroimaging Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michel AlHilani
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Fetal-Neonatal Neuroimaging Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Naoaki Tanaka
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Sapporo Neuroimaging Research Group, Sapporo, Japan
| | - Melissa Tsuboyama
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jurriaan M Peters
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Christos Papadelis
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Fetal-Neonatal Neuroimaging Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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Guo J, Yang K, Liu H, Yin C, Xiang J, Li H, Ji R, Gao Y. A Stacked Sparse Autoencoder-Based Detector for Automatic Identification of Neuromagnetic High Frequency Oscillations in Epilepsy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:2474-2482. [PMID: 29994761 PMCID: PMC6299455 DOI: 10.1109/tmi.2018.2836965] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
High-frequency oscillations (HFOs) are spontaneous magnetoencephalography (MEG) patterns that have been acknowledged as a putative biomarker to identify epileptic foci. Correct detection of HFOs in the MEG signals is crucial for the accurate and timely clinical evaluation. Since the visual examination of HFOs is time-consuming, error-prone, and with poor inter-reviewer reliability, an automatic HFOs detector is highly desirable in clinical practice. However, the existing approaches for HFOs detection may not be applicable for MEG signals with noisy background activity. Therefore, we employ the stacked sparse autoencoder (SSAE) and propose an SSAE-based MEG HFOs (SMO) detector to facilitate the clinical detection of HFOs. To the best of our knowledge, this is the first attempt to conduct HFOs detection in MEG using deep learning methods. After configuration optimization, our proposed SMO detector is outperformed other classic peer models by achieving 89.9% in accuracy, 88.2% in sensitivity, and 91.6% in specificity. Furthermore, we have tested the performance consistency of our model using various validation schemes. The distribution of performance metrics demonstrates that our model can achieve steady performance.
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Tamilia E, Park EH, Percivati S, Bolton J, Taffoni F, Peters JM, Grant PE, Pearl PL, Madsen JR, Papadelis C. Surgical resection of ripple onset predicts outcome in pediatric epilepsy. Ann Neurol 2018; 84:331-346. [PMID: 30022519 DOI: 10.1002/ana.25295] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 07/05/2018] [Accepted: 07/06/2018] [Indexed: 12/31/2022]
Abstract
OBJECTIVE In patients with medically refractory epilepsy (MRE), interictal ripples (80-250Hz) are observed in large brain areas whose resection may be unnecessary for seizure freedom. This limits their utility as epilepsy biomarkers for surgery. We assessed the spatiotemporal propagation of interictal ripples on intracranial electroencephalography (iEEG) in children with MRE, compared it with the propagation of spikes, identified ripples that initiated propagation (onset-ripples), and evaluated their clinical value as epilepsy biomarkers. METHODS Twenty-seven children who underwent epilepsy surgery were studied. We identified propagation sequences of ripples and spikes across multiple iEEG contacts and calculated each ripple or spike latency from the propagation onset. We classified ripples and spikes into categories (ie, onset, spread, and isolated) based on their spatiotemporal characteristics and correlated their mean rate inside and outside resection with outcome (good outcome, Engel 1 versus poor outcome, Engel≥2). We determined, as onset-zone, spread-zone, and isolated-zone, the areas generating the corresponding ripple or spike category and evaluated the predictive value of their resection. RESULTS We observed ripple propagation in all patients and spike propagation in 25 patients. Mean rate of onset-ripples inside resection predicted the outcome (odds ratio = 5.37; p = 0.02) and correlated with Engel class (rho = -0.55; p = 0.003). Resection of the onset-ripple-zone was associated with good outcome (p = 0.047). No association was found for the spread-ripple-zone, isolated-ripple-zone, or any spike-zone. INTERPRETATION Interictal ripples propagate across iEEG contacts in children with MRE. The association between the onset-ripple-zone resection and good outcome indicates that onset-ripples are promising epilepsy biomarkers, which estimate the epileptogenic tissue better than spread-ripples or onset-spikes. Ann Neurol 2018;84:331-346.
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Affiliation(s)
- Eleonora Tamilia
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Eun-Hyoung Park
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Stefania Percivati
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA.,Unit of Biomedical Robotics and Biomicrosystems, Engineering Department, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Fabrizio Taffoni
- Unit of Biomedical Robotics and Biomicrosystems, Engineering Department, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Jurriaan M Peters
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Christos Papadelis
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA
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Magnetoencephalography: Clinical and Research Practices. Brain Sci 2018; 8:brainsci8080157. [PMID: 30126121 PMCID: PMC6120049 DOI: 10.3390/brainsci8080157] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 08/07/2018] [Accepted: 08/11/2018] [Indexed: 11/25/2022] Open
Abstract
Magnetoencephalography (MEG) is a neurophysiological technique that detects the magnetic fields associated with brain activity. Synthetic aperture magnetometry (SAM), a MEG magnetic source imaging technique, can be used to construct both detailed maps of global brain activity as well as virtual electrode signals, which provide information that is similar to invasive electrode recordings. This innovative approach has demonstrated utility in both clinical and research settings. For individuals with epilepsy, MEG provides valuable, nonredundant information. MEG accurately localizes the irritative zone associated with interictal spikes, often detecting epileptiform activity other methods cannot, and may give localizing information when other methods fail. These capabilities potentially greatly increase the population eligible for epilepsy surgery and improve planning for those undergoing surgery. MEG methods can be readily adapted to research settings, allowing noninvasive assessment of whole brain neurophysiological activity, with a theoretical spatial range down to submillimeter voxels, and in both humans and nonhuman primates. The combination of clinical and research activities with MEG offers a unique opportunity to advance translational research from bench to bedside and back.
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High Frequency Oscillations in the Ripple Band (80–250 Hz) in Scalp EEG: Higher Density of Electrodes Allows for Better Localization of the Seizure Onset Zone. Brain Topogr 2018; 31:1059-1072. [DOI: 10.1007/s10548-018-0658-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2017] [Accepted: 06/29/2018] [Indexed: 10/28/2022]
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Abstract
PURPOSE OF REVIEW Three randomized controlled trials demonstrate that surgical treatment is safe and effective for drug-resistant epilepsy (DRE), yet fewer than 1% of patients are referred for surgery. This is a review of recent trends in surgical referral for DRE, and advances in the field. Reasons for continued underutilization are discussed. RECENT FINDINGS Recent series indicate no increase in surgical referral for DRE over the past two decades. One study suggests that decreased referrals to major epilepsy centers can be accounted for by increased referrals to low-volume nonacademic hospitals where results are poorer, and complication rates higher. The increasing ability of high-resolution MRI to identify small neocortical lesions and an increase in pediatric surgeries, in part, explain a relative greater decrease in temporal lobe surgeries. Misconceptions continue to restrict referral. Consequently, advocacy for referral of all patients with DRE to epilepsy centers that offer specialized diagnosis and other alternative treatments, as well as psychosocial support, is recommended. Recent advances will continue to improve the safety and efficacy of surgical treatment and expand the types of patients who benefit from surgical intervention. SUMMARY Surgical treatment for epilepsy remains underutilized, in part because of persistent misconceptions. Rather than promote referral for surgery, it would be more appropriate to advocate that all patients with DRE deserve a consultation at a full-service epilepsy center that offers many options for eliminating or reducing disability.
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Affiliation(s)
- Jerome Engel
- Departments of Neurology, Neurobiology and Psychiatry and Biobehavioral Sciences and the Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
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