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Schlafly ED, Carbonero D, Chu CJ, Kramer MA. A data augmentation procedure to improve detection of spike ripples in brain voltage recordings. Neurosci Res 2024:S0168-0102(24)00096-8. [PMID: 39102943 DOI: 10.1016/j.neures.2024.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Revised: 06/04/2024] [Accepted: 07/30/2024] [Indexed: 08/07/2024]
Abstract
Epilepsy is a major neurological disorder characterized by recurrent, spontaneous seizures. For patients with drug-resistant epilepsy, treatments include neurostimulation or surgical removal of the epileptogenic zone (EZ), the brain region responsible for seizure generation. Precise targeting of the EZ requires reliable biomarkers. Spike ripples - high-frequency oscillations that co-occur with large amplitude epileptic discharges - have gained prominence as a candidate biomarker. However, spike ripple detection remains a challenge. The gold-standard approach requires an expert manually visualize and interpret brain voltage recordings, which limits reproducibility and high-throughput analysis. Addressing these limitations requires more objective, efficient, and automated methods for spike ripple detection, including approaches that utilize deep neural networks. Despite advancements, dataset heterogeneity and scarcity severely limit machine learning performance. Our study explores long-short term memory (LSTM) neural network architectures for spike ripple detection, leveraging data augmentation to improve classifier performance. We highlight the potential of combining training on augmented and in vivo data for enhanced spike ripple detection and ultimately improving diagnostic accuracy in epilepsy treatment.
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Affiliation(s)
- Emily D Schlafly
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA.
| | - Daniel Carbonero
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Mark A Kramer
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA; Center for Systems Neuroscience, Boston University, Boston, MA, USA.
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Stergiadis C, Kazis D, Klados MA. Epileptic tissue localization using graph-based networks in the high frequency oscillation range of intracranial electroencephalography. Seizure 2024; 117:28-35. [PMID: 38308906 DOI: 10.1016/j.seizure.2024.01.015] [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] [Revised: 01/08/2024] [Accepted: 01/24/2024] [Indexed: 02/05/2024] Open
Abstract
PURPOSE High frequency oscillations (HFOs) are an emerging biomarker of epilepsy. However, very few studies have investigated the functional connectivity of interictal iEEG signals in the frequency range of HFOs. Here, we study the corresponding functional networks using graph theory, and we assess their predictive value for automatic electrode classification in a cohort of 20 drug resistant patients. METHODS Coherence-based connectivity analysis was performed on the iEEG recordings, and six different local graph measures were computed in both sub-bands of the HFO frequency range (80-250 Hz and 250-500 Hz). Correlation analysis was implemented between the local graph measures and the ripple and fast ripple rates. Finally, the WEKA software was employed for training and testing different predictive models on the aforementioned local graph measures. RESULTS The ripple rate was significantly correlated with five out of six local graph measures in the functional network. For fast ripples, their rate was also significantly (but negatively) correlated with most of the local metrics. The results from WEKA showed that the Logistic Regression algorithm was able to classify highly HFO-contaminated electrodes with an accuracy of 82.5 % for ripples and 75.4 % for fast ripples. CONCLUSION Functional connectivity networks in the HFO band could represent an alternative to the direct use of distinct HFO events, while also providing important insights about hub epileptic areas that can represent possible surgical targets. Automatic electrode classification through FC-based classifiers can help bypass the burden of manual HFO annotation, providing at the same time similar amount of information about the epileptic tissue.
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Affiliation(s)
- Christos Stergiadis
- Department of Electronic Engineering, University of York, York, YO10 5DD, UK
| | - Dimitrios Kazis
- 3rd Neurological Department, Aristotle University of Thessaloniki Faculty of Health Sciences, Exohi, 57010 Thessaloniki, Greece
| | - Manousos A Klados
- Department of Psychology, University of York Europe Campus, CITY College 24, Proxenou Koromila Street, 546 22 Thessaloniki, Greece; Neuroscience Research Center (NEUREC), University of York Europe Campus, City College, Thessaloniki, Greece.
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de Font-Réaulx E, Solis-Santamaria A, Arch-Tirado E, González-Astiazarán A. Thermosensitive/thermochromic silicone and infrared thermography mapping in 60 consecutive cases of epilepsy surgery. Surg Neurol Int 2024; 15:63. [PMID: 38468653 PMCID: PMC10927215 DOI: 10.25259/sni_763_2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 02/07/2024] [Indexed: 03/13/2024] Open
Abstract
Background Epilepsy surgery represents a therapeutic opportunity for those patients who do not respond to drug therapy. However, an important challenge is the precise identification of the epileptogenic area during surgery. Since it can be hard to delineate, it makes it necessary to use auxiliary tools as a guide during the surgical procedure. Electrocorticography (ECoG), despite having shown favorable results in terms of reducing post-surgical seizures, have certain limitations. Brain mapping using infrared thermography mapping and a new thermosensitive/thermochromic silicone (TTS) in epilepsy surgery has introduced a new resource of noninvasive and real-time devices that allow the localization of irritative zones. Methods Sixty consecutive patients with drug-resistant epilepsy with surgical indications who decided to participate voluntarily in the study were included in the study. We measured brain temperature using two quantitative methods and a qualitative method: the TTS sheet. In all cases, we used ECoG as the gold standard to identify irritative areas, and all brain tissue samples obtained were sent to pathology for diagnosis. Results In the subgroup in which the ECoG detected irritative areas (n = 51), adding the results in which there was a correlation with the different methods, the efficiency obtained to detect irritative areas is 94.11% (n = 48/51, P ≤ 0.0001) while the infrared thermography mapping method independently has an efficiency of 91.66% (P ≤ 0.0001). The TTS has a sensitivity of 95.71% and a specificity of 97.9% (P ≤ 0.0001) to detect hypothermic areas that correlate with the irritative zones detected by ECoG. No postoperative infections or wound dehiscence were documented, so the different methodologies used do not represent an additional risk for the surgical proceedings. Conclusion We consider that the infrared thermography mapping using high-resolution infrared thermography cameras and the TTS are both accurate and safe methods to identify irritative areas in epilepsy surgeries.
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Affiliation(s)
- Enrique de Font-Réaulx
- Head, Department of Epilepsy Surgery, Neurological Center, Centro Médico ABC, Mexico City, Mexico
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Cserpan D, Guidi G, Alessandri B, Fedele T, Rüegger A, Pisani F, Sarnthein J, Ramantani G. Scalp high-frequency oscillations differentiate neonates with seizures from healthy neonates. Epilepsia Open 2023; 8:1491-1502. [PMID: 37702021 PMCID: PMC10690668 DOI: 10.1002/epi4.12827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 09/02/2023] [Indexed: 09/14/2023] Open
Abstract
OBJECTIVE We aimed to investigate (1) whether an automated detector can capture scalp high-frequency oscillations (HFO) in neonates and (2) whether scalp HFO rates can differentiate neonates with seizures from healthy neonates. METHODS We considered 20 neonates with EEG-confirmed seizures and four healthy neonates. We applied a previously validated automated HFO detector to determine scalp HFO rates in quiet sleep. RESULTS Etiology in neonates with seizures included hypoxic-ischemic encephalopathy in 11 cases, structural vascular lesions in 6, and genetic causes in 3. The HFO rates were significantly higher in neonates with seizures (0.098 ± 0.091 HFO/min) than in healthy neonates (0.038 ± 0.025 HFO/min; P = 0.02) with a Hedge's g value of 0.68 indicating a medium effect size. The HFO rate of 0.1 HFO/min/ch yielded the highest Youden index in discriminating neonates with seizures from healthy neonates. In neonates with seizures, etiology, status epilepticus, EEG background activity, and seizure patterns did not significantly impact HFO rates. SIGNIFICANCE Neonatal scalp HFO can be detected automatically and differentiate neonates with seizures from healthy neonates. Our observations have significant implications for neuromonitoring in neonates. This is the first step in establishing neonatal HFO as a biomarker for neonatal seizures.
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Affiliation(s)
- Dorottya Cserpan
- Department of NeuropediatricsUniversity Children's HospitalZurichSwitzerland
| | - Greta Guidi
- Department of NeuropediatricsUniversity Children's HospitalZurichSwitzerland
| | - Beatrice Alessandri
- Department of NeuropediatricsUniversity Children's HospitalZurichSwitzerland
| | - Tommaso Fedele
- Department of NeuropediatricsUniversity Children's HospitalZurichSwitzerland
| | - Andrea Rüegger
- Department of NeuropediatricsUniversity Children's HospitalZurichSwitzerland
| | - Francesco Pisani
- Department of Human Neurosciences, Child Neurology and Psychiatry UnitSapienza University of RomeRomeItaly
| | - Johannes Sarnthein
- Department of NeurosurgeryUniversity Hospital ZurichZurichSwitzerland
- University of ZurichZurichSwitzerland
| | - Georgia Ramantani
- Department of NeuropediatricsUniversity Children's HospitalZurichSwitzerland
- University of ZurichZurichSwitzerland
- Children's Research CenterUniversity Children's HospitalZurichSwitzerland
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Ramantani G, Westover MB, Gliske S, Sarnthein J, Sarma S, Wang Y, Baud MO, Stacey WC, Conrad EC. Passive and active markers of cortical excitability in epilepsy. Epilepsia 2023; 64 Suppl 3:S25-S36. [PMID: 36897228 PMCID: PMC10512778 DOI: 10.1111/epi.17578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/07/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023]
Abstract
Electroencephalography (EEG) has been the primary diagnostic tool in clinical epilepsy for nearly a century. Its review is performed using qualitative clinical methods that have changed little over time. However, the intersection of higher resolution digital EEG and analytical tools developed in the past decade invites a re-exploration of relevant methodology. In addition to the established spatial and temporal markers of spikes and high-frequency oscillations, novel markers involving advanced postprocessing and active probing of the interictal EEG are gaining ground. This review provides an overview of the EEG-based passive and active markers of cortical excitability in epilepsy and of the techniques developed to facilitate their identification. Several different emerging tools are discussed in the context of specific EEG applications and the barriers we must overcome to translate these tools into clinical practice.
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Affiliation(s)
- Georgia Ramantani
- Department of Neuropediatrics and Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - M Brandon Westover
- Department of Neurology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Data Science, Massachusetts General Hospital McCance Center for Brain Health, Boston, Massachusetts, USA
- Research Affiliate Faculty, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Research Affiliate Faculty, Broad Institute, Cambridge, Massachusetts, USA
| | - Stephen Gliske
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Johannes Sarnthein
- Department of Neurosurgery, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Sridevi Sarma
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems, School of Computing Science, Newcastle University, Newcastle Upon Tyne, UK
| | - Maxime O Baud
- Sleep-Wake-Epilepsy Center, NeuroTec, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | - William C Stacey
- Department of Neurology, BioInterfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
- Division of Neurology, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
| | - Erin C Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Maeda K, Hosoda N, Fukumoto J, Kawai S, Hayafuji M, Tsuboi H, Fujita S, Ichino N, Osakabe K, Sugimoto K, Ishihara N. Association of Scalp High-Frequency Oscillation Detection and Characteristics With Disease Activity in Pediatric Epilepsy. J Clin Neurophysiol 2023:00004691-990000000-00106. [PMID: 37934062 DOI: 10.1097/wnp.0000000000001052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023] Open
Abstract
INTRODUCTION High-frequency oscillation (HFO) in scalp electroencephalography is a promising new noninvasive prognostic epilepsy biomarker, but further data are needed to ascertain the utility of this parameter. The present work investigated the association between epileptic activity and scalp HFO in pediatric patients with various types of epilepsy, using multivariable regression models to correct for possible confounding factors. METHODS The authors analyzed 97 subjects who were divided into groups with active epilepsy (within 1 year of seizure), seizure-free epilepsy (>1 year without seizure), and nonepilepsy. Regarding the frequency of seizure occurrence as an indicator of epileptic activity, we categorized subjects into four groups (Daily/Weekly, Monthly, Yearly, and Rarely). RESULTS Multiple linear regression analysis showed that the scalp HFO detection rate was significantly higher in patients with active epilepsy than in those with nonepilepsy (β [95% confidence interval] = 2.77 [1.79-4.29]; P < 0.001). The association between scalp HFO detection rate and frequency of seizure occurrence was highest in the Daily/Weekly group (β [95% confidence interval] = 3.38 [1.57-7.27]; P = 0.002), followed by Monthly and Yearly groups (β [95% confidence interval] = 2.42 [1.02-5.73]; P = 0.046 and 0.36 [0.16-0.83]; P = 0.017). In addition, HFO duration, number of peaks, and number of channels detected were significantly higher in patients with active epilepsy. CONCLUSIONS Pediatric patients with active epilepsy and high frequency of seizure occurrence exhibited a higher scalp HFO detection rate. These results may help to establish HFO detectable by noninvasive scalp electroencephalography as a biomarker of active epilepsy in pediatric patients.
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Affiliation(s)
- Keisuke Maeda
- Department of Clinical Physiology, Fujita Health University School of Medical Sciences, Toyoake, Japan
| | - Nami Hosoda
- Department of Clinical Laboratory, Fujita Health University Hospital, Toyoake, Japan
| | - Junichi Fukumoto
- Department of Clinical Laboratory, Fujita Health University Hospital, Toyoake, Japan
| | - Shun Kawai
- Department of Clinical Laboratory, Fujita Health University Hospital, Toyoake, Japan
| | - Mizuki Hayafuji
- Department of Clinical Laboratory, Fujita Health University Hospital, Toyoake, Japan
| | - Himari Tsuboi
- Department of Clinical Laboratory, Fujita Health University Hospital, Toyoake, Japan
| | - Shiho Fujita
- Department of Clinical Laboratory, Fujita Health University Hospital, Toyoake, Japan
| | - Naohiro Ichino
- Department of Clinical Physiology, Fujita Health University School of Medical Sciences, Toyoake, Japan
| | - Keisuke Osakabe
- Department of Clinical Physiology, Fujita Health University School of Medical Sciences, Toyoake, Japan
| | - Keiko Sugimoto
- Department of Medical Sciences Education, Fujita Health University School of Medical Sciences, Toyoake, Japan; and
| | - Naoko Ishihara
- Department of Pediatrics, Fujita Health University School of Medicine, Toyoake, Japan
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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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Dimakopoulos V, Selmin G, Regli L, Sarnthein J. Optimization of signal-to-noise ratio in short-duration SEP recordings by variation of stimulation rate. Clin Neurophysiol 2023; 150:89-97. [PMID: 37030046 DOI: 10.1016/j.clinph.2023.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 02/24/2023] [Accepted: 03/10/2023] [Indexed: 03/29/2023]
Abstract
OBJECTIVE The intraoperative averaging of the somatosensory evoked potential (SEP) requires reliable recordings within the shortest possible duration. We here systematically optimized the repetition rate of stimulus presentation. METHODS We recorded medianus and tibial nerve SEP during 22 surgeries and varied the rate of stimulus presentation between 2.7 Hz and 28.7 Hz. We randomly sampled a number of sweeps corresponding to recording durations up to 20 s and calculated the signal-to-noise ratio (SNR). RESULTS For the medianus nerve at 5 s recording duration, SEP stimulation rate at 12.7 Hz obtained the highest median SNR = 22.9 for the N20, which was higher than for rate 4.7 Hz (p = 1.5e-4). When increasing the stimulation rate, latency increased and amplitude decayed for cortical but not for peripheral recording sites. For the tibial nerve, the rate 4.7 Hz achieved the highest SNR for all durations. CONCLUSIONS We determined the time-dependence of SNR for N20 and elucidated the underlying physiology. For short recordings, rapid reduction of noise through averaging at high stimulation rate outweighs the disadvantage of smaller amplitude. SIGNIFICANCE For a short duration of medianus nerve SEP recording only, it may be advantageous to stimulate with a repetition rate of 12.7 Hz.
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Gallotto S, Seeck M. EEG biomarker candidates for the identification of epilepsy. Clin Neurophysiol Pract 2022; 8:32-41. [PMID: 36632368 PMCID: PMC9826889 DOI: 10.1016/j.cnp.2022.11.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 10/14/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
Electroencephalography (EEG) is one of the main pillars used for the diagnosis and study of epilepsy, readily employed after a possible first seizure has occurred. The most established biomarker of epilepsy, in case seizures are not recorded, are interictal epileptiform discharges (IEDs). In clinical practice, however, IEDs are not always present and the EEG may appear completely normal despite an underlying epileptic disorder, often leading to difficulties in the diagnosis of the disease. Thus, finding other biomarkers that reliably predict whether an individual suffers from epilepsy even in the absence of evident epileptic activity would be extremely helpful, since they could allow shortening the period of diagnostic uncertainty and consequently decreasing the risk of seizure. To date only a few EEG features other than IEDs seem to be promising candidates able to distinguish between epilepsy, i.e. > 60 % risk of recurrent seizures, or other (pathological) conditions. The aim of this narrative review is to provide an overview of the EEG-based biomarker candidates for epilepsy and the techniques employed for their identification.
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Karpychev V, Balatskaya A, Utyashev N, Pedyash N, Zuev A, Dragoy O, Fedele T. Epileptogenic high-frequency oscillations present larger amplitude both in mesial temporal and neocortical regions. Front Hum Neurosci 2022; 16:984306. [PMID: 36248681 PMCID: PMC9557004 DOI: 10.3389/fnhum.2022.984306] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/12/2022] [Indexed: 11/17/2022] Open
Abstract
High-frequency oscillations (HFO) are a promising biomarker for the identification of epileptogenic tissue. While HFO rates have been shown to predict seizure outcome, it is not yet clear whether their morphological features might improve this prediction. We validated HFO rates against seizure outcome and delineated the distribution of HFO morphological features. We collected stereo-EEG recordings from 20 patients (231 electrodes; 1,943 contacts). We computed HFO rates (the co-occurrence of ripples and fast ripples) through a validated automated detector during non-rapid eye movement sleep. Applying machine learning, we delineated HFO morphological features within and outside epileptogenic tissue across mesial temporal lobe (MTL) and Neocortex. HFO rates predicted seizure outcome with 85% accuracy, 79% specificity, 100% sensitivity, 100% negative predictive value, and 67% positive predictive value. The analysis of HFO features showed larger amplitude in the epileptogenic tissue, similar morphology for epileptogenic HFO in MTL and Neocortex, and larger amplitude for physiological HFO in MTL. We confirmed HFO rates as a reliable biomarker for epilepsy surgery and characterized the potential clinical relevance of HFO morphological features. Our results support the prospective use of HFO in epilepsy surgery and contribute to the anatomical mapping of HFO morphology.
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Affiliation(s)
- Victor Karpychev
- Center for Language and Brain, HSE University, Moscow, Russia
- *Correspondence: Victor Karpychev,
| | | | - Nikita Utyashev
- National Medical and Surgical Center named after N.I. Pirogov, Moscow, Russia
| | - Nikita Pedyash
- National Medical and Surgical Center named after N.I. Pirogov, Moscow, Russia
| | - Andrey Zuev
- National Medical and Surgical Center named after N.I. Pirogov, Moscow, Russia
| | - Olga Dragoy
- Center for Language and Brain, HSE University, Moscow, Russia
- Institute of Linguistics, Russian Academy of Sciences, Moscow, Russia
| | - Tommaso Fedele
- Institute for Cognitive Neuroscience, HSE University, Moscow, Russia
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Shoeibi A, Moridian P, Khodatars M, Ghassemi N, Jafari M, Alizadehsani R, Kong Y, Gorriz JM, Ramírez J, Khosravi A, Nahavandi S, Acharya UR. An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works. Comput Biol Med 2022; 149:106053. [DOI: 10.1016/j.compbiomed.2022.106053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 08/17/2022] [Accepted: 08/17/2022] [Indexed: 02/01/2023]
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Li X, Zhang H, Lai H, Wang J, Wang W, Yang X. High-Frequency Oscillations and Epileptogenic Network. Curr Neuropharmacol 2022; 20:1687-1703. [PMID: 34503414 PMCID: PMC9881061 DOI: 10.2174/1570159x19666210908165641] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/26/2021] [Accepted: 08/31/2021] [Indexed: 11/22/2022] Open
Abstract
Epilepsy is a network disease caused by aberrant neocortical large-scale connectivity spanning regions on the scale of several centimeters. High-frequency oscillations, characterized by the 80-600 Hz signals in electroencephalography, have been proven to be a promising biomarker of epilepsy that can be used in assessing the severity and susceptibility of epilepsy as well as the location of the epileptogenic zone. However, the presence of a high-frequency oscillation network remains a topic of debate as high-frequency oscillations have been previously thought to be incapable of propagation, and the relationship between high-frequency oscillations and the epileptogenic network has rarely been discussed. Some recent studies reported that high-frequency oscillations may behave like networks that are closely relevant to the epileptogenic network. Pathological highfrequency oscillations are network-driven phenomena and elucidate epileptogenic network development; high-frequency oscillations show different characteristics coincident with the epileptogenic network dynamics, and cross-frequency coupling between high-frequency oscillations and other signals may mediate the generation and propagation of abnormal discharges across the network.
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Affiliation(s)
- Xiaonan Li
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | | | | | - Jiaoyang Wang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | - Wei Wang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | - Xiaofeng Yang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China,Address correspondence to this author at the Bioland Laboratory, Guangzhou, China; Tel: 86+ 18515855127; E-mail:
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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] [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
- *Correspondence: Johannes Sarnthein,
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14
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Varatharajah Y, Joseph B, Brinkmann B, Morita-Sherman M, Fitzgerald Z, Vegh D, Nair D, Burgess R, Cendes F, Jehi L, Worrell G. Quantitative Analysis of Visually Reviewed Normal Scalp EEG Predicts Seizure Freedom Following Anterior Temporal Lobectomy. Epilepsia 2022; 63:1630-1642. [PMID: 35416285 PMCID: PMC9283304 DOI: 10.1111/epi.17257] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 04/10/2022] [Accepted: 04/11/2022] [Indexed: 11/28/2022]
Abstract
Objective Anterior temporal lobectomy (ATL) is a widely performed and successful intervention for drug‐resistant temporal lobe epilepsy (TLE). However, up to one third of patients experience seizure recurrence within 1 year after ATL. Despite the extensive literature on presurgical electroencephalography (EEG) and magnetic resonance imaging (MRI) abnormalities to prognosticate seizure freedom following ATL, the value of quantitative analysis of visually reviewed normal interictal EEG in such prognostication remains unclear. In this retrospective multicenter study, we investigate whether machine learning analysis of normal interictal scalp EEG studies can inform the prediction of postoperative seizure freedom outcomes in patients who have undergone ATL. Methods We analyzed normal presurgical scalp EEG recordings from 41 Mayo Clinic (MC) and 23 Cleveland Clinic (CC) patients. We used an unbiased automated algorithm to extract eyes closed awake epochs from scalp EEG studies that were free of any epileptiform activity and then extracted spectral EEG features representing (a) spectral power and (b) interhemispheric spectral coherence in frequencies between 1 and 25 Hz across several brain regions. We analyzed the differences between the seizure‐free and non–seizure‐free patients and employed a Naïve Bayes classifier using multiple spectral features to predict surgery outcomes. We trained the classifier using a leave‐one‐patient‐out cross‐validation scheme within the MC data set and then tested using the out‐of‐sample CC data set. Finally, we compared the predictive performance of normal scalp EEG‐derived features against MRI abnormalities. Results We found that several spectral power and coherence features showed significant differences correlated with surgical outcomes and that they were most pronounced in the 10–25 Hz range. The Naïve Bayes classification based on those features predicted 1‐year seizure freedom following ATL with area under the curve (AUC) values of 0.78 and 0.76 for the MC and CC data sets, respectively. Subsequent analyses revealed that (a) interhemispheric spectral coherence features in the 10–25 Hz range provided better predictability than other combinations and (b) normal scalp EEG‐derived features provided superior and potentially distinct predictive value when compared with MRI abnormalities (>10% higher F1 score). Significance These results support that quantitative analysis of even a normal presurgical scalp EEG may help prognosticate seizure freedom following ATL in patients with drug‐resistant TLE. Although the mechanism for this result is not known, the scalp EEG spectral and coherence properties predicting seizure freedom may represent activity arising from the neocortex or the networks responsible for temporal lobe seizure generation within vs outside the margins of an ATL.
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Affiliation(s)
- Yogatheesan Varatharajah
- Department of Bioengineering, University of Illinois, Urbana, IL, 61801, USA.,Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
| | - Boney Joseph
- Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
| | - Benjamin Brinkmann
- Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
| | | | | | - Deborah Vegh
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, 44195, USA
| | - Dileep Nair
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, 44195, USA
| | - Richard Burgess
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, 44195, USA
| | - Fernando Cendes
- Department of Neurology, University of Campinas UNICAMP, Campinas, Brazil
| | - Lara Jehi
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, 44195, USA
| | - Gregory Worrell
- Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
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15
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Cserpan D, Gennari A, Gaito L, Lo Biundo SP, Tuura R, Sarnthein J, Ramantani G. Scalp HFO rates are higher for larger lesions. Epilepsia Open 2022; 7:496-503. [PMID: 35357778 PMCID: PMC9436296 DOI: 10.1002/epi4.12596] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/23/2022] [Accepted: 03/28/2022] [Indexed: 11/30/2022] Open
Abstract
High‐frequency oscillations (HFO) in scalp EEG are a new and promising noninvasive epilepsy biomarker, providing added prognostic value, particularly in pediatric lesional epilepsy. However, it is unclear if lesion characteristics, such as lesion volume, depth, type, and localization, impact scalp HFO rates. We analyzed scalp EEG from 13 children and adolescents with focal epilepsy associated with focal cortical dysplasia (FCD), low‐grade tumors, or hippocampal sclerosis. We applied a validated automated detector to determine HFO rates in bipolar channels. We identified the lesion characteristics in MRI. Larger lesions defined by MRI volumetric analysis corresponded to higher cumulative scalp HFO rates (P = .01) that were detectable in a higher number of channels (P = .05). Both superficial and deep lesions generated HFO detectable in the scalp EEG. Lesion type (FCD vs tumor) and lobar localization (temporal vs extratemporal) did not affect scalp HFO rates in our study. Our observations support that all lesions may generate HFO detectable in scalp EEG, irrespective of their characteristics, whereas larger epileptogenic lesions generate higher scalp HFO rates over larger areas that are thus more accessible to detection. Our study provides crucial insight into scalp HFO detectability in pediatric lesional epilepsy, facilitating their implementation as an epilepsy biomarker in a clinical setting.
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Affiliation(s)
- Dorottya Cserpan
- Department of Neuropediatrics University Children's Hospital Zurich Switzerland
| | - Antonio Gennari
- Department of Neuropediatrics University Children's Hospital Zurich Switzerland
- MR‐Research Centre University Children's Hospital Zurich Switzerland
| | - Luca Gaito
- Department of Neuropediatrics University Children's Hospital Zurich Switzerland
- Department of Neurosurgery University Hospital Zurich Switzerland
| | | | - Ruth Tuura
- MR‐Research Centre University Children's Hospital Zurich Switzerland
- University of Zurich Switzerland
- Children’s Research Centre University Children's Hospital Zurich Switzerland
| | - Johannes Sarnthein
- Department of Neurosurgery University Hospital Zurich Switzerland
- University of Zurich Switzerland
| | - Georgia Ramantani
- Department of Neuropediatrics University Children's Hospital Zurich Switzerland
- University of Zurich Switzerland
- Children’s Research Centre University Children's Hospital Zurich Switzerland
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16
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Behavior of olfactory-related frontal lobe oscillations in Alzheimer's disease and MCI: A pilot study. Int J Psychophysiol 2022; 175:43-53. [PMID: 35217110 DOI: 10.1016/j.ijpsycho.2022.02.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 12/19/2021] [Accepted: 02/17/2022] [Indexed: 11/20/2022]
Abstract
Slow-gamma (35-45 Hz) phase synchronization and the coupling between slow-gamma and low-frequency theta oscillations (4-8 Hz) are closely related to memory retrieval and cognitive functions. In this pilot study, we assess the Phase Amplitude Coupling (PAC) between theta and slow-gamma oscillatory bands and the quality of synchronization in slow-gamma oscillations using Phase Locking Value (PLV) on EEG data from healthy individuals and patients diagnosed with amnestic Mild Cognitive Impairment (aMCI) and Alzheimer's Disease (AD) during an oddball olfactory task. Our study indicates noticeable differences between the PLV and PAC values corresponding to olfactory stimulation in the three groups of participants. These differences can help explain the underlying processes involved in these cognitive disorders and the differences between aMCI and AD patients in performing cognitive tasks. Our study also proposes a diagnosis method for aMCI through comparing the brain's response characteristics during olfactory stimulation and rest. Early diagnosis of aMCI can potentially lead to its timely treatment and prevention from progression to AD.
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17
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Burelo K, Ramantani G, Indiveri G, Sarnthein J. A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG. Sci Rep 2022; 12:1798. [PMID: 35110665 PMCID: PMC8810784 DOI: 10.1038/s41598-022-05883-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 01/17/2022] [Indexed: 12/04/2022] Open
Abstract
Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This development has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The demand for therapy monitoring in epilepsy has kindled interest in compact wearable electronic devices for long-term EEG recording. Spiking neural networks (SNN) have emerged as optimal architectures for embedding in compact low-power signal processing hardware. We analyzed 20 scalp EEG recordings from 11 pediatric focal lesional epilepsy patients. We designed a custom SNN to detect events of interest (EoI) in the 80–250 Hz ripple band and reject artifacts in the 500–900 Hz band. We identified the optimal SNN parameters to detect EoI and reject artifacts automatically. The occurrence of HFO thus detected was associated with active epilepsy with 80% accuracy. The HFO rate mirrored the decrease in seizure frequency in 8 patients (p = 0.0047). Overall, the HFO rate correlated with seizure frequency (rho = 0.90 CI [0.75 0.96], p < 0.0001, Spearman’s correlation). The fully automated SNN detected clinically relevant HFO in the scalp EEG. This study is a further step towards non-invasive epilepsy monitoring with a low-power wearable device.
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Affiliation(s)
- Karla Burelo
- Klinik für Neurochirurgie, Universitätsspital und Universität Zürich, 8091, Zurich, Switzerland.,Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Georgia Ramantani
- Neuropädiatrie, Universitäts-Kinderspital und Universität Zürich, Zurich, Switzerland.,Forschungszentrum für das Kind, Universitäts-Kinderspital Zürich, Zurich, Switzerland.,Zentrum für Neurowissenschaften Zürich, ETH und Universität Zürich, Zurich, Switzerland
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.,Zentrum für Neurowissenschaften Zürich, ETH und Universität Zürich, Zurich, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, Universitätsspital und Universität Zürich, 8091, Zurich, Switzerland. .,Zentrum für Neurowissenschaften Zürich, ETH und Universität Zürich, Zurich, Switzerland.
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18
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Windhager PF, Marcu AV, Trinka E, Bathke A, Höller Y. Are High Frequency Oscillations in Scalp EEG Related to Age? Front Neurol 2022; 12:722657. [PMID: 35153968 PMCID: PMC8829347 DOI: 10.3389/fneur.2021.722657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 12/20/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND High-frequency oscillations (HFOs) have received much attention in recent years, particularly in the clinical context. In addition to their application as a marker for pathological changes in patients with epilepsy, HFOs have also been brought into context with several physiological mechanisms. Furthermore, recent studies reported a relation between an increase of HFO rate and age in invasive EEG recordings. The present study aimed to investigate whether this relation can be replicated in scalp-EEG. METHODS We recorded high-density EEG from 11 epilepsy patients at rest as well as during motor performance. Manual detection of HFOs was performed by two independent raters following a standardized protocol. Patients were grouped by age into younger (<25 years) and older (>50 years) participants. RESULTS No significant difference of HFO-rates was found between groups [U = 10.5, p = 0.429, r = 0.3]. CONCLUSIONS Lack of replicability of the age effect of HFOs may be due to the local propagation patterns of age-related HFOs occurring in deep structures. However, limitations such as small sample size, decreased signal-to-noise ratio as compared to invasive recordings, as well as HFO-mimicking artifacts must be considered.
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Affiliation(s)
- Philipp Franz Windhager
- Department of Neurology, Christian-Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,*Correspondence: Philipp Franz Windhager
| | - Adrian V. Marcu
- Department of Neurology, Christian-Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian-Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Arne Bathke
- Department of Mathematics, Paris Lodron University Salzburg, Salzburg, Austria
| | - Yvonne Höller
- Faculty of Psychology, University of Akureyri, Akureyri, Iceland
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19
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Scalp HFO rates decrease after successful epilepsy surgery and are not impacted by the skull defect resulting from craniotomy. Sci Rep 2022; 12:1301. [PMID: 35079091 PMCID: PMC8789862 DOI: 10.1038/s41598-022-05373-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/05/2022] [Indexed: 02/06/2023] Open
Abstract
Epilepsy surgery can achieve seizure freedom in selected pediatric candidates, but reliable postsurgical predictors of seizure freedom are missing. High frequency oscillations (HFO) in scalp EEG are a new and promising biomarker of treatment response. However, it is unclear if the skull defect resulting from craniotomy interferes with HFO detection in postsurgical recordings. We considered 14 children with focal lesional epilepsy who underwent presurgical evaluation, epilepsy surgery, and postsurgical follow-up of ≥ 1 year. We identified the nearest EEG electrodes to the skull defect in the postsurgical MRI. We applied a previously validated automated HFO detector to determine HFO rates in presurgical and postsurgical EEG. Overall, HFO rates showed a positive correlation with seizure frequency (p < 0.001). HFO rates in channels over the HFO area decreased following successful epilepsy surgery, irrespective of their proximity to the skull defect (p = 0.005). HFO rates in channels outside the HFO area but near the skull defect showed no increase following surgery (p = 0.091) and did not differ from their contralateral channels (p = 0.726). Our observations show that the skull defect does not interfere with postsurgical HFO detection. This supports the notion that scalp HFO can predict postsurgical seizure freedom and thus guide therapy management in focal lesional epilepsy.
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20
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Cserpan D, Rosch R, Pietro Lo Biundo S, Sarnthein J, Ramantani G. Variation of scalp EEG high frequency oscillation rate with sleep stage and time spent in sleep in patients with pediatric epilepsy. Clin Neurophysiol 2022; 135:117-125. [DOI: 10.1016/j.clinph.2021.12.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 11/07/2021] [Accepted: 12/14/2021] [Indexed: 12/15/2022]
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21
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High-frequency oscillations in scalp EEG: A systematic review of methodological choices and clinical findings. Clin Neurophysiol 2022; 137:46-58. [DOI: 10.1016/j.clinph.2021.12.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/13/2021] [Accepted: 12/21/2021] [Indexed: 02/08/2023]
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22
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Santana‐Gomez CE, Engel J, Staba R. Drug-resistant epilepsy and the hypothesis of intrinsic severity: What about the high-frequency oscillations? Epilepsia Open 2021; 7 Suppl 1:S59-S67. [PMID: 34861102 PMCID: PMC9340307 DOI: 10.1002/epi4.12565] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 11/23/2021] [Accepted: 11/30/2021] [Indexed: 11/19/2022] Open
Abstract
Drug‐resistant epilepsy (DRE) affects approximately one‐third of the patients with epilepsy. Based on experimental findings from animal models and brain tissue from patients with DRE, different hypotheses have been proposed to explain the cause(s) of drug resistance. One is the intrinsic severity hypothesis that posits that drug resistance is an inherent property of epilepsy related to disease severity. Seizure frequency is one measure of epilepsy severity, but frequency alone is an incomplete measure of severity and does not fully explain basic research and clinical studies on drug resistance; thus, other measures of epilepsy severity are needed. One such measure could be pathological high‐frequency oscillations (HFOs), which are believed to reflect the neuronal disturbances responsible for the development of epilepsy and the generation of spontaneous seizures. In this manuscript, we will briefly review the intrinsic severity hypothesis, describe basic and clinical research on HFOs in the epileptic brain, and based on this evidence discuss whether HFOs could be a clinical measure of epilepsy severity. Understanding the mechanisms of DRE is critical for producing breakthroughs in the development and testing of novel strategies for treatment.
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Affiliation(s)
| | - Jerome Engel
- Department of NeurologyDavid Geffen School of Medicine at UCLALos AngelesCaliforniaUSA
- Brain Research InstituteDavid Geffen School of Medicine at UCLALos AngelesCaliforniaUSA
- Department of NeurobiologyDavid Geffen School of Medicine at UCLALos AngelesCaliforniaUSA
- Department of Psychiatry and Biobehavioral SciencesDavid Geffen School of Medicine at UCLALos AngelesCaliforniaUSA
| | - Richard Staba
- Department of NeurologyDavid Geffen School of Medicine at UCLALos AngelesCaliforniaUSA
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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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Dimakopoulos V, Mégevand P, Boran E, Momjian S, Seeck M, Vulliémoz S, Sarnthein J. Blinded study: prospectively defined high-frequency oscillations predict seizure outcome in individual patients. Brain Commun 2021; 3:fcab209. [PMID: 34541534 PMCID: PMC8445392 DOI: 10.1093/braincomms/fcab209] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 06/01/2021] [Accepted: 06/14/2020] [Indexed: 11/16/2022] Open
Abstract
Interictal high-frequency oscillations are discussed as biomarkers for epileptogenic brain tissue that should be resected in epilepsy surgery to achieve seizure freedom. The prospective classification of tissue sampled by individual electrode contacts remains a challenge. We have developed an automated, prospective definition of clinically relevant high-frequency oscillations in intracranial EEG from Montreal and tested it in recordings from Zurich. We here validated the algorithm on intracranial EEG that was recorded in an independent epilepsy centre so that the analysis was blinded to seizure outcome. We selected consecutive patients who underwent resective epilepsy surgery in Geneva with post-surgical follow-up > 12 months. We analysed long-term recordings during sleep that we segmented into intervals of 5 min. High-frequency oscillations were defined in the ripple (80–250 Hz) and the fast ripple (250–500 Hz) frequency bands. Contacts with the highest rate of ripples co-occurring with fast ripples designated the relevant area. As a validity criterion, we calculated the test–retest reliability of the high-frequency oscillations area between the 5 min intervals (dwell time ≥50%). If the area was not fully resected and the patient suffered from recurrent seizures, this was classified as a true positive prediction. We included recordings from 16 patients (median age 32 years, range 18–53 years) with stereotactic depth electrodes and/or with subdural electrode grids (median follow-up 27 months, range 12–55 months). For each patient, we included several 5 min intervals (median 17 intervals). The relevant area had high test–retest reliability across intervals (median dwell time 95%). In two patients, the test–retest reliability was too low (dwell time < 50%) so that outcome prediction was not possible. The area was fully included in the resected volume in 2/4 patients who achieved post-operative seizure freedom (specificity 50%) and was not fully included in 9/10 patients with recurrent seizures (sensitivity 90%), leading to an accuracy of 79%. An additional exploratory analysis suggested that high-frequency oscillations were associated with interictal epileptic discharges only in channels within the relevant area and not associated in channels outside the area. We thereby validated the automated procedure to delineate the clinically relevant area in each individual patient of an independently recorded dataset and achieved the same good accuracy as in our previous studies. The reproducibility of our results across datasets is promising for a multicentre study to test the clinical application of high-frequency oscillations to guide epilepsy surgery.
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Affiliation(s)
- Vasileios Dimakopoulos
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zürich, Switzerland
| | - Pierre Mégevand
- Département des neurosciences fondamentales, Faculté de médecine, Université de Genève, Geneva, Switzerland.,Service de neurologie, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Ece Boran
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zürich, Switzerland
| | - Shahan Momjian
- Service de neurochirurgie, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Margitta Seeck
- Service de neurologie, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Serge Vulliémoz
- Service de neurologie, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zürich, Switzerland.,Klinisches Neurowissenschaften Zentrum, University Hospital Zurich, Zürich, Switzerland
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Contento M, Bertaccini B, Biggi M, Magliani M, Failli Y, Rosati E, Massacesi L, Paganini M. Prediction of seizure recurrence risk following discontinuation of antiepileptic drugs. Epilepsia 2021; 62:2159-2170. [PMID: 34250596 PMCID: PMC8457060 DOI: 10.1111/epi.16993] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 06/20/2021] [Accepted: 06/21/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Discontinuation of antiepileptic drugs (AEDs) in seizure-free patients is an important goal because of possible long-term side effects and the social stigma burden of epilepsy. The purpose of this work was to assess seizure recurrence risk after suspension of AEDs, to evaluate predictors for recurrence, and to investigate the recovery of seizure control after relapse. In addition, the accuracy of a previously published prediction model of seizure recurrence risk was estimated. METHODS Seizure-free patients with epilepsy who had discontinued AEDs were retrospectively enrolled. The frequency of seizure relapses after AED withdrawal as well as prognosis after recurrence were assessed and the predictive role of baseline clinical-demographic variables was evaluated. The aforementioned prediction model was also validated and its accuracy assessed at different seizure-relapse probability levels. RESULTS The enrolled patients (n = 133) had been followed for a median of 3 years (range 0.8-33 years) after AED discontinuation; 60 (45%) of them relapsed. Previous febrile seizures in childhood (hazard ratio [HR] 3.927; 95% confidence interval [CI] 1.403-10.988), a seizure-free period on therapy of less than 2 years (HR 2.313; 95% CI 1.193-4.486), and persistent motor deficits (HR 4.568; 95% CI 1.412-14.772) were the clinical features associated with relapse risk in univariate analysis. Among these variables, only a seizure-free period on therapy of less than 2 years was associated with seizure recurrence in multivariate analysis (HR 2.365; 95% CI 1.178-4.7444). Pharmacological control of epilepsy was restored in 82.4% of the patients who relapsed. In this population, the aforementioned prediction model showed an unsatisfactory accuracy. SIGNIFICANCE A period of freedom from seizure on therapy of less than 2 years was the main predictor of seizure recurrence. The accuracy of the previously described prediction tool was low in this cohort, thus suggesting its cautious use in real-world clinical practice.
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Affiliation(s)
- Margherita Contento
- Department of Neurosciences, Drug and Child Health, University of Florence, Florence, Italy
| | - Bruno Bertaccini
- Department of Statistics Informatics and Application "G.Parenti", University of Florence, Florence, Italy
| | - Martina Biggi
- Department of Neurosciences, Drug and Child Health, University of Florence, Florence, Italy
| | - Matteo Magliani
- Department of Neurosciences, Drug and Child Health, University of Florence, Florence, Italy
| | - Ylenia Failli
- Department of Neurosciences, Drug and Child Health, University of Florence, Florence, Italy
| | - Eleonora Rosati
- Department Neurology 2, Careggi University Hospital, Florence, Italy
| | - Luca Massacesi
- Department of Neurosciences, Drug and Child Health, University of Florence, Florence, Italy.,Department Neurology 2, Careggi University Hospital, Florence, Italy
| | - Marco Paganini
- Department Neurology 2, Careggi University Hospital, Florence, Italy
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Yan L, Li L, Chen J, Wang L, Jiang L, Hu Y. Application of High-Frequency Oscillations on Scalp EEG in Infant Spasm: A Prospective Controlled Study. Front Hum Neurosci 2021; 15:682011. [PMID: 34177501 PMCID: PMC8223253 DOI: 10.3389/fnhum.2021.682011] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 05/17/2021] [Indexed: 01/22/2023] Open
Abstract
Objective We quantitatively analyzed high-frequency oscillations (HFOs) using scalp electroencephalography (EEG) in patients with infantile spasms (IS). Methods We enrolled 60 children with IS hospitalized from January 2019 to August 2020. Sixty healthy age-matched children comprised the control group. Time-frequency analysis was used to quantify γ, ripple, and fast ripple (FR) oscillation energy changes. Results γ, ripple, and FR oscillations dominated in the temporal and frontal lobes. The average HFO energy of the sleep stage is lower than that of the wake stage in the same frequency bands in both the normal control (NC) and IS groups (P < 0.05). The average HFO energy of the IS group was significantly higher than that of the NC group in γ band during sleep stage (P < 0.01). The average HFO energy of S and Post-S stage were higher than that of sleep stage in γ band (P < 0.05). In the ripple band, the average HFO energy of Pre-S, S, and Post-S stage was higher than that of sleep stage (P < 0.05). Before treatment, there was no significant difference in BASED score between the effective and ineffective groups. The interaction of curative efficacy × frequency and the interaction of curative efficacy × state are statistically significant. The average HFO energy of the effective group was lower than that of the ineffective group in the sleep stage (P < 0.05). For the 16 children deemed "effective" in the IS group, the average HFO energy of three frequency bands was not significantly different before compared with after treatment. Significance Scalp EEG can record HFOs. The energy of HFOs can distinguish physiological HFOs from pathological ones more accurately than frequency. On scalp EEG, γ oscillations can better detect susceptibility to epilepsy than ripple and FR oscillations. HFOs can trigger spasms. The analysis of average HFO energy can be used as a predictor of the effectiveness of epilepsy treatment.
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Affiliation(s)
- Lisi Yan
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Lin Li
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Jin Chen
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Li Wang
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Li Jiang
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Yue Hu
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,National Clinical Research Center for Child Health and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Pediatrics, Chongqing, China
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27
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Nadalin JK, Eden UT, Han X, Richardson RM, Chu CJ, Kramer MA. Application of a convolutional neural network for fully-automated detection of spike ripples in the scalp electroencephalogram. J Neurosci Methods 2021; 360:109239. [PMID: 34090917 DOI: 10.1016/j.jneumeth.2021.109239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 05/17/2021] [Accepted: 05/30/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND A reliable biomarker to identify cortical tissue responsible for generating epileptic seizures is required to guide prognosis and treatment in epilepsy. Combined spike ripple events are a promising biomarker for epileptogenic tissue that currently require expert review for accurate identification. This expert review is time consuming and subjective, limiting reproducibility and high-throughput applications. NEW METHOD To address this limitation, we develop a fully-automated method for spike ripple detection. The method consists of a convolutional neural network trained to compute the probability that a spectrogram image contains a spike ripple. RESULTS We validate the proposed spike ripple detector on expert-labeled data and show that this detector accurately separates subjects with low and high seizure risks. COMPARISON WITH EXISTING METHOD The proposed method performs as well as existing methods that require manual validation of candidate spike ripple events. The introduction of a fully automated method reduces subjectivity and increases rigor and reproducibility of this epilepsy biomarker. CONCLUSION We introduce and validate a fully-automated spike ripple detector to support utilization of this epilepsy biomarker in clinical and translational work.
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Affiliation(s)
- Jessica K Nadalin
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States; Center for Systems Neuroscience, Boston University, Boston, MA 02215, United States
| | - Xue Han
- Center for Systems Neuroscience, Boston University, Boston, MA 02215, United States; Department of Biomedical Engineering, Boston University, Boston, MA 02215, United States
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Mark A Kramer
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States; Center for Systems Neuroscience, Boston University, Boston, MA 02215, United States.
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28
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Shi LJ, Wei BX, Xu L, Lin YC, Wang YP, Zhang JC. Magnetoencephalography for epileptic focus localization based on Tucker decomposition with ripple window. CNS Neurosci Ther 2021; 27:820-830. [PMID: 33942534 PMCID: PMC8193700 DOI: 10.1111/cns.13643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 03/24/2021] [Indexed: 11/30/2022] Open
Abstract
AIMS To improve the Magnetoencephalography (MEG) spatial localization precision of focal epileptic. METHODS 306-channel simulated or real clinical MEG is estimated as a lower-dimensional tensor by Tucker decomposition based on Higher-order orthogonal iteration (HOOI) before the inverse problem using linearly constraint minimum variance (LCMV). For simulated MEG data, the proposed method is compared with dynamic imaging of coherent sources (DICS), multiple signal classification (MUSIC), and LCMV. For clinical real MEG of 31 epileptic patients, the ripples (80-250 Hz) were detected to compare the source location precision with spikes using the proposed method or the dipole-fitting method. RESULTS The experimental results showed that the positional accuracy of the proposed method was higher than that of LCMV, DICS, and MUSIC for simulation data. For clinical real MEG data, the positional accuracy of the proposed method was higher than that of dipole-fitting regardless of whether the time window was ripple window or spike window. Also, the positional accuracy of the ripple window was higher than that of the spike window regardless of whether the source location method was the proposed method or the dipole-fitting method. For both shallow and deep sources, the proposed method provided effective performance. CONCLUSION Tucker estimation of MEG for source imaging by ripple window is a promising approach toward the presurgical evaluation of epileptics.
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Affiliation(s)
- Li-Juan Shi
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Bo-Xuan Wei
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.,Hefei Innovation Research Institute, Beihang University, Hefei, Anhui, China
| | - Lu Xu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Yi-Cong Lin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing, China
| | - Yu-Ping Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing, China
| | - Ji-Cong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.,Hefei Innovation Research Institute, Beihang University, Hefei, Anhui, China
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29
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Noninvasive high-frequency oscillations riding spikes delineates epileptogenic sources. Proc Natl Acad Sci U S A 2021; 118:2011130118. [PMID: 33875582 PMCID: PMC8092606 DOI: 10.1073/pnas.2011130118] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Millions of people affected by epilepsy may undergo surgical resection of the epileptic tissues to stop seizures if such epileptic foci can be accurately delineated. High-frequency oscillations (HFOs), existing in electroencephalography, are highly correlated with epileptic brain, which is promising for guiding successful neurosurgery. However, it is unclear whether and how pathological HFOs can be differentiated to localize the epileptogenic tissues given the presence of various nonepileptic high-frequency activities. Here, we show morphological and source imaging evidence that pathological HFOs can be identified by the concurrence of epileptiform spikes. We describe a framework to delineate the underlying epileptogenicity using this biomarker. Our work may offer translational tools to improve treatments by noninvasively demarking pathological activities and hence epileptic foci. High-frequency oscillations (HFOs) are a promising biomarker for localizing epileptogenic brain and guiding successful neurosurgery. However, the utility and translation of noninvasive HFOs, although highly desirable, is impeded by the difficulty in differentiating pathological HFOs from nonepileptiform high-frequency activities and localizing the epileptic tissue using noninvasive scalp recordings, which are typically contaminated with high noise levels. Here, we show that the consistent concurrence of HFOs with epileptiform spikes (pHFOs) provides a tractable means to identify pathological HFOs automatically, and this in turn demarks an epileptiform spike subgroup with higher epileptic relevance than the other spikes in a cohort of 25 temporal epilepsy patients (including a total of 2,967 interictal spikes and 1,477 HFO events). We found significant morphological distinctions of HFOs and spikes in the presence/absence of this concurrent status. We also demonstrated that the proposed pHFO source imaging enhanced localization of epileptogenic tissue by 162% (∼5.36 mm) for concordance with surgical resection and by 186% (∼12.48 mm) with seizure-onset zone determined by invasive studies, compared to conventional spike imaging, and demonstrated superior congruence with the surgical outcomes. Strikingly, the performance of spike imaging was selectively boosted by the presence of spikes with pHFOs, especially in patients with multitype spikes. Our findings suggest that concurrent HFOs and spikes reciprocally discriminate pathological activities, providing a translational tool for noninvasive presurgical diagnosis and postsurgical evaluation in vulnerable patients.
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30
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Noorlag L, van 't Klooster MA, van Huffelen AC, van Klink NEC, Benders MJNL, de Vries LS, Leijten FSS, Jansen FE, Braun KPJ, Zijlmans M. High-frequency oscillations recorded with surface EEG in neonates with seizures. Clin Neurophysiol 2021; 132:1452-1461. [PMID: 34023627 DOI: 10.1016/j.clinph.2021.02.400] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 02/12/2021] [Indexed: 12/01/2022]
Abstract
OBJECTIVE Neonatal seizures are often the first symptom of perinatal brain injury. High-frequency oscillations (HFOs) are promising new biomarkers for epileptogenic tissue and can be found in intracranial and surface EEG. To date, we cannot reliably predict which neonates with seizures will develop childhood epilepsy. We questioned whether epileptic HFOs can be generated by the neonatal brain and potentially predict epilepsy. METHODS We selected 24 surface EEGs sampled at 2048 Hz with 175 seizures from 16 neonates and visually reviewed them for HFOs. Interictal epochs were also reviewed. RESULTS We found HFOs in thirteen seizures (7%) from four neonates (25%). 5025 ictal ripples (rate 10 to 1311/min; mean frequency 135 Hz; mean duration 66 ms) and 1427 fast ripples (rate 8 to 356/min; mean frequency 298 Hz; mean duration 25 ms) were marked. Two neonates (13%) showed interictal HFOs (285 ripples and 25 fast ripples). Almost all HFOs co-occurred with sharp transients. We could not find a relationship between neonatal HFOs and outcome yet. CONCLUSIONS Neonatal HFOs co-occur with ictal and interictal sharp transients. SIGNIFICANCE The neonatal brain can generate epileptic ripples and fast ripples, particularly during seizures, though their occurrence is not common and potential clinical value not evident yet.
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Affiliation(s)
- Lotte Noorlag
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, the Netherlands.
| | - Maryse A van 't Klooster
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, the Netherlands
| | - Alexander C van Huffelen
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, the Netherlands
| | - Nicole E C van Klink
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, the Netherlands
| | - Manon J N L Benders
- Department of Neonatology, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, the Netherlands
| | - Linda S de Vries
- Department of Neonatology, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, the Netherlands
| | - Frans S S Leijten
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, the Netherlands
| | - Floor E Jansen
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, the Netherlands
| | - Kees P J Braun
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, the Netherlands
| | - Maeike Zijlmans
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Utrecht, Utrecht, the Netherlands; Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede and Zwolle, the Netherlands
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31
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Liu X, Hu L, Xu C, Xu S, Wang S, Chen Z, Shen J. An Automatic HFO Detection Method Combining Visual Inspection Features with Multi-Domain Features. Neurosci Bull 2021; 37:777-788. [PMID: 33768515 DOI: 10.1007/s12264-021-00659-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 11/28/2020] [Indexed: 11/28/2022] Open
Abstract
As an important promising biomarker, high frequency oscillations (HFOs) can be used to track epileptic activity and localize epileptogenic zones. However, visual marking of HFOs from a large amount of intracranial electroencephalogram (iEEG) data requires a great deal of time and effort from researchers, and is also very dependent on visual features and easily influenced by subjective factors. Therefore, we proposed an automatic epileptic HFO detection method based on visual features and non-intuitive multi-domain features. To eliminate the interference of continuous oscillatory activity in detected sporadic short HFO events, the iEEG signals adjacent to the detected events were set as the neighboring environmental range while the number of oscillations and the peak-valley differences were calculated as the environmental reference features. The proposed method was developed as a MatLab-based HFO detector to automatically detect HFOs in multi-channel, long-distance iEEG signals. The performance of our detector was evaluated on iEEG recordings from epileptic mice and patients with intractable epilepsy. More than 90% of the HFO events detected by this method were confirmed by experts, while the average missed-detection rate was < 10%. Compared with recent related research, the proposed method achieved a synchronous improvement of sensitivity and specificity, and a balance between low false-alarm rate and high detection rate. Detection results demonstrated that the proposed method performs well in sensitivity, specificity, and precision. As an auxiliary tool, our detector can greatly improve the efficiency of clinical experts in inspecting HFO events during the diagnosis and treatment of epilepsy.
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Affiliation(s)
- Xiaochen Liu
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Lingli Hu
- School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, 310009, China
| | - Chenglin Xu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Shuai Xu
- School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, 310009, China
| | - Shuang Wang
- School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, 310009, China
| | - Zhong Chen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Jizhong Shen
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China.
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32
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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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33
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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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Ramon C, Holmes MD. Increased Phase Cone Turnover in 80-250 Hz Bands Occurs in the Epileptogenic Zone During Interictal Periods. Front Hum Neurosci 2021; 14:615744. [PMID: 33424570 PMCID: PMC7785702 DOI: 10.3389/fnhum.2020.615744] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 11/26/2020] [Indexed: 11/13/2022] Open
Abstract
We found that phase cone clustering patterns in EEG ripple bands demonstrate an increased turnover rate in epileptogenic zones compared to adjacent regions. We employed 256 channel EEG data collected in four adult subjects with refractory epilepsy. The analysis was performed in the 80-150 and 150-250 Hz ranges. Ictal onsets were documented with intracranial EEG recordings. Interictal scalp recordings, free of epileptiform patterns, of 240-s duration, were selected for analysis for each subject. The data was filtered, and the instantaneous phase was extracted after the Hilbert transformation. Spatiotemporal contour plots of the unwrapped instantaneous phase with 1.0 ms intervals were constructed using a montage layout of the 256 electrode positions. Stable phase cone patterns were selected based on criteria that the sign of spatial gradient did not change for a minimum of three consecutive time samples and the frame velocity was consistent with known propagation velocities of cortical axons. These plots exhibited increased dynamical formation and dissolution of phase cones in the ictal onset zones, compared to surrounding cortical regions, in all four patients. We believe that these findings represent markers of abnormally increased cortical excitability. They are potential tools that may assist in localizing the epileptogenic zone.
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Affiliation(s)
- Ceon Ramon
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, United States.,Regional Epilepsy Center, Harborview Medical Center, University of Washington, Seattle, WA, United States
| | - Mark D Holmes
- Regional Epilepsy Center, Harborview Medical Center, University of Washington, Seattle, WA, United States
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35
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Pisani F, Spagnoli C, Falsaperla R, Nagarajan L, Ramantani G. Seizures in the neonate: A review of etiologies and outcomes. Seizure 2021; 85:48-56. [PMID: 33418166 DOI: 10.1016/j.seizure.2020.12.023] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 12/24/2020] [Accepted: 12/26/2020] [Indexed: 12/21/2022] Open
Abstract
Neonatal seizures occur in their majority in close temporal relation to an acute brain injury or systemic insult, and are accordingly defined as acute symptomatic or provoked seizures. However less frequently, unprovoked seizures may also present in the neonatal period as secondary to structural brain abnormalities, thus corresponding to structural epilepsies, or to genetic conditions, thus corresponding to genetic epilepsies. Unprovoked neonatal seizures should be thus considered as the clinical manifestation of early onset structural or genetic epilepsies that often have the characteristics of early onset epileptic encephalopathies. In this review, we address the conundrum of neonatal seizures including acute symptomatic, remote symptomatic, provoked, and unprovoked seizures, evolving to post-neonatal epilepsies, and neonatal onset epilepsies. The different clinical scenarios involving neonatal seizures, each with their distinct post-neonatal evolution are presented. The structural and functional impact of neonatal seizures on brain development and the concept of secondary epileptogenesis, with or without a following latent period after the acute seizures, are addressed. Finally, we underline the need for an early differential diagnosis between an acute symptomatic seizure and an unprovoked seizure, since it is associated with fundamental differences in clinical evolution. These are crucial aspects for neonatal management, counselling and prognostication. In view of the above aspects, we provide an outlook on future strategies and potential lines of research in this field.
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Affiliation(s)
- Francesco Pisani
- Child Neuropsychiatry Unit, Medicine and Surgery Department, University of Parma, Italy
| | - Carlotta Spagnoli
- Child Neurology Unit, Department of Pediatrics, Azienda USL-IRCCS, Reggio Emilia, Italy
| | - Raffaele Falsaperla
- Neonatal Intensive Care Unit, University-Hospital Policlinico Vittorio Emanuele, Catania, Italy
| | - Lakshmi Nagarajan
- Children's Neuroscience Service, Department of Neurology, Perth Children's Hospital, Australia
| | - Georgia Ramantani
- Department of Neuropediatrics, University Children's Hospital Zurich, Switzerland.
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36
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Fan Y, Dong L, Liu X, Wang H, Liu Y. Recent advances in the noninvasive detection of high-frequency oscillations in the human brain. Rev Neurosci 2020; 32:305-321. [PMID: 33661582 DOI: 10.1515/revneuro-2020-0073] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 10/23/2020] [Indexed: 01/10/2023]
Abstract
In recent decades, a significant body of evidence based on invasive clinical research has showed that high-frequency oscillations (HFOs) are a promising biomarker for localization of the seizure onset zone (SOZ), and therefore, have the potential to improve postsurgical outcomes in patients with epilepsy. Emerging clinical literature has demonstrated that HFOs can be recorded noninvasively using methods such as scalp electroencephalography (EEG) and magnetoencephalography (MEG). Not only are HFOs considered to be a useful biomarker of the SOZ, they also have the potential to gauge disease severity, monitor treatment, and evaluate prognostic outcomes. In this article, we review recent clinical research on noninvasively detected HFOs in the human brain, with a focus on epilepsy. Noninvasively detected scalp HFOs have been investigated in various types of epilepsy. HFOs have also been studied noninvasively in other pathologic brain disorders, such as migraine and autism. Herein, we discuss the challenges reported in noninvasive HFO studies, including the scarcity of MEG and high-density EEG equipment in clinical settings, low signal-to-noise ratio, lack of clinically approved automated detection methods, and the difficulty in differentiating between physiologic and pathologic HFOs. Additional studies on noninvasive recording methods for HFOs are needed, especially prospective multicenter studies. Further research is fundamental, and extensive work is needed before HFOs can routinely be assessed in clinical settings; however, the future appears promising.
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Affiliation(s)
- Yuying Fan
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Liping Dong
- Library of China Medical University, Shenyang, China
| | - Xueyan Liu
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hua Wang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yunhui Liu
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, China
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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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