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Qu Z, Luo J, Chen X, Zhang Y, Yu S, Shu H. Association between Removal of High-Frequency Oscillations and the Effect of Epilepsy Surgery: A Meta-Analysis. J Neurol Surg A Cent Eur Neurosurg 2024; 85:294-301. [PMID: 37918885 PMCID: PMC10984718 DOI: 10.1055/a-2202-9344] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/11/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND High-frequency oscillations (HFOs) are spontaneous electroencephalographic (EEG) events that occur within the frequency range of 80 to 500 Hz and consist of at least four distinct oscillations that stand out from the background activity. They can be further classified into "ripples" (80-250 Hz) and "fast ripples" (FR; 250-500 Hz) based on different frequency bands. Studies have indicated that HFOs may serve as important markers for identifying epileptogenic regions and networks in patients with refractory epilepsy. Furthermore, a higher extent of removal of brain regions generating HFOs could potentially lead to improved prognosis. However, the clinical application criteria for HFOs remain controversial, and the results from different research groups exhibit inconsistencies. Given this controversy, the aim of this study was to conduct a meta-analysis to explore the utility of HFOs in predicting postoperative seizure outcomes by examining the prognosis of refractory epilepsy patients with varying ratios of HFO removal. METHODS Prospective and retrospective studies that analyzed HFOs and postoperative seizure outcomes in epilepsy patients who underwent resective surgery were included in the meta-analysis. The patients in these studies were grouped based on the ratio of HFOs removed, resulting in four groups: completely removed FR (C-FR), completely removed ripples (C-Ripples), mostly removed FR (P-FR), and partial ripples removal (P-Ripples). The prognosis of patients within each group was compared to investigate the correlation between the ratio of HFO removal and patient prognosis. RESULTS A total of nine studies were included in the meta-analysis. The prognosis of patients in the C-FR group was significantly better than that of patients with incomplete FR removal (odds ratio [OR] = 6.62; 95% confidence interval [CI]: 3.10-14.15; p < 0.00001). Similarly, patients in the C-Ripples group had a more favorable prognosis compared with those with incomplete ripples removal (OR = 4.45; 95% CI: 1.33-14.89; p = 0.02). Patients in the P-FR group had better prognosis than those with a majority of FR remaining untouched (OR = 6.23; 95% CI: 2.04-19.06; p = 0.001). In the P-Ripples group, the prognosis of patients with a majority of ripples removed was superior to that of patients with a majority of ripples remaining untouched (OR = 8.14; 95% CI: 2.62-25.33; p = 0.0003). CONCLUSIONS There is a positive correlation between the greater removal of brain regions generating HFOs and more favorable postoperative seizure outcomes. However, further investigations, particularly through clinical trials, are necessary to justify the clinical application of HFOs in guiding epilepsy surgery.
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Affiliation(s)
- Zhichuang Qu
- Department of Neurosurgery, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
| | - Juan Luo
- Department of Neurosurgery, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
| | - Xin Chen
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
| | - Yuanyuan Zhang
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
- Southwest Jiaotong University, Chengdu, China
| | - Sixun Yu
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
| | - Haifeng Shu
- Department of Neurosurgery, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
- Southwest Jiaotong University, Chengdu, China
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Sarnthein J, Neidert MC. A profile on the WISE cortical strip for intraoperative neurophysiological monitoring. Expert Rev Med Devices 2024; 21:373-379. [PMID: 38629964 DOI: 10.1080/17434440.2024.2343421] [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: 11/20/2023] [Accepted: 04/11/2024] [Indexed: 05/31/2024]
Abstract
INTRODUCTION During intraoperative neurophysiological monitoring in neurosurgery, brain electrodes are placed to record electrocorticography or to inject current for direct cortical stimulation. A low impedance electrode may improve signal quality. AREAS COVERED We review here a brain electrode (WISE Cortical Strip, WCS®), where a thin polymer strip embeds platinum nanoparticles to create conductive electrode contacts. The low impedance contacts enable a high signal-to-noise ratio, allowing for better detection of small signals such as high-frequency oscillations (HFO). The softness of the WCS may hinder sliding the electrode under the dura or advancing it to deeper structures as the hippocampus but assures conformability with the cortex even in the resection cavity. We provide an extensive review on WCS including a market overview, an introduction to the device (mechanistics, cost aspects, performance standards, safety and contraindications) and an overview of the available pre- and post-approval data. EXPERT OPINION The WCS improves signal detection by lower impedance and better conformability to the cortex. The higher signal-to-noise ratio improves the detection of challenging signals. The softness of the electrode may be a disadvantage in some applications and an advantage in others.
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Affiliation(s)
- Johannes Sarnthein
- Klinik für Neurochirurgie, Universitätsspital Zürich, Universität Zürich, Zurich, Switzerland
- Klinisches Neurozentrum, Universitätsspital Zürich, Zurich, Switzerland
| | - Marian C Neidert
- Klinik für Neurochirurgie, Universitätsspital Zürich, Universität Zürich, Zurich, Switzerland
- Klinik für Neurochirurgie, Kantonsspital St. Gallen, St. Gallen, Switzerland
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Costa F, Schaft EV, Huiskamp G, Aarnoutse EJ, Van't Klooster MA, Krayenbühl N, Ramantani G, Zijlmans M, Indiveri G, Sarnthein J. Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework. Nat Commun 2024; 15:3255. [PMID: 38627406 PMCID: PMC11021517 DOI: 10.1038/s41467-024-47495-y] [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: 11/10/2023] [Accepted: 04/04/2024] [Indexed: 04/19/2024] Open
Abstract
Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-signal neuromorphic device to process the ECoG in real-time. We exploit the variability of the inhomogeneous silicon neurons to achieve efficient sparse and decorrelated temporal signal encoding. We interface the full-custom SNN device to the BCI2000 real-time framework and configure the setup to detect HFO and IED co-occurring with HFO (IED-HFO). We validate the setup on pre-recorded data and obtain HFO rates that are concordant with a previously validated offline algorithm (Spearman's ρ = 0.75, p = 1e-4), achieving the same postsurgical seizure freedom predictions for all patients. In a remote on-line analysis, intraoperative ECoG recorded in Utrecht was compressed and transferred to Zurich for SNN processing and successful IED-HFO detection in real-time. These results further demonstrate how automated remote real-time detection may enable the use of HFO in clinical practice.
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Affiliation(s)
- Filippo Costa
- Klinik für Neurochirurgie, Universitätsspital Zürich und Universität Zürich, Zürich, Switzerland.
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
| | - Eline V Schaft
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Geertjan Huiskamp
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Erik J Aarnoutse
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Maryse A Van't Klooster
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Niklaus Krayenbühl
- Division of Pediatric Neurosurgery, University Children's Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Georgia Ramantani
- Division of Pediatric Neurosurgery, University Children's Hospital Zurich and University of Zurich, Zurich, Switzerland
- Zentrum für Neurowissenschaften (ZNZ) Neuroscience Center Zurich, Universität Zürich und ETH Zürich, Zurich, Switzerland
| | - Maeike Zijlmans
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
- Zentrum für Neurowissenschaften (ZNZ) Neuroscience Center Zurich, Universität Zürich und ETH Zürich, Zurich, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, Universitätsspital Zürich und Universität Zürich, Zürich, Switzerland.
- Zentrum für Neurowissenschaften (ZNZ) Neuroscience Center Zurich, Universität Zürich und ETH Zürich, Zurich, Switzerland.
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Gerstl JVE, Kiseleva A, Imbach L, Sarnthein J, Fedele T. High frequency oscillations in relation to interictal spikes in predicting postsurgical seizure freedom. Sci Rep 2023; 13:21313. [PMID: 38042925 PMCID: PMC10693609 DOI: 10.1038/s41598-023-48764-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 11/30/2023] [Indexed: 12/04/2023] Open
Abstract
We evaluate whether interictal spikes, epileptiform HFOs and their co-occurrence (Spike + HFO) were included in the resection area with respect to seizure outcome. We also characterise the relationship between high frequency oscillations (HFOs) and propagating spikes. We analysed intracranial EEG of 20 patients that underwent resective epilepsy surgery. The co-occurrence of ripples and fast ripples was considered an HFO event; the co-occurrence of an interictal spike and HFO was considered a Spike + HFO event. HFO distribution and spike onset were compared in cases of spike propagation. Accuracy in predicting seizure outcome was 85% for HFO, 60% for Spikes, and 79% for Spike + HFO. Sensitivity was 57% for HFO, 71% for Spikes and 67% for Spikes + HFO. Specificity was 100% for HFO, 54% for Spikes and 85% for Spikes + HFO. In 2/2 patients with spike propagation, the spike onset included the HFO area. Combining interictal spikes with HFO had comparable accuracy to HFO. In patients with propagating spikes, HFO rate was maximal at the onset of spike propagation.
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Affiliation(s)
- Jakob V E Gerstl
- University College London Medical School, London, UK
- Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Alina Kiseleva
- Institute for Cognitive Neuroscience, HSE University, Myasnitskaya Ulitsa, 20, Moscow, Russian Federation, 101000
| | - Lukas Imbach
- Swiss Epilepsy Center, Klinik Lengg, Zurich, Switzerland
| | - Johannes Sarnthein
- Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Tommaso Fedele
- Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland.
- Institute for Cognitive Neuroscience, HSE University, Myasnitskaya Ulitsa, 20, Moscow, Russian Federation, 101000.
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Dimakopoulos V, Neidert MC, Sarnthein J. Low impedance electrodes improve detection of high frequency oscillations in the intracranial EEG. Clin Neurophysiol 2023; 153:133-140. [PMID: 37487419 DOI: 10.1016/j.clinph.2023.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 07/03/2023] [Indexed: 07/26/2023]
Abstract
OBJECTIVE Epileptic fast ripple oscillations (FR, 250-500 Hz) indicate epileptogenic tissue with high specificity. However, their low amplitude makes detection demanding against noise. Since thermal noise is reduced by low impedance electrodes (LoZ), we investigate here whether this noise reduction is relevant in the FR frequency range. METHODS We analyzed intracranial electrocorticography during neurosurgery of 10 patients where a low impedance electrode was compared to a standard electrode (HiZ) with equal surface area during stimulation of the somatosensory evoked potential, which evokes a robust response in the FR frequency range. To estimate the noise level, we computed the difference between sweep 2n and sweep 2n + 1 for all sweeps. RESULTS The power spectral density of the noise spectrum improved for the LoZ over all frequencies. In the FR range, the median noise level improved from HiZ (0.153 µV) to LoZ (0.089 µV). For evoked FR, the detection rate improved (91% for HiZ vs. 100% for LoZ). CONCLUSIONS Low impedance electrodes for intracranial EEG reduce noise in the FR frequency range and may thereby improve FR detection. SIGNIFICANCE Improving the measurement chain may enhance the diagnostic value of FR as biomarkers for epileptogenic tissue.
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Affiliation(s)
| | - Marian C Neidert
- Klinik für Neurochirurgie, Universitätsspital Zürich, Universität Zürich, Switzerland; Klinik für Neurochirurgie, Kantonsspital St. Gallen, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, Universitätsspital Zürich, Universität Zürich, Switzerland; Klinisches Neurozentrum, Universitätsspital Zürich, Switzerland.
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Frauscher B, Bénar CG, Engel JJ, Grova C, Jacobs J, Kahane P, Wiebe S, Zjilmans M, Dubeau F. Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy. Epilepsy Behav 2023; 143:109221. [PMID: 37119580 DOI: 10.1016/j.yebeh.2023.109221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 05/01/2023]
Abstract
Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to localize epileptic seizures. New signal analysis techniques and the prospects of artificial intelligence and big data will offer unprecedented opportunities to further advance the field in the near future, ultimately resulting in improved quality of life for many patients with drug-resistant epilepsy. This article summarizes selected presentations from Day 1 of the two-day symposium "Neurophysiology, Neuropsychology, Epilepsy, 2022: Hills We Have Climbed and the Hills Ahead". Day 1 was dedicated to highlighting and honoring the work of Dr. Jean Gotman, a pioneer in EEG, intracranial EEG, simultaneous EEG/ functional magnetic resonance imaging, and signal analysis of epilepsy. The program focused on two main research directions of Dr. Gotman, and was dedicated to "High-frequency oscillations, a new biomarker of epilepsy" and "Probing the epileptic focus from inside and outside". All talks were presented by colleagues and former trainees of Dr. Gotman. The extended summaries provide an overview of historical and current work in the neurophysiology of epilepsy with emphasis on novel EEG biomarkers of epilepsy and source imaging and concluded with an outlook on the future of epilepsy research, and what is needed to bring the field to the next level.
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Affiliation(s)
- B Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - C G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - J Jr Engel
- David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - C Grova
- Multimodal Functional Imaging Lab, PERFORM Centre, Department of Physics, Concordia University, Montreal, QC, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, QC, Canada; Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
| | - J Jacobs
- Department of Pediatric and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - P Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institute Neurosciences, Department of Neurology, 38000 Grenoble, France
| | - S Wiebe
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - M Zjilmans
- Stichting Epilepsie Instellingen Nederland, The Netherlands; Brain Center, University Medical Center Utrecht, The Netherlands
| | - F Dubeau
- Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
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Kuhnke N, Wusthoff CJ, Swarnalingam E, Yanoussi M, Jacobs J. Epileptic high-frequency oscillations occur in neonates with a high risk for seizures. Front Neurol 2023; 13:1048629. [PMID: 36686542 PMCID: PMC9848430 DOI: 10.3389/fneur.2022.1048629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 11/30/2022] [Indexed: 01/05/2023] Open
Abstract
Introduction Scalp high-frequency oscillations (HFOs, 80-250 Hz) are increasingly recognized as EEG markers of epileptic brain activity. It is, however, unclear what level of brain maturity is necessary to generate these oscillations. Many studies have reported the occurrence of scalp HFOs in children with a correlation between treatment success of epileptic seizures and the reduction of HFOs. More recent studies describe the reliable detection of HFOs on scalp EEG during the neonatal period. Methods In the present study, continuous EEGs of 38 neonates at risk for seizures were analyzed visually for the scalp HFOs using 30 min of quiet sleep EEG. EEGs of 14 patients were of acceptable quality to analyze HFOs. Results The average rate of HFOs was 0.34 ± 0.46/min. About 3.2% of HFOs occurred associated with epileptic spikes. HFOs were significantly more frequent in EEGs with abnormal vs. normal background activities (p = 0.005). Discussion Neonatal brains are capable of generating HFOs. HFO could be a viable biomarker for neonates at risk of developing seizures. Our preliminary data suggest that HFOs mainly occur in those neonates who have altered background activity. Larger data sets are needed to conclude whether HFO occurrence is linked to seizure generation and whether this might predict the development of epilepsy.
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Affiliation(s)
- Nicola Kuhnke
- Department of Pediatric Neurology and Muscular Disease, University Medical Center, Freiburg, Germany
| | | | - Eroshini Swarnalingam
- Department of Pediatrics, University of Calgary, Alberta Children's Hospital, Calgary, AB, Canada
| | - Mina Yanoussi
- Department of Pediatric Neurology and Muscular Disease, University Medical Center, Freiburg, Germany
| | - Julia Jacobs
- Department of Pediatrics, University of Calgary, Alberta Children's Hospital, Calgary, AB, Canada,*Correspondence: Julia Jacobs ✉
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Gunnarsdottir KM, Li A, Smith RJ, Kang JY, Korzeniewska A, Crone NE, Rouse AG, Cheng JJ, Kinsman MJ, Landazuri P, Uysal U, Ulloa CM, Cameron N, Cajigas I, Jagid J, Kanner A, Elarjani T, Bicchi MM, Inati S, Zaghloul KA, Boerwinkle VL, Wyckoff S, Barot N, Gonzalez-Martinez J, Sarma SV. Source-sink connectivity: a novel interictal EEG marker for seizure localization. Brain 2022; 145:3901-3915. [PMID: 36412516 PMCID: PMC10200292 DOI: 10.1093/brain/awac300] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 07/05/2022] [Accepted: 08/01/2022] [Indexed: 07/26/2023] Open
Abstract
Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone, the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the epileptogenic zone exist. Localizing the epileptogenic zone is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in epileptogenic zone localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. IEEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aimed to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the epileptogenic zone. We hypothesized that when a patient is not clinically seizing, it is because the epileptogenic zone is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighbouring nodes ('sources') and the inhibited nodes themselves ('sinks'). Specifically, patient-specific dynamical network models were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics. We validated the algorithm in a retrospective analysis of 65 patients. The source-sink metrics identified epileptogenic regions with 73% accuracy and clinicians agreed with the algorithm in 93% of seizure-free patients. The algorithm was further validated by using the metrics of the annotated epileptogenic zone to predict surgical outcomes. The source-sink metrics predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians' predictions (surgical success rate of this dataset). In failed outcomes, we identified brain regions with high metrics that were untreated. When compared with high frequency oscillations, the most commonly proposed interictal iEEG feature for epileptogenic zone localization, source-sink metrics outperformed in predictive power (by a factor of 1.2), suggesting they may be an interictal iEEG fingerprint of the epileptogenic zone.
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Affiliation(s)
| | - Adam Li
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Rachel J Smith
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Joon-Yi Kang
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Anna Korzeniewska
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Nathan E Crone
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Adam G Rouse
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Jennifer J Cheng
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Michael J Kinsman
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Patrick Landazuri
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Utku Uysal
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Carol M Ulloa
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Nathaniel Cameron
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Iahn Cajigas
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Jonathan Jagid
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Andres Kanner
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Turki Elarjani
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Manuel Melo Bicchi
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Sara Inati
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Varina L Boerwinkle
- Barrow Neurological Institute, Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
| | - Sarah Wyckoff
- Barrow Neurological Institute, Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
| | - Niravkumar Barot
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | | | - Sridevi V Sarma
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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Johannes S, Kathleen S, Marian Christoph N, Andreas R, Francesco S, Joerg Christian T, Niklas T, Andrea S. Evaluation of a new cortical strip electrode for intraoperative somatosensory monitoring during perirolandic brain surgery. Clin Neurophysiol 2022; 142:44-51. [DOI: 10.1016/j.clinph.2022.07.497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/22/2022] [Accepted: 07/17/2022] [Indexed: 11/03/2022]
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Burelo K, Sharifshazileh M, Indiveri G, Sarnthein J. Automatic Detection of High-Frequency Oscillations With Neuromorphic Spiking Neural Networks. Front Neurosci 2022; 16:861480. [PMID: 35720714 PMCID: PMC9205405 DOI: 10.3389/fnins.2022.861480] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Interictal high-frequency oscillations (HFO) detected in electroencephalography recordings have been proposed as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. Automatic HFO detectors typically analyze the data offline using complex time-consuming algorithms, which limits their clinical application. Neuromorphic circuits offer the possibility of building compact and low-power processing systems that can analyze data on-line and in real time. In this review, we describe a fully automated detection pipeline for HFO that uses, for the first time, spiking neural networks and neuromorphic technology. We demonstrated that our HFO detection pipeline can be applied to recordings from different modalities (intracranial electroencephalography, electrocorticography, and scalp electroencephalography) and validated its operation in a custom-designed neuromorphic processor. Our HFO detection approach resulted in high accuracy and specificity in the prediction of seizure outcome in patients implanted with intracranial electroencephalography and electrocorticography, and in the prediction of epilepsy severity in patients recorded with scalp electroencephalography. Our research provides a further step toward the real-time detection of HFO using compact and low-power neuromorphic devices. The real-time detection of HFO in the operation room may improve the seizure outcome of epilepsy surgery, while the use of our neuromorphic processor for non-invasive therapy monitoring might allow for more effective medication strategies to achieve seizure control. Therefore, this work has the potential to improve the quality of life in patients with epilepsy by improving epilepsy diagnostics and treatment.
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Affiliation(s)
- Karla Burelo
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zurich, Switzerland
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | | | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
- Zentrum für Neurowissenschaften Zurich, ETH und Universität Zürich, Zurich, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zurich, Switzerland
- Zentrum für Neurowissenschaften Zurich, ETH und Universität Zürich, Zurich, Switzerland
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MacDonald DB, Simon MV, Nuwer MR. Neurophysiology during epilepsy surgery. HANDBOOK OF CLINICAL NEUROLOGY 2022; 186:103-121. [PMID: 35772880 DOI: 10.1016/b978-0-12-819826-1.00017-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Intraoperative neuromonitoring (IONM) complements modern presurgical investigations by providing information about the epileptic focus as well as real-time identification of critical functional tissue and assessment of ongoing neural integrity during resective epilepsy surgery. This chapter summarizes current IONM methods for mapping the epileptic focus and for mapping and monitoring functionally important structures with direct brain stimulation and evoked potentials. These techniques include electrocorticography, computerized high-frequency oscillation mapping, single-pulse electric stimulation, cortical and subcortical motor evoked potentials, somatosensory evoked potentials, visual evoked potentials, and cortico-cortical evoked potentials. They may help to maximize epileptic tissue resection while avoiding permanent postoperative neurologic deficits.
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Affiliation(s)
| | - Mirela V Simon
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Marc R Nuwer
- Departments of Neurology and Clinical Neurophysiology, David Geffen School of Medicine, University of California Los Angeles, and Ronald Reagan UCLA Medical Center, Los Angeles, CA, United States
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12
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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: 18] [Impact Index Per Article: 6.0] [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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13
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Sun Y, Ren G, Ren J, Wang Q. High-frequency oscillations detected by electroencephalography as biomarkers to evaluate treatment outcome, mirror pathological severity and predict susceptibility to epilepsy. ACTA EPILEPTOLOGICA 2021. [DOI: 10.1186/s42494-021-00063-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
AbstractHigh-frequency oscillations (HFOs) in the electroencephalography (EEG) have been extensively investigated as a potential biomarker of epileptogenic zones. The understanding of the role of HFOs in epilepsy has been advanced considerably over the past decade, and the use of scalp EEG facilitates recordings of HFOs. HFOs were initially applied in large scale in epilepsy surgery and are now being utilized in other applications. In this review, we summarize applications of HFOs in 3 subtopics: (1) HFOs as biomarkers to evaluate epilepsy treatment outcome; (2) HFOs as biomarkers to measure seizure propensity; (3) HFOs as biomarkers to reflect the pathological severity of epilepsy. Nevertheless, knowledge regarding the above clinical applications of HFOs remains limited at present. Further validation through prospective studies is required for its reliable application in the clinical management of individual epileptic patients.
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14
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Foley E, Quitadamo LR, Walsh AR, Bill P, Hillebrand A, Seri S. MEG detection of high frequency oscillations and intracranial-EEG validation in pediatric epilepsy surgery. Clin Neurophysiol 2021; 132:2136-2145. [PMID: 34284249 DOI: 10.1016/j.clinph.2021.06.005] [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: 09/18/2020] [Revised: 05/23/2021] [Accepted: 06/15/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVE To assess the feasibility of automatically detecting high frequency oscillations (HFOs) in magnetoencephalography (MEG) recordings in a group of ten paediatric epilepsy surgery patients who had undergone intracranial electroencephalography (iEEG). METHODS A beamforming source-analysis method was used to construct virtual sensors and an automatic algorithm was applied to detect HFOs (80-250 Hz). We evaluated the concordance of MEG findings with the sources of iEEG HFOs, the clinically defined seizure onset zone (SOZ), the location of resected brain structures, and with post-operative outcome. RESULTS In 8/9 patients there was good concordance between the sources of MEG HFOs and iEEG HFOs and the SOZ. Significantly more HFOs were detected in iEEG relative to MEG t(71) = 2.85, p < .05. There was good concordance between sources of MEG HFOs and the resected area in patients with good and poor outcome, however HFOs were also detected outside of the resected area in patients with poor outcome. CONCLUSION Our findings demonstrate the feasibility of automatically detecting HFOs non-invasively in MEG recordings in paediatric patients, and confirm compatibility of results with invasive recordings. SIGNIFICANCE This approach provides support for the non-invasive detection of HFOs to aid surgical planning and potentially reduce the need for invasive monitoring, which is pertinent to paediatric patients.
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Affiliation(s)
- Elaine Foley
- Aston Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham, UK.
| | - Lucia R Quitadamo
- Aston Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham, UK
| | - A Richard Walsh
- Children's Epilepsy Surgery Program, The Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | - Peter Bill
- Children's Epilepsy Surgery Program, The Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | - Arjan Hillebrand
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, De Boelelaan, 1117 Amsterdam, the Netherlands
| | - Stefano Seri
- Aston Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham, UK; Children's Epilepsy Surgery Program, The Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
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15
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Sharifshazileh M, Burelo K, Sarnthein J, Indiveri G. An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG. Nat Commun 2021; 12:3095. [PMID: 34035249 PMCID: PMC8149394 DOI: 10.1038/s41467-021-23342-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 04/20/2021] [Indexed: 02/04/2023] Open
Abstract
The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time. An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue. Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time. Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity. This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies.
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Affiliation(s)
- Mohammadali Sharifshazileh
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Karla Burelo
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Johannes Sarnthein
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
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16
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Burelo K, Sharifshazileh M, Krayenbühl N, Ramantani G, Indiveri G, Sarnthein J. A spiking neural network (SNN) for detecting high frequency oscillations (HFOs) in the intraoperative ECoG. Sci Rep 2021; 11:6719. [PMID: 33762590 PMCID: PMC7990937 DOI: 10.1038/s41598-021-85827-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 03/05/2021] [Indexed: 12/17/2022] Open
Abstract
To achieve seizure freedom, epilepsy surgery requires the complete resection of the epileptogenic brain tissue. In intraoperative electrocorticography (ECoG) recordings, high frequency oscillations (HFOs) generated by epileptogenic tissue can be used to tailor the resection margin. However, automatic detection of HFOs in real-time remains an open challenge. Here we present a spiking neural network (SNN) for automatic HFO detection that is optimally suited for neuromorphic hardware implementation. We trained the SNN to detect HFO signals measured from intraoperative ECoG on-line, using an independently labeled dataset (58 min, 16 recordings). We targeted the detection of HFOs in the fast ripple frequency range (250-500 Hz) and compared the network results with the labeled HFO data. We endowed the SNN with a novel artifact rejection mechanism to suppress sharp transients and demonstrate its effectiveness on the ECoG dataset. The HFO rates (median 6.6 HFO/min in pre-resection recordings) detected by this SNN are comparable to those published in the dataset (Spearman's [Formula: see text] = 0.81). The postsurgical seizure outcome was "predicted" with 100% (CI [63 100%]) accuracy for all 8 patients. These results provide a further step towards the construction of a real-time portable battery-operated HFO detection system that can be used during epilepsy surgery to guide the resection of the epileptogenic zone.
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Affiliation(s)
- Karla Burelo
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, 8057, Zurich, Switzerland
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, 8091, Zurich, Switzerland
| | - Mohammadali Sharifshazileh
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, 8057, Zurich, Switzerland
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, 8091, Zurich, Switzerland
| | - Niklaus Krayenbühl
- University Children's Hospital, University of Zurich, 8032, Zurich, Switzerland
- Klinisches Neurozentrum Zürich, University Hospital Zurich, University of Zurich, 8006 Zurich, Switzerland
| | - Georgia Ramantani
- University Children's Hospital, University of Zurich, 8032, Zurich, Switzerland
- Klinisches Neurozentrum Zürich, University Hospital Zurich, University of Zurich, 8006 Zurich, Switzerland
- Neuroscience Center Zurich, ETH Zurich, 8092, Zurich, Switzerland
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, 8057, Zurich, Switzerland
- Neuroscience Center Zurich, ETH Zurich, 8092, Zurich, Switzerland
| | - Johannes Sarnthein
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, 8091, Zurich, Switzerland.
- Klinisches Neurozentrum Zürich, University Hospital Zurich, University of Zurich, 8006 Zurich, Switzerland.
- Neuroscience Center Zurich, ETH Zurich, 8092, Zurich, Switzerland.
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17
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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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18
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Waterstraat G, Körber R, Storm JH, Curio G. Noninvasive neuromagnetic single-trial analysis of human neocortical population spikes. Proc Natl Acad Sci U S A 2021; 118:e2017401118. [PMID: 33707209 PMCID: PMC7980398 DOI: 10.1073/pnas.2017401118] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Neuronal spiking is commonly recorded by invasive sharp microelectrodes, whereas standard noninvasive macroapproaches (e.g., electroencephalography [EEG] and magnetoencephalography [MEG]) predominantly represent mass postsynaptic potentials. A notable exception are low-amplitude high-frequency (∼600 Hz) somatosensory EEG/MEG responses that can represent population spikes when averaged over hundreds of trials to raise the signal-to-noise ratio. Here, a recent leap in MEG technology-featuring a factor 10 reduction in white noise level compared with standard systems-is leveraged to establish an effective single-trial portrayal of evoked cortical population spike bursts in healthy human subjects. This time-resolved approach proved instrumental in revealing a significant trial-to-trial variability of burst amplitudes as well as time-correlated (∼10 s) fluctuations of burst response latencies. Thus, ultralow-noise MEG enables noninvasive single-trial analyses of human cortical population spikes concurrent with low-frequency mass postsynaptic activity and thereby could comprehensively characterize cortical processing, potentially also in diseases not amenable to invasive microelectrode recordings.
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Affiliation(s)
- Gunnar Waterstraat
- Neurophysics Group, Department of Neurology, Charité-Universitätsmedizin Berlin, 12203 Berlin, Germany;
| | - Rainer Körber
- Department of Biosignals, Physikalisch-Technische Bundesanstalt, 10587 Berlin, Germany
| | - Jan-Hendrik Storm
- Department of Biosignals, Physikalisch-Technische Bundesanstalt, 10587 Berlin, Germany
| | - Gabriel Curio
- Neurophysics Group, Department of Neurology, Charité-Universitätsmedizin Berlin, 12203 Berlin, Germany
- Bernstein Center for Computational Neuroscience, 10115 Berlin, Germany
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19
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Boran E, Stieglitz L, Sarnthein J. Epileptic High-Frequency Oscillations in Intracranial EEG Are Not Confounded by Cognitive Tasks. Front Hum Neurosci 2021; 15:613125. [PMID: 33746723 PMCID: PMC7971186 DOI: 10.3389/fnhum.2021.613125] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 01/27/2021] [Indexed: 11/13/2022] Open
Abstract
Rationale: High-frequency oscillations (HFOs) in intracranial EEG (iEEG) are used to delineate the epileptogenic zone during presurgical diagnostic assessment in patients with epilepsy. HFOs are historically divided into ripples (80-250 Hz), fast ripples (FR, >250 Hz), and their co-occurrence (FRandR). In a previous study, we had validated the rate of FRandRs during deep sleep to predict seizure outcome. Here, we ask whether epileptic FRandRs might be confounded by physiological FRandRs that are unrelated to epilepsy. Methods: We recorded iEEG in the medial temporal lobe MTL (hippocampus, entorhinal cortex, and amygdala) in 17 patients while they performed cognitive tasks. The three cognitive tasks addressed verbal working memory, visual working memory, and emotional processing. In our previous studies, these tasks activated the MTL. We re-analyzed the data of these studies with the automated detector that focuses on the co-occurrence of ripples and FRs (FRandR). Results: For each task, we identified those channels in which the HFO rate was modulated during the task condition compared to the control condition. However, the number of these channels did not exceed the chance level. Interestingly, even during wakefulness, the HFO rate was higher for channels within the seizure onset zone (SOZ) than for channels outside the SOZ. Conclusion: Our prospective definition of an epileptic HFO, the FRandR, is not confounded by physiological HFOs that might be elicited by our cognitive tasks. This is reassuring for the clinical use of FRandR as a biomarker of the EZ.
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Affiliation(s)
- Ece Boran
- Klinik für Neurochirurgie, Universitäts Spital und Universität Zürich, Zurich, Switzerland
| | - Lennart Stieglitz
- Klinik für Neurochirurgie, Universitäts Spital und Universität Zürich, Zurich, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, Universitäts Spital und Universität Zürich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
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20
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Chen Z, Maturana MI, Burkitt AN, Cook MJ, Grayden DB. High-Frequency Oscillations in Epilepsy: What Have We Learned and What Needs to be Addressed. Neurology 2021; 96:439-448. [PMID: 33408149 DOI: 10.1212/wnl.0000000000011465] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 11/10/2020] [Indexed: 11/15/2022] Open
Abstract
For the past 2 decades, high-frequency oscillations (HFOs) have been enthusiastically studied by the epilepsy community. Emerging evidence shows that HFOs harbor great promise to delineate epileptogenic brain areas and possibly predict the likelihood of seizures. Investigations into HFOs in clinical epilepsy have advanced from small retrospective studies relying on visual identification and correlation analysis to larger prospective assessments using automatic detection and prediction strategies. Although most studies have yielded promising results, some have revealed significant obstacles to clinical application of HFOs, thus raising debate about the reliability and practicality of HFOs as clinical biomarkers. In this review, we give an overview of the current state of HFO research and pinpoint the conceptual and methodological issues that have hampered HFO translation. We highlight recent insights gained from long-term data, high-density recordings, and multicenter collaborations and discuss the open questions that need to be addressed in future research.
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Affiliation(s)
- Zhuying Chen
- From the Department of Biomedical Engineering (Z.C., A.N.B., M.J.C., D.B.G.), The University of Melbourne and Department of Medicine (Z.C., M.I.M., M.J.C., D.B.G.), St Vincent's Hospital, The University of Melbourne; Seer Medical (M.I.M.), Melbourne; and Graeme Clark Institute (M.J.C.), The University of Melbourne, VIC, Australia.
| | - Matias I Maturana
- From the Department of Biomedical Engineering (Z.C., A.N.B., M.J.C., D.B.G.), The University of Melbourne and Department of Medicine (Z.C., M.I.M., M.J.C., D.B.G.), St Vincent's Hospital, The University of Melbourne; Seer Medical (M.I.M.), Melbourne; and Graeme Clark Institute (M.J.C.), The University of Melbourne, VIC, Australia
| | - Anthony N Burkitt
- From the Department of Biomedical Engineering (Z.C., A.N.B., M.J.C., D.B.G.), The University of Melbourne and Department of Medicine (Z.C., M.I.M., M.J.C., D.B.G.), St Vincent's Hospital, The University of Melbourne; Seer Medical (M.I.M.), Melbourne; and Graeme Clark Institute (M.J.C.), The University of Melbourne, VIC, Australia
| | - Mark J Cook
- From the Department of Biomedical Engineering (Z.C., A.N.B., M.J.C., D.B.G.), The University of Melbourne and Department of Medicine (Z.C., M.I.M., M.J.C., D.B.G.), St Vincent's Hospital, The University of Melbourne; Seer Medical (M.I.M.), Melbourne; and Graeme Clark Institute (M.J.C.), The University of Melbourne, VIC, Australia
| | - David B Grayden
- From the Department of Biomedical Engineering (Z.C., A.N.B., M.J.C., D.B.G.), The University of Melbourne and Department of Medicine (Z.C., M.I.M., M.J.C., D.B.G.), St Vincent's Hospital, The University of Melbourne; Seer Medical (M.I.M.), Melbourne; and Graeme Clark Institute (M.J.C.), The University of Melbourne, VIC, Australia
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21
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Jacobs J, Zijlmans M. HFO to Measure Seizure Propensity and Improve Prognostication in Patients With Epilepsy. Epilepsy Curr 2020; 20:338-347. [PMID: 33081501 PMCID: PMC7818207 DOI: 10.1177/1535759720957308] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
The study of high frequency oscillations (HFO) in the electroencephalogram (EEG)
as biomarkers of epileptic activity has merely focused on their spatial location
and relationship to the epileptogenic zone. It has been suggested in several
ways that the amount of HFO at a certain point in time may reflect the disease
activity or severity. This could be clinically useful in several ways,
especially as noninvasive recording of HFO appears feasible. We grouped the
potential hypotheses into 4 categories: (1) HFO as biomarkers to predict the
development of epilepsy; (2) HFO as biomarkers to predict the occurrence of
seizures; (3) HFO as biomarkers linked to the severity of epilepsy, and (4) HFO
as biomarkers to evaluate outcome of treatment. We will review the literature
that addresses these 4 hypotheses and see to what extent HFO can be used to
measure seizure propensity and help determine prognosis of this unpredictable
disease.
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Affiliation(s)
- Julia Jacobs
- 157744Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada
| | - Maeike Zijlmans
- 36512UMC Utrecht Brain Center Rudolf Magnus, Utrecht, the Netherlands
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22
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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: 39] [Impact Index Per Article: 9.8] [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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Remakanthakurup Sindhu K, Staba R, Lopour BA. Trends in the use of automated algorithms for the detection of high-frequency oscillations associated with human epilepsy. Epilepsia 2020; 61:1553-1569. [PMID: 32729943 DOI: 10.1111/epi.16622] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 06/17/2020] [Accepted: 06/29/2020] [Indexed: 12/11/2022]
Abstract
High-frequency oscillations (HFOs) in intracranial electroencephalography (EEG) are a promising biomarker of the epileptogenic zone and tool for surgical planning. Many studies have shown that a high rate of HFOs (number per minute) is correlated with the seizure-onset zone, and complete removal of HFO-generating brain regions has been associated with seizure-free outcome after surgery. In order to use HFOs as a biomarker, these transient events must first be detected in electrophysiological data. Because visual detection of HFOs is time-consuming and subject to low interrater reliability, many automated algorithms have been developed, and they are being used increasingly for such studies. However, there is little guidance on how to select an algorithm, implement it in a clinical setting, and validate the performance. Therefore, we aim to review automated HFO detection algorithms, focusing on conceptual similarities and differences between them. We summarize the standard steps for data pre-processing, as well as post-processing strategies for rejection of false-positive detections. We also detail four methods for algorithm testing and validation, and we describe the specific goal achieved by each one. We briefly review direct comparisons of automated algorithms applied to the same data set, emphasizing the importance of optimizing detection parameters. Then, to assess trends in the use of automated algorithms and their potential for use in clinical studies, we review evidence for the relationship between automatically detected HFOs and surgical outcome. We conclude with practical recommendations and propose standards for the selection, implementation, and validation of automated HFO-detection algorithms.
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Affiliation(s)
| | | | - Beth A Lopour
- Biomedical Engineering, UC Irvine, Irvine, California, USA
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24
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Boran E, Sarnthein J, Krayenbühl N, Ramantani G, Fedele T. High-frequency oscillations in scalp EEG mirror seizure frequency in pediatric focal epilepsy. Sci Rep 2019; 9:16560. [PMID: 31719543 PMCID: PMC6851354 DOI: 10.1038/s41598-019-52700-w] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 10/17/2019] [Indexed: 11/10/2022] Open
Abstract
High-frequency oscillations (HFO) are promising EEG biomarkers of epileptogenicity. While the evidence supporting their significance derives mainly from invasive recordings, recent studies have extended these observations to HFO recorded in the widely accessible scalp EEG. Here, we investigated whether scalp HFO in drug-resistant focal epilepsy correspond to epilepsy severity and how they are affected by surgical therapy. In eleven children with drug-resistant focal epilepsy that underwent epilepsy surgery, we prospectively recorded pre- and postsurgical scalp EEG with a custom-made low-noise amplifier (LNA). In four of these children, we also recorded intraoperative electrocorticography (ECoG). To detect clinically relevant HFO, we applied a previously validated automated detector. Scalp HFO rates showed a significant positive correlation with seizure frequency (R2 = 0.80, p < 0.001). Overall, scalp HFO rates were higher in patients with active epilepsy (19 recordings, p = 0.0066, PPV = 86%, NPV = 80%, accuracy = 84% CI [62% 94%]) and decreased following successful epilepsy surgery. The location of the highest HFO rates in scalp EEG matched the location of the highest HFO rates in ECoG. This study is the first step towards using non-invasively recorded scalp HFO to monitor disease severity in patients affected by epilepsy.
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Affiliation(s)
- Ece Boran
- Klinik für Neurochirurgie, UniversitätsSpital & Universität Zürich, Zürich, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, UniversitätsSpital & Universität Zürich, Zürich, Switzerland.,Zentrum für Neurowissenschaften Zürich, ETH Zürich, Zürich, Switzerland
| | - Niklaus Krayenbühl
- Klinik für Neurochirurgie, UniversitätsSpital & Universität Zürich, Zürich, Switzerland.,Pädiatrische Neurochirurgie, Universitäts-Kinderspital Zürich, Zürich, Switzerland
| | - Georgia Ramantani
- Neuropädiatrie, Universitäts-Kinderspital Zürich, Zürich, Switzerland
| | - Tommaso Fedele
- Institute of Cognitive Neuroscience, Higher School of Economics - National Research University, Moscow, Russian Federation.
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25
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Boran E, Ramantani G, Krayenbühl N, Schreiber M, König K, Fedele T, Sarnthein J. High-density ECoG improves the detection of high frequency oscillations that predict seizure outcome. Clin Neurophysiol 2019; 130:1882-1888. [DOI: 10.1016/j.clinph.2019.07.008] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 05/31/2019] [Accepted: 07/06/2019] [Indexed: 01/11/2023]
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26
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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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27
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In search of epileptic scalp high-frequency oscillations. Clin Neurophysiol 2019; 130:1172-1174. [PMID: 31064718 DOI: 10.1016/j.clinph.2019.04.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 04/11/2019] [Indexed: 11/20/2022]
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28
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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: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [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
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29
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Roessler K, Heynold E, Buchfelder M, Stefan H, Hamer HM. Current value of intraoperative electrocorticography (iopECoG). Epilepsy Behav 2019; 91:20-24. [PMID: 30420228 DOI: 10.1016/j.yebeh.2018.06.053] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 06/28/2018] [Accepted: 06/29/2018] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Intraoperative electrocorticography (iopECoG) can contribute to delineate the resection borders of the anticipated epileptogenic zone in epilepsy surgery. However, it has several caveats that should be considered to avoid incorrect interpretation during intraoperative monitoring. METHODS The literature on iopECoG application was reviewed, and pros and cons as well as obstacles to this technique were analyzed. RESULTS The literature of the first half of the nineties was very enthusiastic in using iopECoG for tailoring the resection in temporal as well as extratemporal epilepsy surgery. Mostly, this resulted in a good correlation of postresection ECoG and excellent seizure outcome. In the second half of the nineties, many authors demonstrated lack of correlation between iopECoG and postoperative seizure outcome, especially in surgery for temporal lobe epilepsy with hippocampal sclerosis. In the noughties, investigators found that ECoG was significantly useful in neocortical lesional temporal lobe epilepsy as well as in extratemporal lesional epilepsies. Extratemporal epilepsy without lesions proved to be more a domain of chronic extraoperative ECoG, especially using depth electrode recordings. In recent years, iopECoG detecting high-frequency oscillations (ripples, 80-250 Hz, fast ripples, 250-500 Hz) for tailored resection was found to allow intraoperative prediction of postoperative seizure outcome. CONCLUSION After a period of scepticism, iopECoG seems back in the focus of interest for intraoperative guidance of resecting epileptogenic tissue to raise postoperative favorable seizure outcome. In temporal and extratemporal lesional epilepsies, especially in cases of focal cortical dysplasia, tuberous sclerosis, or cavernous malformations, an excellent correlation between iopECoG-guided resection and postoperative seizure relief was found.
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Affiliation(s)
- Karl Roessler
- Neurosurgical Clinic, University Hospital Erlangen, Germany.
| | | | | | - Hermann Stefan
- Epilepsy Center, Neurological Clinic, University Hospital Erlangen, Germany
| | - Hajo M Hamer
- Epilepsy Center, Neurological Clinic, University Hospital Erlangen, Germany
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30
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Kogias E, Schmeiser B, Doostkam S, Brandt A, Hammen T, Zentner J, Ramantani G. Multilobar Resections for 3T MRI-Negative Epilepsy: Worth the Trouble? World Neurosurg 2018; 123:e338-e347. [PMID: 30502474 DOI: 10.1016/j.wneu.2018.11.170] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 11/12/2018] [Accepted: 11/19/2018] [Indexed: 12/01/2022]
Abstract
OBJECTIVE Multilobar resection in magnetic resonance imaging (MRI)-negative drug-resistant epilepsy warrants attention because they account for up to one third of MRI-negative epilepsy surgery. Despite their high prevalence, data are sparse, and the risk/benefit ratio continues to be debated. The present study investigated the postoperative seizure outcomes in this especially challenging subgroup. METHODS We retrospectively analyzed the data of 4 consecutive patients with 3T MRI-negative findings and drug-resistant focal epilepsy who had undergone multilobar epilepsy surgery at our institution. RESULTS The mean age at first surgery was 28.5 years (range, 14-48); 1 patient required 2 consecutive reoperations. The final resection was in the frontotemporal and temporo-parieto-occipital regions in 2 patients each. Histopathological examination revealed mild malformations of cortical development in 2 patients and focal cortical dysplasia type Ia and type IIa in 1 patient each. At the last follow-up examination (median, 3.3 years; range, 1-11), 2 patients were completely seizure free (Engel class Ia), 1 patient had experienced some disabling seizures after surgery but had been free of disabling seizures for 2 years at the last follow-up examination (Engel class Ic), and 1 patient had experienced worthwhile improvement (Engel class IIb) and had been seizure free for 1 year at the last follow-up examination. No surgical complications developed. CONCLUSIONS Our results have demonstrated that multilobar epilepsy surgery is effective for lasting seizure control for selected 3T MRI-negative candidates, leading to favorable outcomes for all 4 of our patients. Comprehensive multimodal preoperative evaluation is a prerequisite for postoperative success. Reevaluation should be considered for patients with seizure recurrence, because reoperation could be especially beneficial for selected patients who have not responded to an initially limited resection.
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Affiliation(s)
- Evangelos Kogias
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Barbara Schmeiser
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Soroush Doostkam
- Institute of Neuropathology, Medical Center - University of Freiburg, Freiburg, Germany
| | - Armin Brandt
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Thilo Hammen
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Josef Zentner
- Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Georgia Ramantani
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany; Department of Neuropediatrics, University Children's Hospital Zurich, Zurich, Switzerland.
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Simultaneously recorded intracranial and scalp high frequency oscillations help identify patients with poor postsurgical seizure outcome. Clin Neurophysiol 2018; 130:128-137. [PMID: 30529879 DOI: 10.1016/j.clinph.2018.10.016] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2017] [Revised: 09/20/2018] [Accepted: 10/15/2018] [Indexed: 11/22/2022]
Abstract
OBJECTIVE High frequency oscillations (HFO) between 80-500 Hz are markers of epileptic areas in intracranial and maybe also scalp EEG. We investigate simultaneous recordings of scalp and intracranial EEG and hypothesize that scalp HFOs provide important additional clinical information in the presurgical setting. METHODS Spikes and HFOs were visually identified in all intracranial scalp EEG channels. Analysis of correlation of event location between intracranial and scalp EEG as well as relationship between events and the SOZ and zone of surgical removal was performed. RESULTS 24 patients could be included, 23 showed spikes and 19 HFOs on scalp recordings. In 15/19 patients highest scalp HFO rate was located over the implantation side, with 13 patients having the highest scalp and intracranial HFO rate over the same region. 17 patients underwent surgery, 7 became seizure free. Patients with poor post-operative outcome showed significantly more regions with HFO than those with seizure free outcome. CONCLUSIONS Scalp HFOs are mostly located over the SOZ. Widespread scalp HFOs are indicative of a larger epileptic network and associated with poor postsurgical outcome. SIGNIFICANCE Analysis of scalp HFO add clinically important information about the extent of epileptic areas during presurgical simultaneous scalp and intracranial EEG recordings.
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Fedele T, Schönenberger C, Curio G, Serra C, Krayenbühl N, Sarnthein J. Intraoperative subdural low-noise EEG recording of the high frequency oscillation in the somatosensory evoked potential. Clin Neurophysiol 2017; 128:1851-1857. [PMID: 28826015 DOI: 10.1016/j.clinph.2017.07.400] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Revised: 06/30/2017] [Accepted: 07/10/2017] [Indexed: 11/28/2022]
Abstract
OBJECTIVE The detectability of high frequency oscillations (HFO, >200Hz) in the intraoperative ECoG is restricted by their low signal-to-noise ratio (SNR). Using the somatosensory evoked HFO, we quantify how HFO detectability can benefit from a custom-made low-noise amplifier (LNA). METHODS In 9 patients undergoing tumor surgery in the central region, subdural strip electrodes were placed for intraoperative neurophysiological monitoring. We recorded the somatosensory evoked potential (SEP) simultaneously by custom-made LNA and by a commercial device (CD). We varied the stimulation rate between 1.3 and 12.7Hz to tune the SNR of the N20 component and the evoked HFO and quantified HFO detectability at the single trial level. In three patients we compared Propofol® and Sevoflurane® anesthesia. RESULTS In the average, amplitude decreased in both in N20 and evoked HFO amplitude with increasing stimulation rate (p<0.05). We detected a higher percentage of single trial evoked HFO with the LNA (p<0.001) for recordings with low impedance (<5kΩ). Average amplitudes were indistinguishable between anesthesia compounds. CONCLUSION Low-noise amplification improves the detection of the evoked HFO in recordings with subdural electrodes with low impedance. SIGNIFICANCE Low-noise EEG might critically improve the detectability of interictal spontaneous HFO in subdural and possibly in scalp recordings.
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Affiliation(s)
- Tommaso Fedele
- Neurosurgery Department, University Hospital Zurich, Zurich, Switzerland.
| | | | - Gabriel Curio
- Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité, Berlin, Germany
| | - Carlo Serra
- Neurosurgery Department, University Hospital Zurich, Zurich, Switzerland
| | - Niklaus Krayenbühl
- Neurosurgery Department, University Hospital Zurich, Zurich, Switzerland
| | - Johannes Sarnthein
- Neurosurgery Department, University Hospital Zurich, Zurich, Switzerland; University of Zurich, Neuroscience Center Zurich, Zurich, Switzerland
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Intraoperative fast ripples independently predict postsurgical epilepsy outcome: Comparison with other electrocorticographic phenomena. Epilepsy Res 2017. [PMID: 28644979 DOI: 10.1016/j.eplepsyres.2017.06.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
In the surgical management of epilepsy, the resection of cortex exhibiting interictal fast ripples (250-500Hz) on electrocorticography has been linked to postoperative seizure-freedom. Although fast ripples appear to accurately identify the epileptogenic zone-the minimum tissue that must be removed at surgery to achieve seizure-freedom-it has not been established that fast ripples are a superior biomarker in comparison with multimodal presurgical neuroimaging and other electrocorticography abnormalities. Hence, in the prediction of postoperative seizure-freedom, we compared the value of fast ripples with other intraoperative electocorticography abnormalities including focal slowing, paroxysmal fast activity, intermittent spike discharges, continuous epileptiform discharges, focal attenuation, and intraoperative seizures, as well as complete resection of the lesion defined by MRI and other neuroimaging. In a cohort of 60 children with lesional epilepsy and median postsurgical follow-up exceeding 4 years, who underwent resective epilepsy surgery with intraoperative electrocorticography, we evaluated the extent to which removal of each intraoperative electrocorticography abnormality impacts time to first postoperative seizure using the Kaplan-Meier method and Cox proportional hazards regression. Secondly, we contrasted the predictive value of resection of each competing electrocorticography abnormality using standard test metrics (sensitivity, specificity, positive predictive value, and negative predictive value). In contrast with all other intraoperative electrocorticography abnormalities, fast ripples demonstrated the most favorable combination of positive predictive value (100%) and negative predictive value (76%) in the prediction of postoperative seizures. Among all candidate electrocorticography features, time to first postoperative seizure was most strongly associated with incomplete resection of fast ripples (hazard ratio=19.8, p<0.001). In multivariate survival analyses, postoperative seizures were independently predicted by incomplete resection of cortex generating fast ripples (hazard ratio=25.4, 95%CI 6.71-96.0, p<0.001) and focal slowing (hazard ratio=5.79, 95%CI 1.76-19.0, p=0.004), even after adjustment for the impact of an otherwise complete resection. All children with incomplete resection of interictal FR-generating cortex exhibited postoperative seizures within six months. Notably, this cohort included many patients with large resections and thus limited opportunity to exhibit unresected fast ripples. Future study in a cohort with small resection volume, or a clinical trial in which resection margins are guided by fast ripple distribution, would likely yield a more precise estimate of the risk posed by unresected fast ripples. With a high detection rate during brief intraoperative electrocorticography and favorable positive and negative predictive value, interictal fast ripple characterization during surgery is a feasible and useful adjunct to standard methods for epilepsy surgery planning, and represents a valuable spatially-localizing biomarker of the epileptogenic zone, without the need for prolonged extraoperative electrocorticography.
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