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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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2
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Klimes P, Nejedly P, Hrtonova V, Cimbalnik J, Travnicek V, Pail M, Peter-Derex L, Hall J, Pana R, Halamek J, Jurak P, Brazdil M, Frauscher B. Interictal stereo-electroencephalography features below 45 Hz are sufficient for correct localization of the epileptogenic zone and postsurgical outcome prediction. Epilepsia 2024. [PMID: 39180407 DOI: 10.1111/epi.18081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 07/18/2024] [Accepted: 07/25/2024] [Indexed: 08/26/2024]
Abstract
OBJECTIVE Evidence suggests that the most promising results in interictal localization of the epileptogenic zone (EZ) are achieved by a combination of multiple stereo-electroencephalography (SEEG) biomarkers in machine learning models. These biomarkers usually include SEEG features calculated in standard frequency bands, but also high-frequency (HF) bands. Unfortunately, HF features require extra effort to record, store, and process. Here we investigate the added value of these HF features for EZ localization and postsurgical outcome prediction. METHODS In 50 patients we analyzed 30 min of SEEG recorded during non-rapid eye movement sleep and tested a logistic regression model with three different sets of features. The first model used broadband features (1-500 Hz); the second model used low-frequency features up to 45 Hz; and the third model used HF features above 65 Hz. The EZ localization by each model was evaluated by various metrics including the area under the precision-recall curve (AUPRC) and the positive predictive value (PPV). The differences between the models were tested by the Wilcoxon signed-rank tests and Cliff's Delta effect size. The differences in outcome predictions based on PPV values were further tested by the McNemar test. RESULTS The AUPRC score of the random chance classifier was .098. The models (broad-band, low-frequency, high-frequency) achieved median AUPRCs of .608, .582, and .522, respectively, and correctly predicted outcomes in 38, 38, and 33 patients. There were no statistically significant differences in AUPRC or any other metric between the three models. Adding HF features to the model did not have any additional contribution. SIGNIFICANCE Low-frequency features are sufficient for correct localization of the EZ and outcome prediction with no additional value when considering HF features. This finding allows significant simplification of the feature calculation process and opens the possibility of using these models in SEEG recordings with lower sampling rates, as commonly performed in clinical routines.
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Affiliation(s)
- Petr Klimes
- Institute of Scientific Instruments of the CAS, Brno, Czech Republic
| | - Petr Nejedly
- Institute of Scientific Instruments of the CAS, Brno, Czech Republic
- Department of Neurology, Faculty of Medicine, Brno Epilepsy Center, St. Anne's University Hospital, Member of ERN-EpiCARE, Masaryk University, Brno, Czech Republic
| | | | - Jan Cimbalnik
- Department of Neurology, Faculty of Medicine, Brno Epilepsy Center, St. Anne's University Hospital, Member of ERN-EpiCARE, Masaryk University, Brno, Czech Republic
- International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Vojtech Travnicek
- Institute of Scientific Instruments of the CAS, Brno, Czech Republic
- International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Martin Pail
- Institute of Scientific Instruments of the CAS, Brno, Czech Republic
- Department of Neurology, Faculty of Medicine, Brno Epilepsy Center, St. Anne's University Hospital, Member of ERN-EpiCARE, Masaryk University, Brno, Czech Republic
| | - Laure Peter-Derex
- Center for Sleep Medicine and Respiratory Diseases, Lyon University Hospital, Lyon 1 University, Lyon, France
- Lyon Neuroscience Research Center, INSERM U1028/CNRS UMR5292, Lyon, France
| | - Jeffery Hall
- Montreal Neurological Hospital, McGill University, Montreal, Quebec, Canada
| | - Raluca Pana
- Montreal Neurological Hospital, McGill University, Montreal, Quebec, Canada
| | - Josef Halamek
- Institute of Scientific Instruments of the CAS, Brno, Czech Republic
| | - Pavel Jurak
- Institute of Scientific Instruments of the CAS, Brno, Czech Republic
| | - Milan Brazdil
- Department of Neurology, Faculty of Medicine, Brno Epilepsy Center, St. Anne's University Hospital, Member of ERN-EpiCARE, Masaryk University, Brno, Czech Republic
- Behavioral and Social Neuroscience Research Group, CEITEC Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Birgit Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Quebec, Canada
- Department of Neurology, Duke University Medical Center, Durham, North Carolina, USA
- Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, North Carolina, USA
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3
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Shi W, Eden U, Kramer MA, Chu CJ. Reply: The challenge of assessing invasive biomarkers for epilepsy surgery and To plan efficacious epilepsy surgery. Brain 2024; 147:e58-e60. [PMID: 38753652 DOI: 10.1093/brain/awae165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 05/18/2024] Open
Affiliation(s)
- Wen Shi
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Uri Eden
- Center for Systems Neuroscience, Boston University, Boston, MA 02215, USA
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
| | - Mark A Kramer
- Center for Systems Neuroscience, Boston University, Boston, MA 02215, USA
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
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Kozma C, Schroeder G, Owen T, de Tisi J, McEvoy AW, Miserocchi A, Duncan J, Wang Y, Taylor PN. Identifying epileptogenic abnormality by decomposing intracranial EEG and MEG power spectra. J Neurosci Methods 2024; 408:110180. [PMID: 38795977 DOI: 10.1016/j.jneumeth.2024.110180] [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: 02/15/2024] [Revised: 05/08/2024] [Accepted: 05/22/2024] [Indexed: 05/28/2024]
Abstract
BACKGROUND Accurate identification of abnormal electroencephalographic (EEG) activity is pivotal for diagnosing and treating epilepsy. Recent studies indicate that decomposing brain activity into periodic (oscillatory) and aperiodic (trend across all frequencies) components can illuminate the drivers of spectral activity changes. NEW METHODS We analysed intracranial EEG (iEEG) data from 234 subjects, creating a normative map. This map was compared to a cohort of 63 patients with refractory focal epilepsy under consideration for neurosurgery. The normative map was computed using three approaches: (i) relative complete band power, (ii) relative band power with the aperiodic component removed, and (iii) the aperiodic exponent. Abnormalities were calculated for each approach in the patient cohort. We evaluated the spatial profiles, assessed their ability to localize abnormalities, and replicated the findings using magnetoencephalography (MEG). RESULTS Normative maps of relative complete band power and relative periodic band power exhibited similar spatial profiles, while the aperiodic normative map revealed higher exponent values in the temporal lobe. Abnormalities estimated through complete band power effectively distinguished between good and bad outcome patients. Combining periodic and aperiodic abnormalities enhanced performance, like the complete band power approach. COMPARISON WITH EXISTING METHODS AND CONCLUSIONS Sparing cerebral tissue with abnormalities in both periodic and aperiodic activity may result in poor surgical outcomes. Both periodic and aperiodic components do not carry sufficient information in isolation. The relative complete band power solution proved to be the most reliable method for this purpose. Future studies could investigate how cerebral location or pathology influences periodic or aperiodic abnormalities.
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Affiliation(s)
- Csaba Kozma
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.
| | - Gabrielle Schroeder
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Tom Owen
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Jane de Tisi
- UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - Andrew W McEvoy
- UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - Anna Miserocchi
- UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - John Duncan
- UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
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5
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Mendoza T, Trevino CL, Shrey DW, Lin JJ, Sen-Gupta I, Lopour BA. Optimizing automated detection of high frequency oscillations using visual markings does not improve SOZ localization. Clin Neurophysiol 2024; 164:30-39. [PMID: 38843758 DOI: 10.1016/j.clinph.2024.05.010] [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: 01/11/2024] [Revised: 02/28/2024] [Accepted: 05/20/2024] [Indexed: 07/15/2024]
Abstract
OBJECTIVE High frequency oscillations (HFOs) are a biomarker of the seizure onset zone (SOZ) and can be visually or automatically detected. In theory, one can optimize an automated algorithm's parameters to maximize SOZ localization accuracy; however, there is no consensus on whether or how this should be done. Therefore, we optimized an automated detector using visually identified HFOs and evaluated the impact on SOZ localization accuracy. METHODS We detected HFOs in intracranial EEG from 20 patients with refractory epilepsy from two centers using (1) unoptimized automated detection, (2) visual identification, and (3) automated detection optimized to match visually detected HFOs. RESULTS SOZ localization accuracy based on HFO rate was not significantly different between the three methods. Across patients, visually optimized detector settings varied, and no single set of settings produced universally accurate SOZ localization. Exploratory analysis suggests that, for many patients, detection settings exist that would improve SOZ localization. CONCLUSIONS SOZ localization accuracy was similar for all three methods, was not improved by visually optimizing detector settings, and may benefit from patient-specific parameter optimization. SIGNIFICANCE Visual HFO marking is laborious, and optimizing automated detection using visual markings does not improve localization accuracy. New patient-specific detector optimization methods are needed.
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Affiliation(s)
- Trisha Mendoza
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA
| | - Casey L Trevino
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA
| | - Daniel W Shrey
- Division of Neurology, Children's Hospital of Orange County, Orange, CA, USA; Department of Pediatrics, University of California, Irvine, Orange, CA, USA
| | - Jack J Lin
- UC Davis Comprehensive Epilepsy Program, Department of Neurology, Davis, CA, USA; UC Davis Center for Mind and Brain, Davis, CA, USA
| | - Indranil Sen-Gupta
- Comprehensive Epilepsy Program, Department of Neurology, University of California, Irvine, Irvine, CA, USA
| | - Beth A Lopour
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA.
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Roehri N, Vulliemoz S, Lagarde S. The challenge of assessing invasive biomarkers for epilepsy surgery. Brain 2024; 147:e52-e54. [PMID: 38753681 PMCID: PMC11292892 DOI: 10.1093/brain/awae164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 05/13/2024] [Indexed: 05/18/2024] Open
Affiliation(s)
- Nicolas Roehri
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, CH-1211 Geneva, Switzerland
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, CH-1211 Geneva, Switzerland
- Center for Biomedical Imaging (CIBM), Lausanne and Geneva, 1015 Lausanne, Switzerland
| | - Stanislas Lagarde
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, CH-1211 Geneva, Switzerland
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, 13005 Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, 13005 Marseille, France
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7
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Zhang Y, Daida A, Liu L, Kuroda N, Ding Y, Oana S, Monsoor T, Hussain SA, Qiao JX, Salamon N, Fallah A, Sim MS, Sankar R, Staba RJ, Engel J, Asano E, Roychowdhury V, Nariai H. Discovering Neurophysiological Characteristics of Pathological High-Frequency Oscillations in Epilepsy with an Explainable Deep Generative Model. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.10.24310189. [PMID: 39040207 PMCID: PMC11261948 DOI: 10.1101/2024.07.10.24310189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Interictal high-frequency oscillation (HFO) is a promising biomarker of the epileptogenic zone (EZ). However, objective definitions to distinguish between pathological and physiological HFOs have remained elusive, impeding HFOs' clinical applications. We employed self-supervised deep generative variational autoencoders to learn such discriminative HFO features directly from their morphologies in a data-driven manner. We studied a large retrospective cohort of 185 patients who underwent intracranial monitoring and analyzed 686,410 candidate HFO events collected from 18,265 brain contacts across diverse brain regions. The model automatically clustered HFOs into distinct morphological groups in the latent space. One cluster consisted of putative morphologically defined pathological HFOs (mpHFOs): HFOs in that cluster were observed to be associated with spikes and exhibited high signal intensity both in the HFO band (>80 Hz) at detection and in the sub-HFO band (10-80 Hz) surrounding the detection and were primarily localized in the seizure onset zone (SOZ). Moreover, resection of brain regions based on a higher prevalence of interictal mpHFOs better predicted postoperative seizure outcomes than current clinical standards based on SOZ removal. Our self-supervised, explainable, deep generative model distills pathological HFOs and thus potentially helps delineate the EZ purely from interictal intracranial EEG data.
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Affiliation(s)
- Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Atsuro Daida
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Lawrence Liu
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Naoto Kuroda
- Department of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, MI, USA
| | - Yuanyi Ding
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Shingo Oana
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Shaun A. Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Joe X Qiao
- Division of Neuroradiology, Department of Radiology, UCLA Medical Center, David Geffen School of Medicine, Los 6 Angeles, CA, USA
| | - Noriko Salamon
- Division of Neuroradiology, Department of Radiology, UCLA Medical Center, David Geffen School of Medicine, Los 6 Angeles, CA, USA
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Myung Shin Sim
- Department of Medicine, Statistics Core, University of California, Los Angeles, CA, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Richard J. Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Jerome Engel
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Neurobiology, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
- The Brain Research Institute, University of California, Los Angeles, CA, USA
| | - Eishi Asano
- Department of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, MI, USA
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA
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Fang C, Li X, Na M, Jiang W, He Y, Wei A, Huang J, Zhou M. Epilepsy lesion localization method based on brain function network. Front Hum Neurosci 2024; 18:1431153. [PMID: 39050383 PMCID: PMC11266299 DOI: 10.3389/fnhum.2024.1431153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Accepted: 06/26/2024] [Indexed: 07/27/2024] Open
Abstract
Objective In the past, the localization of seizure onset zone (SOZ) primarily relied on traditional EEG signal analysis methods. However, due to their limited spatial and temporal resolution, accurately pinpointing neural activity was challenging, thereby restricting their clinical applicability. Compared with traditional EEG signals, SEEG signals have superior spatial and temporal resolution, and can more accurately record neural activity near epileptic foci, making them better suited for studying SOZ. In addition, the traditional EEG signal analysis methods still have limitations, mainly focusing on the analysis of local signal features, while ignoring the complexity and interconnection of the overall brain network. How to more accurately locate SOZ is still not well resolved. The purpose of this study is to develop an effective positioning method for more accurate positioning. Method To overcome these limitations, this study proposed a model integrating brain functional network analysis with nonlinear dynamics. We utilized weighted phase lag index (WPLI) to construct brain functional network, epilepic network connectivity strength (ENCS) as the feature, and introduced persistence entropy (PE) for feature fusion, subsequently employing support vector machine (SVM) classification. Results The proposed method was verified on the HUP-iEEG dataset, our solution identified the SOZ with 0.9440 accuracy, 0.9848 precision, 0.8974 recall rate, 0.9340 F1 score and 0.9697 area under the ROC curve across patients, which outperforms the existing approaches. It exhibits a 2.30 percentage point enhancement in localisation accuracy along with a 2.97 percentage points in AUC compared to others. Conclusion Our method consider the interactions between nodes in brain network connections, as well as the inherent nonlinear and non-stationary properties of neural signals, to be more robust.
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Affiliation(s)
- Chunying Fang
- School of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin, China
| | - Xingyu Li
- School of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin, China
| | - Meng Na
- Department of Neurosurgery, The First Hospital of Harbin Medical University, Harbin, China
| | - Wenhao Jiang
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Yuankun He
- School of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin, China
| | - Aowei Wei
- School of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin, China
| | - Jie Huang
- School of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin, China
| | - Ming Zhou
- School of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin, China
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Jin BZ, Capiglioni M, Federspiel A, Ahmadli U, Schindler K, Kiefer C, Wiest R. Neuronal current imaging of epileptic activity: An MRI study in patients with a first unprovoked epileptic seizure. Epilepsia Open 2024. [PMID: 38970780 DOI: 10.1002/epi4.13001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 06/07/2024] [Accepted: 06/11/2024] [Indexed: 07/08/2024] Open
Abstract
OBJECTIVE This study evaluates the performance of the novel MRI sequence stimulus-induced rotary saturation (SIRS) to map responses to interictal epileptic activity in the human cortex. Spin-lock pulses have been applied to indirectly detect neuronal activity through magnetic field perturbations. Following initial reports about the feasibility of the method in humans and animals with epilepsy, we aimed to investigate the diagnostic yield of spin-lock MR pulses in comparison with scalp-EEG in first seizure patients. METHODS We employed a novel method for measurements of neuronal activity through the detection of a resonant oscillating field, stimulus-induced rotary saturation contrast (SIRS) at spin-lock frequencies of 120 and 240 Hz acquired at a single 3T MRI system. Within a prospective observational study, we conducted SIRS experiments in 55 patients within 7 days after a suspected first unprovoked epileptic seizure and 61 healthy control subjects. In this study, we report on the analysis of data from a single 3T MRI system, encompassing 35 first seizure patients and 31 controls. RESULTS The SIRS method was applicable in all patients and healthy controls at frequencies of 120 and 240 Hz. We did not observe any significant age- or sex-related differences. Specificity of SIRS at 120 Hz was 90.3% and 93.5% at 240 Hz. Sensitivity was 17.1% at 120 Hz and 40.0% at 240 Hz. SIGNIFICANCE SIRS targets neuronal oscillating magnetic fields in patients with epilepsy. The coupling of presaturated spins to epilepsy-related magnetic field perturbations may serve as a-at this stage experimental-diagnostic test in first seizure patients to complement EEG findings as a standard screening test. PLAIN LANGUAGE SUMMARY Routine diagnostic tests carry several limitations when applied after a suspected first seizure. SIRS is a noninvasive MRI method to enable time-sensitive diagnosis of image correlates of epileptic activity with increased sensitivity compared to routine EEG.
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Affiliation(s)
- Baudouin Zongxin Jin
- Support Center of Advanced Neuroimaging, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Graduate School of Health Sciences, Medical Faculty, University of Bern, Bern, Switzerland
| | - Milena Capiglioni
- Support Center of Advanced Neuroimaging, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
| | - Andrea Federspiel
- Support Center of Advanced Neuroimaging, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
| | - Uzeyir Ahmadli
- Support Center of Advanced Neuroimaging, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
| | - Kaspar Schindler
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Claus Kiefer
- Support Center of Advanced Neuroimaging, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
| | - Roland Wiest
- Support Center of Advanced Neuroimaging, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
- Swiss Institute for Translational and Entrepreneurial Medicine, Sitem-Insel, Bern, Switzerland
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10
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Shi W, Shaw D, Walsh KG, Han X, Eden UT, Richardson RM, Gliske SV, Jacobs J, Brinkmann BH, Worrell GA, Stacey WC, Frauscher B, Thomas J, Kramer MA, Chu CJ. Spike ripples localize the epileptogenic zone best: an international intracranial study. Brain 2024; 147:2496-2506. [PMID: 38325327 PMCID: PMC11224608 DOI: 10.1093/brain/awae037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 12/10/2023] [Accepted: 01/19/2024] [Indexed: 02/09/2024] Open
Abstract
We evaluated whether spike ripples, the combination of epileptiform spikes and ripples, provide a reliable and improved biomarker for the epileptogenic zone compared with other leading interictal biomarkers in a multicentre, international study. We first validated an automated spike ripple detector on intracranial EEG recordings. We then applied this detector to subjects from four centres who subsequently underwent surgical resection with known 1-year outcomes. We evaluated the spike ripple rate in subjects cured after resection [International League Against Epilepsy Class 1 outcome (ILAE 1)] and those with persistent seizures (ILAE 2-6) across sites and recording types. We also evaluated available interictal biomarkers: spike, spike-gamma, wideband high frequency oscillation (HFO, 80-500 Hz), ripple (80-250 Hz) and fast ripple (250-500 Hz) rates using previously validated automated detectors. The proportion of resected events was computed and compared across subject outcomes and biomarkers. Overall, 109 subjects were included. Most spike ripples were removed in subjects with ILAE 1 outcome (P < 0.001), and this was qualitatively observed across all sites and for depth and subdural electrodes (P < 0.001 and P < 0.001, respectively). Among ILAE 1 subjects, the mean spike ripple rate was higher in the resected volume (0.66/min) than in the non-removed tissue (0.08/min, P < 0.001). A higher proportion of spike ripples were removed in subjects with ILAE 1 outcomes compared with ILAE 2-6 outcomes (P = 0.06). Among ILAE 1 subjects, the proportion of spike ripples removed was higher than the proportion of spikes (P < 0.001), spike-gamma (P < 0.001), wideband HFOs (P < 0.001), ripples (P = 0.009) and fast ripples (P = 0.009) removed. At the individual level, more subjects with ILAE 1 outcomes had the majority of spike ripples removed (79%, 38/48) than spikes (69%, P = 0.12), spike-gamma (69%, P = 0.12), wideband HFOs (63%, P = 0.03), ripples (45%, P = 0.01) or fast ripples (36%, P < 0.001) removed. Thus, in this large, multicentre cohort, when surgical resection was successful, the majority of spike ripples were removed. Furthermore, automatically detected spike ripples localize the epileptogenic tissue better than spikes, spike-gamma, wideband HFOs, ripples and fast ripples.
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Affiliation(s)
- Wen Shi
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Dana Shaw
- Graduate Program in Neuroscience, Boston University, Boston, MA 02215, USA
| | - Katherine G Walsh
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Xue Han
- Center for Systems Neuroscience, Boston University, Boston, MA 02215, USA
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Uri T Eden
- Center for Systems Neuroscience, Boston University, Boston, MA 02215, USA
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
| | - Robert M Richardson
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Stephen V Gliske
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Julia Jacobs
- Department of Neuropediatrics and Muscle Disorders, Medical Center, University of Freiburg, Freiburg 79106, Germany
- Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
- Department of Neuroscience, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
- Hotchkiss Brain Institute and Alberta Children’s Hospital Research Institute, University of Calgary, Calgary T2N 1N4, AB, Canada
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, MN 55905, USA
| | - Gregory A Worrell
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, MN 55905, USA
| | - William C Stacey
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Birgit Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 0G4, Canada
- Analytical Neurophysiology Lab, Department of Neurology, Duke University Medical Center, Durham, NC 27710, USA
- Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC 27708, USA
| | - John Thomas
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 0G4, Canada
| | - Mark A Kramer
- Center for Systems Neuroscience, Boston University, Boston, MA 02215, USA
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
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11
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Wang Z, Guo J, van 't Klooster M, Hoogteijling S, Jacobs J, Zijlmans M. Prognostic Value of Complete Resection of the High-Frequency Oscillation Area in Intracranial EEG: A Systematic Review and Meta-Analysis. Neurology 2024; 102:e209216. [PMID: 38560817 PMCID: PMC11175645 DOI: 10.1212/wnl.0000000000209216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 01/12/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND AND OBJECTIVES High-frequency oscillations (HFOs; ripples 80-250 Hz; fast ripples [FRs] 250-500 Hz) recorded with intracranial electrodes generated excitement and debate about their potential to localize epileptogenic foci. We performed a systematic review and meta-analysis on the prognostic value of complete resection of the HFOs-area (crHFOs-area) for epilepsy surgical outcome in intracranial EEG (iEEG) accessing multiple subgroups. METHODS We searched PubMed, Embase, and Web of Science for original research from inception to October 27, 2022. We defined favorable surgical outcome (FSO) as Engel class I, International League Against Epilepsy class 1, or seizure-free status. The prognostic value of crHFOs-area for FSO was assessed by (1) the pooled FSO proportion after crHFOs-area; (2) FSO for crHFOs-area vs without crHFOs-area; and (3) the predictive performance. We defined high combined prognostic value as FSO proportion >80% + FSO crHFOs-area >without crHFOs-area + area under the curve (AUC) >0.75 and examined this for the clinical subgroups (study design, age, diagnostic type, HFOs-identification method, HFOs-rate thresholding, and iEEG state). Temporal lobe epilepsy (TLE) was compared with extra-TLE through dichotomous variable analysis. Individual patient analysis was performed for sex, affected hemisphere, MRI findings, surgery location, and pathology. RESULTS Of 1,387 studies screened, 31 studies (703 patients) met our eligibility criteria. Twenty-seven studies (602 patients) analyzed FRs and 20 studies (424 patients) ripples. Pooled FSO proportion after crHFOs-area was 81% (95% CI 76%-86%) for FRs and 82% (73%-89%) for ripples. Patients with crHFOs-area achieved more often FSO than those without crHFOs-area (FRs odds ratio [OR] 6.38, 4.03-10.09, p < 0.001; ripples 4.04, 2.32-7.04, p < 0.001). The pooled AUCs were 0.81 (0.77-0.84) for FRs and 0.76 (0.72-0.79) for ripples. Combined prognostic value was high in 10 subgroups: retrospective, children, long-term iEEG, threshold (FRs and ripples) and automated detection and interictal (FRs). FSO after complete resection of FRs-area (crFRs-area) was achieved less often in people with TLE than extra-TLE (OR 0.37, 0.15-0.89, p = 0.006). Individual patient analyses showed that crFRs-area was seen more in patients with FSO with than without MRI lesions (p = 0.02 after multiple correction). DISCUSSION Complete resection of the brain area with HFOs is associated with good postsurgical outcome. Its prognostic value holds, especially for FRs, for various subgroups. The use of HFOs for extra-TLE patients requires further evidence.
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Affiliation(s)
- Ziyi Wang
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
| | - Jiaojiao Guo
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
| | - Maryse van 't Klooster
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
| | - Sem Hoogteijling
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
| | - Julia Jacobs
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
| | - Maeike Zijlmans
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
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12
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Cai Z, Jiang X, Bagić A, Worrell GA, Richardson M, He B. Spontaneous HFO Sequences Reveal Propagation Pathways for Precise Delineation of Epileptogenic Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.02.592202. [PMID: 38746136 PMCID: PMC11092614 DOI: 10.1101/2024.05.02.592202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Epilepsy, a neurological disorder affecting millions worldwide, poses great challenges in precisely delineating the epileptogenic zone - the brain region generating seizures - for effective treatment. High-frequency oscillations (HFOs) are emerging as promising biomarkers; however, the clinical utility is hindered by the difficulties in distinguishing pathological HFOs from non- epileptiform activities at single electrode and single patient resolution and understanding their dynamic role in epileptic networks. Here, we introduce an HFO-sequencing approach to analyze spontaneous HFOs traversing cortical regions in 40 drug-resistant epilepsy patients. This data- driven method automatically detected over 8.9 million HFOs, pinpointing pathological HFO- networks, and unveiled intricate millisecond-scale spatiotemporal dynamics, stability, and functional connectivity of HFOs in prolonged intracranial EEG recordings. These HFO sequences demonstrated a significant improvement in localization of epileptic tissue, with an 818.47% increase in concordance with seizure-onset zone (mean error: 2.92 mm), compared to conventional benchmarks. They also accurately predicted seizure outcomes for 90% AUC based on pre-surgical information using generalized linear models. Importantly, this mapping remained reliable even with short recordings (mean standard deviation: 3.23 mm for 30-minute segments). Furthermore, HFO sequences exhibited distinct yet highly repetitive spatiotemporal patterns, characterized by pronounced synchrony and predominant inward information flow from periphery towards areas involved in propagation, suggesting a crucial role for excitation-inhibition balance in HFO initiation and progression. Together, these findings shed light on the intricate organization of epileptic network and highlight the potential of HFO-sequencing as a translational tool for improved diagnosis, surgical targeting, and ultimately, better outcomes for vulnerable patients with drug-resistant epilepsy. One Sentence Summary Pathological fast brain oscillations travel like traffic along varied routes, outlining recurrently visited neural sites emerging as critical hotspots in epilepsy network.
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13
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Qu Z, Luo J, Chen X, Zhang Y, Yu S, Shu H. Association between Removal of High-Frequency Oscillations and the Effect of Epilepsy Surgery: A Meta-Analysis. J Neurol Surg A Cent Eur Neurosurg 2024; 85:294-301. [PMID: 37918885 PMCID: PMC10984718 DOI: 10.1055/a-2202-9344] [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: 07/05/2023] [Accepted: 09/11/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND High-frequency oscillations (HFOs) are spontaneous electroencephalographic (EEG) events that occur within the frequency range of 80 to 500 Hz and consist of at least four distinct oscillations that stand out from the background activity. They can be further classified into "ripples" (80-250 Hz) and "fast ripples" (FR; 250-500 Hz) based on different frequency bands. Studies have indicated that HFOs may serve as important markers for identifying epileptogenic regions and networks in patients with refractory epilepsy. Furthermore, a higher extent of removal of brain regions generating HFOs could potentially lead to improved prognosis. However, the clinical application criteria for HFOs remain controversial, and the results from different research groups exhibit inconsistencies. Given this controversy, the aim of this study was to conduct a meta-analysis to explore the utility of HFOs in predicting postoperative seizure outcomes by examining the prognosis of refractory epilepsy patients with varying ratios of HFO removal. METHODS Prospective and retrospective studies that analyzed HFOs and postoperative seizure outcomes in epilepsy patients who underwent resective surgery were included in the meta-analysis. The patients in these studies were grouped based on the ratio of HFOs removed, resulting in four groups: completely removed FR (C-FR), completely removed ripples (C-Ripples), mostly removed FR (P-FR), and partial ripples removal (P-Ripples). The prognosis of patients within each group was compared to investigate the correlation between the ratio of HFO removal and patient prognosis. RESULTS A total of nine studies were included in the meta-analysis. The prognosis of patients in the C-FR group was significantly better than that of patients with incomplete FR removal (odds ratio [OR] = 6.62; 95% confidence interval [CI]: 3.10-14.15; p < 0.00001). Similarly, patients in the C-Ripples group had a more favorable prognosis compared with those with incomplete ripples removal (OR = 4.45; 95% CI: 1.33-14.89; p = 0.02). Patients in the P-FR group had better prognosis than those with a majority of FR remaining untouched (OR = 6.23; 95% CI: 2.04-19.06; p = 0.001). In the P-Ripples group, the prognosis of patients with a majority of ripples removed was superior to that of patients with a majority of ripples remaining untouched (OR = 8.14; 95% CI: 2.62-25.33; p = 0.0003). CONCLUSIONS There is a positive correlation between the greater removal of brain regions generating HFOs and more favorable postoperative seizure outcomes. However, further investigations, particularly through clinical trials, are necessary to justify the clinical application of HFOs in guiding epilepsy surgery.
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Affiliation(s)
- Zhichuang Qu
- Department of Neurosurgery, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
| | - Juan Luo
- Department of Neurosurgery, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
| | - Xin Chen
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
| | - Yuanyuan Zhang
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
- Southwest Jiaotong University, Chengdu, China
| | - Sixun Yu
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
| | - Haifeng Shu
- Department of Neurosurgery, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
- Southwest Jiaotong University, Chengdu, China
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14
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Ye H, Chen C, Weiss SA, Wang S. Pathological and Physiological High-frequency Oscillations on Electroencephalography in Patients with Epilepsy. Neurosci Bull 2024; 40:609-620. [PMID: 37999861 PMCID: PMC11127900 DOI: 10.1007/s12264-023-01150-6] [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: 05/21/2023] [Accepted: 09/28/2023] [Indexed: 11/25/2023] Open
Abstract
High-frequency oscillations (HFOs) encompass ripples (80 Hz-200 Hz) and fast ripples (200 Hz-600 Hz), serving as a promising biomarker for localizing the epileptogenic zone in epilepsy. Spontaneous fast ripples are always pathological, while ripples may be physiological or pathological. Distinguishing physiological from pathological ripples is important not only for designating epileptogenic brain regions, but also for investigations that study ripples in the context of memory encoding, consolidation, and recall in patients with epilepsy. Many studies have sought to identify distinguishing features between pathological and physiological ripples over the past two decades. Physiological and pathological ripples differ with respect to their spatial location, cellular mechanisms, morphology, and coupling with background electroencephalographic activity. Retrospective studies have demonstrated that differentiating between pathological and physiological ripples can improve surgical outcome prediction. In this review, we summarize the characteristics, differences, and applications of pathological and physiological HFOs and discuss strategies for their clinical translation.
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Affiliation(s)
- Hongyi Ye
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China
| | - Cong Chen
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China
| | - Shennan A Weiss
- Department of Neurology, State University of New York Downstate, Brooklyn, NY, 11203, USA
- Department of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, NY, 11203, USA
- Department of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY, 11203, USA
| | - Shuang Wang
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China.
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15
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Chaibi S, Mahjoub C, Ayadi W, Kachouri A. Epileptic EEG patterns recognition through machine learning techniques and relevant time-frequency features. BIOMED ENG-BIOMED TE 2024; 69:111-123. [PMID: 37899292 DOI: 10.1515/bmt-2023-0332] [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: 12/13/2022] [Accepted: 10/09/2023] [Indexed: 10/31/2023]
Abstract
OBJECTIVES The present study is designed to explore the process of epileptic patterns' automatic detection, specifically, epileptic spikes and high-frequency oscillations (HFOs), via a selection of machine learning (ML) techniques. The primary motivation for conducting such a research lies mainly in the need to investigate the long-term electroencephalography (EEG) recordings' visual examination process, often considered as a time-consuming and potentially error-prone procedure, requiring a great deal of mental focus and highly experimented neurologists. On attempting to resolve such a challenge, a number of state-of-the-art ML algorithms have been evaluated and compare in terms of performance, to pinpoint the most effective algorithm fit for accurately extracting epileptic EEG patterns. CONTENT Based on intracranial as well as simulated EEG data, the attained findings turn out to reveal that the randomforest (RF) method proved to be the most consistently effective approach, significantly outperforming the entirety of examined methods in terms of EEG recordings epileptic-pattern identification. Indeed, the RF classifier appeared to record an average balanced classification rate (BCR) of 92.38 % in regard to spikes recognition process, and 78.77 % in terms of HFOs detection. SUMMARY Compared to other approaches, our results provide valuable insights into the RF classifier's effectiveness as a powerful ML technique, fit for detecting EEG signals born epileptic bursts. OUTLOOK As a potential future work, we envisage to further validate and sustain our major reached findings through incorporating a larger EEG dataset. We also aim to explore the generative adversarial networks (GANs) application so as to generate synthetic EEG signals or combine signal generation techniques with deep learning approaches. Through this new vein of thought, we actually preconize to enhance and boost the automated detection methods' performance even more, thereby, noticeably enhancing the epileptic EEG pattern recognition area.
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Affiliation(s)
- Sahbi Chaibi
- AFD2E Laboratory, National Engineering School, Sfax University, Sfax, Tunisia
- Faculty of Sciences of Monastir, Monastir University, Monastir, Tunisia
| | - Chahira Mahjoub
- AFD2E Laboratory, National Engineering School, Sfax University, Sfax, Tunisia
| | - Wadhah Ayadi
- Faculty of Sciences of Monastir, Monastir University, Monastir, Tunisia
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16
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Karimi-Rouzbahani H, McGonigal A. Generalisability of epileptiform patterns across time and patients. Sci Rep 2024; 14:6293. [PMID: 38491096 PMCID: PMC10942983 DOI: 10.1038/s41598-024-56990-7] [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: 01/20/2024] [Accepted: 03/13/2024] [Indexed: 03/18/2024] Open
Abstract
The complexity of localising the epileptogenic zone (EZ) contributes to surgical resection failures in achieving seizure freedom. The distinct patterns of epileptiform activity during interictal and ictal phases, varying across patients, often lead to suboptimal localisation using electroencephalography (EEG) features. We posed two key questions: whether neural signals reflecting epileptogenicity generalise from interictal to ictal time windows within each patient, and whether epileptiform patterns generalise across patients. Utilising an intracranial EEG dataset from 55 patients, we extracted a large battery of simple to complex features from stereo-EEG (SEEG) and electrocorticographic (ECoG) neural signals during interictal and ictal windows. Our features (n = 34) quantified many aspects of the signals including statistical moments, complexities, frequency-domain and cross-channel network attributes. Decision tree classifiers were then trained and tested on distinct time windows and patients to evaluate the generalisability of epileptogenic patterns across time and patients, respectively. Evidence strongly supported generalisability from interictal to ictal time windows across patients, particularly in signal power and high-frequency network-based features. Consistent patterns of epileptogenicity were observed across time windows within most patients, and signal features of epileptogenic regions generalised across patients, with higher generalisability in the ictal window. Signal complexity features were particularly contributory in cross-patient generalisation across patients. These findings offer insights into generalisable features of epileptic neural activity across time and patients, with implications for future automated approaches to supplement other EZ localisation methods.
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Affiliation(s)
- Hamid Karimi-Rouzbahani
- Neurosciences Centre, Mater Hospital, South Brisbane, 4101, Australia.
- Mater Research Institute, University of Queensland, South Brisbane, 4101, Australia.
- Queensland Brain Institute, University of Queensland, St Lucia, 4072, Australia.
| | - Aileen McGonigal
- Neurosciences Centre, Mater Hospital, South Brisbane, 4101, Australia
- Mater Research Institute, University of Queensland, South Brisbane, 4101, Australia
- Queensland Brain Institute, University of Queensland, St Lucia, 4072, Australia
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17
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Medina Villalon S, Makhalova J, López-Madrona VJ, Garnier E, Badier JM, Bartolomei F, Bénar CG. Combining independent component analysis and source localization for improving spatial sampling of stereoelectroencephalography in epilepsy. Sci Rep 2024; 14:4071. [PMID: 38374380 PMCID: PMC10876572 DOI: 10.1038/s41598-024-54359-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 02/12/2024] [Indexed: 02/21/2024] Open
Abstract
Stereoelectroencephalography is a powerful intracerebral EEG recording method for the presurgical evaluation of epilepsy. It consists in implanting depth electrodes in the patient's brain to record electrical activity and map the epileptogenic zone, which should be resected to render the patient seizure-free. Stereoelectroencephalography has high spatial accuracy and signal-to-noise ratio but remains limited in the coverage of the explored brain regions. Thus, the implantation might provide a suboptimal sampling of epileptogenic regions. We investigate the potential of improving a suboptimal stereoelectroencephalography recording by performing source localization on stereoelectroencephalography signals. We propose combining independent component analysis, connectivity measures to identify components of interest, and distributed source modelling. This approach was tested on two patients with two implantations each, the first failing to characterize the epileptogenic zone and the second giving a better diagnosis. We demonstrate that ictal and interictal source localization performed on the first stereoelectroencephalography recordings matches the findings of the second stereo-EEG exploration. Our findings suggest that independent component analysis followed by source localization on the topographies of interest is a promising method for retrieving the epileptogenic zone in case of suboptimal implantation.
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Affiliation(s)
- Samuel Medina Villalon
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Julia Makhalova
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | | | - Elodie Garnier
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | | | - Fabrice Bartolomei
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Christian G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
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18
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Geller AS, Teale P, Kronberg E, Ebersole JS. Magnetoencephalography for Epilepsy Presurgical Evaluation. Curr Neurol Neurosci Rep 2024; 24:35-46. [PMID: 38148387 DOI: 10.1007/s11910-023-01328-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/13/2023] [Indexed: 12/28/2023]
Abstract
PURPOSE OF THE REVIEW Magnetoencephalography (MEG) is a functional neuroimaging technique that records neurophysiology data with millisecond temporal resolution and localizes it with subcentimeter accuracy. Its capability to provide high resolution in both of these domains makes it a powerful tool both in basic neuroscience as well as clinical applications. In neurology, it has proven useful in its ability to record and localize epileptiform activity. Epilepsy workup typically begins with scalp electroencephalography (EEG), but in many situations, EEG-based localization of the epileptogenic zone is inadequate. The complementary sensitivity of MEG can be crucial in such cases, and MEG has been adopted at many centers as an important resource in building a surgical hypothesis. In this paper, we review recent work evaluating the extent of MEG influence of presurgical evaluations, novel analyses of MEG data employed in surgical workup, and new MEG instrumentation that will likely affect the field of clinical MEG. RECENT FINDINGS MEG consistently contributes to presurgical evaluation and these contributions often change the plan for epilepsy surgery. Extensive work has been done to develop new analytic methods for localizing the source of epileptiform activity with MEG. Systems using optically pumped magnetometry (OPM) have been successfully deployed to record and localize epileptiform activity. MEG remains an important noninvasive tool for epilepsy presurgical evaluation. Continued improvements in analytic methodology will likely increase the diagnostic yield of the test. Novel instrumentation with OPM may contribute to this as well, and may increase accessibility of MEG by decreasing cost.
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Affiliation(s)
- Aaron S Geller
- Department of Neurology, CU Anschutz Medical School, Aurora, CO, USA.
| | - Peter Teale
- Department of Neurology, CU Anschutz Medical School, Aurora, CO, USA
| | - Eugene Kronberg
- Department of Neurology, CU Anschutz Medical School, Aurora, CO, USA
| | - John S Ebersole
- Department of Neurology, Atlantic Neuroscience Institute, Summit, NJ, USA
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19
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Feys O, Wens V, Rovai A, Schuind S, Rikir E, Legros B, De Tiège X, Gaspard N. Delayed effective connectivity characterizes the epileptogenic zone during stereo-EEG. Clin Neurophysiol 2024; 158:59-68. [PMID: 38183887 DOI: 10.1016/j.clinph.2023.12.013] [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: 09/25/2023] [Revised: 11/11/2023] [Accepted: 12/19/2023] [Indexed: 01/08/2024]
Abstract
OBJECTIVE Single-pulse electrical stimulations (SPES) can elicit normal and abnormal responses that might characterize the epileptogenic zone, including spikes, high-frequency oscillations and cortico-cortical evoked potentials (CCEPs). In this study, we investigate their association with the epileptogenic zone during stereoelectroencephalography (SEEG) in 28 patients with refractory focal epilepsy. METHODS Characteristics of CCEPs (distance-corrected or -uncorrected latency, amplitude and the connectivity index) and the occurrence of spikes and ripples were assessed. Responses within the epileptogenic zone and within the non-involved zone were compared using receiver operating characteristics curves and analysis of variance (ANOVA) either in all patients, patients with well-delineated epileptogenic zone, and patients older than 15 years old. RESULTS We found an increase in distance-corrected CCEPs latency after stimulation within the epileptogenic zone (area under the curve = 0.71, 0.72, 0.70, ANOVA significant after false discovery rate correction). CONCLUSIONS The increased distance-corrected CCEPs latency suggests that neuronal propagation velocity is altered within the epileptogenic network. This association might reflect effective connectivity changes at cortico-cortical or cortico-subcortico-cortical levels. Other responses were not associated with the epileptogenic zone, including the CCEPs amplitude, the connectivity index, the occurrences of induced ripples and spikes. The discrepancy with previous descriptions may be explained by different spatial brain sampling between subdural and depth electrodes. SIGNIFICANCE Increased distance-corrected CCEPs latency, indicating delayed effective connectivity, characterizes the epileptogenic zone. This marker could be used to help tailor surgical resection limits after SEEG.
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Affiliation(s)
- Odile Feys
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB) - Hôpital Erasme, Department of Neurology, Bruxelles, Belgium; Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et Neuroimagerie translationnelles (LN(2)T), Bruxelles, Belgium.
| | - Vincent Wens
- Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et Neuroimagerie translationnelles (LN(2)T), Bruxelles, Belgium; Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB) - Hôpital Erasme, Department of Translational Neuroimaging, Bruxelles, Belgium
| | - Antonin Rovai
- Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et Neuroimagerie translationnelles (LN(2)T), Bruxelles, Belgium; Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB) - Hôpital Erasme, Department of Translational Neuroimaging, Bruxelles, Belgium
| | - Sophie Schuind
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB) - Hôpital Erasme, Department of Neurosurgery, Bruxelles, Belgium
| | - Estelle Rikir
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB) - Hôpital Erasme, Department of Neurology, Bruxelles, Belgium
| | - Benjamin Legros
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB) - Hôpital Erasme, Department of Neurology, Bruxelles, Belgium
| | - Xavier De Tiège
- Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratoire de Neuroanatomie et Neuroimagerie translationnelles (LN(2)T), Bruxelles, Belgium; Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB) - Hôpital Erasme, Department of Translational Neuroimaging, Bruxelles, Belgium
| | - Nicolas Gaspard
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB) - Hôpital Erasme, Department of Neurology, Bruxelles, Belgium; Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Laboratory of Experimental Neurology, Bruxelles, Belgium; Yale University, Department of Neurology, New Haven, CT, USA
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20
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Gerstl JVE, Kiseleva A, Imbach L, Sarnthein J, Fedele T. High frequency oscillations in relation to interictal spikes in predicting postsurgical seizure freedom. Sci Rep 2023; 13:21313. [PMID: 38042925 PMCID: PMC10693609 DOI: 10.1038/s41598-023-48764-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 11/30/2023] [Indexed: 12/04/2023] Open
Abstract
We evaluate whether interictal spikes, epileptiform HFOs and their co-occurrence (Spike + HFO) were included in the resection area with respect to seizure outcome. We also characterise the relationship between high frequency oscillations (HFOs) and propagating spikes. We analysed intracranial EEG of 20 patients that underwent resective epilepsy surgery. The co-occurrence of ripples and fast ripples was considered an HFO event; the co-occurrence of an interictal spike and HFO was considered a Spike + HFO event. HFO distribution and spike onset were compared in cases of spike propagation. Accuracy in predicting seizure outcome was 85% for HFO, 60% for Spikes, and 79% for Spike + HFO. Sensitivity was 57% for HFO, 71% for Spikes and 67% for Spikes + HFO. Specificity was 100% for HFO, 54% for Spikes and 85% for Spikes + HFO. In 2/2 patients with spike propagation, the spike onset included the HFO area. Combining interictal spikes with HFO had comparable accuracy to HFO. In patients with propagating spikes, HFO rate was maximal at the onset of spike propagation.
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Affiliation(s)
- Jakob V E Gerstl
- University College London Medical School, London, UK
- Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Alina Kiseleva
- Institute for Cognitive Neuroscience, HSE University, Myasnitskaya Ulitsa, 20, Moscow, Russian Federation, 101000
| | - Lukas Imbach
- Swiss Epilepsy Center, Klinik Lengg, Zurich, Switzerland
| | - Johannes Sarnthein
- Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Tommaso Fedele
- Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland.
- Institute for Cognitive Neuroscience, HSE University, Myasnitskaya Ulitsa, 20, Moscow, Russian Federation, 101000.
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21
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Ramantani G, Westover MB, Gliske S, Sarnthein J, Sarma S, Wang Y, Baud MO, Stacey WC, Conrad EC. Passive and active markers of cortical excitability in epilepsy. Epilepsia 2023; 64 Suppl 3:S25-S36. [PMID: 36897228 PMCID: PMC10512778 DOI: 10.1111/epi.17578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/07/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023]
Abstract
Electroencephalography (EEG) has been the primary diagnostic tool in clinical epilepsy for nearly a century. Its review is performed using qualitative clinical methods that have changed little over time. However, the intersection of higher resolution digital EEG and analytical tools developed in the past decade invites a re-exploration of relevant methodology. In addition to the established spatial and temporal markers of spikes and high-frequency oscillations, novel markers involving advanced postprocessing and active probing of the interictal EEG are gaining ground. This review provides an overview of the EEG-based passive and active markers of cortical excitability in epilepsy and of the techniques developed to facilitate their identification. Several different emerging tools are discussed in the context of specific EEG applications and the barriers we must overcome to translate these tools into clinical practice.
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Affiliation(s)
- Georgia Ramantani
- Department of Neuropediatrics and Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - M Brandon Westover
- Department of Neurology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Data Science, Massachusetts General Hospital McCance Center for Brain Health, Boston, Massachusetts, USA
- Research Affiliate Faculty, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Research Affiliate Faculty, Broad Institute, Cambridge, Massachusetts, USA
| | - Stephen Gliske
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Johannes Sarnthein
- Department of Neurosurgery, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Sridevi Sarma
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems, School of Computing Science, Newcastle University, Newcastle Upon Tyne, UK
| | - Maxime O Baud
- Sleep-Wake-Epilepsy Center, NeuroTec, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | - William C Stacey
- Department of Neurology, BioInterfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
- Division of Neurology, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
| | - Erin C Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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22
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Makhalova J, Madec T, Medina Villalon S, Jegou A, Lagarde S, Carron R, Scavarda D, Garnier E, Bénar CG, Bartolomei F. The role of quantitative markers in surgical prognostication after stereoelectroencephalography. Ann Clin Transl Neurol 2023; 10:2114-2126. [PMID: 37735846 PMCID: PMC10646998 DOI: 10.1002/acn3.51900] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/26/2023] [Accepted: 09/08/2023] [Indexed: 09/23/2023] Open
Abstract
OBJECTIVE Stereoelectroencephalography (SEEG) is the reference method in the presurgical exploration of drug-resistant focal epilepsy. However, prognosticating surgery on an individual level is difficult. A quantified estimation of the most epileptogenic regions by searching for relevant biomarkers can be proposed for this purpose. We investigated the performances of ictal (Epileptogenicity Index, EI; Connectivity EI, cEI), interictal (spikes, high-frequency oscillations, HFO [80-300 Hz]; Spikes × HFO), and combined (Spikes × EI; Spikes × cEI) biomarkers in predicting surgical outcome and searched for prognostic factors based on SEEG-signal quantification. METHODS Fifty-three patients operated on following SEEG were included. We compared, using precision-recall, the epileptogenic zone quantified using different biomarkers (EZq ) against the visual analysis (EZC ). Correlations between the EZ resection rates or the EZ extent and surgical prognosis were analyzed. RESULTS EI and Spikes × EI showed the best precision against EZc (0.74; 0.70), followed by Spikes × cEI and cEI, whereas interictal markers showed lower precision. The EZ resection rates were greater in seizure-free than in non-seizure-free patients for the EZ defined by ictal biomarkers and were correlated with the outcome for EI and Spikes × EI. No such correlation was found for interictal markers. The extent of the quantified EZ did not correlate with the prognosis. INTERPRETATION Ictal or combined ictal-interictal markers overperformed the interictal markers both for detecting the EZ and predicting seizure freedom. Combining ictal and interictal epileptogenicity markers improves detection accuracy. Resection rates of the quantified EZ using ictal markers were the only statistically significant determinants for surgical prognosis.
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Affiliation(s)
- Julia Makhalova
- APHM, Timone Hospital, Epileptology and Cerebral RhythmologyMarseilleFrance
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
- Aix Marseille Univ, CNRS, CRMBMMarseilleFrance
| | - Tanguy Madec
- APHM, Timone Hospital, Epileptology and Cerebral RhythmologyMarseilleFrance
| | - Samuel Medina Villalon
- APHM, Timone Hospital, Epileptology and Cerebral RhythmologyMarseilleFrance
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
| | - Aude Jegou
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
| | - Stanislas Lagarde
- APHM, Timone Hospital, Epileptology and Cerebral RhythmologyMarseilleFrance
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
| | - Romain Carron
- APHM, Timone Hospital, Functional, and Stereotactic NeurosurgeryMarseilleFrance
| | | | - Elodie Garnier
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
| | | | - Fabrice Bartolomei
- APHM, Timone Hospital, Epileptology and Cerebral RhythmologyMarseilleFrance
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
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23
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Shamas M, Yeh HJ, Fried I, Engel J, Staba RJ. High-rate leading spikes in propagating spike sequences predict seizure outcome in surgical patients with temporal lobe epilepsy. Brain Commun 2023; 5:fcad289. [PMID: 37953846 PMCID: PMC10636565 DOI: 10.1093/braincomms/fcad289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 08/14/2023] [Accepted: 10/23/2023] [Indexed: 11/14/2023] Open
Abstract
Inter-ictal spikes aid in the diagnosis of epilepsy and in planning surgery of medication-resistant epilepsy. However, the localizing information from spikes can be unreliable because spikes can propagate, and the burden of spikes, often assessed as a rate, does not always correlate with the seizure onset zone or seizure outcome. Recent work indicates identifying where spikes regularly emerge and spread could localize the seizure network. Thus, the current study sought to better understand where and how rates of single and coupled spikes, and especially brain regions with high-rate and leading spike of a propagating sequence, informs the extent of the seizure network. In 37 patients with medication-resistant temporal lobe seizures, who had surgery to treat their seizure disorder, an algorithm detected spikes in the pre-surgical depth inter-ictal EEG. A separate algorithm detected spike propagation sequences and identified the location of leading and downstream spikes in each sequence. We analysed the rate and power of single spikes on each electrode and coupled spikes between pairs of electrodes, and the proportion of sites with high-rate, leading spikes in relation to the seizure onset zone of patients seizure free (n = 19) and those with continuing seizures (n = 18). We found increased rates of single spikes in mesial temporal seizure onset zone (ANOVA, P < 0.001, η2 = 0.138), and increased rates of coupled spikes within, but not between, mesial-, lateral- and extra-temporal seizure onset zone of patients with continuing seizures (P < 0.001; η2 = 0.195, 0.113 and 0.102, respectively). In these same patients, there was a higher proportion of brain regions with high-rate leaders, and each sequence contained a greater number of spikes that propagated with a higher efficiency over a longer distance outside the seizure onset zone than patients seizure free (Wilcoxon, P = 0.0172). The proportion of high-rate leaders in and outside the seizure onset zone could predict seizure outcome with area under curve = 0.699, but not rates of single or coupled spikes (0.514 and 0.566). Rates of coupled spikes to a greater extent than single spikes localize the seizure onset zone and provide evidence for inter-ictal functional segregation, which could be an adaptation to avert seizures. Spike rates, however, have little value in predicting seizure outcome. High-rate spike sites leading propagation could represent sources of spikes that are important components of an efficient seizure network beyond the clinical seizure onset zone, and like the seizure onset zone these, too, need to be removed, disconnected or stimulated to increase the likelihood for seizure control.
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Affiliation(s)
- Mohamad Shamas
- David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Hsiang J Yeh
- David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Itzhak Fried
- David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Jerome Engel
- David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Richard J Staba
- David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
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24
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Sakakura K, Kuroda N, Sonoda M, Mitsuhashi T, Firestone E, Luat AF, Marupudi NI, Sood S, Asano E. Developmental atlas of phase-amplitude coupling between physiologic high-frequency oscillations and slow waves. Nat Commun 2023; 14:6435. [PMID: 37833252 PMCID: PMC10575956 DOI: 10.1038/s41467-023-42091-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
We investigated the developmental changes in high-frequency oscillation (HFO) and Modulation Index (MI) - the coupling measure between HFO and slow-wave phase. We generated normative brain atlases, using subdural EEG signals from 8251 nonepileptic electrode sites in 114 patients (ages 1.0-41.5 years) who achieved seizure control following resective epilepsy surgery. We observed a higher MI in the occipital lobe across all ages, and occipital MI increased notably during early childhood. The cortical areas exhibiting MI co-growth were connected via the vertical occipital fasciculi and posterior callosal fibers. While occipital HFO rate showed no significant age-association, the temporal, frontal, and parietal lobes exhibited an age-inversed HFO rate. Assessment of 1006 seizure onset sites revealed that z-score normalized MI and HFO rate were higher at seizure onset versus nonepileptic electrode sites. We have publicly shared our intracranial EEG data to enable investigators to validate MI and HFO-centric presurgical evaluations to identify the epileptogenic zone.
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Affiliation(s)
- Kazuki Sakakura
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
- Department of Neurosurgery, Rush University Medical Center, Chicago, IL, 60612, USA
- Department of Neurosurgery, University of Tsukuba, Tsukuba, 3058575, Japan
| | - Naoto Kuroda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, 9808575, Japan
| | - Masaki Sonoda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
- Department of Neurosurgery, Yokohama City University, Yokohama-shi, 2360004, Japan
| | - Takumi Mitsuhashi
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
- Department of Neurosurgery, Juntendo University, Tokyo, 1138421, Japan
| | - Ethan Firestone
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
- Department of Physiology, Wayne State University, Detroit, MI, 48201, USA
| | - Aimee F Luat
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
- Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
- Department of Pediatrics, Central Michigan University, Mount Pleasant, MI, 48858, USA
| | - Neena I Marupudi
- Department of Neurosurgery, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
| | - Sandeep Sood
- Department of Neurosurgery, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
| | - Eishi Asano
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA.
- Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA.
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25
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Tani H, Tateishi Y, Kobayashi Y, Ishikawa N. HFA analysis using scalp electroencephalograms in two cases of Rasmussen's syndrome. Epilepsy Res 2023; 196:107205. [PMID: 37611340 DOI: 10.1016/j.eplepsyres.2023.107205] [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: 06/12/2023] [Revised: 08/07/2023] [Accepted: 08/07/2023] [Indexed: 08/25/2023]
Abstract
OBJECTIVE In recent years, wide-band EEGs have been used to assess brain activity, and their effectiveness in the pathological analysis of epilepsy has been demonstrated. This report describes two cases of Rasmussen's syndrome (RS) in which high-frequency scalp EEGs were retrospectively analyzed to assess the pathological condition of epilepsy in RS. METHODS The two RS cases were divided into three periods: incipient, stable, and frequent seizure periods. Using the EEG record of each period, interictal epileptiform discharges (IEDs) were visually extracted. Subsequently, a time-frequency analysis was performed to calculate the rate of high-frequency activities (HFAs) (IED-HFA rate). Finally, differences between the three periods were examined. RESULTS IED-HFA rates significantly increased in the frequent seizure period compared with the stable period in both cases(P < 0.05). CONCLUSION there was a significant increase in HFAs superimposed over IEDs during the frequent seizure period compared to the stable period. HFAs are thought to be associated with epileptogenicity. Similarly, HFAs could be a useful biomarker for the pathological condition of epilepsy in RS.
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Affiliation(s)
- Hiroo Tani
- Department of Pediatrics, Chugoku Rosai Hospital, Japan; Department of Pediatrics, Hiroshima University Hospital, Japan; Epilepsy Center, Hiroshima University Hospital, Japan.
| | - Yuichi Tateishi
- Department of Pediatrics, Hiroshima University Hospital, Japan; Epilepsy Center, Hiroshima University Hospital, Japan
| | - Yoshiyuki Kobayashi
- Department of Pediatrics, Hiroshima University Hospital, Japan; Epilepsy Center, Hiroshima University Hospital, Japan
| | - Nobutsune Ishikawa
- Department of Pediatrics, Hiroshima University Hospital, Japan; Epilepsy Center, Hiroshima University Hospital, Japan; Department of Pediatrics, Hiroshima Prefectural Hospital, Japan
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26
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Weiss SA, Fried I, Engel J, Sperling MR, Wong RKS, Nir Y, Staba RJ. Fast ripples reflect increased excitability that primes epileptiform spikes. Brain Commun 2023; 5:fcad242. [PMID: 37869578 PMCID: PMC10587774 DOI: 10.1093/braincomms/fcad242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/08/2023] [Accepted: 09/07/2023] [Indexed: 10/24/2023] Open
Abstract
The neuronal circuit disturbances that drive inter-ictal and ictal epileptiform discharges remain elusive. Using a combination of extra-operative macro-electrode and micro-electrode inter-ictal recordings in six pre-surgical patients during non-rapid eye movement sleep, we found that, exclusively in the seizure onset zone, fast ripples (200-600 Hz), but not ripples (80-200 Hz), frequently occur <300 ms before an inter-ictal intra-cranial EEG spike with a probability exceeding chance (bootstrapping, P < 1e-5). Such fast ripple events are associated with higher spectral power (P < 1e-10) and correlated with more vigorous neuronal firing than solitary fast ripple (generalized linear mixed-effects model, P < 1e-9). During the intra-cranial EEG spike that follows a fast ripple, action potential firing is lower than during an intra-cranial EEG spike alone (generalized linear mixed-effects model, P < 0.05), reflecting an inhibitory restraint of intra-cranial EEG spike initiation. In contrast, ripples do not appear to prime epileptiform spikes. We next investigated the clinical significance of pre-spike fast ripple in a separate cohort of 23 patients implanted with stereo EEG electrodes, who underwent resections. In non-rapid eye movement sleep recordings, sites containing a high proportion of fast ripple preceding intra-cranial EEG spikes correlate with brain areas where seizures begin more than solitary fast ripple (P < 1e-5). Despite this correlation, removal of these sites does not guarantee seizure freedom. These results are consistent with the hypothesis that fast ripple preceding EEG spikes reflect an increase in local excitability that primes EEG spike discharges preferentially in the seizure onset zone and that epileptogenic brain regions are necessary, but not sufficient, for initiating inter-ictal epileptiform discharges.
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Affiliation(s)
- Shennan A Weiss
- Department of Neurology, State University of New York Downstate, Brooklyn, NY 11203, USA
- Department of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, NY 11203, USA
- Department of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY 11203, USA
| | - Itzhak Fried
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Jerome Engel
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
- Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
- Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Michael R Sperling
- Departments of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Robert K S Wong
- Department of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, NY 11203, USA
| | - Yuval Nir
- Department of Physiology and Pharmacology, Sackler School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- The Sieratzki-Sagol Center for Sleep Medicine, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel
| | - Richard J Staba
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
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27
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Dellavale D, Bonini F, Pizzo F, Makhalova J, Wendling F, Badier JM, Bartolomei F, Bénar CG. Spontaneous fast-ultradian dynamics of polymorphic interictal events in drug-resistant focal epilepsy. Epilepsia 2023; 64:2027-2043. [PMID: 37199673 DOI: 10.1111/epi.17655] [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: 01/10/2023] [Revised: 05/16/2023] [Accepted: 05/16/2023] [Indexed: 05/19/2023]
Abstract
OBJECTIVE We studied the rate dynamics of interictal events occurring over fast-ultradian time scales, as commonly examined in clinics to guide surgical planning in epilepsy. METHODS Stereo-electroencephalography (SEEG) traces of 35 patients with good surgical outcome (Engel I) were analyzed. For this we developed a general data mining method aimed at clustering the plethora of transient waveform shapes including interictal epileptiform discharges (IEDs) and assessed the temporal fluctuations in the capability of mapping the epileptogenic zone (EZ) of each type of event. RESULTS We found that the fast-ultradian dynamics of the IED rate may effectively impair the precision of EZ identification, and appear to occur spontaneously, that is, not triggered by or exclusively associated with a particular cognitive task, wakefulness, sleep, seizure occurrence, post-ictal state, or antiepileptic drug withdrawal. Propagation of IEDs from the EZ to the propagation zone (PZ) could explain the observed fast-ultradian fluctuations in a reduced fraction of the analyzed patients, suggesting that other factors like the excitability of the epileptogenic tissue could play a more relevant role. A novel link was found between the fast-ultradian dynamics of the overall rate of polymorphic events and the rate of specific IEDs subtypes. We exploited this feature to estimate in each patient the 5 min interictal epoch for near-optimal EZ and resected-zone (RZ) localization. This approach produces at the population level a better EZ/RZ classification when compared to both (1) the whole time series available in each patient (p = .084 for EZ, p < .001 for RZ, Wilcoxon signed-rank test) and (2) 5 min epochs sampled randomly from the interictal recordings of each patient (p < .05 for EZ, p < .001 for RZ, 105 random samplings). SIGNIFICANCE Our results highlight the relevance of the fast-ultradian IED dynamics in mapping the EZ, and show how this dynamics can be estimated prospectively to inform surgical planning in epilepsy.
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Affiliation(s)
- Damián Dellavale
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
- Centro Atómico Bariloche and Instituto Balseiro, Comisión Nacional de Energía Atómica (CNEA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Cuyo (UNCUYO), Río Negro, San Carlos de Bariloche, Argentina
| | - Francesca Bonini
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
- Epileptology and Cerebral Rhythmology, APHM, Timone Hospital, Marseille, France
| | - Francesca Pizzo
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
- Epileptology and Cerebral Rhythmology, APHM, Timone Hospital, Marseille, France
| | - Julia Makhalova
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
- Epileptology and Cerebral Rhythmology, APHM, Timone Hospital, Marseille, France
| | | | - Jean-Michel Badier
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
| | - Fabrice Bartolomei
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
- Epileptology and Cerebral Rhythmology, APHM, Timone Hospital, Marseille, France
| | - Christian-George Bénar
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
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Sadoun MSN, Laleg-Kirati TM. Seizure Onset Localization in Focal Epilepsy using intracranial-EEG data and the Schrodinger Operator's Spectrum. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083081 DOI: 10.1109/embc40787.2023.10339987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Epilepsy is a neurological disorder characterized by recurrent, unprovoked seizures that vary from short attention failure to convulsions. Despite its threats and limitations, existing medications target only specific types of seizures while up to 33% of epileptic conditions are drug-resistant. The best available treatment is surgical resection or neurostimulation and both require accurate localization of the Seizure Onset Zone. Its delineation is performed by analyzing neuronal activity by epileptologists, however, it is time-consuming and error-prone. Therefore, if the said zone could be located faster and more accurately, the seizure freedom of patients would be significantly enhanced. An effort within the field is aiming at developing computer-aided methods to assist medical experts and this starts with characterizing electrical neural activity. In the present paper, a new method for characterizing the epileptic intracranial EEG is proposed. The method is based on a semi-classical signal analysis (SCSA) method. Functional connectivity measures are used to compare patterns observed when feeding these measures with the raw time-series and when feeding them with SCSA features. The obtained results are undeniably promising for further investigation and improvement of the framework.Clinical relevance- The paper contributes to the design methods and algorithms to build reliable software solutions to assist medical experts in identifying Seizure Onset Zone in focal epilepsy.
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Fabo D, Bokodi V, Szabó JP, Tóth E, Salami P, Keller CJ, Hajnal B, Thesen T, Devinsky O, Doyle W, Mehta A, Madsen J, Eskandar E, Erőss L, Ulbert I, Halgren E, Cash SS. The role of superficial and deep layers in the generation of high frequency oscillations and interictal epileptiform discharges in the human cortex. Sci Rep 2023; 13:9620. [PMID: 37316509 DOI: 10.1038/s41598-022-22497-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023] Open
Abstract
Describing intracortical laminar organization of interictal epileptiform discharges (IED) and high frequency oscillations (HFOs), also known as ripples. Defining the frequency limits of slow and fast ripples. We recorded potential gradients with laminar multielectrode arrays (LME) for current source density (CSD) and multi-unit activity (MUA) analysis of interictal epileptiform discharges IEDs and HFOs in the neocortex and mesial temporal lobe of focal epilepsy patients. IEDs were observed in 20/29, while ripples only in 9/29 patients. Ripples were all detected within the seizure onset zone (SOZ). Compared to hippocampal HFOs, neocortical ripples proved to be longer, lower in frequency and amplitude, and presented non-uniform cycles. A subset of ripples (≈ 50%) co-occurred with IEDs, while IEDs were shown to contain variable high-frequency activity, even below HFO detection threshold. The limit between slow and fast ripples was defined at 150 Hz, while IEDs' high frequency components form clusters separated at 185 Hz. CSD analysis of IEDs and ripples revealed an alternating sink-source pair in the supragranular cortical layers, although fast ripple CSD appeared lower and engaged a wider cortical domain than slow ripples MUA analysis suggested a possible role of infragranularly located neural populations in ripple and IED generation. Laminar distribution of peak frequencies derived from HFOs and IEDs, respectively, showed that supragranular layers were dominated by slower (< 150 Hz) components. Our findings suggest that cortical slow ripples are generated primarily in upper layers while fast ripples and associated MUA in deeper layers. The dissociation of macro- and microdomains suggests that microelectrode recordings may be more selective for SOZ-linked ripples. We found a complex interplay between neural activity in the neocortical laminae during ripple and IED formation. We observed a potential leading role of cortical neurons in deeper layers, suggesting a refined utilization of LMEs in SOZ localization.
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Affiliation(s)
- Daniel Fabo
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary.
| | - Virag Bokodi
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- Roska Tamás Doctoral School of Sciences and Technologies, Budapest, Hungary
| | - Johanna-Petra Szabó
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Budapest, Hungary
| | - Emilia Tóth
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- Department of Neurology, University of Texas, McGovern Medical School, Houston, TX, USA
| | - Pariya Salami
- Epilepsy Division, Department of Neurology, Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Corey J Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Boglárka Hajnal
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Budapest, Hungary
| | - Thomas Thesen
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, USA
- Department of Biomedical Sciences, College of Medicine, University of Houston, Houston, TX, USA
| | - Orrin Devinsky
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, USA
| | - Werner Doyle
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, USA
| | - Ashesh Mehta
- Department of Neurosurgery, Zucker School of Medicine at Hofstra/Northwell and Feinstein Institute for Medical Research, Manhasset, NY, USA
| | | | - Emad Eskandar
- Massachusetts General Hospital Neurosurgery Research, Boston, MA, USA
| | - Lorand Erőss
- Department of Functional Neurosurgery, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
| | - István Ulbert
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- Institute of Psychology, Eötvös Loránd Research Network, Budapest, Hungary
| | - Eric Halgren
- Department of Radiology, Neurosciences and Psychiatry, University of California, San Diego, San Diego, CA, USA
| | - Sydney S Cash
- Epilepsy Division, Department of Neurology, Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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Firestone E, Sonoda M, Kuroda N, Sakakura K, Jeong JW, Lee MH, Wada K, Takayama Y, Iijima K, Iwasaki M, Miyazaki T, Asano E. Sevoflurane-induced high-frequency oscillations, effective connectivity and intraoperative classification of epileptic brain areas. Clin Neurophysiol 2023; 150:17-30. [PMID: 36989866 PMCID: PMC10192072 DOI: 10.1016/j.clinph.2023.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/27/2023] [Accepted: 03/02/2023] [Indexed: 03/18/2023]
Abstract
OBJECTIVE To determine how sevoflurane anesthesia modulates intraoperative epilepsy biomarkers on electrocorticography, including high-frequency oscillation (HFO) effective connectivity (EC), and to investigate their relation to epileptogenicity and anatomical white matter. METHODS We studied eight pediatric drug-resistant focal epilepsy patients who achieved seizure control after invasive monitoring and resective surgery. We visualized spatial distributions of the electrocorticography biomarkers at an oxygen baseline, three time-points while sevoflurane was increasing, and at a plateau of 2 minimum alveolar concentration (MAC) sevoflurane. HFO EC was combined with diffusion-weighted imaging, in dynamic tractography. RESULTS Intraoperative HFO EC diffusely increased as a function of sevoflurane concentration, although most in epileptogenic sites (defined as those included in the resection); their ability to classify epileptogenicity was optimized at sevoflurane 2 MAC. HFO EC could be visualized on major white matter tracts, as a function of sevoflurane level. CONCLUSIONS The results strengthened the hypothesis that sevoflurane-activated HFO biomarkers may help intraoperatively localize the epileptogenic zone. SIGNIFICANCE Our results help characterize how HFOs at non-epileptogenic and epileptogenic networks respond to sevoflurane. It may be warranted to establish a normative HFO atlas incorporating the modifying effects of sevoflurane and major white matter pathways, as critical reference in epilepsy presurgical evaluation.
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Affiliation(s)
- Ethan Firestone
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Physiology, Wayne State University, Detroit, MI 48201, USA
| | - Masaki Sonoda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Neurosurgery, Yokohama City University Graduate School of Medicine, Yokohama 2360004, Japan
| | - Naoto Kuroda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai 9808575, Japan
| | - Kazuki Sakakura
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Neurosurgery, University of Tsukuba, Tsukuba 3058575, Japan
| | - Jeong-Won Jeong
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Min-Hee Lee
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA
| | - Keiko Wada
- Department of Anesthesiology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan; Department of Anesthesiology and Critical Care, Yokohama City University Graduate School of Medicine, Yokohama 2360004, Japan
| | - Yutaro Takayama
- Department of Neurosurgery, Yokohama City University Graduate School of Medicine, Yokohama 2360004, Japan; Department of Neurosurgery, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan
| | - Keiya Iijima
- Department of Neurosurgery, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan
| | - Masaki Iwasaki
- Department of Neurosurgery, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan
| | - Tomoyuki Miyazaki
- Department of Anesthesiology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan; Department of Physiology, Yokohama City University Graduate School of Medicine, Yokohama 2360004, Japan
| | - Eishi Asano
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA.
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Bernabei JM, Li A, Revell AY, Smith RJ, Gunnarsdottir KM, Ong IZ, Davis KA, Sinha N, Sarma S, Litt B. Quantitative approaches to guide epilepsy surgery from intracranial EEG. Brain 2023; 146:2248-2258. [PMID: 36623936 PMCID: PMC10232272 DOI: 10.1093/brain/awad007] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 12/11/2022] [Accepted: 12/28/2022] [Indexed: 01/11/2023] Open
Abstract
Over the past 10 years, the drive to improve outcomes from epilepsy surgery has stimulated widespread interest in methods to quantitatively guide epilepsy surgery from intracranial EEG (iEEG). Many patients fail to achieve seizure freedom, in part due to the challenges in subjective iEEG interpretation. To address this clinical need, quantitative iEEG analytics have been developed using a variety of approaches, spanning studies of seizures, interictal periods, and their transitions, and encompass a range of techniques including electrographic signal analysis, dynamical systems modeling, machine learning and graph theory. Unfortunately, many methods fail to generalize to new data and are sensitive to differences in pathology and electrode placement. Here, we critically review selected literature on computational methods of identifying the epileptogenic zone from iEEG. We highlight shared methodological challenges common to many studies in this field and propose ways that they can be addressed. One fundamental common pitfall is a lack of open-source, high-quality data, which we specifically address by sharing a centralized high-quality, well-annotated, multicentre dataset consisting of >100 patients to support larger and more rigorous studies. Ultimately, we provide a road map to help these tools reach clinical trials and hope to improve the lives of future patients.
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Affiliation(s)
- John M Bernabei
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Adam Li
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Andrew Y Revell
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Rachel J Smith
- Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Neuroengineering Program, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Kristin M Gunnarsdottir
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ian Z Ong
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A Davis
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nishant Sinha
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sridevi Sarma
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Brian Litt
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Frauscher B, Bénar CG, Engel JJ, Grova C, Jacobs J, Kahane P, Wiebe S, Zjilmans M, Dubeau F. Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy. Epilepsy Behav 2023; 143:109221. [PMID: 37119580 DOI: 10.1016/j.yebeh.2023.109221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 05/01/2023]
Abstract
Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to localize epileptic seizures. New signal analysis techniques and the prospects of artificial intelligence and big data will offer unprecedented opportunities to further advance the field in the near future, ultimately resulting in improved quality of life for many patients with drug-resistant epilepsy. This article summarizes selected presentations from Day 1 of the two-day symposium "Neurophysiology, Neuropsychology, Epilepsy, 2022: Hills We Have Climbed and the Hills Ahead". Day 1 was dedicated to highlighting and honoring the work of Dr. Jean Gotman, a pioneer in EEG, intracranial EEG, simultaneous EEG/ functional magnetic resonance imaging, and signal analysis of epilepsy. The program focused on two main research directions of Dr. Gotman, and was dedicated to "High-frequency oscillations, a new biomarker of epilepsy" and "Probing the epileptic focus from inside and outside". All talks were presented by colleagues and former trainees of Dr. Gotman. The extended summaries provide an overview of historical and current work in the neurophysiology of epilepsy with emphasis on novel EEG biomarkers of epilepsy and source imaging and concluded with an outlook on the future of epilepsy research, and what is needed to bring the field to the next level.
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Affiliation(s)
- B Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - C G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - J Jr Engel
- David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - C Grova
- Multimodal Functional Imaging Lab, PERFORM Centre, Department of Physics, Concordia University, Montreal, QC, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, QC, Canada; Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
| | - J Jacobs
- Department of Pediatric and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - P Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institute Neurosciences, Department of Neurology, 38000 Grenoble, France
| | - S Wiebe
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - M Zjilmans
- Stichting Epilepsie Instellingen Nederland, The Netherlands; Brain Center, University Medical Center Utrecht, The Netherlands
| | - F Dubeau
- Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
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Weiss SA, Fried I, Engel J, Sperling MR, Wong RK, Nir Y, Staba RJ. Fast ripples reflect increased excitability that primes epileptiform spikes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.26.23287702. [PMID: 37034609 PMCID: PMC10081394 DOI: 10.1101/2023.03.26.23287702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
The neuronal circuit disturbances that drive interictal and ictal epileptiform discharges remains elusive. Using a combination of extraoperative macro- and micro-electrode interictal recordings in six presurgical patients during non-rapid eye movement (REM) sleep we found that, exclusively in the seizure onset zone, fast ripples (FR; 200-600Hz), but not ripples (80-200 Hz), frequently occur <300 msec before an interictal intracranial EEG (iEEG) spike with a probability exceeding chance (bootstrapping, p<1e-5). Such FR events are associated with higher spectral power (p<1e-10) and correlated with more vigorous neuronal firing than solitary FR (generalized linear mixed-effects model, GLMM, p<1e-3) irrespective of FR power. During the iEEG spike that follows a FR, action potential firing is lower than during a iEEG spike alone (GLMM, p<1e-10), reflecting an inhibitory restraint of iEEG spike initiation. In contrast, ripples do not appear to prime epileptiform spikes. We next investigated the clinical significance of pre-spike FR in a separate cohort of 23 patients implanted with stereo EEG electrodes who underwent resections. In non-REM sleep recordings, sites containing a high proportion of FR preceding iEEG spikes correlate with brain areas where seizures begin more than solitary FR (p<1e-5). Despite this correlation, removal of these sites does not guarantee seizure freedom. These results are consistent with the hypothesis that FR preceding EEG spikes reflect an increase in local excitability that primes EEG spike discharges preferentially in the seizure onset zone and that epileptogenic brain regions are necessary, but not sufficient, for initiating interictal epileptiform discharges.
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Affiliation(s)
- Shennan A Weiss
- Dept. of Neurology, State University of New York Downstate, Brooklyn, New York, 11203 USA
- Dept. of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York, 11203 USA
- Dept. of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY, USA
| | - Itzhak Fried
- Dept. of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Jerome Engel
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Michael R. Sperling
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Robert K.S. Wong
- Dept. of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York, 11203 USA
| | - Yuval Nir
- Department of Physiology and Pharmacology, Sackler School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- The Sieratzki-Sagol Center for Sleep Medicine, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel
| | - Richard J Staba
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
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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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Shahabi H, Nair DR, Leahy RM. Multilayer brain networks can identify the epileptogenic zone and seizure dynamics. eLife 2023; 12:e68531. [PMID: 36929752 PMCID: PMC10065796 DOI: 10.7554/elife.68531] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 03/16/2023] [Indexed: 03/18/2023] Open
Abstract
Seizure generation, propagation, and termination occur through spatiotemporal brain networks. In this paper, we demonstrate the significance of large-scale brain interactions in high-frequency (80-200Hz) for the identification of the epileptogenic zone (EZ) and seizure evolution. To incorporate the continuity of neural dynamics, here we have modeled brain connectivity constructed from stereoelectroencephalography (SEEG) data during seizures using multilayer networks. After introducing a new measure of brain connectivity for temporal networks, named multilayer eigenvector centrality (mlEVC), we applied a consensus hierarchical clustering on the developed model to identify the EZ as a cluster of nodes with distinctive brain connectivity in the ictal period. Our algorithm could successfully predict electrodes inside the resected volume as EZ for 88% of participants, who all were seizure-free for at least 12 months after surgery. Our findings illustrated significant and unique desynchronization between EZ and the rest of the brain in the early to mid-seizure. We showed that aging and the duration of epilepsy intensify this desynchronization, which can be the outcome of abnormal neuroplasticity. Additionally, we illustrated that seizures evolve with various network topologies, confirming the existence of different epileptogenic networks in each patient. Our findings suggest not only the importance of early intervention in epilepsy but possible factors that correlate with disease severity. Moreover, by analyzing the propagation patterns of different seizures, we demonstrate the necessity of collecting sufficient data for identifying epileptogenic networks.
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Affiliation(s)
- Hossein Shahabi
- Signal and Image Processing Institute, University of Southern CaliforniaLos AngelesUnited States
| | - Dileep R Nair
- Epilepsy Center, Cleveland Clinic Neurological InstituteClevelandUnited States
| | - Richard M Leahy
- Signal and Image Processing Institute, University of Southern CaliforniaLos AngelesUnited States
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Thomas J, Kahane P, Abdallah C, Avigdor T, Zweiphenning WJEM, Chabardes S, Jaber K, Latreille V, Minotti L, Hall J, Dubeau F, Gotman J, Frauscher B. A Subpopulation of Spikes Predicts Successful Epilepsy Surgery Outcome. Ann Neurol 2023; 93:522-535. [PMID: 36373178 DOI: 10.1002/ana.26548] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 11/09/2022] [Accepted: 11/11/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVE Epileptic spikes are the traditional interictal electroencephalographic (EEG) biomarker for epilepsy. Given their low specificity for identifying the epileptogenic zone (EZ), they are given only moderate attention in presurgical evaluation. This study aims to demonstrate that it is possible to identify specific spike features in intracranial EEG that optimally define the EZ and predict surgical outcome. METHODS We analyzed spike features on stereo-EEG segments from 83 operated patients from 2 epilepsy centers (37 Engel IA) in wakefulness, non-rapid eye movement sleep, and rapid eye movement sleep. After automated spike detection, we investigated 135 spike features based on rate, morphology, propagation, and energy to determine the best feature or feature combination to discriminate the EZ in seizure-free and non-seizure-free patients by applying 4-fold cross-validation. RESULTS The rate of spikes with preceding gamma activity in wakefulness performed better for surgical outcome classification (4-fold area under receiver operating characteristics curve [AUC] = 0.755 ± 0.07) than the seizure onset zone, the current gold standard (AUC = 0.563 ± 0.05, p = 0.015) and the ripple rate, an emerging seizure-independent biomarker (AUC = 0.537 ± 0.07, p = 0.006). Channels with a spike-gamma rate exceeding 1.9/min had an 80% probability of being in the EZ. Combining features did not improve the results. INTERPRETATION Resection of brain regions with high spike-gamma rates in wakefulness is associated with a high probability of achieving seizure freedom. This rate could be applied to determine the minimal number of spiking channels requiring resection. In addition to quantitative analysis, this feature is easily accessible to visual analysis, which could aid clinicians during presurgical evaluation. ANN NEUROL 2023;93:522-535.
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Affiliation(s)
- John Thomas
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Philippe Kahane
- Grenoble Alpes University Hospital Center, Grenoble Alpes University, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France
| | - Chifaou Abdallah
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Tamir Avigdor
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Willemiek J E M Zweiphenning
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.,University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Stephan Chabardes
- Grenoble Alpes University Hospital Center, Grenoble Alpes University, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France
| | - Kassem Jaber
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Véronique Latreille
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Lorella Minotti
- Grenoble Alpes University Hospital Center, Grenoble Alpes University, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France
| | - Jeff Hall
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - François Dubeau
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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37
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Zhao B, McGonigal A, Hu W, Zhang C, Wang X, Mo J, Zhao X, Ai L, Shao X, Zhang K, Zhang J. Interictal HFO and FDG-PET correlation predicts surgical outcome following SEEG. Epilepsia 2023; 64:667-677. [PMID: 36510851 DOI: 10.1111/epi.17485] [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: 10/19/2022] [Revised: 12/09/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVE This study aimed to investigate the quantitative relationship between interictal 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and interictal high-frequency oscillations (HFOs) from stereo-electroencephalography (SEEG) recordings in patients with refractory epilepsy. METHODS We retrospectively included 32 patients. FDG-PET data were quantified through statistical parametric mapping (SPM) t test modeling with normal controls. Interictal SEEG segments with four, 10-min segments were selected randomly. HFO detection and classification procedures were automatically performed. Channel-based HFOs separating ripple (80-250 Hz) and fast ripple (FR; 250-500 Hz) counts were correlated with the surrounding metabolism T score at the individual and group level, respectively. The association was further validated across anatomic seizure origins and sleep vs wake states. We built a joint feature FR × T reflecting the FR and hypometabolism concordance to predict surgical outcomes in 28 patients who underwent surgery. RESULTS We found a negative correlation between interictal FDG-PET and HFOs through the linear mixed-effects model (R2 = .346 and .457 for ripples and FRs, respectively, p < .001); these correlations were generalizable to different epileptogenic-zone lobar localizations and vigilance states. The FR × T inside the resection volume could be used as a predictor for surgical outcomes with an area under the curve of 0.81. SIGNIFICANCE The degree of hypometabolism is associated with HFO generation rate, especially for FRs. This relationship would be meaningful for selection of SEEG candidates and for optimizing SEEG scheme planning. The concordance between FRs and hypometabolism inside the resection volume could provide prognostic information regarding surgical outcome.
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Affiliation(s)
- Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Aileen McGonigal
- Epilepsy Unit, Neurosciences Centre, Mater Hospital and Mater Research Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaobin Zhao
- Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lin Ai
- Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaoqiu Shao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
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38
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Hashemi M, Vattikonda AN, Jha J, Sip V, Woodman MM, Bartolomei F, Jirsa VK. Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators. Neural Netw 2023; 163:178-194. [PMID: 37060871 DOI: 10.1016/j.neunet.2023.03.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 03/24/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
Whole-brain modeling of epilepsy combines personalized anatomical data with dynamical models of abnormal activities to generate spatio-temporal seizure patterns as observed in brain imaging data. Such a parametric simulator is equipped with a stochastic generative process, which itself provides the basis for inference and prediction of the local and global brain dynamics affected by disorders. However, the calculation of likelihood function at whole-brain scale is often intractable. Thus, likelihood-free algorithms are required to efficiently estimate the parameters pertaining to the hypothetical areas, ideally including the uncertainty. In this study, we introduce the simulation-based inference for the virtual epileptic patient model (SBI-VEP), enabling us to amortize the approximate posterior of the generative process from a low-dimensional representation of whole-brain epileptic patterns. The state-of-the-art deep learning algorithms for conditional density estimation are used to readily retrieve the statistical relationships between parameters and observations through a sequence of invertible transformations. We show that the SBI-VEP is able to efficiently estimate the posterior distribution of parameters linked to the extent of the epileptogenic and propagation zones from sparse intracranial electroencephalography recordings. The presented Bayesian methodology can deal with non-linear latent dynamics and parameter degeneracy, paving the way for fast and reliable inference on brain disorders from neuroimaging modalities.
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Travnicek V, Klimes P, Cimbalnik J, Halamek J, Jurak P, Brinkmann B, Balzekas I, Abdallah C, Dubeau F, Frauscher B, Worrell G, Brazdil M. Relative entropy is an easy-to-use invasive electroencephalographic biomarker of the epileptogenic zone. Epilepsia 2023; 64:962-972. [PMID: 36764672 DOI: 10.1111/epi.17539] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 02/12/2023]
Abstract
OBJECTIVE High-frequency oscillations are considered among the most promising interictal biomarkers of the epileptogenic zone in patients suffering from pharmacoresistant focal epilepsy. However, there is no clear definition of pathological high-frequency oscillations, and the existing detectors vary in methodology, performance, and computational costs. This study proposes relative entropy as an easy-to-use novel interictal biomarker of the epileptic tissue. METHODS We evaluated relative entropy and high-frequency oscillation biomarkers on intracranial electroencephalographic data from 39 patients with seizure-free postoperative outcome (Engel Ia) from three institutions. We tested their capability to localize the epileptogenic zone, defined as resected contacts located in the seizure onset zone. The performance was compared using areas under the receiver operating curves (AUROCs) and precision-recall curves. Then we tested whether a universal threshold can be used to delineate the epileptogenic zone across patients from different institutions. RESULTS Relative entropy in the ripple band (80-250 Hz) achieved an average AUROC of .85. The normalized high-frequency oscillation rate in the ripple band showed an identical AUROC of .85. In contrast to high-frequency oscillations, relative entropy did not require any patient-level normalization and was easy and fast to calculate due to its clear and straightforward definition. One threshold could be set across different patients and institutions, because relative entropy is independent of signal amplitude and sampling frequency. SIGNIFICANCE Although both relative entropy and high-frequency oscillations have a similar performance, relative entropy has significant advantages such as straightforward definition, computational speed, and universal interpatient threshold, making it an easy-to-use promising biomarker of the epileptogenic zone.
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Affiliation(s)
- Vojtech Travnicek
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Petr Klimes
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Jan Cimbalnik
- International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Josef Halamek
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Pavel Jurak
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Benjamin Brinkmann
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Irena Balzekas
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Chifaou Abdallah
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - François Dubeau
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Greg Worrell
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Milan Brazdil
- Department of Neurology, Brno Epilepsy Center, St. Anne's University Hospital and Medical Faculty of Masaryk University, Brno, Czech Republic.,Central European Institute of Technology, Masaryk University, Brno, Czech Republic
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40
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Lai N, Li Z, Xu C, Wang Y, Chen Z. Diverse nature of interictal oscillations: EEG-based biomarkers in epilepsy. Neurobiol Dis 2023; 177:105999. [PMID: 36638892 DOI: 10.1016/j.nbd.2023.105999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/07/2023] [Accepted: 01/09/2023] [Indexed: 01/11/2023] Open
Abstract
Interictal electroencephalogram (EEG) patterns, including high-frequency oscillations (HFOs), interictal spikes (ISs), and slow wave activities (SWAs), are defined as specific oscillations between seizure events. These interictal oscillations reflect specific dynamic changes in network excitability and play various roles in epilepsy. In this review, we briefly describe the electrographic characteristics of HFOs, ISs, and SWAs in the interictal state, and discuss the underlying cellular and network mechanisms. We also summarize representative evidence from experimental and clinical epilepsy to address their critical roles in ictogenesis and epileptogenesis, indicating their potential as electrophysiological biomarkers of epilepsy. Importantly, we put forwards some perspectives for further research in the field.
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Affiliation(s)
- Nanxi Lai
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zhisheng Li
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Cenglin Xu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yi Wang
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China; Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhong Chen
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China; Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, China; Epilepsy Center, Department of Neurology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
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41
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Goel K, Pek V, Shlobin NA, Chen JS, Wang A, Ibrahim GM, Hadjinicolaou A, Roessler K, Dudley RW, Nguyen DK, El-Tahry R, Fallah A, Weil AG. Clinical utility of intraoperative electrocorticography for epilepsy surgery: A systematic review and meta-analysis. Epilepsia 2023; 64:253-265. [PMID: 36404579 DOI: 10.1111/epi.17472] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 11/16/2022] [Accepted: 11/17/2022] [Indexed: 11/22/2022]
Abstract
Despite the widespread use of intraoperative electrocorticography (iECoG) during resective epilepsy surgery, there are conflicting data on its overall efficacy and inability to predict benefit per pathology. Given the heterogeneity of iECoG use in resective epilepsy surgery, it is important to assess the utility of interictal-based iECoG. This individual patient data (IPD) meta-analysis seeks to identify the benefit of iECoG during resective epilepsy surgery in achieving seizure freedom for various pathologies. Embase, Scopus, and PubMed were searched from inception to January 31, 2021 using the following terms: "ecog", "electrocorticography", and "epilepsy". Articles were included if they reported seizure freedom at ≥12-month follow-up in cohorts with and without iECoG for epilepsy surgery. Non-English articles, noncomparative iECoG cohorts, and studies with <10% iECoG use were excluded. This meta-analysis followed the PRISMA 2020 guidelines. The primary outcome was seizure freedom at last follow-up and time to seizure recurrence, if applicable. Forest plots with random effects modeling assessed the relationship between iECoG use and seizure freedom. Cox regression of IPD was performed to identify predictors of longer duration of seizure freedom. Kaplan-Meier curves with log-rank test were created to visualize differences in time to seizure recurrence. Of 7504 articles identified, 18 were included for study-level analysis. iECoG was not associated with higher seizure freedom at the study level (relative risk = 1.09, 95% confidence interval [CI] = 0.96-1.23, p = .19, I2 = 64%), but on IPD (n = 7 studies, 231 patients) iECoG use was independently associated with more favorable seizure outcomes (hazard ratio = 0.47, 95% CI = .23-.95, p = .037). In Kaplan-Meier analysis of specific pathologies, iECoG use was significantly associated with longer seizure freedom only for focal cortical dysplasia (FCD; p < .001) etiology. Number needed to treat for iECoG was 8.8, and for iECoG in FCD it was 4.7. We show iECoG seizure freedom is not achieved uniformly across centers. iECoG is particularly beneficial for FCD etiology in improving seizure freedom.
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Affiliation(s)
- Keshav Goel
- David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California, USA
| | - Valérie Pek
- Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Nathan A Shlobin
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Jia-Shu Chen
- Warren Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Andrew Wang
- David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California, USA
- College of Medicine, Charles R. Drew University of Medicine and Science, Los Angeles, Los Angeles, California, USA
| | - George M Ibrahim
- Division of Neurosurgery, Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
| | - Aristides Hadjinicolaou
- Division of Neurology, Department of Pediatrics, Sainte-Justine University Hospital Centre, Montreal, Quebec, Canada
- Brain and Development Research Axis, Sainte-Justine Research Centre, Montreal, Quebec, Canada
| | - Karl Roessler
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Roy W Dudley
- Division of Pediatric Neurosurgery, Department of Pediatric Surgery, Montreal Children's Hospital, McGill University, Montreal, Quebec, Canada
| | - Dang K Nguyen
- Department of Neurology, University of Montreal Hospital Centre, Montreal, Quebec, Canada
| | - Riëm El-Tahry
- Saint Luc University Hospital, Brussels, Belgium
- Institute of Neuroscience, Neurology pole, Catholic University of Louvain, Brussels, Belgium
| | - Aria Fallah
- David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California, USA
- Department of Neurosurgery, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California, USA
| | - Alexander G Weil
- Brain and Development Research Axis, Sainte-Justine Research Centre, Montreal, Quebec, Canada
- Division of Neurosurgery, Department of Surgery, Sainte-Justine University Hospital Centre, Montreal, Quebec, Canada
- Division of Neurosurgery, Department of Surgery, University of Montreal Hospital Centre, Montreal, Quebec, Canada
- Department of Neuroscience, University of Montreal, Montreal, Quebec, Canada
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Macdonald‐Laurs E, Warren AEL, Lee WS, Yang JY, MacGregor D, Lockhart PJ, Leventer RJ, Neal A, Harvey AS. Intrinsic and secondary epileptogenicity in focal cortical dysplasia type II. Epilepsia 2023; 64:348-363. [PMID: 36527426 PMCID: PMC10952144 DOI: 10.1111/epi.17495] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 12/15/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
OBJECTIVE Favorable seizure outcome is reported following resection of bottom-of-sulcus dysplasia (BOSD). We assessed the distribution of epileptogenicity and dysplasia in and around BOSD to better understand this clinical outcome and the optimal surgical approach. METHODS We studied 27 children and adolescents with magnetic resonance imaging (MRI)-positive BOSD who underwent epilepsy surgery; 85% became seizure-free postresection (median = 5.0 years follow-up). All patients had resection of the dysplastic sulcus, and 11 had additional resection of the gyral crown (GC) or adjacent gyri (AG). Markers of epileptogenicity were relative cortical hypometabolism on preoperative 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET), and spiking, ripples, fast ripples, spike-high-frequency oscillation cross-rate, and phase amplitude coupling (PAC) on preresection and postresection electrocorticography (ECoG), all analyzed at the bottom-of-sulcus (BOS), top-of-sulcus (TOS), GC, and AG. Markers of dysplasia were increased cortical thickness on preoperative MRI, and dysmorphic neuron density and variant allele frequency of somatic MTOR mutations in resected tissue, analyzed at similar locations. RESULTS Relative cortical metabolism was significantly reduced and ECoG markers were significantly increased at the BOS compared to other regions. Apart from spiking and PAC, which were greater at the TOS compared to the GC, there were no significant differences in PET and other ECoG markers between the TOS, GC, and AG, suggesting a cutoff of epileptogenicity at the TOS rather than a tapering gradient on the cortical surface. MRI and tissue markers of dysplasia were all maximal in the BOS, reduced in the TOS, and mostly absent in the GC. Spiking and PAC reduced significantly over the GC after resection of the dysplastic sulcus. SIGNIFICANCE These findings support the concept that dysplasia and intrinsic epileptogenicity are mostly limited to the dysplastic sulcus in BOSD and support resection or ablation confined to the MRI-visible lesion as a first-line surgical approach. 18 F-FDG PET and ECoG abnormalities in surrounding cortex seem to be secondary phenomena.
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Affiliation(s)
- Emma Macdonald‐Laurs
- Department of NeurologyRoyal Children's HospitalParkvilleVictoriaAustralia
- Murdoch Children's Research InstituteParkvilleVictoriaAustralia
- Department of PaediatricsUniversity of MelbourneParkvilleVictoriaAustralia
| | - Aaron E. L. Warren
- Murdoch Children's Research InstituteParkvilleVictoriaAustralia
- Department of MedicineUniversity of MelbourneParkvilleVictoriaAustralia
| | - Wei Shern Lee
- Murdoch Children's Research InstituteParkvilleVictoriaAustralia
- Department of PaediatricsUniversity of MelbourneParkvilleVictoriaAustralia
| | - Joseph Yuan‐Mou Yang
- Murdoch Children's Research InstituteParkvilleVictoriaAustralia
- Department of PaediatricsUniversity of MelbourneParkvilleVictoriaAustralia
- Department of NeurosurgeryRoyal Children's HospitalParkvilleVictoriaAustralia
| | - Duncan MacGregor
- Murdoch Children's Research InstituteParkvilleVictoriaAustralia
- Department of PathologyRoyal Children's HospitalParkvilleVictoriaAustralia
| | - Paul J. Lockhart
- Murdoch Children's Research InstituteParkvilleVictoriaAustralia
- Department of PaediatricsUniversity of MelbourneParkvilleVictoriaAustralia
| | - Richard J. Leventer
- Department of NeurologyRoyal Children's HospitalParkvilleVictoriaAustralia
- Murdoch Children's Research InstituteParkvilleVictoriaAustralia
- Department of PaediatricsUniversity of MelbourneParkvilleVictoriaAustralia
| | - Andrew Neal
- Department of Neuroscience, Faculty of Medicine, Nursing, and Health Sciences, Central Clinical SchoolMonash UniversityMelbourneVictoriaAustralia
| | - A. Simon Harvey
- Department of NeurologyRoyal Children's HospitalParkvilleVictoriaAustralia
- Murdoch Children's Research InstituteParkvilleVictoriaAustralia
- Department of PaediatricsUniversity of MelbourneParkvilleVictoriaAustralia
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43
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Vasickova Z, Klimes P, Cimbalnik J, Travnicek V, Pail M, Halamek J, Jurak P, Brazdil M. Shadows of very high-frequency oscillations can be detected in lower frequency bands of routine stereoelectroencephalography. Sci Rep 2023; 13:1065. [PMID: 36658267 PMCID: PMC9852423 DOI: 10.1038/s41598-023-27797-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 01/09/2023] [Indexed: 01/20/2023] Open
Abstract
Very high-frequency oscillations (VHFOs, > 500 Hz) are more specific in localizing the epileptogenic zone (EZ) than high-frequency oscillations (HFOs, < 500 Hz). Unfortunately, VHFOs are not visible in standard clinical stereo-EEG (SEEG) recordings with sampling rates of 1 kHz or lower. Here we show that "shadows" of VHFOs can be found in frequencies below 500 Hz and can help us to identify SEEG channels with a higher probability of increased VHFO rates. Subsequent analysis of Logistic regression models on 141 SEEG channels from thirteen patients shows that VHFO "shadows" provide additional information to gold standard HFO analysis and can potentially help in precise EZ delineation in standard clinical recordings.
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Affiliation(s)
- Zuzana Vasickova
- Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital, Faculty of Medicine, Masaryk University, Brno, Czech Republic.,Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic
| | - Petr Klimes
- Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic.
| | - Jan Cimbalnik
- International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Vojtech Travnicek
- International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic.,Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic
| | - Martin Pail
- Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital, Faculty of Medicine, Masaryk University, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic.,Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic
| | - Josef Halamek
- Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic
| | - Pavel Jurak
- Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic
| | - Milan Brazdil
- Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital, Faculty of Medicine, Masaryk University, Brno, Czech Republic.,Behavioral and Social Neuroscience Research Group, CEITEC Central European Institute of Technology, Masaryk University, Brno, Czech Republic
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Gallotto S, Seeck M. EEG biomarker candidates for the identification of epilepsy. Clin Neurophysiol Pract 2022; 8:32-41. [PMID: 36632368 PMCID: PMC9826889 DOI: 10.1016/j.cnp.2022.11.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 10/14/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
Electroencephalography (EEG) is one of the main pillars used for the diagnosis and study of epilepsy, readily employed after a possible first seizure has occurred. The most established biomarker of epilepsy, in case seizures are not recorded, are interictal epileptiform discharges (IEDs). In clinical practice, however, IEDs are not always present and the EEG may appear completely normal despite an underlying epileptic disorder, often leading to difficulties in the diagnosis of the disease. Thus, finding other biomarkers that reliably predict whether an individual suffers from epilepsy even in the absence of evident epileptic activity would be extremely helpful, since they could allow shortening the period of diagnostic uncertainty and consequently decreasing the risk of seizure. To date only a few EEG features other than IEDs seem to be promising candidates able to distinguish between epilepsy, i.e. > 60 % risk of recurrent seizures, or other (pathological) conditions. The aim of this narrative review is to provide an overview of the EEG-based biomarker candidates for epilepsy and the techniques employed for their identification.
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Zhang Y, Chung H, Ngo JP, Monsoor T, Hussain SA, Matsumoto JH, Walshaw PD, Fallah A, Sim MS, Asano E, Sankar R, Staba RJ, Engel J, Speier W, Roychowdhury V, Nariai H. Characterizing physiological high-frequency oscillations using deep learning. J Neural Eng 2022; 19:10.1088/1741-2552/aca4fa. [PMID: 36541546 PMCID: PMC10364130 DOI: 10.1088/1741-2552/aca4fa] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 11/22/2022] [Indexed: 11/23/2022]
Abstract
Objective.Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. However, HFOs can also be recorded in the healthy brain regions, which complicates the interpretation of HFOs. The present study aimed to characterize salient features of physiological HFOs using deep learning (DL).Approach.We studied children with neocortical epilepsy who underwent intracranial strip/grid evaluation. Time-series EEG data were transformed into DL training inputs. The eloquent cortex (EC) was defined by functional cortical mapping and used as a DL label. Morphological characteristics of HFOs obtained from EC (ecHFOs) were distilled and interpreted through a novel weakly supervised DL model.Main results.A total of 63 379 interictal intracranially-recorded HFOs from 18 children were analyzed. The ecHFOs had lower amplitude throughout the 80-500 Hz frequency band around the HFO onset and also had a lower signal amplitude in the low frequency band throughout a one-second time window than non-ecHFOs, resembling a bell-shaped template in the time-frequency map. A minority of ecHFOs were HFOs with spikes (22.9%). Such morphological characteristics were confirmed to influence DL model prediction via perturbation analyses. Using the resection ratio (removed HFOs/detected HFOs) of non-ecHFOs, the prediction of postoperative seizure outcomes improved compared to using uncorrected HFOs (area under the ROC curve of 0.82, increased from 0.76).Significance.We characterized salient features of physiological HFOs using a DL algorithm. Our results suggested that this DL-based HFO classification, once trained, might help separate physiological from pathological HFOs, and efficiently guide surgical resection using HFOs.
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Affiliation(s)
- Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Hoyoung Chung
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Jacquline P. Ngo
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Shaun A. Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Joyce H. Matsumoto
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Patricia D. Walshaw
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Myung Shin Sim
- Department of Medicine, Statistics Core, University of California, Los Angeles, CA, USA
| | - Eishi Asano
- Department of Pediatrics and Neurology, Children’s Hospital of Michigan, Wayne State University School of Medicine, Detroit, MI, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Richard J. Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Jerome Engel
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Neurobiology, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
- The Brain Research Institute, University of California, Los Angeles, CA, USA
| | - William Speier
- Department of Radiological Sciences, University of California, Los Angeles, CA, USA
- Department of Bioengineering, University of California, Los Angeles, CA, USA
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
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46
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Curot J, Barbeau E, Despouy E, Denuelle M, Sol JC, Lotterie JA, Valton L, Peyrache A. Local neuronal excitation and global inhibition during epileptic fast ripples in humans. Brain 2022; 146:561-575. [PMID: 36093747 PMCID: PMC9924905 DOI: 10.1093/brain/awac319] [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: 02/09/2022] [Revised: 07/01/2022] [Accepted: 08/01/2022] [Indexed: 11/12/2022] Open
Abstract
Understanding the neuronal basis of epileptic activity is a major challenge in neurology. Cellular integration into larger scale networks is all the more challenging. In the local field potential, interictal epileptic discharges can be associated with fast ripples (200-600 Hz), which are a promising marker of the epileptogenic zone. Yet, how neuronal populations in the epileptogenic zone and in healthy tissue are affected by fast ripples remain unclear. Here, we used a novel 'hybrid' macro-micro depth electrode in nine drug-resistant epileptic patients, combining classic depth recording of local field potentials (macro-contacts) and two or three tetrodes (four micro-wires bundled together) enabling up to 15 neurons in local circuits to be simultaneously recorded. We characterized neuronal responses (190 single units) with the timing of fast ripples (2233 fast ripples) on the same hybrid and other electrodes that target other brain regions. Micro-wire recordings reveal signals that are not visible on macro-contacts. While fast ripples detected on the closest macro-contact to the tetrodes were always associated with fast ripples on the tetrodes, 82% of fast ripples detected on tetrodes were associated with detectable fast ripples on the nearest macro-contact. Moreover, neuronal recordings were taken in and outside the epileptogenic zone of implanted epileptic subjects and they revealed an interlay of excitation and inhibition across anatomical scales. While fast ripples were associated with increased neuronal activity in very local circuits only, they were followed by inhibition in large-scale networks (beyond the epileptogenic zone, even in healthy cortex). Neuronal responses to fast ripples were homogeneous in local networks but differed across brain areas. Similarly, post-fast ripple inhibition varied across recording locations and subjects and was shorter than typical inter-fast ripple intervals, suggesting that this inhibition is a fundamental refractory process for the networks. These findings demonstrate that fast ripples engage local and global networks, including healthy tissue, and point to network features that pave the way for new diagnostic and therapeutic strategies. They also reveal how even localized pathological brain dynamics can affect a broad range of cognitive functions.
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Affiliation(s)
- Jonathan Curot
- Correspondence to: Jonathan Curot, MD, PhD CerCo CNRS UMR 5549, Université Toulouse III CHU Purpan, Pavillon Baudot, 31052 Toulouse Cedex, France E-mail:
| | - Emmanuel Barbeau
- Brain and Cognition Research Center (CerCo), Centre National de la Recherche Scientifique, UMR5549, Toulouse, France,Faculty of Health, University of Toulouse, Paul Sabatier University, Toulouse, France
| | - Elodie Despouy
- Brain and Cognition Research Center (CerCo), Centre National de la Recherche Scientifique, UMR5549, Toulouse, France
| | - Marie Denuelle
- Departments of Neurology and Neurosurgery, Toulouse University Hospital, Toulouse, France,Brain and Cognition Research Center (CerCo), Centre National de la Recherche Scientifique, UMR5549, Toulouse, France
| | - Jean Christophe Sol
- Departments of Neurology and Neurosurgery, Toulouse University Hospital, Toulouse, France,Faculty of Health, University of Toulouse, Paul Sabatier University, Toulouse, France,Toulouse Neuro Imaging Center (ToNIC), INSERM, U1214, Toulouse, France
| | - Jean-Albert Lotterie
- Departments of Neurology and Neurosurgery, Toulouse University Hospital, Toulouse, France,Toulouse Neuro Imaging Center (ToNIC), INSERM, U1214, Toulouse, France
| | - Luc Valton
- Departments of Neurology and Neurosurgery, Toulouse University Hospital, Toulouse, France,Brain and Cognition Research Center (CerCo), Centre National de la Recherche Scientifique, UMR5549, Toulouse, France
| | - Adrien Peyrache
- Correspondence may also be addressed to: Adrien Peyrache, PhD Montreal Neurological Institute Department of Neurology and Neurosurgery McGill University, 3810 University Street Montreal, Quebec, Canada E-mail:
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47
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Mo J, Zhang J, Hu W, Sang L, Shao X, Zhang C, Zhang K. Metabolism and Intracranial Epileptogenicity in Temporal Lobe Long-Term Epilepsy-Associated Tumor. J Clin Med 2022; 11:jcm11185309. [PMID: 36142957 PMCID: PMC9504693 DOI: 10.3390/jcm11185309] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 09/06/2022] [Accepted: 09/07/2022] [Indexed: 11/16/2022] Open
Abstract
Brain tumors are common in epilepsy surgery and frequently occur in the temporal lobe, but the optimal surgical strategies to remove the tumor and epileptogenic zone remain controversial. We aim at illustrating the positron emission tomography (PET) metabolism and the stereoelectroencephalography (SEEG) epileptogenicity of temporal lobe long-term epilepsy-associated tumors (LEAT). In this study, 70 patients and 25 healthy controls were included. Our analysis leveraged group-level analysis to reveal the whole-brain metabolic pattern of temporal lobe LEATs. The SEEG-based epileptogenicity mapping was performed to verify the PET findings in the epileptic network. Compared to controls, patients with a temporal lobe LEAT showed a more widespread epileptic network based on 18FDG-PET in patients with a mesial temporal lobe LEAT than in those with a lateral temporal lobe LEAT. The significant brain clusters mainly involved the paracentral lobule (ANOVA F = 9.731, p < 0.001), caudate nucleus (ANOVA F = 20.749, p < 0.001), putamen (Kruskal−Wallis H = 19.258, p < 0.001), and thalamus (ANOVA F = 4.754, p = 0.011). Subgroup analysis and SEEG-based epileptogenicity mapping are similar to the metabolic pattern. Our findings demonstrate the metabolic and electrophysiological organization of the temporal lobe LEAT epileptic network, which may assist in a patient-specific surgical strategy.
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Affiliation(s)
- Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, China
| | - Lin Sang
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing 100070, China
| | - Xiaoqiu Shao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
- Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, China
- Correspondence: ; Tel.: +86-010-59975051
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Acerbo E, Jegou A, Luff C, Dzialecka P, Botzanowski B, Missey F, Ngom I, Lagarde S, Bartolomei F, Cassara A, Neufeld E, Jirsa V, Carron R, Grossman N, Williamson A. Focal non-invasive deep-brain stimulation with temporal interference for the suppression of epileptic biomarkers. Front Neurosci 2022; 16:945221. [PMID: 36061593 PMCID: PMC9431367 DOI: 10.3389/fnins.2022.945221] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/12/2022] [Indexed: 11/15/2022] Open
Abstract
Introduction Neurostimulation applied from deep brain stimulation (DBS) electrodes is an effective therapeutic intervention in patients suffering from intractable drug-resistant epilepsy when resective surgery is contraindicated or failed. Inhibitory DBS to suppress seizures and associated epileptogenic biomarkers could be performed with high-frequency stimulation (HFS), typically between 100 and 165 Hz, to various deep-seated targets, such as the Mesio-temporal lobe (MTL), which leads to changes in brain rhythms, specifically in the hippocampus. The most prominent alterations concern high-frequency oscillations (HFOs), namely an increase in ripples, a reduction in pathological Fast Ripples (FRs), and a decrease in pathological interictal epileptiform discharges (IEDs). Materials and methods In the current study, we use Temporal Interference (TI) stimulation to provide a non-invasive DBS (130 Hz) of the MTL, specifically the hippocampus, in both mouse models of epilepsy, and scale the method using human cadavers to demonstrate the potential efficacy in human patients. Simulations for both mice and human heads were performed to calculate the best coordinates to reach the hippocampus. Results This non-invasive DBS increases physiological ripples, and decreases the number of FRs and IEDs in a mouse model of epilepsy. Similarly, we show the inability of 130 Hz transcranial current stimulation (TCS) to achieve similar results. We therefore further demonstrate the translatability to human subjects via measurements of the TI stimulation vs. TCS in human cadavers. Results show a better penetration of TI fields into the human hippocampus as compared with TCS. Significance These results constitute the first proof of the feasibility and efficiency of TI to stimulate at depth an area without impacting the surrounding tissue. The data tend to show the sufficiently focal character of the induced effects and suggest promising therapeutic applications in epilepsy.
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Affiliation(s)
- Emma Acerbo
- Aix Marseille University:, INSERM, Institut de Neurosciences des Systèmes, Marseille, France
| | - Aude Jegou
- Aix Marseille University:, INSERM, Institut de Neurosciences des Systèmes, Marseille, France
| | - Charlotte Luff
- Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Patrycja Dzialecka
- Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Boris Botzanowski
- Aix Marseille University:, INSERM, Institut de Neurosciences des Systèmes, Marseille, France
| | - Florian Missey
- Aix Marseille University:, INSERM, Institut de Neurosciences des Systèmes, Marseille, France
| | - Ibrahima Ngom
- Aix Marseille University:, INSERM, Institut de Neurosciences des Systèmes, Marseille, France
| | - Stanislas Lagarde
- Aix Marseille University:, INSERM, Institut de Neurosciences des Systèmes, Marseille, France
- Department of Epileptology, APHM, Timone Hospital, Marseille, France
| | - Fabrice Bartolomei
- Aix Marseille University:, INSERM, Institut de Neurosciences des Systèmes, Marseille, France
- Department of Epileptology, APHM, Timone Hospital, Marseille, France
| | - Antonino Cassara
- Foundation for Research on Information Technologies in Society, Zurich, Switzerland
| | - Esra Neufeld
- Foundation for Research on Information Technologies in Society, Zurich, Switzerland
| | - Viktor Jirsa
- Aix Marseille University:, INSERM, Institut de Neurosciences des Systèmes, Marseille, France
| | - Romain Carron
- Aix Marseille University:, INSERM, Institut de Neurosciences des Systèmes, Marseille, France
- Department of Functional and Stereotactic Neurosurgery, Timone University Hospital, Marseille, France
| | - Nir Grossman
- Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Adam Williamson
- Aix Marseille University:, INSERM, Institut de Neurosciences des Systèmes, Marseille, France
- Department of Medicine, Center for Bioelectronic Medicine, Karolinska Institute, Stockholm, Sweden
- *Correspondence: Adam Williamson, ;
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49
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Maccabeo A, van 't Klooster MA, Schaft E, Demuru M, Zweiphenning W, Gosselaar P, Gebbink T, Otte WM, Zijlmans M. Spikes and High Frequency Oscillations in Lateral Neocortical Temporal Lobe Epilepsy: Can They Predict the Success Chance of Hippocampus-Sparing Resections? Front Neurol 2022; 13:797075. [PMID: 35983430 PMCID: PMC9379925 DOI: 10.3389/fneur.2022.797075] [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: 10/18/2021] [Accepted: 05/23/2022] [Indexed: 11/27/2022] Open
Abstract
Purpose We investigated the distribution of spikes and HFOs recorded during intraoperative electrocorticography (ioECoG) and tried to elaborate a predictive model for postsurgical outcomes of patients with lateral neocortical temporal lobe epilepsy (TLE) whose mesiotemporal structures are left in situ. Methods We selected patients with temporal lateral neocortical epilepsy focus who underwent ioECoG-tailored resections without amygdalo–hippocampectomies. We visually marked spikes, ripples (80–250 Hz), and fast ripples (FRs; 250–500 Hz) on neocortical and mesiotemporal channels before and after resections. We looked for differences in event rates and resection ratios between good (Engel 1A) and poor outcome groups and performed logistic regression analysis to identify outcome predictors. Results Fourteen out of 24 included patients had a good outcome. The poor-outcome patients showed higher rates of ripples on neocortical channels distant from the resection in pre- and post-ioECoG than people with good outcomes (ppre = 0.04, ppost = 0.05). Post-ioECoG FRs were found only in poor-outcome patients (N = 3). A prediction model based on regression analysis showed low rates of mesiotemporal post-ioECoG ripples (ORmesio = 0.13, pmesio = 0.04) and older age at epilepsy onset (OR = 1.76, p = 0.04) to be predictors of good seizure outcome. Conclusion HFOs in ioECoG may help to inform the neurosurgeon of the hippocampus-sparing resection success chance in patients with lateral neocortical TLE.
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Affiliation(s)
- Alessandra Maccabeo
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Maryse A. van 't Klooster
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Eline Schaft
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Matteo Demuru
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
- Stichting Epilepsie Instellingen Nederland, Heemstede, Netherlands
| | - Willemiek Zweiphenning
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Peter Gosselaar
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Tineke Gebbink
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Wim M. Otte
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Maeike Zijlmans
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
- Stichting Epilepsie Instellingen Nederland, Heemstede, Netherlands
- *Correspondence: Maeike Zijlmans
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50
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Wang Y, Xu J, Liu T, Chen F, Chen S, Yuan L, Zhai F, Liang S. Diagnostic value of high-frequency oscillations for the epileptogenic zone: A systematic review and meta-analysis. Seizure 2022; 99:82-90. [DOI: 10.1016/j.seizure.2022.05.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 05/04/2022] [Accepted: 05/06/2022] [Indexed: 11/30/2022] Open
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