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Chybowski B, Klimes P, Cimbalnik J, Travnicek V, Nejedly P, Pail M, Peter-Derex L, Hall J, Dubeau F, Jurak P, Brazdil M, Frauscher B. Timing matters for accurate identification of the epileptogenic zone. Clin Neurophysiol 2024; 161:1-9. [PMID: 38430856 DOI: 10.1016/j.clinph.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 12/12/2023] [Accepted: 01/01/2024] [Indexed: 03/05/2024]
Abstract
OBJECTIVE Interictal biomarkers of the epileptogenic zone (EZ) and their use in machine learning models open promising avenues for improvement of epilepsy surgery evaluation. Currently, most studies restrict their analysis to short segments of intracranial EEG (iEEG). METHODS We used 2381 hours of iEEG data from 25 patients to systematically select 5-minute segments across various interictal conditions. Then, we tested machine learning models for EZ localization using iEEG features calculated within these individual segments or across them and evaluated the performance by the area under the precision-recall curve (PRAUC). RESULTS On average, models achieved a score of 0.421 (the result of the chance classifier was 0.062). However, the PRAUC varied significantly across the segments (0.323-0.493). Overall, NREM sleep achieved the highest scores, with the best results of 0.493 in N2. When using data from all segments, the model performed significantly better than single segments, except NREM sleep segments. CONCLUSIONS The model based on a short segment of iEEG recording can achieve similar results as a model based on prolonged recordings. The analyzed segment should, however, be carefully and systematically selected, preferably from NREM sleep. SIGNIFICANCE Random selection of short iEEG segments may give rise to inaccurate localization of the EZ.
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Affiliation(s)
- Bartlomiej Chybowski
- University of Edinburgh, School of Medicine, Deanery of Clinical Sciences, 47 Little France Crescent, EH164TJ Edinburgh, Scotland
| | - Petr Klimes
- Institute of Scientific Instruments of the CAS, v. v. i., Královopolská 147, 612 00 Brno, Czech Republic
| | - Jan Cimbalnik
- International Clinical Research Center, St. Anne's University Hospital, Pekařská 53, 602 00 Brno, Czech Republic
| | - Vojtech Travnicek
- Institute of Scientific Instruments of the CAS, v. v. i., Královopolská 147, 612 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital, Pekařská 53, 602 00 Brno, Czech Republic
| | - Petr Nejedly
- Institute of Scientific Instruments of the CAS, v. v. i., Královopolská 147, 612 00 Brno, Czech Republic
| | - Martin Pail
- Institute of Scientific Instruments of the CAS, v. v. i., Královopolská 147, 612 00 Brno, Czech Republic; Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital, Member of ERN-EpiCARE, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; Behavioral and Social Neuroscience Research Group, CEITEC Central European Institute of Technology, Masaryk University, Žerotínovo nám 617/9, 601 77 Brno, Czech Republic
| | - Laure Peter-Derex
- Center for Sleep Medicine and Respiratory Diseases, Lyon University Hospital, Lyon 1 University, 103 Grande Rue de la Croix-Rousse, 69004 Lyon, France; Lyon Neuroscience Research Center, CH Le Vinatier - Bâtiment 462 - Neurocampus, 95 Bd Pinel, 69500 Lyon, France
| | - Jeff Hall
- Montreal Neurological Hospital, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Quebec, Canada
| | - François Dubeau
- Montreal Neurological Hospital, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Quebec, Canada
| | - Pavel Jurak
- Institute of Scientific Instruments of the CAS, v. v. i., Královopolská 147, 612 00 Brno, Czech Republic
| | - Milan Brazdil
- Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital, Member of ERN-EpiCARE, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; Behavioral and Social Neuroscience Research Group, CEITEC Central European Institute of Technology, Masaryk University, Žerotínovo nám 617/9, 601 77 Brno, Czech Republic
| | - Birgit Frauscher
- Montreal Neurological Hospital, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Quebec, Canada; Department of Neurology, Duke University Medical School and Department of Biomedical Engineering, Pratt School of Engineering, 2424 Erwin Road, Durham, NC, 27705, USA.
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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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Latreille V, Avigdor T, Thomas J, Crane J, Sziklas V, Jones-Gotman M, Frauscher B. Scalp and hippocampal sleep correlates of memory function in drug-resistant temporal lobe epilepsy. Sleep 2024; 47:zsad228. [PMID: 37658793 PMCID: PMC10851866 DOI: 10.1093/sleep/zsad228] [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: 04/24/2023] [Revised: 07/22/2023] [Indexed: 09/05/2023] Open
Abstract
Seminal animal studies demonstrated the role of sleep oscillations such as cortical slow waves, thalamocortical spindles, and hippocampal ripples in memory consolidation. In humans, whether ripples are involved in sleep-related memory processes is less clear. Here, we explored the interactions between sleep oscillations (measured as traits) and general episodic memory abilities in 26 adults with drug-resistant temporal lobe epilepsy who performed scalp-intracranial electroencephalographic recordings and neuropsychological testing, including two analogous hippocampal-dependent verbal and nonverbal memory tasks. We explored the relationships between hemispheric scalp (spindles, slow waves) and hippocampal physiological and pathological oscillations (spindles, slow waves, ripples, and epileptic spikes) and material-specific memory function. To differentiate physiological from pathological ripples, we used multiple unbiased data-driven clustering approaches. At the individual level, we found material-specific cerebral lateralization effects (left-verbal memory, right-nonverbal memory) for all scalp spindles (rs > 0.51, ps < 0.01) and fast spindles (rs > 0.61, ps < 0.002). Hippocampal epileptic spikes and short pathological ripples, but not physiological oscillations, were negatively (rs > -0.59, ps < 0.01) associated with verbal learning and retention scores, with left lateralizing and antero-posterior effects. However, data-driven clustering failed to separate the ripple events into defined clusters. Correlation analyses with the resulting clusters revealed no meaningful or significant associations with the memory scores. Our results corroborate the role of scalp spindles in memory processes in patients with drug-resistant temporal lobe epilepsy. Yet, physiological and pathological ripples were not separable when using data-driven clustering, and thus our findings do not provide support for a role of sleep ripples as trait-like characteristics of general memory abilities in epilepsy.
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Affiliation(s)
- Véronique Latreille
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| | - Tamir Avigdor
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| | - John Thomas
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| | - Joelle Crane
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
- Department of Psychology, McGill University, Montreal, Canada
| | - Viviane Sziklas
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
- Department of Psychology, McGill University, Montreal, Canada
| | - Marilyn Jones-Gotman
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
- Department of Psychology, McGill University, Montreal, Canada
| | - Birgit Frauscher
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
- Analytical Neurophysiology (ANPHY) Lab, Duke University Medical Center, Durham, NC, USA
- Department of Neurology, Duke University Medical Center, Durham, NC, USA
- Department of Biomedical Engineering. Duke Pratt School of Engineering, Durham NC, USA
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Frauscher B, Mansilla D, Abdallah C, Astner-Rohracher A, Beniczky S, Brazdil M, Gnatkovsky V, Jacobs J, Kalamangalam G, Perucca P, Ryvlin P, Schuele S, Tao J, Wang Y, Zijlmans M, McGonigal A. Learn how to interpret and use intracranial EEG findings. Epileptic Disord 2024; 26:1-59. [PMID: 38116690 DOI: 10.1002/epd2.20190] [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: 07/18/2023] [Revised: 10/21/2023] [Accepted: 11/29/2023] [Indexed: 12/21/2023]
Abstract
Epilepsy surgery is the therapy of choice for many patients with drug-resistant focal epilepsy. Recognizing and describing ictal and interictal patterns with intracranial electroencephalography (EEG) recordings is important in order to most efficiently leverage advantages of this technique to accurately delineate the seizure-onset zone before undergoing surgery. In this seminar in epileptology, we address learning objective "1.4.11 Recognize and describe ictal and interictal patterns with intracranial recordings" of the International League against Epilepsy curriculum for epileptologists. We will review principal considerations of the implantation planning, summarize the literature for the most relevant ictal and interictal EEG patterns within and beyond the Berger frequency spectrum, review invasive stimulation for seizure and functional mapping, discuss caveats in the interpretation of intracranial EEG findings, provide an overview on special considerations in children and in subdural grids/strips, and review available quantitative/signal analysis approaches. To be as practically oriented as possible, we will provide a mini atlas of the most frequent EEG patterns, highlight pearls for its not infrequently challenging interpretation, and conclude with two illustrative case examples. This article shall serve as a useful learning resource for trainees in clinical neurophysiology/epileptology by providing a basic understanding on the concepts of invasive intracranial EEG.
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Affiliation(s)
- B Frauscher
- Department of Neurology, Duke University Medical Center and Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, North Carolina, USA
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, Montreal, Québec, Canada
| | - D Mansilla
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, Montreal, Québec, Canada
- Neurophysiology Unit, Institute of Neurosurgery Dr. Asenjo, Santiago, Chile
| | - C Abdallah
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, Montreal, Québec, Canada
| | - A Astner-Rohracher
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - S Beniczky
- Danish Epilepsy Centre, Dianalund, Denmark
- Aarhus University, Aarhus, Denmark
| | - M Brazdil
- Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital and Medical Faculty of Masaryk University, Member of the ERN-EpiCARE, Brno, Czechia
- Behavioral and Social Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - V Gnatkovsky
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - J Jacobs
- Department of Paediatrics and Department of Neuroscience, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute and Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - G Kalamangalam
- Department of Neurology, University of Florida, Gainesville, Florida, USA
- Wilder Center for Epilepsy Research, University of Florida, Gainesville, Florida, USA
| | - P Perucca
- Epilepsy Research Centre, Department of Medicine (Austin Health), University of Melbourne, Melbourne, Victoria, Australia
- Bladin-Berkovic Comprehensive Epilepsy Program, Department of Neurology, Austin Health, Melbourne, Victoria, Australia
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
- Department of Neurology, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - P Ryvlin
- Department of Clinical Neurosciences, CHUV, Lausanne University Hospital, Lausanne, Switzerland
| | - S Schuele
- Department of Neurology, Feinberg School of Medicine, Northwestern Memorial Hospital, Chicago, Illinois, USA
| | - J Tao
- Department of Neurology, The University of Chicago, Chicago, Illinois, USA
| | - Y Wang
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Wilder Center for Epilepsy Research, University of Florida, Gainesville, Florida, USA
| | - M Zijlmans
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
| | - A McGonigal
- Department of Neurosciences, Mater Misericordiae Hospital, Brisbane, Queensland, Australia
- Mater Research Institute, Faculty of Medicine, University of Queensland, St Lucia, Queensland, Australia
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Weiss SA, Fried I, Engel J, Bragin A, Wang S, Sperling MR, Wong RK, Nir Y, Staba RJ. Pathological neurons generate ripples at the UP-DOWN transition disrupting information transfer. Epilepsia 2024; 65:362-377. [PMID: 38041560 PMCID: PMC10922301 DOI: 10.1111/epi.17845] [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: 08/03/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/03/2023]
Abstract
OBJECTIVE To confirm and investigate why pathological high-frequency oscillations (pHFOs), including ripples (80-200 Hz) and fast ripples (200-600 Hz), are generated during the UP-DOWN transition of the slow wave and if information transmission mediated by ripple temporal coupling is disrupted in the seizure-onset zone (SOZ). METHODS We isolated 217 total units from 175.95 intracranial electroencephalography (iEEG) contact-hours of synchronized macro- and microelectrode recordings from 6 patients. Sleep slow oscillation (.1-2 Hz) epochs were identified in the iEEG recording. iEEG HFOs that occurred superimposed on the slow wave were transformed to phasors and adjusted by the phase of maximum firing in nearby units (i.e., maximum UP). We tested whether, in the SOZ, HFOs and associated action potentials (APs) occur more often at the UP-DOWN transition. We also examined ripple temporal correlations using cross-correlograms. RESULTS At the group level in the SOZ, HFO and HFO-associated AP probability was highest during the UP-DOWN transition of slow wave excitability (p < < .001). In the non-SOZ, HFO and HFO-associated AP was highest during the DOWN-UP transition (p < < .001). At the unit level in the SOZ, 15.6% and 20% of units exhibited more robust firing during ripples (Cohen's d = .11-.83) and fast ripples (d = .36-.90) at the UP-DOWN transition (p < .05 f.d.r. corrected), respectively. By comparison, also in the SOZ, 6.6% (d = .14-.30) and 8.5% (d = .33-.41) of units had significantly less firing during ripples and fast ripples at the UP-DOWN transition, respectively. Additional data shows that ripple and fast ripple temporal correlations, involving global slow waves, between the hippocampus, entorhinal cortex, and parahippocampal gyrus were reduced by >50% in the SOZ compared to the non-SOZ (N = 3). SIGNIFICANCE The UP-DOWN transition of slow wave excitability facilitates the activation of pathological neurons to generate pHFOs. Ripple temporal correlations across brain regions may be important in memory consolidation and are disrupted in the SOZ, perhaps by pHFO generation.
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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
| | - Anatol Bragin
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Shuang Wang
- Depts of Neurology, Epilepsy Center, Second Affiliated Hospital of Medical College, Zhejiang University, Zhejiang, China
| | - 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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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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Revajová K, Trávníček V, Jurák P, Vašíčková Z, Halámek J, Klimeš P, Cimbálník J, Brázdil M, Pail M. Interictal invasive very high-frequency oscillations in resting awake state and sleep. Sci Rep 2023; 13:19225. [PMID: 37932365 PMCID: PMC10628183 DOI: 10.1038/s41598-023-46024-z] [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: 06/21/2023] [Accepted: 10/26/2023] [Indexed: 11/08/2023] Open
Abstract
Interictal very high-frequency oscillations (VHFOs, 500-2000 Hz) in a resting awake state seem to be, according to a precedent study of our team, a more specific predictor of a good outcome of the epilepsy surgery compared to traditional interictal high-frequency oscillations (HFOs, 80-500 Hz). In this study, we retested this hypothesis on a larger cohort of patients. In addition, we also collected patients' sleep data and hypothesized that the occurrence of VHFOs in sleep will be greater than in resting state. We recorded interictal invasive electroencephalographic (iEEG) oscillations in 104 patients with drug-resistant epilepsy in a resting state and in 35 patients during sleep. 21 patients in the rest study and 11 patients in the sleep study met the inclusion criteria (interictal HFOs and VHFOs present in iEEG recordings, a surgical intervention and a postoperative follow-up of at least 1 year) for further evaluation of iEEG data. In the rest study, patients with good postoperative outcomes had significantly higher ratio of resected contacts with VHFOs compared to HFOs. In sleep, VHFOs were more abundant than in rest and the percentage of resected contacts in patients with good and poor outcomes did not considerably differ in any type of oscillations. In conclusion, (1) our results confirm, in a larger patient cohort, our previous work about VHFOs being a specific predictor of the area which needs to be resected; and (2) that more frequent sleep VHFOs do not further improve the results.
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Affiliation(s)
- Karin Revajová
- Brno Epilepsy Center, Department of Neurology, member of ERN-EpiCARE, St Anne's University Hospital and Medical Faculty of Masaryk University, Brno, 602 00, Czech Republic.
| | - Vojtěch Trávníček
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, 602 00, Czech Republic
- International Clinical Research Center, St Anne's University Hospital, Brno, 602 00, Czech Republic
| | - Pavel Jurák
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, 602 00, Czech Republic
| | - Zuzana Vašíčková
- Brno Epilepsy Center, Department of Neurology, member of ERN-EpiCARE, St Anne's University Hospital and Medical Faculty of Masaryk University, Brno, 602 00, Czech Republic
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, 602 00, Czech Republic
- International Clinical Research Center, St Anne's University Hospital, Brno, 602 00, Czech Republic
| | - Josef Halámek
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, 602 00, Czech Republic
| | - Petr Klimeš
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, 602 00, Czech Republic
- International Clinical Research Center, St Anne's University Hospital, Brno, 602 00, Czech Republic
| | - Jan Cimbálník
- Brno Epilepsy Center, Department of Neurology, member of ERN-EpiCARE, St Anne's University Hospital and Medical Faculty of Masaryk University, Brno, 602 00, Czech Republic
- International Clinical Research Center, St Anne's University Hospital, Brno, 602 00, Czech Republic
| | - Milan Brázdil
- Brno Epilepsy Center, Department of Neurology, member of ERN-EpiCARE, St Anne's University Hospital and Medical Faculty of Masaryk University, Brno, 602 00, Czech Republic
- Central European Institute of Technology, Masaryk University, Brno, 602 00, Czech Republic
- International Clinical Research Center, St Anne's University Hospital, Brno, 602 00, Czech Republic
| | - Martin Pail
- Brno Epilepsy Center, Department of Neurology, member of ERN-EpiCARE, St Anne's University Hospital and Medical Faculty of Masaryk University, Brno, 602 00, Czech Republic
- International Clinical Research Center, St Anne's University Hospital, Brno, 602 00, Czech Republic
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8
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Ho A, Hannan S, Thomas J, Avigdor T, Abdallah C, Dubeau F, Gotman J, Frauscher B. Rapid eye movement sleep affects interictal epileptic activity differently in mesiotemporal and neocortical areas. Epilepsia 2023; 64:3036-3048. [PMID: 37714213 DOI: 10.1111/epi.17763] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/22/2023] [Accepted: 08/22/2023] [Indexed: 09/17/2023]
Abstract
OBJECTIVE Rapid eye movement (REM) sleep reduces the rate and extent of interictal epileptiform discharges (IEDs). Breakthrough epileptic activity during REM sleep is therefore thought to best localize the seizure onset zone (SOZ). We utilized polysomnography combined with direct cortical recordings to investigate the influences of anatomical locations and the time of night on the suppressive effect of REM sleep on IEDs. METHODS Forty consecutive patients with drug-resistant focal epilepsy underwent combined polysomnography and stereo-electroencephalography during presurgical evaluation. Ten-minute interictal epochs were selected 2 h prior to sleep onset (wakefulness), and from the first and second half of the night during non-REM (NREM) sleep and REM sleep. IEDs were detected automatically across all channels. Anatomic localization, time of night, and channel type (within or outside the SOZ) were tested as modulating factors. RESULTS Relative to wakefulness, there was a suppression of IEDs by REM sleep in neocortical regions (median = -27.6%), whereas mesiotemporal regions showed an increase in IEDs (19.1%, p = .01, d = .39). This effect was reversed when comparing the regional suppression of IEDs by REM sleep relative to NREM sleep (-35.1% in neocortical, -58.7% in mesiotemporal, p < .001, d = .39). Across all patients, no clinically relevant novel IED regions were observed in REM sleep versus NREM or wakefulness based on our predetermined thresholds (4 IEDs/min in REM, 0 IEDs/min in NREM and wakefulness). Finally, there was a reduction in IEDs in late (NREM: 1.08/min, REM: .61/min) compared to early sleep (NREM: 1.22/min, REM: .69/min) for both NREM (p < .001, d = .21) and REM (p = .04, d = .14). SIGNIFICANCE Our results demonstrate a spatiotemporal effect of IED suppression by REM sleep relative to wakefulness in neocortical but not mesiotemporal regions, and in late versus early sleep. This suggests the importance of considering sleep stage interactions and the potential influences of anatomical locations when using IEDs to define the epileptic focus.
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Affiliation(s)
- Alyssa Ho
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Sana Hannan
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - John Thomas
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Tamir Avigdor
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Chifaou Abdallah
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, 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
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Montreal Neurological Institute and Hospital, McGill University, Montreal, 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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9
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Monsoor T, Zhang Y, Daida A, Oana S, Lu Q, Hussain SA, Fallah A, Sankar R, Staba RJ, Speier W, Roychowdhury V, Nariai H. Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy. Clin Neurophysiol 2023; 154:129-140. [PMID: 37603979 PMCID: PMC10861270 DOI: 10.1016/j.clinph.2023.07.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 06/30/2023] [Accepted: 07/26/2023] [Indexed: 08/23/2023]
Abstract
OBJECTIVE This study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in epilepsy surgery. METHODS We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for spike association and time-frequency plot characteristics. A deep learning (DL)-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method. RESULTS The MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors had the highest spike association rate. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification. CONCLUSIONS HFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs. SIGNIFICANCE Enhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes.
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Affiliation(s)
- Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - 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
| | - Shingo Oana
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Qiujing Lu
- 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
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, 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
| | - William Speier
- Department of Bioengineering, University of California, Los Angeles, CA, USA; Department of Radiological Sciences, 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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10
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Lemus HN, Sarkis RA. Interictal epileptiform discharges in Alzheimer's disease: prevalence, relevance, and controversies. Front Neurol 2023; 14:1261136. [PMID: 37808503 PMCID: PMC10551146 DOI: 10.3389/fneur.2023.1261136] [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: 07/18/2023] [Accepted: 09/08/2023] [Indexed: 10/10/2023] Open
Abstract
Alzheimer's disease (AD) is the most common type of dementia and remains an incurable, progressive disease with limited disease-modifying interventions available. In patients with AD, interictal epileptiform discharges (IEDs) have been identified in up to 54% of combined cohorts of mild cognitive impairment (MCI) or mild dementia and are a marker of a more aggressive disease course. Studies assessing the role of IEDs in AD are limited by the lack of standardization in the definition of IEDs or the different neurophysiologic techniques used to capture them. IEDs are an appealing treatment target given the availability of EEG and anti-seizure medications. There remains uncertainty regarding when to treat IEDs, the optimal drug and dose for treatment, and the impact of treatment on disease course. This review covers the state of knowledge of the field of IEDs in AD, and the steps needed to move the field forward.
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Affiliation(s)
| | - Rani A. Sarkis
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, United States
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11
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Weiss SA, Fried I, Engel J, Bragin A, Wang S, Sperling MR, Wong RK, Nir Y, Staba RJ. Pathological neurons generate ripples at the UP-DOWN transition disrupting information transfer. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.01.23293365. [PMID: 37609251 PMCID: PMC10441494 DOI: 10.1101/2023.08.01.23293365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Objective To confirm and investigate why pathological HFOs (pHFOs), including Ripples [80-200 Hz] and fast ripples [200-600 Hz], are generated during the UP-DOWN transition of the slow wave and if pHFOs interfere with information transmission. Methods We isolated 217 total units from 175.95 iEEG contact-hours of synchronized macro- and microelectrode recordings from 6 patients. Sleep slow oscillation (0.1-2 Hz) epochs were identified in the iEEG recording. iEEG HFOs that occurred superimposed on the slow wave were transformed to phasors and adjusted by the phase of maximum firing in nearby units (i.e., maximum UP). We tested whether, in the seizure onset zone (SOZ), HFOs and associated action potentials (AP) occur more often at the UP-DOWN transition. We also examined ripple temporal correlations using cross correlograms. Results At the group level in the SOZ, HFO and HFO-associated AP probability was highest during the UP-DOWN transition of slow wave excitability (p<<0.001). In the non-SOZ, HFO and HFO-associated AP was highest during the DOWN-UP transition (p<<0.001). At the unit level in the SOZ, 15.6% and 20% of units exhibited more robust firing during ripples (Cohen's d=0.11-0.83) and fast ripples (d=0.36-0.90) at the UP-DOWN transition (p<0.05 f.d.r corrected), respectively. By comparison, also in the SOZ, 6.6% (d=0.14-0.30) and 8.5% (d=0.33-0.41) of units had significantly less firing during ripples and fast ripples at the UP-DOWN transition, respectively. Additional data shows ripple temporal correlations, involving global slow waves, between the hippocampus, entorhinal cortex, and parahippocampal gyrus were reduced by ~50-80% in the SOZ compared to the non-SOZ (N=3). Significance The UP-DOWN transition of slow wave excitability facilitates the activation of pathological neurons to generate pHFOs. The pathological neurons and pHFOs disrupt ripple temporal correlations across brain regions that transfer information and may be important in memory consolidation.
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Affiliation(s)
- Shennan A Weiss
- Dept. of Neurology
- 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
| | | | - Jerome Engel
- Dept. of Neurology
- Dept. of Neurosurgery
- Dept. of Neurobiology
- Dept. of Psychiatry and Biobehavioral Sciences
- Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | | | - Shuang Wang
- Depts of Neurology, Epilepsy Center, Second Affiliated Hospital of Medical College, Zhejiang University, Zhejiang, China
| | - 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
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12
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Lévesque M, Wang S, Macey-Dare ADB, Salami P, Avoli M. Evolution of interictal activity in models of mesial temporal lobe epilepsy. Neurobiol Dis 2023; 180:106065. [PMID: 36907521 DOI: 10.1016/j.nbd.2023.106065] [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: 01/23/2023] [Revised: 02/22/2023] [Accepted: 03/02/2023] [Indexed: 03/12/2023] Open
Abstract
Interictal activity and seizures are the hallmarks of focal epileptic disorders (which include mesial temporal lobe epilepsy, MTLE) in humans and in animal models. Interictal activity, which is recorded with cortical and intracerebral EEG recordings, comprises spikes, sharp waves and high-frequency oscillations, and has been used in clinical practice to identify the epileptic zone. However, its relation with seizures remains debated. Moreover, it is unclear whether specific EEG changes in interictal activity occur during the time preceding the appearance of spontaneous seizures. This period, which is termed "latent", has been studied in rodent models of MTLE in which spontaneous seizures start to occur following an initial insult (most often a status epilepticus induced by convulsive drugs such as kainic acid or pilocarpine) and may mirror epileptogenesis, i.e., the process leading the brain to develop an enduring predisposition to seizure generation. Here, we will address this topic by reviewing experimental studies performed in MTLE models. Specifically, we will review data highlighting the dynamic changes in interictal spiking activity and high-frequency oscillations occurring during the latent period, and how optogenetic stimulation of specific cell populations can modulate them in the pilocarpine model. These findings indicate that interictal activity: (i) is heterogeneous in its EEG patterns and thus, presumably, in its underlying neuronal mechanisms; and (ii) can pinpoint to the epileptogenic processes occurring in focal epileptic disorders in animal models and, perhaps, in epileptic patients.
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Affiliation(s)
- Maxime Lévesque
- Montreal Neurological Institute-Hospital and Departments of Neurology & Neurosurgery, McGill University, 3801 Rue University, Montreal, H3A 2B4, QC, Canada.
| | - Siyan Wang
- Montreal Neurological Institute-Hospital and Departments of Neurology & Neurosurgery, McGill University, 3801 Rue University, Montreal, H3A 2B4, QC, Canada
| | - Anežka D B Macey-Dare
- Montreal Neurological Institute-Hospital and Departments of Neurology & Neurosurgery, McGill University, 3801 Rue University, Montreal, H3A 2B4, QC, Canada; Department of Pharmacology, University of Oxford, Mansfield Road, Oxford OX1 3QT, UK
| | - Pariya Salami
- Montreal Neurological Institute-Hospital and Departments of Neurology & Neurosurgery, McGill University, 3801 Rue University, Montreal, H3A 2B4, QC, Canada; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 55 Fruit St., Boston, MA 02114, USA
| | - Massimo Avoli
- Montreal Neurological Institute-Hospital and Departments of Neurology & Neurosurgery, McGill University, 3801 Rue University, Montreal, H3A 2B4, QC, Canada; Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, H3G 1Y6, QC, Canada
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13
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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: 18] [Impact Index Per Article: 18.0] [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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14
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Schiller K, von Ellenrieder N, Avigdor T, El Kosseifi C, Abdallah C, Minato E, Gotman J, Frauscher B. Focal epilepsy impacts rapid eye movement sleep microstructure. Sleep 2023; 46:zsac250. [PMID: 36242588 PMCID: PMC9905780 DOI: 10.1093/sleep/zsac250] [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: 06/30/2022] [Revised: 08/16/2022] [Indexed: 12/12/2022] Open
Abstract
STUDY OBJECTIVES Whereas there is plenty of evidence on the influence of epileptic activity on non-rapid eye movement (NREM) sleep macro- and micro-structure, data on the impact of epilepsy on rapid eye movement (REM) sleep remains sparse. Using high-density electroencephalography (HD-EEG), we assessed global and focal disturbances of sawtooth waves (STW) as cortically generated sleep oscillations of REM sleep in patients with focal epilepsy. METHODS Twenty-two patients with drug-resistant focal epilepsy (13 females; mean age, 32.6 ± 10.7 years; 12 temporal lobe epilepsy) and 12 healthy controls (3 females; 24.0 ± 3.2 years) underwent combined overnight HD-EEG and polysomnography. STW rate, duration, frequency, power, spatial extent, IED rates and sleep homeostatic properties were analyzed. RESULTS STW rate and duration were reduced in patients with focal epilepsy compared to healthy controls (rate: 0.64/min ± 0.46 vs. 1.12/min ± 0.41, p = .005, d = -0.98; duration: 3.60 s ± 0.76 vs. 4.57 ± 1.00, p = .003, d = -1.01). Not surprisingly given the fronto-central maximum of STW, the reductions were driven by extratemporal lobe epilepsy patients (rate: 0.45/min ± 0.31 vs. 1.12/min ± 0.41, p = .0004, d = -1.35; duration: 3.49 s ± 0.92 vs. 4.57 ± 1.00, p = .017, d = -0.99) and were more pronounced in the first vs. the last sleep cycle (rate first cycle patients vs. controls: 0.60/min ± 0.49 vs. 1.10/min ± 0.55, p = .016, d = -0.90, rate last cycle patients vs. controls: 0.67/min ± 0.51 vs. 0.99/min ± 0.49, p = .11, d = -0.62; duration first cycle patients vs. controls: 3.60s ± 0.76 vs. 4.57 ± 1.00, p = .003, d = -1.01, duration last cycle patients vs. controls: 3.66s ± 0.84 vs. 4.51 ± 1.26, p = .039, d = -0.80). There was no regional decrease of STWs in the region with the epileptic focus vs. the contralateral side (all p > .05). CONCLUSION Patients with focal epilepsy and in particular extratemporal lobe epilepsy show a global reduction of STW activity in REM sleep. This may suggest that epilepsy impacts cortically generated sleep oscillations even in REM sleep when epileptic activity is low.
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Affiliation(s)
- Katharina Schiller
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
- Hospital Group Ostallgaeu-Kaufbeuren, Department of Pediatrics, Kaufbeuren, Germany
- Medical University Innsbruck, Department of Pediatrics, Innsbruck, Austria
| | | | - Tamir Avigdor
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Charbel El Kosseifi
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Chifaou Abdallah
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Erica Minato
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
- Department of Medicine and Center for Neuroscience Studies, Queen’s University; Kingston, Ontario, Canada
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15
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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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16
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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: 6] [Impact Index Per Article: 3.0] [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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17
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Urriola J, Bollmann S, Tremayne F, Burianová H, Marstaller L, Reutens D. Spikes with and without concurrent high-frequency oscillations: Topographic relationship and neural correlates using EEG-fMRI. Epilepsy Res 2022; 188:107039. [DOI: 10.1016/j.eplepsyres.2022.107039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 09/11/2022] [Accepted: 10/17/2022] [Indexed: 11/03/2022]
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18
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Li X, Zhang H, Lai H, Wang J, Wang W, Yang X. High-Frequency Oscillations and Epileptogenic Network. Curr Neuropharmacol 2022; 20:1687-1703. [PMID: 34503414 PMCID: PMC9881061 DOI: 10.2174/1570159x19666210908165641] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/26/2021] [Accepted: 08/31/2021] [Indexed: 11/22/2022] Open
Abstract
Epilepsy is a network disease caused by aberrant neocortical large-scale connectivity spanning regions on the scale of several centimeters. High-frequency oscillations, characterized by the 80-600 Hz signals in electroencephalography, have been proven to be a promising biomarker of epilepsy that can be used in assessing the severity and susceptibility of epilepsy as well as the location of the epileptogenic zone. However, the presence of a high-frequency oscillation network remains a topic of debate as high-frequency oscillations have been previously thought to be incapable of propagation, and the relationship between high-frequency oscillations and the epileptogenic network has rarely been discussed. Some recent studies reported that high-frequency oscillations may behave like networks that are closely relevant to the epileptogenic network. Pathological highfrequency oscillations are network-driven phenomena and elucidate epileptogenic network development; high-frequency oscillations show different characteristics coincident with the epileptogenic network dynamics, and cross-frequency coupling between high-frequency oscillations and other signals may mediate the generation and propagation of abnormal discharges across the network.
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Affiliation(s)
- Xiaonan Li
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | | | | | - Jiaoyang Wang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | - Wei Wang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | - Xiaofeng Yang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China,Address correspondence to this author at the Bioland Laboratory, Guangzhou, China; Tel: 86+ 18515855127; E-mail:
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19
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McLeod GA, Abbasian P, Toutant D, Ghassemi A, Duke T, Rycyk C, Serletis D, Moussavi Z, Ng MC. Sleep-wake states change the interictal localization of candidate epileptic source generators. Sleep 2022; 45:6547903. [PMID: 35279715 PMCID: PMC9189983 DOI: 10.1093/sleep/zsac062] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 02/28/2022] [Indexed: 11/12/2022] Open
Abstract
STUDY OBJECTIVES To compare estimated epileptic source localizations from 5 sleep-wake states (SWS): wakefulness (W), rapid eye movement sleep (REM), and non-REM 1-3. METHODS Electrical source localization (sLORETA) of interictal spikes from different SWS on surface EEG from the epilepsy monitoring unit at spike peak and take-off, with results mapped to individual brain models for 75% of patients. Concordance was defined as source localization voxels shared between 2 and 5 SWS, and discordance as those unique to 1 SWS against 1-4 other SWS. RESULTS 563 spikes from 16 prospectively recruited focal epilepsy patients across 161 day-nights. SWS exerted significant differences at spike peak but not take-off. Source localization size did not vary between SWS. REM localizations were smaller in multifocal than unifocal patients (28.8% vs. 54.4%, p = .0091). All five SWS contributed about 45% of their localizations to converge onto 17.0 ± 15.5% voxels. Against any one other SWS, REM was least concordant (54.4% vs. 66.9%, p = .0006) and most discordant (39.3% vs. 29.6%, p = .0008). REM also yielded the most unique localizations (20.0% vs. 8.6%, p = .0059). CONCLUSIONS REM was best suited to identify candidate epileptic sources. sLORETA proposes a model in which an "omni-concordant core" of source localizations shared by all five SWS is surrounded by a "penumbra" of source localizations shared by some but not all SWS. Uniquely, REM spares this core to "move" source voxels from the penumbra to unique cortex not localized by other SWS. This may reflect differential intra-spike propagation in REM, which may account for its reported superior localizing abilities.
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Affiliation(s)
- Graham A McLeod
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Parandoush Abbasian
- Medical Physics, Department of Physics and Astronomy, University of Manitoba, Winnipeg, MB, Canada.,CancerCare Manitoba Research Institute, Winnipeg, MB, Canada
| | - Darion Toutant
- Biomedical Engineering, University of Manitoba, Winnipeg, MB, Canada
| | | | - Tyler Duke
- Biomedical Engineering, University of Manitoba, Winnipeg, MB, Canada
| | - Conrad Rycyk
- Biomedical Engineering, University of Manitoba, Winnipeg, MB, Canada
| | - Demitre Serletis
- Charles Shor Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA.,Department of Neurosurgery, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Zahra Moussavi
- Biomedical Engineering, University of Manitoba, Winnipeg, MB, Canada
| | - Marcus C Ng
- Biomedical Engineering, University of Manitoba, Winnipeg, MB, Canada.,Section of Neurology, University of Manitoba, Winnipeg, MB, Canada
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20
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Dimakopoulos V, Gotman J, Stacey W, von Ellenrieder N, Jacobs J, Papadelis C, Cimbalnik J, Worrell G, Sperling MR, Zijlmans M, Imbach L, Frauscher B, Sarnthein J. Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom. Brain Commun 2022; 4:fcac151. [PMID: 35770134 PMCID: PMC9234061 DOI: 10.1093/braincomms/fcac151] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 04/29/2022] [Accepted: 06/07/2022] [Indexed: 11/17/2022] Open
Abstract
In drug-resistant focal epilepsy, interictal high-frequency oscillations (HFOs) recorded from intracranial EEG (iEEG) may provide clinical information for delineating epileptogenic brain tissue. The iEEG electrode contacts that contain HFO are hypothesized to delineate the epileptogenic zone; their resection should then lead to postsurgical seizure freedom. We test whether our prospective definition of clinically relevant HFO is in agreement with postsurgical seizure outcome. The algorithm is fully automated and is equally applied to all data sets. The aim is to assess the reliability of the proposed detector and analysis approach. We use an automated data-independent prospective definition of clinically relevant HFO that has been validated in data from two independent epilepsy centres. In this study, we combine retrospectively collected data sets from nine independent epilepsy centres. The analysis is blinded to clinical outcome. We use iEEG recordings during NREM sleep with a minimum of 12 epochs of 5 min of NREM sleep. We automatically detect HFO in the ripple (80-250 Hz) and in the fast ripple (250-500 Hz) band. There is no manual rejection of events in this fully automated algorithm. The type of HFO that we consider clinically relevant is defined as the simultaneous occurrence of a fast ripple and a ripple. We calculate the temporal consistency of each patient's HFO rates over several data epochs within and between nights. Patients with temporal consistency <50% are excluded from further analysis. We determine whether all electrode contacts with high HFO rate are included in the resection volume and whether seizure freedom (ILAE 1) was achieved at ≥2 years follow-up. Applying a previously validated algorithm to a large cohort from several independent epilepsy centres may advance the clinical relevance and the generalizability of HFO analysis as essential next step for use of HFO in clinical practice.
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Affiliation(s)
- Vasileios Dimakopoulos
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zürich, Switzerland
| | - Jean Gotman
- Montreal Neurological Institute & Hospital, McGill University, Montreal, Quebec, Canada
| | - William Stacey
- Department of Neurology and Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, MI, USA
| | | | - Julia Jacobs
- Alberta Children’s Hospital, University of Calgary, Calgary, Canada
| | | | - Jan Cimbalnik
- St. Anne’s University Hospital, Brno, Czech Republic
| | | | - Michael R Sperling
- Department of Neurology, Jefferson University Hospitals, Philadelphia, PA, USA
| | - Maike Zijlmans
- University Medical Center, Utrecht, and Stichting Epilepsie Instellingen Nederland (SEIN), Utrecht, The Netherlands
| | - Lucas Imbach
- Schweizerisches Epilepsie Zentrum, Zurich, Switzerland
| | - Birgit Frauscher
- Montreal Neurological Institute & Hospital, McGill University, Montreal, Quebec, Canada
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zürich, Switzerland
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21
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Liu B, Ran X, Yi Y, Zhang X, Chen H, Hu Y. Anticonvulsant Effect of Carbenoxolone on Chronic Epileptic Rats and Its Mechanism Related to Connexin and High-Frequency Oscillations. Front Mol Neurosci 2022; 15:870947. [PMID: 35615064 PMCID: PMC9125185 DOI: 10.3389/fnmol.2022.870947] [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: 02/07/2022] [Accepted: 04/04/2022] [Indexed: 12/03/2022] Open
Abstract
Objective This study was designed to investigate the influence and mechanism of gap junction carbenoxolone (CBX) on dynamic changes in the spectral power of ripples and fast ripples (FRs) in the hippocampus of chronic epileptic rats. Methods The lithium-pilocarpine (PILO) status epilepticus (SE) model (PILO group) and the CBX pretreatment model (CBX + PILO group) were established to analyze dynamic changes in the spectral power of ripples and FRs, and the dynamic expression of connexin (CX)26, CX32, CX36, and CX43 in the hippocampus of chronic epileptic rats. Results Within 28 days after SE, the number of spontaneous recurrent seizures (SRSs) in the PILO group was significantly higher than that in the CBX + PILO group. The average spectral power of FRs in the PILO group was significantly higher than the baseline level at 1 and 7 days after SE. The average spectral power of FRs in the PILO group was significantly higher than that in the CBX + PILO group at 1, 7, and 14 days after SE. Seizures induced an increase in CX43 expression at 1 and 7 days after SE, but had no significant effect on CX26, CX36, or CX32. CBX pretreatment did not affect the expression of CXs in the hippocampus of normal rats, but it inhibited the expression of CX43 in epileptic rats. The number of SRSs at 2 and 4 weeks after SE had the highest correlation with the average spectral power of FRs; the average spectral power of FRs was moderately correlated with the expression of CX43. Conclusion The results of this study indicate that the energy of FRs may be regulated by its interference with the expression of CX43, and thus, affect seizures. Blocking the expression of CX43 thereby reduces the formation of pathological high-frequency oscillations (HFOs), making it a promising strategy for the treatment of chronic epilepsy.
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Affiliation(s)
- Benke Liu
- Department of Neurology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
- Shenzhen Baoan Women’s and Children’s Hospital, Jinan University, Shenzhen, China
| | - Xiao Ran
- Department of Neurology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
| | - Yanjun Yi
- Department of Neurology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
| | - Xinyu Zhang
- Department of Neurology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
| | - Hengsheng Chen
- Department of Neurology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
| | - Yue Hu
- Department of Neurology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
- *Correspondence: Yue Hu,
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22
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Nunez MD, Charupanit K, Sen-Gupta I, Lopour BA, Lin JJ. Beyond rates: time-varying dynamics of high frequency oscillations as a biomarker of the seizure onset zone. J Neural Eng 2022; 19:10.1088/1741-2552/ac520f. [PMID: 35120337 PMCID: PMC9258635 DOI: 10.1088/1741-2552/ac520f] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 02/04/2022] [Indexed: 11/11/2022]
Abstract
Objective. High frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection. However, the number of HFOs per minute (i.e. the HFO 'rate') is not stable over the duration of intracranial recordings; for example, the rate of HFOs increases during periods of slow-wave sleep. Moreover, HFOs that are predictive of epileptic tissue may occur in oscillatory patterns due to phase coupling with lower frequencies. Therefore, we sought to further characterize between-seizure (i.e. 'interictal') HFO dynamics both within and outside the seizure onset zone (SOZ).Approach. Using long-term intracranial EEG (mean duration 10.3 h) from 16 patients, we automatically detected HFOs using a new algorithm. We then fit a hierarchical negative binomial model to the HFO counts. To account for differences in HFO dynamics and rates between sleep and wakefulness, we also fit a mixture model to the same data that included the ability to switch between two discrete brain states that were automatically determined during the fitting process. The ability to predict the SOZ by model parameters describing HFO dynamics (i.e. clumping coefficients and coefficients of variation) was assessed using receiver operating characteristic curves.Main results. Parameters that described HFO dynamics were predictive of SOZ. In fact, these parameters were found to be more consistently predictive than HFO rate. Using concurrent scalp EEG in two patients, we show that the model-found brain states corresponded to (1) non-REM sleep and (2) awake and rapid eye movement sleep. However the brain state most likely corresponding to slow-wave sleep in the second model improved SOZ prediction compared to the first model for only some patients.Significance. This work suggests that delineation of SOZ with interictal data can be improved by the inclusion of time-varying HFO dynamics.
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Affiliation(s)
- Michael D. Nunez
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands,Department of Biomedical Engineering, University of California, Irvine CA, USA,Corresponding author (Michael D. Nunez), (Beth A. Lopour)
| | - Krit Charupanit
- Department of Biomedical Engineering, University of California, Irvine CA, USA,Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand
| | - Indranil Sen-Gupta
- Neurology, University of California Irvine Medical Center, Orange CA, USA
| | - Beth A. Lopour
- Department of Biomedical Engineering, University of California, Irvine CA, USA,Corresponding author (Michael D. Nunez), (Beth A. Lopour)
| | - Jack J. Lin
- Department of Neurology, University of California, Irvine CA, USA
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23
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Burelo K, Ramantani G, Indiveri G, Sarnthein J. A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG. Sci Rep 2022; 12:1798. [PMID: 35110665 PMCID: PMC8810784 DOI: 10.1038/s41598-022-05883-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 01/17/2022] [Indexed: 12/04/2022] Open
Abstract
Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This development has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The demand for therapy monitoring in epilepsy has kindled interest in compact wearable electronic devices for long-term EEG recording. Spiking neural networks (SNN) have emerged as optimal architectures for embedding in compact low-power signal processing hardware. We analyzed 20 scalp EEG recordings from 11 pediatric focal lesional epilepsy patients. We designed a custom SNN to detect events of interest (EoI) in the 80–250 Hz ripple band and reject artifacts in the 500–900 Hz band. We identified the optimal SNN parameters to detect EoI and reject artifacts automatically. The occurrence of HFO thus detected was associated with active epilepsy with 80% accuracy. The HFO rate mirrored the decrease in seizure frequency in 8 patients (p = 0.0047). Overall, the HFO rate correlated with seizure frequency (rho = 0.90 CI [0.75 0.96], p < 0.0001, Spearman’s correlation). The fully automated SNN detected clinically relevant HFO in the scalp EEG. This study is a further step towards non-invasive epilepsy monitoring with a low-power wearable device.
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Affiliation(s)
- Karla Burelo
- Klinik für Neurochirurgie, Universitätsspital und Universität Zürich, 8091, Zurich, Switzerland.,Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Georgia Ramantani
- Neuropädiatrie, Universitäts-Kinderspital und Universität Zürich, Zurich, Switzerland.,Forschungszentrum für das Kind, Universitäts-Kinderspital Zürich, Zurich, Switzerland.,Zentrum für Neurowissenschaften Zürich, ETH und Universität Zürich, Zurich, Switzerland
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.,Zentrum für Neurowissenschaften Zürich, ETH und Universität Zürich, Zurich, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, Universitätsspital und Universität Zürich, 8091, Zurich, Switzerland. .,Zentrum für Neurowissenschaften Zürich, ETH und Universität Zürich, Zurich, Switzerland.
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24
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Cserpan D, Rosch R, Pietro Lo Biundo S, Sarnthein J, Ramantani G. Variation of scalp EEG high frequency oscillation rate with sleep stage and time spent in sleep in patients with pediatric epilepsy. Clin Neurophysiol 2022; 135:117-125. [DOI: 10.1016/j.clinph.2021.12.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 11/07/2021] [Accepted: 12/14/2021] [Indexed: 12/15/2022]
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25
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High-frequency oscillations in scalp EEG: A systematic review of methodological choices and clinical findings. Clin Neurophysiol 2022; 137:46-58. [DOI: 10.1016/j.clinph.2021.12.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/13/2021] [Accepted: 12/21/2021] [Indexed: 02/08/2023]
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26
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Zweiphenning WJEM, von Ellenrieder N, Dubeau F, Martineau L, Minotti L, Hall JA, Chabardes S, Dudley R, Kahane P, Gotman J, Frauscher B. Correcting for physiological ripples improves epileptic focus identification and outcome prediction. Epilepsia 2021; 63:483-496. [PMID: 34919741 PMCID: PMC9300035 DOI: 10.1111/epi.17145] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 11/30/2021] [Accepted: 11/30/2021] [Indexed: 11/30/2022]
Abstract
Objective The integration of high‐frequency oscillations (HFOs; ripples [80–250 Hz], fast ripples [250–500 Hz]) in epilepsy evaluation is hampered by physiological HFOs, which cannot be reliably differentiated from pathological HFOs. We evaluated whether defining abnormal HFO rates by statistical comparison to region‐specific physiological HFO rates observed in the healthy brain improves identification of the epileptic focus and surgical outcome prediction. Methods We detected HFOs in 151 consecutive patients who underwent stereo‐electroencephalography and subsequent resective epilepsy surgery at two tertiary epilepsy centers. We compared how HFOs identified the resection cavity and predicted seizure‐free outcome using two thresholds from the literature (HFO rate > 1/min; 50% of the total number of a patient's HFOs) and three thresholds based on normative rates from the Montreal Neurological Institute Open iEEG Atlas (https://mni‐open‐ieegatlas.research.mcgill.ca/): global Atlas threshold, regional Atlas threshold, and regional + 10% threshold after regional Atlas correction. Results Using ripples, the regional + 10% threshold performed best for focus identification (77.3% accuracy, 27% sensitivity, 97.1% specificity, 80.6% positive predictive value [PPV], 78.2% negative predictive value [NPV]) and outcome prediction (69.5% accuracy, 58.6% sensitivity, 76.3% specificity, 60.7% PPV, 74.7% NPV). This was an improvement for focus identification (+1.1% accuracy, +17.0% PPV; p < .001) and outcome prediction (+12.0% sensitivity, +1.0% PPV; p = .05) compared to the 50% threshold. The improvement was particularly marked for foci in cortex, where physiological ripples are frequent (outcome: +35.3% sensitivity, +5.3% PPV; p = .014). In these cases, the regional + 10% threshold outperformed fast ripple rate > 1/min (+3.6% accuracy, +26.5% sensitivity, +21.6% PPV; p < .001) and seizure onset zone (+13.5% accuracy, +29.4% sensitivity, +17.0% PPV; p < .05–.01) for outcome prediction. Normalization did not improve the performance of fast ripples. Significance Defining abnormal HFO rates by statistical comparison to rates in healthy tissue overcomes an important weakness in the clinical use of ripples. It improves focus identification and outcome prediction compared to standard HFO measures, increasing their clinical applicability.
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Affiliation(s)
- Willemiek J E M Zweiphenning
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.,University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | | | - François Dubeau
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Laurence Martineau
- Department of Neurology, Grenoble-Alpes University Hospital and Grenoble-Alpes University, Grenoble, France
| | - Lorella Minotti
- Department of Neurology, Grenoble-Alpes University Hospital and Grenoble-Alpes University, Grenoble, France
| | - Jeffery A Hall
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Stephan Chabardes
- Department of Neurosurgery, Grenoble-Alpes University Hospital and Grenoble-Alpes University, Grenoble, France
| | - Roy Dudley
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Philippe Kahane
- Department of Neurology, Grenoble-Alpes University Hospital and Grenoble-Alpes University, Grenoble, France
| | - 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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27
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Weiss SA, Staba RJ, Sharan A, Wu C, Rubinstein D, Das S, Waldman Z, Orosz I, Worrell G, Engel J, Sperling MR. Accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions. Sci Rep 2021; 11:21388. [PMID: 34725412 PMCID: PMC8560764 DOI: 10.1038/s41598-021-00894-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 10/19/2021] [Indexed: 11/10/2022] Open
Abstract
To see whether acute intraoperative recordings using stereo EEG (SEEG) electrodes can replace prolonged interictal intracranial EEG (iEEG) recording, making the process more efficient and safer, 10 min of iEEG were recorded following electrode implantation in 16 anesthetized patients, and 1-2 days later during non-rapid eye movement (REM) sleep. Ripples on oscillations (RonO, 80-250 Hz), ripples on spikes (RonS), sharp-spikes, fast RonO (fRonO, 250-600 Hz), and fast RonS (fRonS) were semi-automatically detected. HFO power and frequency were compared between the conditions using a generalized linear mixed-effects model. HFO rates were compared using a two-way repeated measures ANOVA with anesthesia type and SOZ as factors. A receiver-operating characteristic (ROC) curve analysis quantified seizure onset zone (SOZ) classification accuracy, and the scalar product was used to assess spatial reliability. Resection of contacts with the highest rate of events was compared with outcome. During sleep, all HFOs, except fRonO, were larger in amplitude compared to intraoperatively (p < 0.01). HFO frequency was also affected (p < 0.01). Anesthesia selection affected HFO and sharp-spike rates. In both conditions combined, sharp-spikes and all HFO subtypes were increased in the SOZ (p < 0.01). However, the increases were larger during the sleep recordings (p < 0.05). The area under the ROC curves for SOZ classification were significantly smaller for intraoperative sharp-spikes, fRonO, and fRonS rates (p < 0.05). HFOs and spikes were only significantly spatially reliable for a subset of the patients (p < 0.05). A failure to resect fRonO areas in the sleep recordings trended the most sensitive and accurate for predicting failure. In summary, HFO morphology is altered by anesthesia. Intraoperative SEEG recordings exhibit increased rates of HFOs in the SOZ, but their spatial distribution can differ from sleep recordings. Recording these biomarkers during non-REM sleep offers a more accurate delineation of the SOZ and possibly the epileptogenic zone.
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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, USA
| | - Richard J Staba
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Ashwini Sharan
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Chengyuan Wu
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Daniel Rubinstein
- Department of Neurology and Neuroscience, Thomas Jefferson University, 901 Walnut St. Suite 400, Philadelphia, PA, 19107, USA
| | - Sandhitsu Das
- Penn Image Computing & Science Lab, University of Pennsylvania, Philadelphia, PA, 19143, USA
| | - Zachary Waldman
- Department of Neurology and Neuroscience, Thomas Jefferson University, 901 Walnut St. Suite 400, Philadelphia, PA, 19107, USA
| | - Iren Orosz
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Gregory Worrell
- Department of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), Rochester, USA.,Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
| | - Jerome Engel
- 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
- Department of Neurology and Neuroscience, Thomas Jefferson University, 901 Walnut St. Suite 400, Philadelphia, PA, 19107, USA.
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Papadelis C, Perry MS. Localizing the Epileptogenic Zone with Novel Biomarkers. Semin Pediatr Neurol 2021; 39:100919. [PMID: 34620466 PMCID: PMC8501232 DOI: 10.1016/j.spen.2021.100919] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/09/2021] [Accepted: 08/11/2021] [Indexed: 01/01/2023]
Abstract
Several noninvasive methods, such as high-density EEG or magnetoencephalography, are currently used to delineate the epileptogenic zone (EZ) during the presurgical evaluation of patients with drug resistant epilepsy (DRE). Yet, none of these methods can reliably identify the EZ by their own. In most cases a multimodal approach is needed. Challenging cases often require the implantation of intracranial electrodes, either through stereo-taxic EEG or electro-corticography. Recently, a growing body of literature introduces novel biomarkers of epilepsy that can be used for analyzing both invasive as well as noninvasive electrophysiological data. Some of these biomarkers are able to delineate the EZ with high precision, augment the presurgical evaluation, and predict the surgical outcome of patients with DRE undergoing surgery. However, the use of these epilepsy biomarkers in clinical practice is limited. Here, we summarize and discuss the latest technological advances in the presurgical neurophysiological evaluation of children with DRE with emphasis on electric and magnetic source imaging, high frequency oscillations, and functional connectivity.
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Affiliation(s)
- Christos Papadelis
- Jane and John Justin Neurosciences Center, Cook Children's Health Care System, Fort Worth, TX; School of Medicine, Texas Christian University and University of North Texas Health Science Center, Fort Worth, TX; Department of Bioengineering, University of Texas at Arlington, Arlington, TX; Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA.
| | - M Scott Perry
- Jane and John Justin Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
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Yang Y, Wang W, Wang J, Wang M, Li X, Yan Z, Deng Q, Feng X, Luan G, Yang X, Li T. Scalp-HFO indexes are biomarkers for the lateralization and localization of the epileptogenic zone in preoperative assessment. J Neurophysiol 2021; 126:1148-1158. [PMID: 34495792 DOI: 10.1152/jn.00212.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
During the noninvasive evaluation phase for refractory epilepsy, the localization of the epileptogenic zone (EZ) is essential for the surgical protocols. Confirmation of laterality is required when the preoperative evaluation limits the EZ to bilateral anterior temporal lobes or bilateral frontal lobes. High-frequency oscillations (HFOs) are considered to be promising biological markers for the EZ. However, a large number of studies on HFOs stem from intracranial research. There were few quantitative measures for scalp HFOs, so we proposed a new method to quantify and analyze scalp HFOs. This method was called the "scalp-HFO index" (HI) and calculated in both the EZ and non-EZ. The calculation was based on the numbers and spectral power of scalp HFOs automatically detected. We labeled the brain lobes involved in the EZ as regions of interest (ROIs). The HIs based on the ripple numbers (n-HI) and spectral power (s-HI) were significantly higher in the ROI than in the contra-ROI (P = 0.012, P = 0.003), indicating that HIs contributed to the lateralization of EZ. The sensitivity and specificity of n-HI for the localization of the EZ were 90% and 79.58%, respectively, suggesting that n-HI was valuable in localizing the EZ. HI may contribute to the implantation strategy of invasive electrodes. However, few scalp HFOs were recorded when the EZ was located in the medial cortex region.NEW & NOTEWORTHY We proposed the scalp-high-frequency oscillation (HFO) index (HI) as a quantitative assessment method for scalp HFOs to locate the epileptogenic zone (EZ). Our results showed that the HI in regions of interest (ROIs) was significantly higher than in contra-ROIs. Sensitivity and specificity of HI based on ripple rates (n-HI) for EZ localization were 90% and 79.58%, respectively. If the n-HI of the brain region was >1.35, it was more likely to be an epileptogenic region. Clinical application of HIs as an indicator may facilitate localization of the EZ.
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Affiliation(s)
- Yujiao Yang
- Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Wei Wang
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China.,Bioland Laboratory, Guangzhou Regenerative Medicine and Health, Guangdong Laboratory, Guangzhou, China
| | - Jing Wang
- Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Mengyang Wang
- Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Xiaonan Li
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China.,Bioland Laboratory, Guangzhou Regenerative Medicine and Health, Guangdong Laboratory, Guangzhou, China
| | - Zhaofen Yan
- Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Qinqin Deng
- Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Xing Feng
- Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Guoming Luan
- Department of Functional Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Epilepsy, Sanbo Brain Hospital, Capital Medical University, Beijing, China.,Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Xiaofeng Yang
- Bioland Laboratory, Guangzhou Regenerative Medicine and Health, Guangdong Laboratory, Guangzhou, China
| | - Tianfu Li
- Department of Neurology, Sanbo Brain Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Epilepsy, Sanbo Brain Hospital, Capital Medical University, Beijing, China.,Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
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30
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Dimakopoulos V, Mégevand P, Boran E, Momjian S, Seeck M, Vulliémoz S, Sarnthein J. Blinded study: prospectively defined high-frequency oscillations predict seizure outcome in individual patients. Brain Commun 2021; 3:fcab209. [PMID: 34541534 PMCID: PMC8445392 DOI: 10.1093/braincomms/fcab209] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 06/01/2021] [Accepted: 06/14/2020] [Indexed: 11/16/2022] Open
Abstract
Interictal high-frequency oscillations are discussed as biomarkers for epileptogenic brain tissue that should be resected in epilepsy surgery to achieve seizure freedom. The prospective classification of tissue sampled by individual electrode contacts remains a challenge. We have developed an automated, prospective definition of clinically relevant high-frequency oscillations in intracranial EEG from Montreal and tested it in recordings from Zurich. We here validated the algorithm on intracranial EEG that was recorded in an independent epilepsy centre so that the analysis was blinded to seizure outcome. We selected consecutive patients who underwent resective epilepsy surgery in Geneva with post-surgical follow-up > 12 months. We analysed long-term recordings during sleep that we segmented into intervals of 5 min. High-frequency oscillations were defined in the ripple (80–250 Hz) and the fast ripple (250–500 Hz) frequency bands. Contacts with the highest rate of ripples co-occurring with fast ripples designated the relevant area. As a validity criterion, we calculated the test–retest reliability of the high-frequency oscillations area between the 5 min intervals (dwell time ≥50%). If the area was not fully resected and the patient suffered from recurrent seizures, this was classified as a true positive prediction. We included recordings from 16 patients (median age 32 years, range 18–53 years) with stereotactic depth electrodes and/or with subdural electrode grids (median follow-up 27 months, range 12–55 months). For each patient, we included several 5 min intervals (median 17 intervals). The relevant area had high test–retest reliability across intervals (median dwell time 95%). In two patients, the test–retest reliability was too low (dwell time < 50%) so that outcome prediction was not possible. The area was fully included in the resected volume in 2/4 patients who achieved post-operative seizure freedom (specificity 50%) and was not fully included in 9/10 patients with recurrent seizures (sensitivity 90%), leading to an accuracy of 79%. An additional exploratory analysis suggested that high-frequency oscillations were associated with interictal epileptic discharges only in channels within the relevant area and not associated in channels outside the area. We thereby validated the automated procedure to delineate the clinically relevant area in each individual patient of an independently recorded dataset and achieved the same good accuracy as in our previous studies. The reproducibility of our results across datasets is promising for a multicentre study to test the clinical application of high-frequency oscillations to guide epilepsy surgery.
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Affiliation(s)
- Vasileios Dimakopoulos
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zürich, Switzerland
| | - Pierre Mégevand
- Département des neurosciences fondamentales, Faculté de médecine, Université de Genève, Geneva, Switzerland.,Service de neurologie, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Ece Boran
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zürich, Switzerland
| | - Shahan Momjian
- Service de neurochirurgie, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Margitta Seeck
- Service de neurologie, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Serge Vulliémoz
- Service de neurologie, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zürich, Switzerland.,Klinisches Neurowissenschaften Zentrum, University Hospital Zurich, Zürich, Switzerland
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Migliorelli C, Romero S, Bachiller A, Aparicio J, Alonso JF, Mañanas MA, Antonio-Arce VS. Improving the ripple classification in focal pediatric epilepsy: identifying pathological high-frequency oscillations by Gaussian mixture model clustering. J Neural Eng 2021; 18. [PMID: 34384061 DOI: 10.1088/1741-2552/ac1d31] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 08/12/2021] [Indexed: 11/11/2022]
Abstract
Objective. High-frequency oscillations (HFOs) have emerged as a promising clinical biomarker for presurgical evaluation in childhood epilepsy. HFOs are commonly classified in stereo-encephalography as ripples (80-200 Hz) and fast ripples (200-500 Hz). Ripples are less specific and not so directly associated with epileptogenic activity because of their physiological and pathological origin. The aim of this paper is to distinguish HFOs in the ripple band and to improve the evaluation of the epileptogenic zone (EZ).Approach. This study constitutes a novel modeling approach evaluated in ten patients from Sant Joan de Deu Pediatric Hospital (Barcelona, Spain), with clearly-defined seizure onset zones (SOZ) during presurgical evaluation. A subject-by-subject basis analysis is proposed: a probabilistic Gaussian mixture model (GMM) based on the combination of specific ripple features is applied for estimating physiological and pathological ripple subpopulations.Main Results. Clear pathological and physiological ripples are identified. Features differ considerably among patients showing within-subject variability, suggesting that individual models are more appropriate than a traditional whole-population approach. The difference in rates inside and outside the SOZ for pathological ripples is significantly higher than when considering all the ripples. These significant differences also appear in signal segments without epileptiform activity. Pathological ripple rates show a sharp decline from SOZ to non-SOZ contacts and a gradual decrease with distance.Significance. This novel individual GMM approach improves ripple classification and helps to refine the delineation of the EZ, as well as being appropriate to investigate the interaction of epileptogenic and propagation networks.
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Affiliation(s)
- Carolina Migliorelli
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.,Universitat Politecnica de Catalunya, Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Barcelona, Spain.,Institut de recerca pediatrica Hospital Sant Joan de Déu, Barcelona, Spain
| | - Sergio Romero
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.,Universitat Politecnica de Catalunya, Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Barcelona, Spain.,Institut de recerca pediatrica Hospital Sant Joan de Déu, Barcelona, Spain
| | - Alejandro Bachiller
- Universitat Politecnica de Catalunya, Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Barcelona, Spain.,Institut de recerca pediatrica Hospital Sant Joan de Déu, Barcelona, Spain
| | - Javier Aparicio
- Universitary Hospital Sant Joan de Déu, Epilepsy Unit. Department of Neuropediatrics (member of the European Reference Network for rare and complex epilepsies EpiCARE), Barcelona, Spain
| | - Joan F Alonso
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.,Universitat Politecnica de Catalunya, Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Barcelona, Spain.,Institut de recerca pediatrica Hospital Sant Joan de Déu, Barcelona, Spain
| | - Miguel A Mañanas
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.,Universitat Politecnica de Catalunya, Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Barcelona, Spain.,Institut de recerca pediatrica Hospital Sant Joan de Déu, Barcelona, Spain
| | - Victoria San Antonio-Arce
- Universitary Hospital Sant Joan de Déu, Epilepsy Unit. Department of Neuropediatrics (member of the European Reference Network for rare and complex epilepsies EpiCARE), Barcelona, Spain.,Freiburg Epilepsy Center, Medical Center-University of Freiburg, Faculty of Medicine (member of the European Reference Network for rare and complex epilepsies EpiCARE), Freiburg, Germany
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32
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Yan L, Li L, Chen J, Wang L, Jiang L, Hu Y. Application of High-Frequency Oscillations on Scalp EEG in Infant Spasm: A Prospective Controlled Study. Front Hum Neurosci 2021; 15:682011. [PMID: 34177501 PMCID: PMC8223253 DOI: 10.3389/fnhum.2021.682011] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 05/17/2021] [Indexed: 01/22/2023] Open
Abstract
Objective We quantitatively analyzed high-frequency oscillations (HFOs) using scalp electroencephalography (EEG) in patients with infantile spasms (IS). Methods We enrolled 60 children with IS hospitalized from January 2019 to August 2020. Sixty healthy age-matched children comprised the control group. Time-frequency analysis was used to quantify γ, ripple, and fast ripple (FR) oscillation energy changes. Results γ, ripple, and FR oscillations dominated in the temporal and frontal lobes. The average HFO energy of the sleep stage is lower than that of the wake stage in the same frequency bands in both the normal control (NC) and IS groups (P < 0.05). The average HFO energy of the IS group was significantly higher than that of the NC group in γ band during sleep stage (P < 0.01). The average HFO energy of S and Post-S stage were higher than that of sleep stage in γ band (P < 0.05). In the ripple band, the average HFO energy of Pre-S, S, and Post-S stage was higher than that of sleep stage (P < 0.05). Before treatment, there was no significant difference in BASED score between the effective and ineffective groups. The interaction of curative efficacy × frequency and the interaction of curative efficacy × state are statistically significant. The average HFO energy of the effective group was lower than that of the ineffective group in the sleep stage (P < 0.05). For the 16 children deemed "effective" in the IS group, the average HFO energy of three frequency bands was not significantly different before compared with after treatment. Significance Scalp EEG can record HFOs. The energy of HFOs can distinguish physiological HFOs from pathological ones more accurately than frequency. On scalp EEG, γ oscillations can better detect susceptibility to epilepsy than ripple and FR oscillations. HFOs can trigger spasms. The analysis of average HFO energy can be used as a predictor of the effectiveness of epilepsy treatment.
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Affiliation(s)
- Lisi Yan
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Lin Li
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Jin Chen
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Li Wang
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Li Jiang
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Yue Hu
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
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33
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Schönberger J, Knopf A, Klotz KA, Dümpelmann M, Schulze-Bonhage A, Jacobs J. Distinction of Physiologic and Epileptic Ripples: An Electrical Stimulation Study. Brain Sci 2021; 11:brainsci11050538. [PMID: 33923317 PMCID: PMC8146715 DOI: 10.3390/brainsci11050538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/15/2021] [Accepted: 04/19/2021] [Indexed: 11/16/2022] Open
Abstract
Ripple oscillations (80-250 Hz) are a promising biomarker of epileptic activity, but are also involved in memory consolidation, which impairs their value as a diagnostic tool. Distinguishing physiologic from epileptic ripples has been particularly challenging because usually, invasive recordings are only performed in patients with refractory epilepsy. Here, we identified 'healthy' brain areas based on electrical stimulation and hypothesized that these regions specifically generate 'pure' ripples not coupled to spikes. Intracranial electroencephalography (EEG) recorded with subdural grid electrodes was retrospectively analyzed in 19 patients with drug-resistant focal epilepsy. Interictal spikes and ripples were automatically detected in slow-wave sleep using the publicly available Delphos software. We found that rates of spikes, ripples and ripples coupled to spikes ('spike-ripples') were higher inside the seizure-onset zone (p < 0.001). A comparison of receiver operating characteristic curves revealed that spike-ripples slightly delineated the seizure-onset zone channels, but did this significantly better than spikes (p < 0.001). Ripples were more frequent in the eloquent neocortex than in the remaining non-seizure onset zone areas (p < 0.001). This was due to the higher rates of 'pure' ripples (p < 0.001; median rates 3.3/min vs. 1.4/min), whereas spike-ripple rates were not significantly different (p = 0.87). 'Pure' ripples identified 'healthy' channels significantly better than chance (p < 0.001). Our findings suggest that, in contrast to epileptic spike-ripples, 'pure' ripples are mainly physiological. They may be considered, in addition to electrical stimulation, to delineate eloquent cortex in pre-surgical patients. Since we applied open source software for detection, our approach may be generally suited to tackle a variety of research questions in epilepsy and cognitive science.
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Affiliation(s)
- Jan Schönberger
- Epilepsy Center, Medical Center, University of Freiburg, 79106 Freiburg, Germany; (A.K.); (K.A.K.); (M.D.); (A.S.-B.)
- Department of Neuropediatrics and Muscle Disorders, Medical Center, University of Freiburg, 79106 Freiburg, Germany;
- Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany
- Correspondence:
| | - Anja Knopf
- Epilepsy Center, Medical Center, University of Freiburg, 79106 Freiburg, Germany; (A.K.); (K.A.K.); (M.D.); (A.S.-B.)
- Department of Neuropediatrics and Muscle Disorders, Medical Center, University of Freiburg, 79106 Freiburg, Germany;
- Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany
| | - Kerstin Alexandra Klotz
- Epilepsy Center, Medical Center, University of Freiburg, 79106 Freiburg, Germany; (A.K.); (K.A.K.); (M.D.); (A.S.-B.)
- Department of Neuropediatrics and Muscle Disorders, Medical Center, University of Freiburg, 79106 Freiburg, Germany;
- Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Medical Center, University of Freiburg, 79106 Freiburg, Germany; (A.K.); (K.A.K.); (M.D.); (A.S.-B.)
- Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center, University of Freiburg, 79106 Freiburg, Germany; (A.K.); (K.A.K.); (M.D.); (A.S.-B.)
- Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany
| | - Julia Jacobs
- Department of Neuropediatrics and Muscle Disorders, Medical Center, University of Freiburg, 79106 Freiburg, Germany;
- Faculty of Medicine, University of Freiburg, 79085 Freiburg, Germany
- Department of Paediatrics and Department of Neuroscience, Cumming School of Medicine, University of Calgary, Calgary, AB T3B 6A8, Canada
- Hotchkiss Brain Institute and Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
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34
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Cserpan D, Boran E, Lo Biundo SP, Rosch R, Sarnthein J, Ramantani G. Scalp high-frequency oscillation rates are higher in younger children. Brain Commun 2021; 3:fcab052. [PMID: 33870193 PMCID: PMC8042248 DOI: 10.1093/braincomms/fcab052] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 01/30/2021] [Accepted: 02/15/2021] [Indexed: 12/15/2022] Open
Abstract
High-frequency oscillations in scalp EEG are promising non-invasive biomarkers of epileptogenicity. However, it is unclear how high-frequency oscillations are impacted by age in the paediatric population. We prospectively recorded whole-night scalp EEG in 30 children and adolescents with focal or generalized epilepsy. We used an automated and clinically validated high-frequency oscillation detector to determine ripple rates (80-250 Hz) in bipolar channels. Children < 7 years had higher high-frequency oscillation rates (P = 0.021) when compared with older children. The median test-retest reliability of high-frequency oscillation rates reached 100% (iqr 50) for a data interval duration of 10 min. Scalp high-frequency oscillation frequency decreased with age (r = -0.558, P = 0.002), whereas scalp high-frequency oscillation duration and amplitude were unaffected. The signal-to-noise ratio improved with age (r = 0.37, P = 0.048), and the background ripple band activity decreased with age (r = -0.463, P = 0.011). We characterize the relationship of scalp high-frequency oscillation features and age in paediatric patients. EEG intervals of ≥ 10 min duration are required for reliable measurements of high-frequency oscillation rates. This study is a further step towards establishing scalp high-frequency oscillations as a valid epileptogenicity biomarker in this vulnerable age group.
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Affiliation(s)
- Dorottya Cserpan
- Department of Neuropediatrics, University Children's Hospital Zurich, 8032 Zurich, Switzerland,Department of Neurosurgery, University Hospital Zurich, 8006 Zurich, Switzerland
| | - Ece Boran
- Department of Neurosurgery, University Hospital Zurich, 8006 Zurich, Switzerland
| | - Santo Pietro Lo Biundo
- Department of Neuropediatrics, University Children's Hospital Zurich, 8032 Zurich, Switzerland
| | - Richard Rosch
- Department of Neuropediatrics, University Children's Hospital Zurich, 8032 Zurich, Switzerland
| | - Johannes Sarnthein
- Department of Neurosurgery, University Hospital Zurich, 8006 Zurich, Switzerland,University of Zurich, 8006 Zurich, Switzerland,Klinisches Neurozentrum Zurich, University Hospital Zurich, 8006 Zurich, Switzerland
| | - Georgia Ramantani
- Department of Neuropediatrics, University Children's Hospital Zurich, 8032 Zurich, Switzerland,University of Zurich, 8006 Zurich, Switzerland,Children’s Research Centre, University Children's Hospital Zurich, 8032 Zurich, Switzerland,Correspondence to: Georgia Ramantani, MD, PhD Department of Neuropediatrics, University Children's Hospital Zurich Steinwiesstrasse 75, 8032 Zurich, Switzerland. E-mail:
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35
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Fan Y, Dong L, Liu X, Wang H, Liu Y. Recent advances in the noninvasive detection of high-frequency oscillations in the human brain. Rev Neurosci 2020; 32:305-321. [PMID: 33661582 DOI: 10.1515/revneuro-2020-0073] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 10/23/2020] [Indexed: 01/10/2023]
Abstract
In recent decades, a significant body of evidence based on invasive clinical research has showed that high-frequency oscillations (HFOs) are a promising biomarker for localization of the seizure onset zone (SOZ), and therefore, have the potential to improve postsurgical outcomes in patients with epilepsy. Emerging clinical literature has demonstrated that HFOs can be recorded noninvasively using methods such as scalp electroencephalography (EEG) and magnetoencephalography (MEG). Not only are HFOs considered to be a useful biomarker of the SOZ, they also have the potential to gauge disease severity, monitor treatment, and evaluate prognostic outcomes. In this article, we review recent clinical research on noninvasively detected HFOs in the human brain, with a focus on epilepsy. Noninvasively detected scalp HFOs have been investigated in various types of epilepsy. HFOs have also been studied noninvasively in other pathologic brain disorders, such as migraine and autism. Herein, we discuss the challenges reported in noninvasive HFO studies, including the scarcity of MEG and high-density EEG equipment in clinical settings, low signal-to-noise ratio, lack of clinically approved automated detection methods, and the difficulty in differentiating between physiologic and pathologic HFOs. Additional studies on noninvasive recording methods for HFOs are needed, especially prospective multicenter studies. Further research is fundamental, and extensive work is needed before HFOs can routinely be assessed in clinical settings; however, the future appears promising.
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Affiliation(s)
- Yuying Fan
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Liping Dong
- Library of China Medical University, Shenyang, China
| | - Xueyan Liu
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hua Wang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yunhui Liu
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, China
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Klotz KA, Sag Y, Schönberger J, Jacobs J. Scalp Ripples Can Predict Development of Epilepsy After First Unprovoked Seizure in Childhood. Ann Neurol 2020; 89:134-142. [PMID: 33070359 DOI: 10.1002/ana.25939] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 01/14/2023]
Abstract
OBJECTIVE Identification of children at risk of developing epilepsy after a first unprovoked seizure can be challenging. Interictal epileptiform discharges are associated with higher risk but have limited sensitivity and specificity. High frequency oscillations (HFOs) are newer biomarkers for epileptogenesis. We prospectively evaluated the predictive value of HFOs for developing epilepsy in scalp electroencephalogram (EEG) of children after a first unprovoked seizure. METHODS After their first seizure, 56 children were followed prospectively over 12 months and then grouped in "epilepsy" or "no epilepsy." Initial EEGs were visually analyzed for spikes, spike ripples, and ripples. Inter-group comparisons of spike-rates and HFO-rates were done by Mann-Whitney U test. Predictive values and optimal thresholds were calculated by receiver operating characteristic (ROC) curves. RESULTS In the epilepsy group (n = 26, 46%), mean rates of ripples (0.3 vs 0.09 / minute, p < 0.0001) and spike ripples (0.6 vs 0.06 / minute, p < 0.05) were significantly higher, with no difference in spike rates (1.7 vs 3.0 / minute, p = 0.38). Of those 3 markers, ripples showed the best predictive value (area under the curve [AUC]ripples = 0.88). The optimal threshold for ripples was calculated to be ≥ 0.125 / minute with a sensitivity of 87% and specificity of 85%. Ripple rates were negatively correlated to days passing before epilepsy-diagnosis (R = -0.59, p < 0.0001) and time to a second seizure (R = -0.64, 95% confidence interval [CI] = -0.77 to 0.43, p < 0.0001). INTERPRETATION We could show that in a cohort of children with a first unprovoked seizure, ripples predict the development of epilepsy better than spikes or spike ripples and might be useful biomarkers in the estimation of prognosis and question of treatment. ANN NEUROL 2021;89:134-142.
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Affiliation(s)
- Kerstin A Klotz
- Department of Neuropediatrics and Muscle Disorders, Center for Pediatrics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Yusuf Sag
- Department of Neuropediatrics and Muscle Disorders, Center for Pediatrics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jan Schönberger
- Department of Neuropediatrics and Muscle Disorders, Center for Pediatrics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Julia Jacobs
- Department of Neuropediatrics and Muscle Disorders, Center for Pediatrics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Section of Pediatric Neurology, Alberta Children's Hospital, University of Calgary, Calgary, AB, Canada
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Nevalainen P, von Ellenrieder N, Klimeš P, Dubeau F, Frauscher B, Gotman J. Association of fast ripples on intracranial EEG and outcomes after epilepsy surgery. Neurology 2020; 95:e2235-e2245. [PMID: 32753439 DOI: 10.1212/wnl.0000000000010468] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 05/12/2020] [Indexed: 01/16/2023] Open
Abstract
OBJECTIVE To examine whether fast ripples (FRs) are an accurate marker of the epileptogenic zone, we analyzed overnight stereo-EEG recordings from 43 patients and hypothesized that FR resection ratio, maximal FR rate, and FR distribution predict postsurgical seizure outcome. METHODS We detected FRs automatically from an overnight recording edited for artifacts and visually from a 5-minute period of slow-wave sleep. We examined primarily the accuracy of removing ≥50% of total FR events or of channels with FRs to predict postsurgical seizure outcome (Engel class I = good, classes II-IV = poor) according to the whole-night and 5-minute analysis approaches. Secondarily, we examined the association of low overall FR rates or absence or incomplete resection of 1 dominant FR area with poor outcome. RESULTS The accuracy of outcome prediction was highest (81%, 95% confidence interval [CI] 67%-92%) with the use of the FR event resection ratio and whole-night recording (vs 72%, 95% CI 56%-85%, for the visual 5-minute approach). Absence of channels with FR rates >6/min (p = 0.001) and absence or incomplete resection of 1 dominant FR area (p < 0.001) were associated with poor outcome. CONCLUSIONS FRs are accurate in predicting epilepsy surgery outcome at the individual level when overnight recordings are used. Absence of channels with high FR rates or absence of 1 dominant FR area is a poor prognostic factor that may reflect suboptimal spatial sampling of the epileptogenic zone or multifocality, rather than an inherently low sensitivity of FRs. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that FRs are accurate in predicting epilepsy surgery outcome.
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Affiliation(s)
- Päivi Nevalainen
- From the Montreal Neurological Institute and Hospital (P.N., N.v.E., P.K., F.D., B.F., J.G.), McGill University, Quebec, Canada; and Department of Clinical Neurophysiology (P.N.), Children´s Hospital, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Finland.
| | - Nicolás von Ellenrieder
- From the Montreal Neurological Institute and Hospital (P.N., N.v.E., P.K., F.D., B.F., J.G.), McGill University, Quebec, Canada; and Department of Clinical Neurophysiology (P.N.), Children´s Hospital, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Finland
| | - Petr Klimeš
- From the Montreal Neurological Institute and Hospital (P.N., N.v.E., P.K., F.D., B.F., J.G.), McGill University, Quebec, Canada; and Department of Clinical Neurophysiology (P.N.), Children´s Hospital, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Finland
| | - François Dubeau
- From the Montreal Neurological Institute and Hospital (P.N., N.v.E., P.K., F.D., B.F., J.G.), McGill University, Quebec, Canada; and Department of Clinical Neurophysiology (P.N.), Children´s Hospital, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Finland
| | - Birgit Frauscher
- From the Montreal Neurological Institute and Hospital (P.N., N.v.E., P.K., F.D., B.F., J.G.), McGill University, Quebec, Canada; and Department of Clinical Neurophysiology (P.N.), Children´s Hospital, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Finland
| | - Jean Gotman
- From the Montreal Neurological Institute and Hospital (P.N., N.v.E., P.K., F.D., B.F., J.G.), McGill University, Quebec, Canada; and Department of Clinical Neurophysiology (P.N.), Children´s Hospital, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Finland
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McLeod GA, Ghassemi A, Ng MC. Can REM Sleep Localize the Epileptogenic Zone? A Systematic Review and Analysis. Front Neurol 2020; 11:584. [PMID: 32793089 PMCID: PMC7393443 DOI: 10.3389/fneur.2020.00584] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 05/20/2020] [Indexed: 12/31/2022] Open
Abstract
Epilepsy is a common and debilitating neurological disease. When medication cannot control seizures in up to 40% of cases, surgical resection of epileptogenic tissue is a clinically and cost- effective therapy to achieve seizure freedom. To simultaneously resect minimal yet sufficient cortex, exquisite localization of the epileptogenic zone (EZ) is crucial. However, localization is not straightforward, given relative difficulty of capturing seizures, constraints of the inverse problem in source localization, and possible disparate locations of symptomatogenic vs. epileptogenic regions. Thus, attention has been paid to which state of vigilance best localizes the EZ, in the hopes that one or another sleep-wake state may hold the key to improved accuracy of localization. Studies investigating this topic have employed diverse methodologies and produced diverse results. Nonetheless, rapid eye movement sleep (REM) has emerged as a promising sleep-wake state, as epileptic phenomena captured in REM may spatially correspond more closely to the EZ. Cortical neuronal asynchrony in REM may spatially constrain epileptic phenomena to reduce propagation away from the source generator, rendering them of high localizing value. However, some recent work demonstrates best localization in sleep-wake states other than REM, and there are reports of REM providing clearly false localization. Moreover, synchronistic properties and basic mechanisms of human REM remain to be fully characterized. Amidst these uncertainties, there is an urgent need for recording and analytical techniques to improve accuracy of localization. Here we present a systematic review and quantitative analysis of pertinent literature on whether and how REM may help localize epileptogenic foci. To help streamline and accelerate future work on the intriguing anti-epileptic properties of REM, we also introduce a simple, conceptually clear set-theoretic framework to conveniently and rigorously describe the spatial properties of epileptic phenomena in the brain.
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Affiliation(s)
- Graham A McLeod
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | | | - Marcus C Ng
- Biomedical Engineering, University of Manitoba, Winnipeg, MB, Canada.,Section of Neurology, Department of Internal Medicine, University of Manitoba, Winnipeg, MB, Canada
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Wang Y, Zhou D, Yang X, Xu X, Ren L, Yu T, Zhou W, Shao X, Yang Z, Wang S, Cao D, Liu C, Kwan SY, Xiang J. Expert consensus on clinical applications of high-frequency oscillations in epilepsy. ACTA EPILEPTOLOGICA 2020. [DOI: 10.1186/s42494-020-00018-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
AbstractStudies in animal models of epilepsy and pre-surgical patients have unanimously found a strong correlation between high-frequency oscillations (HFOs, > 80 Hz) and the epileptogenic zone, suggesting that HFOs can be a potential biomarker of epileptogenicity and epileptogenesis. This consensus includes the definition and standard detection techniques of HFOs, the localizing value of pathological HFOs for epileptic foci, and different ways to distinguish physiological from epileptic HFOs. The latest clinical applications of HFOs in epilepsy and the related findings are also discussed. HFOs will advance our understanding of the pathophysiology of epilepsy.
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40
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Kang X, Boly M, Findlay G, Jones B, Gjini K, Maganti R, Struck AF. Quantitative spatio-temporal characterization of epileptic spikes using high density EEG: Differences between NREM sleep and REM sleep. Sci Rep 2020; 10:1673. [PMID: 32015406 PMCID: PMC6997449 DOI: 10.1038/s41598-020-58612-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 01/17/2020] [Indexed: 12/13/2022] Open
Abstract
In this study, we applied high-density EEG recordings (HD-EEG) to quantitatively characterize the fine-grained spatiotemporal distribution of inter-ictal epileptiform discharges (IEDs) across different sleep stages. We quantified differences in spatial extent and duration of IEDs at the scalp and cortical levels using HD-EEG source-localization, during non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep, in six medication-refractory focal epilepsy patients during epilepsy monitoring unit admission. Statistical analyses were performed at single subject level and group level across different sleep stages for duration and distribution of IEDs. Tests were corrected for multiple comparisons across all channels and time points. Compared to NREM sleep, IEDs during REM sleep were of significantly shorter duration and spatially more restricted. Compared to NREM sleep, IEDs location in REM sleep also showed a higher concordance with electrographic ictal onset zone from scalp EEG recording. This study supports the localizing value of REM IEDs over NREM IEDs and suggests that HD-EEG may be of clinical utility in epilepsy surgery work-up.
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Affiliation(s)
- Xuan Kang
- University of Wisconsin-Madison Department of Neurology, Madison, Wisconsin, 53705, USA
| | - Melanie Boly
- University of Wisconsin-Madison Department of Neurology, Madison, Wisconsin, 53705, USA.,University of Wisconsin-Madison Department of Psychiatry, Madison, Wisconsin, 53705, USA
| | - Graham Findlay
- University of Wisconsin-Madison Department of Neurology, Madison, Wisconsin, 53705, USA.,University of Wisconsin-Madison Department of Psychiatry, Madison, Wisconsin, 53705, USA
| | - Benjamin Jones
- University of Wisconsin-Madison Department of Neurology, Madison, Wisconsin, 53705, USA.,University of Wisconsin-Madison Department of Psychiatry, Madison, Wisconsin, 53705, USA
| | - Klevest Gjini
- University of Wisconsin-Madison Department of Neurology, Madison, Wisconsin, 53705, USA
| | - Rama Maganti
- University of Wisconsin-Madison Department of Neurology, Madison, Wisconsin, 53705, USA
| | - Aaron F Struck
- University of Wisconsin-Madison Department of Neurology, Madison, Wisconsin, 53705, USA.
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Are high-frequency oscillations better biomarkers of the epileptogenic zone than spikes? Curr Opin Neurol 2020; 32:213-219. [PMID: 30694920 DOI: 10.1097/wco.0000000000000663] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Precise localization of the epileptogenic zone is imperative for the success of resective surgery of drug-resistant epileptic patients. To decrease the number of surgical failures, clinical research has been focusing on finding new biomarkers. For the past decades, high-frequency oscillations (HFOs, 80-500 Hz) have ousted interictal spikes - the classical interictal marker - from the research spotlight. Many studies have claimed that HFOs were more linked to epileptogenicity than spikes. This present review aims at refining this statement in light of recent studies. RECENT FINDINGS Analysis based on single-patient characteristics has not been able to determine which of HFOs or spikes were better marker of epileptogenic tissues. Physiological HFOs are one of the main obstacles to translate HFOs to clinical practice as separating them from pathological HFOs remains a challenge. Fast ripples (a subgroup of HFOs, 250-500 Hz) which are mostly pathological are not found in all epileptogenic tissues. SUMMARY Quantified measures of HFOs and spikes give complementary results, but many barriers still persist in applying them in clinical routine. The current way of testing HFO and spike detectors and their performance in delineating the epileptogenic zone is debatable and still lacks practicality. Solutions to handle physiological HFOs have been proposed but are still at a preliminary stage.
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Mooij AH, Frauscher B, Gotman J, Huiskamp GJM. A skew-based method for identifying intracranial EEG channels with epileptic activity without detecting spikes, ripples, or fast ripples. Clin Neurophysiol 2019; 131:183-192. [PMID: 31805492 DOI: 10.1016/j.clinph.2019.10.025] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 09/11/2019] [Accepted: 10/16/2019] [Indexed: 02/04/2023]
Abstract
OBJECTIVE To develop a method for identifying intracranial EEG (iEEG) channels with epileptic activity without the need to detect spikes, ripples, or fast ripples. METHODS We compared the skew of the distribution of power values from five minutes non-rapid eye movement stage N3 sleep for the 5-80 Hz, 80-250 Hz (ripple), and 250-500 Hz (fast ripple) bands of epileptic (located in seizure-onset or irritative zone) and non-epileptic iEEG channels recorded in patients with drug-resistant focal epilepsy. We optimized settings in 120 bipolar channels from 10 patients, compared the results to 120 channels from another 10 patients, and applied the method to channels of 12 individual patients. RESULTS The distribution of power values was more skewed in epileptic than in non-epileptic channels in all three frequency bands. The differences in skew were correlated with the presence of spikes, ripples, and fast ripples. When classifying epileptic and non-epileptic channels, the mean accuracy over 12 patients was 0.82 (sensitivity: 0.76, specificity: 0.91). CONCLUSIONS The 'skew method' can distinguish epileptic from non-epileptic channels with good accuracy and, in particular, high specificity. SIGNIFICANCE This is an easy-to-apply method that circumvents the need to visually mark or automatically detect interictal epileptic events.
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Affiliation(s)
- Anne H Mooij
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A 2B4, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A 2B4, Canada
| | - Geertjan J M Huiskamp
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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Boran E, Sarnthein J, Krayenbühl N, Ramantani G, Fedele T. High-frequency oscillations in scalp EEG mirror seizure frequency in pediatric focal epilepsy. Sci Rep 2019; 9:16560. [PMID: 31719543 PMCID: PMC6851354 DOI: 10.1038/s41598-019-52700-w] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 10/17/2019] [Indexed: 11/10/2022] Open
Abstract
High-frequency oscillations (HFO) are promising EEG biomarkers of epileptogenicity. While the evidence supporting their significance derives mainly from invasive recordings, recent studies have extended these observations to HFO recorded in the widely accessible scalp EEG. Here, we investigated whether scalp HFO in drug-resistant focal epilepsy correspond to epilepsy severity and how they are affected by surgical therapy. In eleven children with drug-resistant focal epilepsy that underwent epilepsy surgery, we prospectively recorded pre- and postsurgical scalp EEG with a custom-made low-noise amplifier (LNA). In four of these children, we also recorded intraoperative electrocorticography (ECoG). To detect clinically relevant HFO, we applied a previously validated automated detector. Scalp HFO rates showed a significant positive correlation with seizure frequency (R2 = 0.80, p < 0.001). Overall, scalp HFO rates were higher in patients with active epilepsy (19 recordings, p = 0.0066, PPV = 86%, NPV = 80%, accuracy = 84% CI [62% 94%]) and decreased following successful epilepsy surgery. The location of the highest HFO rates in scalp EEG matched the location of the highest HFO rates in ECoG. This study is the first step towards using non-invasively recorded scalp HFO to monitor disease severity in patients affected by epilepsy.
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Affiliation(s)
- Ece Boran
- Klinik für Neurochirurgie, UniversitätsSpital & Universität Zürich, Zürich, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, UniversitätsSpital & Universität Zürich, Zürich, Switzerland.,Zentrum für Neurowissenschaften Zürich, ETH Zürich, Zürich, Switzerland
| | - Niklaus Krayenbühl
- Klinik für Neurochirurgie, UniversitätsSpital & Universität Zürich, Zürich, Switzerland.,Pädiatrische Neurochirurgie, Universitäts-Kinderspital Zürich, Zürich, Switzerland
| | - Georgia Ramantani
- Neuropädiatrie, Universitäts-Kinderspital Zürich, Zürich, Switzerland
| | - Tommaso Fedele
- Institute of Cognitive Neuroscience, Higher School of Economics - National Research University, Moscow, Russian Federation.
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Klimes P, Cimbalnik J, Brazdil M, Hall J, Dubeau F, Gotman J, Frauscher B. NREM sleep is the state of vigilance that best identifies the epileptogenic zone in the interictal electroencephalogram. Epilepsia 2019; 60:2404-2415. [PMID: 31705527 DOI: 10.1111/epi.16377] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 10/09/2019] [Accepted: 10/09/2019] [Indexed: 01/26/2023]
Abstract
OBJECTIVE Interictal epileptiform anomalies such as epileptiform discharges or high-frequency oscillations show marked variations across the sleep-wake cycle. This study investigates which state of vigilance is the best to localize the epileptogenic zone (EZ) in interictal intracranial electroencephalography (EEG). METHODS Thirty patients with drug-resistant epilepsy undergoing stereo-EEG (SEEG)/sleep recording and subsequent open surgery were included; 13 patients (43.3%) had good surgical outcome (Engel class I). Sleep was scored following standard criteria. Multiple features based on the interictal EEG (interictal epileptiform discharges, high-frequency oscillations, univariate and bivariate features) were used to train a support vector machine (SVM) model to classify SEEG contacts placed in the EZ. The performance of the algorithm was evaluated by the mean area under the receiver-operating characteristic (ROC) curves (AUCs) and positive predictive values (PPVs) across 10-minute sections of wake, non-rapid eye movement sleep (NREM) stages N2 and N3, REM sleep, and their combination. RESULTS Highest AUCs were achieved in NREM sleep stages N2 and N3 compared to wakefulness and REM (P < .01). There was no improvement when using a combination of all four states (P > .05); the best performing features in the combined state were selected from NREM sleep. There were differences between good (Engel I) and poor (Engel II-IV) outcomes in PPV (P < .05) and AUC (P < .01) across all states. The SVM multifeature approach outperformed spikes and high-frequency oscillations (P < .01) and resulted in results similar to those of the seizure-onset zone (SOZ; P > .05). SIGNIFICANCE Sleep improves the localization of the EZ with best identification obtained in NREM sleep stages N2 and N3. Results based on the multifeature classification in 10 minutes of NREM sleep were not different from the results achieved by the SOZ based on 12.7 days of seizure monitoring. This finding might ultimately result in a more time-efficient intracranial presurgical investigation of focal epilepsy.
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Affiliation(s)
- Petr Klimes
- Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada.,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
| | - 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
| | - Jeffery Hall
- Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - François Dubeau
- Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
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Frauscher B, Gotman J. Sleep, oscillations, interictal discharges, and seizures in human focal epilepsy. Neurobiol Dis 2019; 127:545-553. [DOI: 10.1016/j.nbd.2019.04.007] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Revised: 04/01/2019] [Accepted: 04/10/2019] [Indexed: 12/20/2022] Open
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Park CJ, Hong SB. High Frequency Oscillations in Epilepsy: Detection Methods and Considerations in Clinical Application. J Epilepsy Res 2019; 9:1-13. [PMID: 31482052 PMCID: PMC6706641 DOI: 10.14581/jer.19001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 01/02/2019] [Accepted: 01/04/2019] [Indexed: 01/10/2023] Open
Abstract
High frequency oscillations (HFOs) is a brain activity observed in electroencephalography (EEG) in frequency ranges between 80–500 Hz. HFOs can be classified into ripples (80–200 Hz) and fast ripples (200–500 Hz) by their distinctive characteristics. Recent studies reported that both ripples and fast fipples can be regarded as a new biomarker of epileptogenesis and ictogenesis. Previous studies verified that HFOs are clinically important both in patients with mesial temporal lobe epilepsy and neocortical epilepsy. Also, in epilepsy surgery, patients with higher resection ratio of brain regions with HFOs showed better outcome than a group with lower resection ratio. For clinical application of HFOs, it is important to delineate HFOs accurately and discriminate them from artifacts. There have been technical improvements in detecting HFOs by developing various detection algorithms. Still, there is a difficult issue on discriminating clinically important HFOs among detected HFOs, where both quantitative and subjective approaches are suggested. This paper is a review on published HFO studies focused on clinical findings and detection techniques of HFOs as well as tips for clinical applications.
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Affiliation(s)
- Chae Jung Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Samsung Biomedical Research Institute (SBRI), Seoul, Korea
| | - Seung Bong Hong
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Samsung Biomedical Research Institute (SBRI), Seoul, Korea
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González Otárula KA, Khoo HM, von Ellenrieder N, Hall JA, Dubeau F, Gotman J. Spike-related haemodynamic responses overlap with high frequency oscillations in patients with focal epilepsy. Brain 2019; 141:731-743. [PMID: 29360943 DOI: 10.1093/brain/awx383] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2017] [Accepted: 11/23/2017] [Indexed: 12/18/2022] Open
Abstract
Simultaneous scalp EEG/functional MRI measures non-invasively haemodynamic responses to interictal epileptic discharges, which are related to the epileptogenic zone. High frequency oscillations are also an excellent indicator of this zone, but are primarily recorded from intracerebral EEG. We studied the spatial overlap of these two important markers in patients with drug-resistant epilepsy to assess if their combination could help better define the extent of the epileptogenic zone. We included patients who underwent EEG-functional MRI and later intracerebral EEG. Based on intracerebral EEG findings, we separated patients with unifocal seizures from patients with multifocal or unknown onset seizures. Haemodynamic t-maps were coregistered with the intracerebral electrode positions. Each EEG channel was classified as pertaining to one of the following categories: primary haemodynamic cluster (maximum t-value), secondary cluster (t-value > 90% of the primary cluster) or outside the primary and secondary clusters. We marked high frequency oscillations (ripples: 80-250 Hz; fast ripples: 250-500 Hz) during 1 h of slow wave sleep, and compared their rates in each haemodynamic category. After classifying channels as high- or low-rate, the proportion of high-rate channels within the primary or primary plus secondary clusters was compared to the proportion expected by chance. Twenty-five patients, 11 with unifocal and 14 with multifocal/unknown seizure onsets, were studied. We found a significantly higher median high frequency oscillation rate in the primary cluster compared to secondary cluster and outside these two clusters for the unifocal group (P < 0.0001), but not for the multifocal/unknown group. For the unifocal group, the number of high-rate channels within the primary or primary plus secondary clusters was significantly higher than expected by chance. This held only for the high-ripple-rate channels in the multifocal/unknown group. At the patient level, most patients (18/25, or 72%) had at least one high-rate channel within a primary cluster. In patients with unifocal epilepsy, the maximum haemodynamic response (primary cluster) related to scalp interictal discharges overlaps with the tissue generating high frequency oscillations at high rates. If intracranial EEG is warranted, this response should be explored. As a tentative clinical use of the combination of these techniques we propose that higher high frequency oscillation rates inside than outside the maximum response indicates that the patient has indeed a focal epileptogenic zone demarcated by this response, whereas similar rates inside and outside may indicate a widespread epileptogenic zone or an epileptogenic zone not covered by the implantation.
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Affiliation(s)
| | - Hui Ming Khoo
- Montreal Neurological Institute and Hospital, McGill University, Canada.,Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan
| | | | - Jeffery A Hall
- Montreal Neurological Institute and Hospital, McGill University, Canada
| | - François Dubeau
- Montreal Neurological Institute and Hospital, McGill University, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Canada
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Höller P, Trinka E, Höller Y. MEEGIPS-A Modular EEG Investigation and Processing System for Visual and Automated Detection of High Frequency Oscillations. Front Neuroinform 2019; 13:20. [PMID: 31024284 PMCID: PMC6460903 DOI: 10.3389/fninf.2019.00020] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2018] [Accepted: 03/11/2019] [Indexed: 11/21/2022] Open
Abstract
High frequency oscillations (HFOs) are electroencephalographic correlates of brain activity detectable in a frequency range above 80 Hz. They co-occur with physiological processes such as saccades, movement execution, and memory formation, but are also related to pathological processes in patients with epilepsy. Localization of the seizure onset zone, and, more specifically, of the to-be resected area in patients with refractory epilepsy seems to be supported by the detection of HFOs. The visual identification of HFOs is very time consuming with approximately 8 h for 10 min and 20 channels. Therefore, automated detection of HFOs is highly warranted. So far, no software for visual marking or automated detection of HFOs meets the needs of everyday clinical practice and research. In the context of the currently available tools and for the purpose of related local HFO study activities we aimed at converging the advantages of clinical and experimental systems by designing and developing a comprehensive and extensible software framework for HFO analysis that, on the one hand, focuses on the requirements of clinical application and, on the other hand, facilitates the integration of experimental code and algorithms. The development project included the definition of use cases, specification of requirements, software design, implementation, and integration. The work comprised the engineering of component-specific requirements, component design, as well as component- and integration-tests. A functional and tested software package is the deliverable of this activity. The project MEEGIPS, a Modular EEG Investigation and Processing System for visual and automated detection of HFOs, introduces a highly user friendly software that includes five of the most prominent automated detection algorithms. Future evaluation of these, as well as implementation of further algorithms is facilitated by the modular software architecture.
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Affiliation(s)
- Peter Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University, Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University, Salzburg, Austria
| | - Yvonne Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,Department of Psychology, University of Akureyri, Akureyri, Iceland,*Correspondence: Yvonne Höller
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High frequency oscillations as markers of epileptogenic tissue - End of the party? Clin Neurophysiol 2019; 130:624-626. [PMID: 30870797 DOI: 10.1016/j.clinph.2019.01.016] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 01/22/2019] [Accepted: 01/31/2019] [Indexed: 11/20/2022]
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Thomschewski A, Hincapié AS, Frauscher B. Localization of the Epileptogenic Zone Using High Frequency Oscillations. Front Neurol 2019; 10:94. [PMID: 30804887 PMCID: PMC6378911 DOI: 10.3389/fneur.2019.00094] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 01/23/2019] [Indexed: 01/22/2023] Open
Abstract
For patients with drug-resistant focal epilepsy, surgery is the therapy of choice in order to achieve seizure freedom. Epilepsy surgery foremost requires the identification of the epileptogenic zone (EZ), defined as the brain area indispensable for seizure generation. The current gold standard for identification of the EZ is the seizure-onset zone (SOZ). The fact, however that surgical outcomes are unfavorable in 40-50% of well-selected patients, suggests that the SOZ is a suboptimal biomarker of the EZ, and that new biomarkers resulting in better postsurgical outcomes are needed. Research of recent years suggested that high-frequency oscillations (HFOs) are a promising biomarker of the EZ, with a potential to improve surgical success in patients with drug-resistant epilepsy without the need to record seizures. Nonetheless, in order to establish HFOs as a clinical biomarker, the following issues need to be addressed. First, evidence on HFOs as a clinically relevant biomarker stems predominantly from retrospective assessments with visual marking, leading to problems of reproducibility and reliability. Prospective assessments of the use of HFOs for surgery planning using automatic detection of HFOs are needed in order to determine their clinical value. Second, disentangling physiologic from pathologic HFOs is still an unsolved issue. Considering the appearance and the topographic location of presumed physiologic HFOs could be immanent for the interpretation of HFO findings in a clinical context. Third, recording HFOs non-invasively via scalp electroencephalography (EEG) and magnetoencephalography (MEG) is highly desirable, as it would provide us with the possibility to translate the use of HFOs to the scalp in a large number of patients. This article reviews the literature regarding these three issues. The first part of the article focuses on the clinical value of invasively recorded HFOs in localizing the EZ, the detection of HFOs, as well as their separation from physiologic HFOs. The second part of the article focuses on the current state of the literature regarding non-invasively recorded HFOs with emphasis on findings and technical considerations regarding their localization.
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Affiliation(s)
- Aljoscha Thomschewski
- Department of Neurology, Christian Doppler Medical Center, Paracelsus Medical University, Salzburg, Austria,Department of Psychology, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Ana-Sofía Hincapié
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada,*Correspondence: Birgit Frauscher
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