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Schaft EV, Sun D, van 't Klooster MA, van Blooijs D, Smits PL, Zweiphenning WJEM, Gosselaar PH, Ferrier CH, Zijlmans M. Spatial and temporal properties of intra-operatively recorded spikes and high frequency oscillations in focal cortical dysplasia. Clin Neurophysiol 2024; 162:210-218. [PMID: 38643614 DOI: 10.1016/j.clinph.2024.03.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 03/04/2024] [Accepted: 03/26/2024] [Indexed: 04/23/2024]
Abstract
OBJECTIVE Focal cortical dysplasias (FCD) are characterized by distinct interictal spike patterns and high frequency oscillations (HFOs; ripples: 80-250 Hz; fast ripples: 250-500 Hz) in the intra-operative electrocorticogram (ioECoG). We studied the temporal relation between intra-operative spikes and HFOs and their relation to resected tissue in people with FCD with a favorable outcome. METHODS We included patients who underwent ioECoG-tailored epilepsy surgery with pathology confirmed FCD and long-term Engel 1A outcome. Spikes and HFOs were automatically detected and visually checked in 1-minute pre-resection-ioECoG. Channels covering resected and non-resected tissue were compared using a logistic mixed model, assessing event numbers, co-occurrence ratios, and time-based properties. RESULTS We found pre-resection spikes, ripples in respectively 21 and 20 out of 22 patients. Channels covering resected tissue showed high numbers of spikes and HFOs, and high ratios of co-occurring events. Spikes, especially with ripples, have a relatively sharp rising flank with a long descending flank and early ripple onset over resected tissue. CONCLUSIONS A combined analysis of event numbers, ratios, and temporal relationships between spikes and HFOs may aid identifying epileptic tissue in epilepsy surgery. SIGNIFICANCE This study shows a promising method for clinically relevant properties of events, closely associated with FCD.
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Affiliation(s)
- Eline V Schaft
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Dongqing Sun
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Maryse A van 't Klooster
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Dorien van Blooijs
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Stichting Epilepsie Instellingen Nederland (SEIN), Hoofddorp, the Netherlands
| | - Paul L Smits
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Willemiek J E M Zweiphenning
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Peter H Gosselaar
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Cyrille H Ferrier
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Maeike Zijlmans
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands; Stichting Epilepsie Instellingen Nederland (SEIN), Hoofddorp, the Netherlands
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Padmasola GP, Friscourt F, Rigoni I, Vulliémoz S, Schaller K, Michel CM, Sheybani L, Quairiaux C. Involvement of the contralateral hippocampus in ictal-like but not interictal epileptic activities in the kainate mouse model of temporal lobe epilepsy. Epilepsia 2024. [PMID: 38758110 DOI: 10.1111/epi.17970] [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: 09/04/2023] [Revised: 03/19/2024] [Accepted: 03/19/2024] [Indexed: 05/18/2024]
Abstract
OBJECTIVE Animal and human studies have shown that the seizure-generating region is vastly dependent on distant neuronal hubs that can decrease duration and propagation of ongoing seizures. However, we still lack a comprehensive understanding of the impact of distant brain areas on specific interictal and ictal epileptic activities (e.g., isolated spikes, spike trains, seizures). Such knowledge is critically needed, because all kinds of epileptic activities are not equivalent in terms of clinical expression and impact on the progression of the disease. METHODS We used surface high-density electroencephalography and multisite intracortical recordings, combined with pharmacological silencing of specific brain regions in the well-known kainate mouse model of temporal lobe epilepsy. We tested the impact of selective regional silencing on the generation of epileptic activities within a continuum ranging from very transient to more sustained and long-lasting discharges reminiscent of seizures. RESULTS Silencing the contralateral hippocampus completely suppresses sustained ictal activities in the focus, as efficiently as silencing the focus itself, but whereas focus silencing abolishes all focus activities, contralateral silencing fails to control transient spikes. In parallel, we observed that sustained focus epileptiform discharges in the focus are preceded by contralateral firing and more strongly phase-locked to bihippocampal delta/theta oscillations than transient spiking activities, reinforcing the presumed dominant role of the contralateral hippocampus in promoting long-lasting, but not transient, epileptic activities. SIGNIFICANCE Altogether, our work provides suggestive evidence that the contralateral hippocampus is necessary for the interictal to ictal state transition and proposes that crosstalk between contralateral neuronal activity and ipsilateral delta/theta oscillation could be a candidate mechanism underlying the progression from short- to long-lasting epileptic activities.
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Affiliation(s)
- Guru Prasad Padmasola
- Functional Brain Mapping Lab, Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
| | - Fabien Friscourt
- Functional Brain Mapping Lab, Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
- Neurosurgery Clinic, Department of Clinical Neuroscience, University Hospital Geneva, Geneva, Switzerland
| | - Isotta Rigoni
- EEG and Epilepsy Unit, Department of Neuroscience, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland
| | - Serge Vulliémoz
- EEG and Epilepsy Unit, Department of Neuroscience, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland
| | - Karl Schaller
- Neurosurgery Clinic, Department of Clinical Neuroscience, University Hospital Geneva, Geneva, Switzerland
| | - Christoph M Michel
- Functional Brain Mapping Lab, Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
| | - Laurent Sheybani
- Neurology Clinic, Department of Clinical Neuroscience, University Hospital Geneva, Geneva, Switzerland
- Department of Clinical and Experimental Epilepsy, Queen's Square Institute of Neurology, London, UK
| | - Charles Quairiaux
- Functional Brain Mapping Lab, Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
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Shibata T, Tsuchiya H, Akiyama M, Akiyama T, Kobayashi K. Modulation index predicts the effect of ethosuximide on developmental and epileptic encephalopathy with spike-and-wave activation in sleep. Epilepsy Res 2024; 202:107359. [PMID: 38582072 DOI: 10.1016/j.eplepsyres.2024.107359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 03/27/2024] [Accepted: 04/03/2024] [Indexed: 04/08/2024]
Abstract
PURPOSE In developmental and epileptic encephalopathy with spike-and-wave activation in sleep (DEE-SWAS), the thalamocortical network is suggested to play an important role in the pathophysiology of the progression from focal epilepsy to DEE-SWAS. Ethosuximide (ESM) exerts effects by blocking T-type calcium channels in thalamic neurons. With the thalamocortical network in mind, we studied the prediction of ESM effectiveness in DEE-SWAS treatment using phase-amplitude coupling (PAC) analysis. METHODS We retrospectively enrolled children with DEE-SWAS who had an electroencephalogram (EEG) recorded between January 2009 and September 2022 and were prescribed ESM at Okayama University Hospital. Only patients whose EEG showed continuous spike-and-wave during sleep were included. We extracted 5-min non-rapid eye movement sleep stage N2 segments from EEG recorded before starting ESM. We calculated the modulation index (MI) as the measure of PAC in pair combination comprising one of two fast oscillation types (gamma, 40-80 Hz; ripples, 80-150 Hz) and one of five slow-wave bands (delta, 0.5-1, 1-2, 2-3, and 3-4 Hz; theta, 4-8 Hz), and compared it between ESM responders and non-responders. RESULTS We identified 20 children with a diagnosis of DEE-SWAS who took ESM. Fifteen were ESM responders. Regarding gamma oscillations, significant differences were seen only in MI with 0.5-1 Hz slow waves in the frontal pole and occipital regions. Regarding ripples, ESM responders had significantly higher MI in coupling with all slow waves in the frontal pole region, 0.5-1, 3-4, and 4-8 Hz slow waves in the frontal region, 3-4 Hz slow waves in the parietal region, 0.5-1, 2-3, 3-4, and 4-8 Hz slow waves in the occipital region, and 3-4 Hz slow waves in the anterior-temporal region. SIGNIFICANCE High MI in a wider area of the brain may represent the epileptic network mediated by the thalamus in DEE-SWAS and may be a predictor of ESM effectiveness.
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Affiliation(s)
- Takashi Shibata
- Department of Child Neurology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences and Okayama University Hospital, Okayama, Japan.
| | - Hiroki Tsuchiya
- Department of Child Neurology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences and Okayama University Hospital, Okayama, Japan
| | - Mari Akiyama
- Department of Child Neurology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences and Okayama University Hospital, Okayama, Japan
| | - Tomoyuki Akiyama
- Department of Child Neurology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences and Okayama University Hospital, Okayama, Japan
| | - Katsuhiro Kobayashi
- Department of Child Neurology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences and Okayama University Hospital, Okayama, Japan
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Ye H, Chen C, Weiss SA, Wang S. Pathological and Physiological High-frequency Oscillations on Electroencephalography in Patients with Epilepsy. Neurosci Bull 2024; 40:609-620. [PMID: 37999861 PMCID: PMC11127900 DOI: 10.1007/s12264-023-01150-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 09/28/2023] [Indexed: 11/25/2023] Open
Abstract
High-frequency oscillations (HFOs) encompass ripples (80 Hz-200 Hz) and fast ripples (200 Hz-600 Hz), serving as a promising biomarker for localizing the epileptogenic zone in epilepsy. Spontaneous fast ripples are always pathological, while ripples may be physiological or pathological. Distinguishing physiological from pathological ripples is important not only for designating epileptogenic brain regions, but also for investigations that study ripples in the context of memory encoding, consolidation, and recall in patients with epilepsy. Many studies have sought to identify distinguishing features between pathological and physiological ripples over the past two decades. Physiological and pathological ripples differ with respect to their spatial location, cellular mechanisms, morphology, and coupling with background electroencephalographic activity. Retrospective studies have demonstrated that differentiating between pathological and physiological ripples can improve surgical outcome prediction. In this review, we summarize the characteristics, differences, and applications of pathological and physiological HFOs and discuss strategies for their clinical translation.
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Affiliation(s)
- Hongyi Ye
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China
| | - Cong Chen
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China
| | - Shennan A Weiss
- Department of Neurology, State University of New York Downstate, Brooklyn, NY, 11203, USA
- Department of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, NY, 11203, USA
- Department of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY, 11203, USA
| | - Shuang Wang
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China.
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Rigoni I, Padmasola GP, Sheybani L, Schaller K, Quairiaux C, Vulliemoz S. Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity. Neurobiol Dis 2024; 190:106382. [PMID: 38114050 DOI: 10.1016/j.nbd.2023.106382] [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: 10/11/2023] [Revised: 11/27/2023] [Accepted: 12/13/2023] [Indexed: 12/21/2023] Open
Abstract
Studying the development of brain network disruptions in epilepsy is challenged by the paucity of data before epilepsy onset. Here, we used the unilateral, kainate mouse model of hippocampal epilepsy to investigate brain network changes before and after epilepsy onset and their stability across time. Using 32 epicranial electrodes distributed over the mouse hemispheres, we analyzed EEG epochs free from epileptic activity in 15 animals before and 28 days after hippocampal injection (group 1) and in 20 animals on two consecutive days (d28 and d29, group 2). Statistical dependencies between electrodes were characterized with the debiased-weighted phase lag index. We analyzed: a) graph metric changes from baseline to chronic stage (d28) in group 1; b) their reliability across d28 and d29, in group 2; c) their correlation with epileptic activity (EA: seizure, spike and fast-ripple rates), averaged over d28 and d29, in group 2. During the chronic stage, intra-hemispheric connections of the non-injected hemisphere strengthened, yielding an asymmetrical network in low (4-8 Hz) and high theta (8-12 Hz) bands. The contralateral hemisphere also became more integrated and segregated within the high theta band. Both network topology and EEG markers of EA were stable over consecutive days but not correlated with each other. Altogether, we show reproducible large-scale network modifications after the development of focal epilepsy. These modifications are mostly specific to the non-injected hemisphere. The absence of correlation with epileptic activity does not allow to specifically ascribe these network changes to mechanisms supporting EA or rather compensatory inhibition but supports the notion that epilepsy extends beyond the sole repetition of EA and impacts network that might not be involved in EA generation.
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Affiliation(s)
- Isotta Rigoni
- EEG and Epilepsy unit, Department of Neuroscience, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland.
| | - Guru Prasad Padmasola
- Department of Basic Neuroscience, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - Laurent Sheybani
- EEG and Epilepsy unit, Department of Neuroscience, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - Karl Schaller
- Department of Neurosurgery, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Charles Quairiaux
- Department of Basic Neuroscience, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Serge Vulliemoz
- EEG and Epilepsy unit, Department of Neuroscience, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland
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Guo F, Cui Y, Li A, Liu M, Jian Z, Chen K, Yao D, Guo D, Xia Y. Differential patterns of very high-frequency oscillations in two seizure types of the pilocarpine-induced TLE model. Brain Res Bull 2023; 204:110805. [PMID: 37925081 DOI: 10.1016/j.brainresbull.2023.110805] [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: 08/20/2023] [Revised: 10/08/2023] [Accepted: 10/30/2023] [Indexed: 11/06/2023]
Abstract
AIMS Very high-frequency oscillations (VHFOs, >500 Hz) are considered a highly sensitive biomarker of seizures. We hypothesized that VHFOs may exhibit specificity towards hypersynchronous (HYP) seizures and low-voltage fast (LVF) seizures in temporal lobe epilepsy (TLE). METHODS Local field potentials were recorded from the hippocampal network in TLE mice induced by pilocarpine. Subsequently, we analyzed the VHFO features, including their temporal-frequency characteristics and VHFO/theta coupling, during three states: baseline, preictal, and postictal for both HYP- and LVF-seizure groups. RESULTS Significant changes in most of the VHFO features were observed during the preictal state in both seizure groups. In the postictal state, VHFO features in the HYP-seizure group exhibited inverse alterations and appeared to align with those observed during baseline conditions. However, such phenomena were not observed after TLE seizures in the LVF-seizure group. CONCLUSION Our findings highlight distinct patterns of VHFO feature changes across different states of HYP seizures and LVF seizures. These results suggest that VHFOs could serve as indicative biomarkers for seizure alterations specifically associated with HYP-seizure states.
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Affiliation(s)
- Fengru Guo
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yan Cui
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Airui Li
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Mingqi Liu
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Zhaoxin Jian
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Ke Chen
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Dezhong Yao
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Daqing Guo
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yang Xia
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China.
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Monsoor T, Zhang Y, Daida A, Oana S, Lu Q, Hussain SA, Fallah A, Sankar R, Staba RJ, Speier W, Roychowdhury V, Nariai H. Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy. Clin Neurophysiol 2023; 154:129-140. [PMID: 37603979 PMCID: PMC10861270 DOI: 10.1016/j.clinph.2023.07.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 06/30/2023] [Accepted: 07/26/2023] [Indexed: 08/23/2023]
Abstract
OBJECTIVE This study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in epilepsy surgery. METHODS We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for spike association and time-frequency plot characteristics. A deep learning (DL)-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method. RESULTS The MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors had the highest spike association rate. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification. CONCLUSIONS HFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs. SIGNIFICANCE Enhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes.
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Affiliation(s)
- Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Atsuro Daida
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Shingo Oana
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Qiujing Lu
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Shaun A Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA; The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Richard J Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - William Speier
- Department of Bioengineering, University of California, Los Angeles, CA, USA; Department of Radiological Sciences, University of California, Los Angeles, CA, USA
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA; The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA.
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Li Z, Zhang H, Niu S, Xing Y. Localizing epileptogenic zones with high-frequency oscillations and directed connectivity. Seizure 2023; 111:9-16. [PMID: 37487273 DOI: 10.1016/j.seizure.2023.07.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 07/26/2023] Open
Abstract
PURPOSE Precise localization of the epileptogenic zone (EZ) is essential for epilepsy surgery. Existing methods often fail to detect slow onset patterns or similar neural activities presented in the recorded signals. To address this issue, we propose a new measure to quantify epileptogenicity, i.e., the connectivity high-frequency epileptogenicity index (cHFEI). METHODS The cHFEI method combines directed connectivity and high-frequency oscillations (HFOs) to measure the epileptogenicity of regions involved in a brain network. By applying this method to stereoelectroencephalography (SEEG) recordings of 49 seizures in 20 patients, we calculated the accuracy, sensitivity, and precision with a visually identified epileptogenic zone as a reference. The performance was evaluated by the confusion matrix and the area under the receiver operating characteristic (ROC) curve. RESULTS Epileptic network estimation based on cHFEI successfully distinguished brain regions involved in seizure onset from the propagation network. Moreover, cHFEI outperformed other existing detection methods in the estimation of EZs in all patients, with an average area under the ROC curve of 0.88 and an accuracy of 0.85. CONCLUSIONS cHFEI can characterize EZ in a robust manner despite various seizure onset patterns and has potential application in epilepsy therapy.
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Affiliation(s)
- Zhaohui Li
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of information transmission and signal processing, Yanshan University, Qinhuangdao 066004, China.
| | - Hao Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Shipeng Niu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Yanyu Xing
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
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9
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Fabo D, Bokodi V, Szabó JP, Tóth E, Salami P, Keller CJ, Hajnal B, Thesen T, Devinsky O, Doyle W, Mehta A, Madsen J, Eskandar E, Erőss L, Ulbert I, Halgren E, Cash SS. The role of superficial and deep layers in the generation of high frequency oscillations and interictal epileptiform discharges in the human cortex. Sci Rep 2023; 13:9620. [PMID: 37316509 DOI: 10.1038/s41598-022-22497-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023] Open
Abstract
Describing intracortical laminar organization of interictal epileptiform discharges (IED) and high frequency oscillations (HFOs), also known as ripples. Defining the frequency limits of slow and fast ripples. We recorded potential gradients with laminar multielectrode arrays (LME) for current source density (CSD) and multi-unit activity (MUA) analysis of interictal epileptiform discharges IEDs and HFOs in the neocortex and mesial temporal lobe of focal epilepsy patients. IEDs were observed in 20/29, while ripples only in 9/29 patients. Ripples were all detected within the seizure onset zone (SOZ). Compared to hippocampal HFOs, neocortical ripples proved to be longer, lower in frequency and amplitude, and presented non-uniform cycles. A subset of ripples (≈ 50%) co-occurred with IEDs, while IEDs were shown to contain variable high-frequency activity, even below HFO detection threshold. The limit between slow and fast ripples was defined at 150 Hz, while IEDs' high frequency components form clusters separated at 185 Hz. CSD analysis of IEDs and ripples revealed an alternating sink-source pair in the supragranular cortical layers, although fast ripple CSD appeared lower and engaged a wider cortical domain than slow ripples MUA analysis suggested a possible role of infragranularly located neural populations in ripple and IED generation. Laminar distribution of peak frequencies derived from HFOs and IEDs, respectively, showed that supragranular layers were dominated by slower (< 150 Hz) components. Our findings suggest that cortical slow ripples are generated primarily in upper layers while fast ripples and associated MUA in deeper layers. The dissociation of macro- and microdomains suggests that microelectrode recordings may be more selective for SOZ-linked ripples. We found a complex interplay between neural activity in the neocortical laminae during ripple and IED formation. We observed a potential leading role of cortical neurons in deeper layers, suggesting a refined utilization of LMEs in SOZ localization.
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Affiliation(s)
- Daniel Fabo
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary.
| | - Virag Bokodi
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- Roska Tamás Doctoral School of Sciences and Technologies, Budapest, Hungary
| | - Johanna-Petra Szabó
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Budapest, Hungary
| | - Emilia Tóth
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- Department of Neurology, University of Texas, McGovern Medical School, Houston, TX, USA
| | - Pariya Salami
- Epilepsy Division, Department of Neurology, Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Corey J Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Boglárka Hajnal
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Budapest, Hungary
| | - Thomas Thesen
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, USA
- Department of Biomedical Sciences, College of Medicine, University of Houston, Houston, TX, USA
| | - Orrin Devinsky
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, USA
| | - Werner Doyle
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, USA
| | - Ashesh Mehta
- Department of Neurosurgery, Zucker School of Medicine at Hofstra/Northwell and Feinstein Institute for Medical Research, Manhasset, NY, USA
| | | | - Emad Eskandar
- Massachusetts General Hospital Neurosurgery Research, Boston, MA, USA
| | - Lorand Erőss
- Department of Functional Neurosurgery, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
| | - István Ulbert
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- Institute of Psychology, Eötvös Loránd Research Network, Budapest, Hungary
| | - Eric Halgren
- Department of Radiology, Neurosciences and Psychiatry, University of California, San Diego, San Diego, CA, USA
| | - Sydney S Cash
- Epilepsy Division, Department of Neurology, Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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10
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Ricci L, Tamilia E, Mercier M, Pepi C, Carfì-Pavia G, De Benedictis A, Assenza G, Di Lazzaro V, Vigevano F, Specchio N, de Palma L. Phase-amplitude coupling between low- and high-frequency activities as preoperative biomarker of focal cortical dysplasia subtypes. Clin Neurophysiol 2023; 150:40-48. [PMID: 37002979 DOI: 10.1016/j.clinph.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 02/08/2023] [Accepted: 03/02/2023] [Indexed: 04/01/2023]
Abstract
OBJECTIVE To evaluate whether ictal phase-amplitude coupling (PAC) between high-frequency activity and low-frequency activity could be used as a preoperative biomarker of Focal Cortical Dysplasia (FCD) subtypes. We hypothesize that FCD seizures present unique PAC characteristics that may be linked to their specific histopathological features. METHODS We retrospectively examined 12 children with FCD and refractory epilepsy who underwent successful epilepsy surgery. We identified ictal onsets recorded with stereo-EEG. We estimated the strength of PAC between low-frequencies and high-frequencies for each seizure by means of modulation index. Generalized mixed effect models and receiver operating characteristic (ROC) curve analysis were used to test the association between ictal PAC and FCD subtypes. RESULTS Ictal PAC was significantly higher in patients with FCD type II compared to type I, only on SOZ-electrodes (p < 0.005). No differences in ictal PAC were found on non-SOZ electrodes. Pre-ictal PAC registered on SOZ electrodes predicted FCD histopathology with a classification accuracy > 0.9 (p < 0.05). CONCLUSIONS The correlations between histopathology and neurophysiology provide evidence for the contribution of ictal PAC as a preoperative biomarker of FCD subtypes. SIGNIFICANCE Developed into a proper clinical application, such a technique may help improve clinical management and facilitate the prediction of surgical outcome in patients with FCD undergoing stereo-EEG monitoring.
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11
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Berry B, Varatharajah Y, Kremen V, Kucewicz M, Guragain H, Brinkmann B, Duque J, Carvalho DZ, Stead M, Sieck G, Worrell G. Phase-Amplitude Coupling Localizes Pathologic Brain with Aid of Behavioral Staging in Sleep. Life (Basel) 2023; 13:life13051186. [PMID: 37240831 DOI: 10.3390/life13051186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/28/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
Low frequency brain rhythms facilitate communication across large spatial regions in the brain and high frequency rhythms are thought to signify local processing among nearby assemblies. A heavily investigated mode by which these low frequency and high frequency phenomenon interact is phase-amplitude coupling (PAC). This phenomenon has recently shown promise as a novel electrophysiologic biomarker, in a number of neurologic diseases including human epilepsy. In 17 medically refractory epilepsy patients undergoing phase-2 monitoring for the evaluation of surgical resection and in whom temporal depth electrodes were implanted, we investigated the electrophysiologic relationships of PAC in epileptogenic (seizure onset zone or SOZ) and non-epileptogenic tissue (non-SOZ). That this biomarker can differentiate seizure onset zone from non-seizure onset zone has been established with ictal and pre-ictal data, but less so with interictal data. Here we show that this biomarker can differentiate SOZ from non-SOZ interictally and is also a function of interictal epileptiform discharges. We also show a differential level of PAC in slow-wave-sleep relative to NREM1-2 and awake states. Lastly, we show AUROC evaluation of the localization of SOZ is optimal when utilizing beta or alpha phase onto high-gamma or ripple band. The results suggest an elevated PAC may reflect an electrophysiology-based biomarker for abnormal/epileptogenic brain regions.
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Affiliation(s)
- Brent Berry
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Yogatheesan Varatharajah
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Biomedical and Electrical/Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Vaclav Kremen
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
- Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University, 160 00 Prague, Czech Republic
| | - Michal Kucewicz
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Hari Guragain
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Benjamin Brinkmann
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Juliano Duque
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Computing and Mathematics, FFCLRP, University of São Paulo, Ribeirão Preto 14040-901, SP, Brazil
| | | | - Matt Stead
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Gary Sieck
- Department of Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Gregory Worrell
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
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12
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Monsoor T, Zhang Y, Daida A, Oana S, Lu Q, Hussain SA, Fallah A, Sankar R, Staba RJ, Speier W, Roychowdhury V, Nariai H. Optimizing Detection and Deep Learning-based Classification of Pathological High-Frequency Oscillations in Epilepsy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.13.23288435. [PMID: 37131743 PMCID: PMC10153337 DOI: 10.1101/2023.04.13.23288435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Objective This study aimed to explore sensitive detection methods and deep learning (DL)-based classification for pathological high-frequency oscillations (HFOs). Methods We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent resection after chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for pathological features based on spike association and time-frequency plot characteristics. A DL-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method. Results The MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors exhibited the most pathological features. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification. Conclusions HFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs. Significance Enhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes. HIGHLIGHTS HFOs detected by the MNI detector showed different traits and higher pathological bias than those detected by the STE detectorHFOs detected by both MNI and STE detectors (the Intersection HFOs) were deemed the most pathologicalA deep learning-based classification was able to distill pathological HFOs, regard-less of the initial HFO detection methods.
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13
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Lévesque M, Wang S, Macey-Dare ADB, Salami P, Avoli M. Evolution of interictal activity in models of mesial temporal lobe epilepsy. Neurobiol Dis 2023; 180:106065. [PMID: 36907521 DOI: 10.1016/j.nbd.2023.106065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/22/2023] [Accepted: 03/02/2023] [Indexed: 03/12/2023] Open
Abstract
Interictal activity and seizures are the hallmarks of focal epileptic disorders (which include mesial temporal lobe epilepsy, MTLE) in humans and in animal models. Interictal activity, which is recorded with cortical and intracerebral EEG recordings, comprises spikes, sharp waves and high-frequency oscillations, and has been used in clinical practice to identify the epileptic zone. However, its relation with seizures remains debated. Moreover, it is unclear whether specific EEG changes in interictal activity occur during the time preceding the appearance of spontaneous seizures. This period, which is termed "latent", has been studied in rodent models of MTLE in which spontaneous seizures start to occur following an initial insult (most often a status epilepticus induced by convulsive drugs such as kainic acid or pilocarpine) and may mirror epileptogenesis, i.e., the process leading the brain to develop an enduring predisposition to seizure generation. Here, we will address this topic by reviewing experimental studies performed in MTLE models. Specifically, we will review data highlighting the dynamic changes in interictal spiking activity and high-frequency oscillations occurring during the latent period, and how optogenetic stimulation of specific cell populations can modulate them in the pilocarpine model. These findings indicate that interictal activity: (i) is heterogeneous in its EEG patterns and thus, presumably, in its underlying neuronal mechanisms; and (ii) can pinpoint to the epileptogenic processes occurring in focal epileptic disorders in animal models and, perhaps, in epileptic patients.
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Affiliation(s)
- Maxime Lévesque
- Montreal Neurological Institute-Hospital and Departments of Neurology & Neurosurgery, McGill University, 3801 Rue University, Montreal, H3A 2B4, QC, Canada.
| | - Siyan Wang
- Montreal Neurological Institute-Hospital and Departments of Neurology & Neurosurgery, McGill University, 3801 Rue University, Montreal, H3A 2B4, QC, Canada
| | - Anežka D B Macey-Dare
- Montreal Neurological Institute-Hospital and Departments of Neurology & Neurosurgery, McGill University, 3801 Rue University, Montreal, H3A 2B4, QC, Canada; Department of Pharmacology, University of Oxford, Mansfield Road, Oxford OX1 3QT, UK
| | - Pariya Salami
- Montreal Neurological Institute-Hospital and Departments of Neurology & Neurosurgery, McGill University, 3801 Rue University, Montreal, H3A 2B4, QC, Canada; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, 55 Fruit St., Boston, MA 02114, USA
| | - Massimo Avoli
- Montreal Neurological Institute-Hospital and Departments of Neurology & Neurosurgery, McGill University, 3801 Rue University, Montreal, H3A 2B4, QC, Canada; Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, H3G 1Y6, QC, Canada
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14
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Epileptic seizure focus detection from interictal electroencephalogram: a survey. Cogn Neurodyn 2023; 17:1-23. [PMID: 36704629 PMCID: PMC9871145 DOI: 10.1007/s11571-022-09816-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/15/2022] [Accepted: 04/21/2022] [Indexed: 01/29/2023] Open
Abstract
Electroencephalogram (EEG) is one of most effective clinical diagnosis modalities for the localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG signals in an automated manner to identify the epileptic seizure focus. To develop AI system for identifying the epileptic focus, there are many recently-published AI solutions based on biomarkers or statistic features that utilize interictal EEGs. In this review, we survey these solutions and find that they can be divided into three main categories: (i) those that use of biomarkers in EEG signals, including high-frequency oscillation, phase-amplitude coupling, and interictal epileptiform discharges, (ii) others that utilize feature-extraction methods, and (iii) solutions based upon neural networks (an end-to-end approach). We provide a detailed description of seizure focus with clinical diagnosis methods, a summary of the public datasets that seek to reduce the research gap in epilepsy, recent novel performance evaluation criteria used to evaluate the AI systems, and guidelines on when and how to use them. This review also suggests a number of future research challenges that must be overcome in order to design more efficient computer-aided solutions to epilepsy focus detection.
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15
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Zhang Y, Chung H, Ngo JP, Monsoor T, Hussain SA, Matsumoto JH, Walshaw PD, Fallah A, Sim MS, Asano E, Sankar R, Staba RJ, Engel J, Speier W, Roychowdhury V, Nariai H. Characterizing physiological high-frequency oscillations using deep learning. J Neural Eng 2022; 19:10.1088/1741-2552/aca4fa. [PMID: 36541546 PMCID: PMC10364130 DOI: 10.1088/1741-2552/aca4fa] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 11/22/2022] [Indexed: 11/23/2022]
Abstract
Objective.Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. However, HFOs can also be recorded in the healthy brain regions, which complicates the interpretation of HFOs. The present study aimed to characterize salient features of physiological HFOs using deep learning (DL).Approach.We studied children with neocortical epilepsy who underwent intracranial strip/grid evaluation. Time-series EEG data were transformed into DL training inputs. The eloquent cortex (EC) was defined by functional cortical mapping and used as a DL label. Morphological characteristics of HFOs obtained from EC (ecHFOs) were distilled and interpreted through a novel weakly supervised DL model.Main results.A total of 63 379 interictal intracranially-recorded HFOs from 18 children were analyzed. The ecHFOs had lower amplitude throughout the 80-500 Hz frequency band around the HFO onset and also had a lower signal amplitude in the low frequency band throughout a one-second time window than non-ecHFOs, resembling a bell-shaped template in the time-frequency map. A minority of ecHFOs were HFOs with spikes (22.9%). Such morphological characteristics were confirmed to influence DL model prediction via perturbation analyses. Using the resection ratio (removed HFOs/detected HFOs) of non-ecHFOs, the prediction of postoperative seizure outcomes improved compared to using uncorrected HFOs (area under the ROC curve of 0.82, increased from 0.76).Significance.We characterized salient features of physiological HFOs using a DL algorithm. Our results suggested that this DL-based HFO classification, once trained, might help separate physiological from pathological HFOs, and efficiently guide surgical resection using HFOs.
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Affiliation(s)
- Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Hoyoung Chung
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Jacquline P. Ngo
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Shaun A. Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Joyce H. Matsumoto
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Patricia D. Walshaw
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Myung Shin Sim
- Department of Medicine, Statistics Core, University of California, Los Angeles, CA, USA
| | - Eishi Asano
- Department of Pediatrics and Neurology, Children’s Hospital of Michigan, Wayne State University School of Medicine, Detroit, MI, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Richard J. Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Jerome Engel
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Neurobiology, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
- The Brain Research Institute, University of California, Los Angeles, CA, USA
| | - William Speier
- Department of Radiological Sciences, University of California, Los Angeles, CA, USA
- Department of Bioengineering, University of California, Los Angeles, CA, USA
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
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16
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Weiss SA, Sheybani L, Seenarine N, Fried I, Wu C, Sharan A, Engel J, Sperling MR, Nir Y, Staba RJ. Delta oscillation coupled propagating fast ripples precede epileptiform discharges in patients with focal epilepsy. Neurobiol Dis 2022; 175:105928. [DOI: 10.1016/j.nbd.2022.105928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/13/2022] [Accepted: 11/15/2022] [Indexed: 11/18/2022] Open
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17
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Wan L, Zhang CT, Zhu G, Chen J, Shi XY, Wang J, Zou LP, Zhang B, Shi WB, Yeh CH, Yang G. Integration of multiscale entropy and BASED scale of electroencephalography after adrenocorticotropic hormone therapy predict relapse of infantile spasms. World J Pediatr 2022; 18:761-770. [PMID: 35906344 DOI: 10.1007/s12519-022-00583-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 06/12/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Even though adrenocorticotropic hormone (ACTH) demonstrated powerful efficacy in the initially successful treatment of infantile spasms (IS), nearly half of patients have experienced a relapse. We sought to investigate whether features of electroencephalogram (EEG) predict relapse in those IS patients without structural brain abnormalities. METHODS We retrospectively reviewed data from children with IS who achieved initial response after ACTH treatment, along with EEG recorded within the last two days of treatment. The recurrence of epileptic spasms following treatment was tracked for 12 months. Subjects were categorized as either non-relapse or relapse groups. General clinical and EEG recordings were collected, burden of amplitudes and epileptiform discharges (BASED) score and multiscale entropy (MSE) were carefully explored for cross-group comparisons. RESULTS Forty-one patients were enrolled in the study, of which 26 (63.4%) experienced a relapse. The BASED score was significantly higher in the relapse group. MSE in the non-relapse group was significantly lower than the relapse group in the γ band but higher in the lower frequency range (δ, θ, α). Sensitivity and specificity were 85.71% and 92.31%, respectively, when combining MSE in the δ/γ frequency of the occipital region, plus BASED score were used to distinguish relapse from non-relapse groups. CONCLUSIONS BASED score and MSE of EEG after ACTH treatment could be used to predict relapse for IS patients without brain structural abnormalities. Patients with BASED score ≥ 3, MSE increased in higher frequency, and decreased in lower frequency had a high risk of relapse.
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Affiliation(s)
- Lin Wan
- Senior Department of Pediatrics, Chinese PLA General Hospital, Beijing, 100000, China.,Department of Pediatrics, the First Medical Centre, Chinese PLA General Hospital, Beijing, China.,Medical School of Chinese People's Liberation Army, Beijing, China
| | - Chu-Ting Zhang
- School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China
| | - Gang Zhu
- Senior Department of Pediatrics, Chinese PLA General Hospital, Beijing, 100000, China.,Department of Pediatrics, the First Medical Centre, Chinese PLA General Hospital, Beijing, China.,Medical School of Chinese People's Liberation Army, Beijing, China
| | - Jian Chen
- Senior Department of Pediatrics, Chinese PLA General Hospital, Beijing, 100000, China.,Department of Pediatrics, the First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Xiu-Yu Shi
- Senior Department of Pediatrics, Chinese PLA General Hospital, Beijing, 100000, China.,Department of Pediatrics, the First Medical Centre, Chinese PLA General Hospital, Beijing, China.,Medical School of Chinese People's Liberation Army, Beijing, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Jing Wang
- Senior Department of Pediatrics, Chinese PLA General Hospital, Beijing, 100000, China.,Department of Pediatrics, the First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Li-Ping Zou
- Senior Department of Pediatrics, Chinese PLA General Hospital, Beijing, 100000, China.,Department of Pediatrics, the First Medical Centre, Chinese PLA General Hospital, Beijing, China.,Medical School of Chinese People's Liberation Army, Beijing, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Bo Zhang
- Department of Neurology and ICCTR Biostatistics and Research Design Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Wen-Bin Shi
- School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China
| | - Chien-Hung Yeh
- School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
| | - Guang Yang
- Senior Department of Pediatrics, Chinese PLA General Hospital, Beijing, 100000, China. .,Department of Pediatrics, the First Medical Centre, Chinese PLA General Hospital, Beijing, China. .,Medical School of Chinese People's Liberation Army, Beijing, China. .,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
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18
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Liu AA, Henin S, Abbaspoor S, Bragin A, Buffalo EA, Farrell JS, Foster DJ, Frank LM, Gedankien T, Gotman J, Guidera JA, Hoffman KL, Jacobs J, Kahana MJ, Li L, Liao Z, Lin JJ, Losonczy A, Malach R, van der Meer MA, McClain K, McNaughton BL, Norman Y, Navas-Olive A, de la Prida LM, Rueckemann JW, Sakon JJ, Skelin I, Soltesz I, Staresina BP, Weiss SA, Wilson MA, Zaghloul KA, Zugaro M, Buzsáki G. A consensus statement on detection of hippocampal sharp wave ripples and differentiation from other fast oscillations. Nat Commun 2022; 13:6000. [PMID: 36224194 PMCID: PMC9556539 DOI: 10.1038/s41467-022-33536-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 09/21/2022] [Indexed: 02/05/2023] Open
Abstract
Decades of rodent research have established the role of hippocampal sharp wave ripples (SPW-Rs) in consolidating and guiding experience. More recently, intracranial recordings in humans have suggested their role in episodic and semantic memory. Yet, common standards for recording, detection, and reporting do not exist. Here, we outline the methodological challenges involved in detecting ripple events and offer practical recommendations to improve separation from other high-frequency oscillations. We argue that shared experimental, detection, and reporting standards will provide a solid foundation for future translational discovery.
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Affiliation(s)
- Anli A Liu
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
- Neuroscience Institute, NYU Langone Medical Center, New York, NY, USA
| | - Simon Henin
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
| | - Saman Abbaspoor
- Department of Psychology, Vanderbilt University, Nashville, TN, USA
| | - Anatol Bragin
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Elizabeth A Buffalo
- Department of Physiology and Biophysics, Washington National Primate Center, University of Washington, Seattle, WA, USA
| | - Jordan S Farrell
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - David J Foster
- Department of Psychology and Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Loren M Frank
- Kavli Institute for Fundamental Neuroscience, Center for Integrative Neuroscience and Department of Physiology, University of California San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Tamara Gedankien
- Department of Biomedical Engineering, Department of Neurological Surgery, Columbia University, New York, NY, USA
| | - Jean Gotman
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Jennifer A Guidera
- Kavli Institute for Fundamental Neuroscience, Center for Integrative Neuroscience and Department of Physiology, University of California San Francisco, San Francisco, CA, USA
- Medical Scientist Training Program, Department of Bioengineering, University of California, San Francisco, San Francisco, CA, USA
| | - Kari L Hoffman
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
| | - Joshua Jacobs
- Department of Biomedical Engineering, Department of Neurological Surgery, Columbia University, New York, NY, USA
| | - Michael J Kahana
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Lin Li
- Department of Biomedical Engineering, University of North Texas, Denton, TX, USA
| | - Zhenrui Liao
- Department of Neuroscience, Columbia University, New York, NY, USA
| | - Jack J Lin
- Department of Neurology, Center for Mind and Brain, University of California Davis, Oakland, CA, USA
| | - Attila Losonczy
- Department of Neuroscience, Columbia University, New York, NY, USA
| | - Rafael Malach
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
| | | | - Kathryn McClain
- Neuroscience Institute, NYU Langone Medical Center, New York, NY, USA
| | - Bruce L McNaughton
- The Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, Canada
| | - Yitzhak Norman
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
| | | | | | - Jon W Rueckemann
- Department of Physiology and Biophysics, Washington National Primate Center, University of Washington, Seattle, WA, USA
| | - John J Sakon
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ivan Skelin
- Department of Neurology, Center for Mind and Brain, University of California Davis, Oakland, CA, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Bernhard P Staresina
- Department of Experimental Psychology, Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Shennan A Weiss
- Brookdale Hospital Medical Center, SUNY Downstate Medical Center, Brooklyn, NY, USA
| | - Matthew A Wilson
- Department of Brain and Cognitive Sciences and Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, MD, USA
| | - Michaël Zugaro
- Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, Université PSL, Paris, France
| | - György Buzsáki
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA.
- Neuroscience Institute, NYU Langone Medical Center, New York, NY, USA.
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19
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Zhou Y, You J, Kumar U, Weiss SA, Bragin A, Engel J, Papadelis C, Li L. An approach for reliably identifying high-frequency oscillations and reducing false-positive detections. Epilepsia Open 2022; 7:674-686. [PMID: 36053171 PMCID: PMC9712470 DOI: 10.1002/epi4.12647] [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: 03/04/2022] [Accepted: 08/31/2022] [Indexed: 12/30/2022] Open
Abstract
OBJECTIVE Aiming to improve the feasibility and reliability of using high-frequency oscillations (HFOs) for translational studies of epilepsy, we present a pipeline with features specifically designed to reject false positives for HFOs to improve the automatic HFO detector. METHODS We presented an integrated, multi-layered procedure capable of automatically rejecting HFOs from a variety of common false positives, such as motion, background signals, and sharp transients. This method utilizes a time-frequency contour approach that embeds three different layers including peak constraints, power thresholds, and morphological identification to discard false positives. Four experts were involved in rating detected HFO events that were randomly selected from different posttraumatic epilepsy (PTE) animals for a comprehensive evaluation. RESULTS The algorithm was run on 768-h recordings of intracranial electrodes in 48 PTE animals. A total of 453 917 HFOs were identified by initial HFO detection, of which 450 917 were implemented for HFO refinement and 203 531 events were retained. Random sampling was used to evaluate the performance of the detector. The HFO detection yielded an overall accuracy of 0.95 ± 0.03 , with precision, recall, and F1 scores of 0.92 ± 0.05 , 0.99 ± 0.01 , and 0.94 ± 0.03 , respectively. For the HFO classification, our algorithm obtained an accuracy of 0.97 ± 0.02 . For the inter-rater reliability of algorithm evaluation, the agreement among four experts was 0.94 ± 0.03 for HFO detection and 0.85 ± 0.04 for HFO classification. SIGNIFICANCE Our approach shows that a segregated pipeline design with a focus on false-positive rejection can improve the detection efficiency and provide reliable results. This pipeline does not require customization and uses fixed parameters, making it highly feasible and translatable for basic and clinical applications of epilepsy.
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Affiliation(s)
- Yufeng Zhou
- Department of Biomedical EngineeringUniversity of North TexasTexasUSA
| | - Jing You
- Department of Biomedical EngineeringUniversity of North TexasTexasUSA
| | - Udaya Kumar
- Department of NeurologyUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Shennan A Weiss
- Departments of Neurology, Department of Physiology and PharmacologyState University of New York DownstateBrooklynNew YorkUSA,Department of NeurologyNew York City Health + Hospitals/Kings CountyBrooklynNew YorkUSA
| | - Anatol Bragin
- Department of NeurologyUniversity of California Los AngelesLos AngelesCaliforniaUSA,Brain Research InstituteUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Jerome Engel
- Department of NeurologyUniversity of California Los AngelesLos AngelesCaliforniaUSA,Brain Research InstituteUniversity of CaliforniaLos AngelesCaliforniaUSA,Department of NeurobiologyDavid Geffen School of Medicine at UCLALos AngelesCaliforniaUSA,Department of Psychiatry and Biobehavioral SciencesDavid Geffen School of Medicine at UCLACaliforniaUSA
| | - Christos Papadelis
- Jane and John Justin Neurosciences CenterCook Children's Health Care SystemFort WorthTexasUSA,School of MedicineTexas Christian UniversityFort WorthTexasUSA,Department of BioengineeringUniversity of Texas at ArlingtonArlingtonTexasUSA
| | - Lin Li
- Department of Biomedical EngineeringUniversity of North TexasTexasUSA,Department of NeurologyUniversity of California Los AngelesLos AngelesCaliforniaUSA
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20
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Tamrakar S, Iimura Y, Suzuki H, Mitsuhashi T, Ueda T, Nishioka K, Karagiozov K, Nakajima M, Miao Y, Tanaka T, Sugano H. Higher phase-amplitude coupling between ripple and slow oscillations indicates the distribution of epileptogenicity in temporal lobe epilepsy with hippocampal sclerosis. Seizure 2022; 100:1-7. [DOI: 10.1016/j.seizure.2022.05.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 05/27/2022] [Accepted: 05/28/2022] [Indexed: 10/18/2022] Open
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21
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Sonoda M, Rothermel R, Carlson A, Jeong JW, Lee MH, Hayashi T, Luat AF, Sood S, Asano E. Naming-related spectral responses predict neuropsychological outcome after epilepsy surgery. Brain 2022; 145:517-530. [PMID: 35313351 PMCID: PMC9014727 DOI: 10.1093/brain/awab318] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 07/14/2021] [Accepted: 07/28/2021] [Indexed: 11/12/2022] Open
Abstract
This prospective study determined the use of intracranially recorded spectral responses during naming tasks in predicting neuropsychological performance following epilepsy surgery. We recruited 65 patients with drug-resistant focal epilepsy who underwent preoperative neuropsychological assessment and intracranial EEG recording. The Clinical Evaluation of Language Fundamentals evaluated the baseline and postoperative language function. During extra-operative intracranial EEG recording, we assigned patients to undergo auditory and picture naming tasks. Time-frequency analysis determined the spatiotemporal characteristics of naming-related amplitude modulations, including high gamma augmentation at 70-110 Hz. We surgically removed the presumed epileptogenic zone based on the intracranial EEG and MRI abnormalities while maximally preserving the eloquent areas defined by electrical stimulation mapping. The multivariate regression model incorporating auditory naming-related high gamma augmentation predicted the postoperative changes in Core Language Score with r2 of 0.37 and in Expressive Language Index with r2 of 0.32. Independently of the effects of epilepsy and neuroimaging profiles, higher high gamma augmentation at the resected language-dominant hemispheric area predicted a more severe postoperative decline in Core Language Score and Expressive Language Index. Conversely, the model incorporating picture naming-related high gamma augmentation predicted the change in Receptive Language Index with an r2 of 0.50. Higher high gamma augmentation independently predicted a more severe postoperative decline in Receptive Language Index. Ancillary regression analysis indicated that naming-related low gamma augmentation and alpha/beta attenuation likewise independently predicted a more severe Core Language Score decline. The machine learning-based prediction model suggested that naming-related high gamma augmentation, among all spectral responses used as predictors, most strongly contributed to the improved prediction of patients showing a >5-point Core Language Score decline (reflecting the lower 25th percentile among patients). We generated the model-based atlas visualizing sites, which, if resected, would lead to such a language decline. With a 5-fold cross-validation procedure, the auditory naming-based model predicted patients who had such a postoperative language decline with an accuracy of 0.80. The model indicated that virtual resection of an electrical stimulation mapping-defined language site would have increased the relative risk of the Core Language Score decline by 5.28 (95% confidence interval: 3.47-8.02). Especially, that of an electrical stimulation mapping-defined receptive language site would have maximized it to 15.90 (95% confidence interval: 9.59-26.33). In summary, naming-related spectral responses predict neuropsychological outcomes after epilepsy surgery. We have provided our prediction model as an open-source material, which will indicate the postoperative language function of future patients and facilitate external validation at tertiary epilepsy centres.
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Affiliation(s)
- Masaki Sonoda
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Neurosurgery, Yokohama City University, Yokohama, Kanagawa 2360004, Japan
| | - Robert Rothermel
- Department of Psychiatry, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Alanna Carlson
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Psychiatry, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Jeong-Won Jeong
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Neurology, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Min-Hee Lee
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Takahiro Hayashi
- Department of Neurosurgery, Yokohama City University, Yokohama, Kanagawa 2360004, Japan
| | - Aimee F Luat
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Neurology, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Pediatrics, Central Michigan University, Mount Pleasant, MI 48858, USA
| | - Sandeep Sood
- Department of Neurosurgery, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Eishi Asano
- Department of Pediatrics, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Department of Neurology, Children’s Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
- Correspondence to: Eishi Asano, MD, PhD, MS (CRDSA) Division of Pediatric Neurology, Children’s Hospital of Michigan Wayne State University. 3901 Beaubien St., Detroit, MI 48201, USA E-mail:
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22
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Weiss SA, Pastore T, Orosz I, Rubinstein D, Gorniak R, Waldman Z, Fried I, Wu C, Sharan A, Slezak D, Worrell G, Engel J, Sperling MR, Staba RJ. Graph theoretical measures of fast ripples support the epileptic network hypothesis. Brain Commun 2022; 4:fcac101. [PMID: 35620169 PMCID: PMC9128387 DOI: 10.1093/braincomms/fcac101] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 02/10/2022] [Accepted: 04/12/2022] [Indexed: 11/16/2022] Open
Abstract
The epileptic network hypothesis and epileptogenic zone hypothesis are two
theories of ictogenesis. The network hypothesis posits that coordinated activity
among interconnected nodes produces seizures. The epileptogenic zone hypothesis
posits that distinct regions are necessary and sufficient for seizure
generation. High-frequency oscillations, and particularly fast ripples, are
thought to be biomarkers of the epileptogenic zone. We sought to test these
theories by comparing high-frequency oscillation rates and networks in surgical
responders and non-responders, with no appreciable change in seizure frequency
or severity, within a retrospective cohort of 48 patients implanted with
stereo-EEG electrodes. We recorded inter-ictal activity during non-rapid eye
movement sleep and semi-automatically detected and quantified high-frequency
oscillations. Each electrode contact was localized in normalized coordinates. We
found that the accuracy of seizure onset zone electrode contact classification
using high-frequency oscillation rates was not significantly different in
surgical responders and non-responders, suggesting that in non-responders the
epileptogenic zone partially encompassed the seizure onset zone(s)
(P > 0.05). We also found that in the
responders, fast ripple on oscillations exhibited a higher spectral content in
the seizure onset zone compared with the non-seizure onset zone
(P < 1 × 10−5).
By contrast, in the non-responders, fast ripple had a lower spectral content in
the seizure onset zone
(P < 1 × 10−5).
We constructed two different networks of fast ripple with a spectral content
>350 Hz. The first was a rate–distance network that
multiplied the Euclidian distance between fast ripple-generating contacts by the
average rate of fast ripple in the two contacts. The radius of the
rate–distance network, which excluded seizure onset zone nodes,
discriminated non-responders, including patients not offered resection or
responsive neurostimulation due to diffuse multifocal onsets, with an accuracy
of 0.77 [95% confidence interval (CI) 0.56–0.98]. The second fast
ripple network was constructed using the mutual information between the timing
of the events to measure functional connectivity. For most non-responders, this
network had a longer characteristic path length, lower mean local efficiency in
the non-seizure onset zone, and a higher nodal strength among non-seizure onset
zone nodes relative to seizure onset zone nodes. The graphical theoretical
measures from the rate–distance and mutual information networks of 22
non- responsive neurostimulation treated patients was used to train a support
vector machine, which when tested on 13 distinct patients classified
non-responders with an accuracy of 0.92 (95% CI 0.75–1). These
results indicate patients who do not respond to surgery or those not selected
for resection or responsive neurostimulation can be explained by the epileptic
network hypothesis that is a decentralized network consisting of widely
distributed, hyperexcitable fast ripple-generating nodes.
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Affiliation(s)
- Shennan A Weiss
- Dept. of Neurology, State University of New York Downstate, Brooklyn, New York, 11203 USA
- Dept. of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York, 11203 USA
- Dept. of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY, USA
| | - Tomas Pastore
- Dept. of Computer Science, University of Buenos Aires, Buenos Aires, Argentina
| | - Iren Orosz
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Daniel Rubinstein
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Richard Gorniak
- Dept. of Neuroradiology, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Zachary Waldman
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Itzhak Fried
- Dept. of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Chengyuan Wu
- Dept. of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Ashwini Sharan
- Dept. of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Diego Slezak
- Dept. of Computer Science, University of Buenos Aires, Buenos Aires, Argentina
| | - Gregory Worrell
- Dept. of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), USA
- Dept. of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Jerome Engel
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Michael R. Sperling
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Richard J Staba
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
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23
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Spike ripples in striatum correlate with seizure risk in two mouse models. Epilepsy Behav Rep 2022; 18:100529. [PMID: 35274094 PMCID: PMC8902602 DOI: 10.1016/j.ebr.2022.100529] [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: 12/06/2021] [Revised: 01/21/2022] [Accepted: 02/05/2022] [Indexed: 11/28/2022] Open
Abstract
Epilepsy biomarkers from electroencephalogram recordings are routinely used to assess seizure risk and localization. Two widely adopted biomarkers include: (i) interictal spikes, and (ii) high frequency ripple oscillations. The combination of these two biomarkers, ripples co-occurring with spikes (spike ripples), has been proposed as an improved biomarker for the epileptogenic zone and epileptogenicity in humans and rodent models. Whether spike ripples translate to predict seizure risk in rodent seizure models is unknown. Further, recent evidence suggests ictal networks can include deep gray nuclei in humans. Whether pathologic spike ripples and seizures are also observed in the basal ganglia in rodent models has not been explored. We addressed these questions using local field potential recordings from mice with and without striatal seizures after carbachol or 6-hydroxydopamine infusions into the striatum. We found increased spike ripples in the interictal and ictal periods in mice with seizures compared to pre-infusion and post-infusion seizure-free recordings. These data provide evidence of electrographic seizures involving the striatum in mice and support the candidacy of spike ripples as a translational biomarker for seizure risk in mouse models.
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24
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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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25
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Miyakoshi M, Nariai H, Rajaraman RR, Bernardo D, Shrey DW, Lopour BA, Sim MS, Staba RJ, Hussain SA. Automated preprocessing and phase-amplitude coupling analysis of scalp EEG discriminates infantile spasms from controls during wakefulness. Epilepsy Res 2021; 178:106809. [PMID: 34823159 DOI: 10.1016/j.eplepsyres.2021.106809] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 10/26/2021] [Accepted: 11/02/2021] [Indexed: 10/19/2022]
Abstract
OBJECTIVE Delta-gamma phase-amplitude coupling in EEG is useful for localizing epileptic sources and to evaluate severity in children with infantile spasms. We (1) develop an automated EEG preprocessing pipeline to clean data using artifact subspace reconstruction (ASR) and independent component (IC) analysis (ICA) and (2) evaluate delta-gamma modulation index (MI) as a method to distinguish children with epileptic spasms (cases) from normal controls during sleep and awake. METHODS Using 400 scalp EEG datasets (200 sleep, 200 awake) from 100 subjects, we calculated MI after applying high-pass and line-noise filters (Clean 0), and after ASR followed by either conservative (Clean 1) or stringent (Clean 2) artifactual IC rejection. Classification of cases and controls using MI was evaluated with Receiver Operating Characteristics (ROC) to obtain area under curve (AUC). RESULTS The artifact rejection algorithm reduced raw signal variance by 29-45% and 38-60% for Clean 1 and Clean 2, respectively. MI derived from sleep data, with or without preprocessing, robustly classified the groups (all AUC > 0.98). In contrast, group classification using MI derived from awake data was successful only after Clean 2 (AUC = 0.85). CONCLUSIONS We have developed an automated EEG preprocessing pipeline to perform artifact rejection and quantify delta-gamma modulation index.
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Affiliation(s)
- Makoto Miyakoshi
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, United States
| | - Hiroki Nariai
- David Geffen School of Medicine, Department of Pediatrics, University of California Los Angeles, United States.
| | - Rajsekar R Rajaraman
- David Geffen School of Medicine, Department of Pediatrics, University of California Los Angeles, United States
| | | | - Daniel W Shrey
- Children's Hospital of Orange County, Neurology, University of California, Irvine, Pediatrics, United States
| | - Beth A Lopour
- Henry Samueli School of Engineering, University of California Irvine, United States
| | - Myung Shin Sim
- Division of General Internal Medicine and Health Services Research, Department of Medicine Statistics Core, University of California Los Angeles, United States
| | - Richard J Staba
- David Geffen School of Medicine, Department of Neurology, University of California Los Angeles, United States
| | - Shaun A Hussain
- David Geffen School of Medicine, Department of Pediatrics, University of California Los Angeles, United States
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26
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Zhang Y, Lu Q, Monsoor T, Hussain SA, Qiao JX, Salamon N, Fallah A, Sim MS, Asano E, Sankar R, Staba RJ, Engel J, Speier W, Roychowdhury V, Nariai H. Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach. Brain Commun 2021; 4:fcab267. [PMID: 35169696 PMCID: PMC8833577 DOI: 10.1093/braincomms/fcab267] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 09/29/2021] [Accepted: 10/04/2021] [Indexed: 11/12/2022] Open
Abstract
Intracranially recorded interictal high-frequency oscillations have been proposed as a promising spatial biomarker of the epileptogenic zone. However, its visual verification is time-consuming and exhibits poor inter-rater reliability. Furthermore, no method is currently available to distinguish high-frequency oscillations generated from the epileptogenic zone (epileptogenic high-frequency oscillations) from those generated from other areas (non-epileptogenic high-frequency oscillations). To address these issues, we constructed a deep learning-based algorithm using chronic intracranial EEG data via subdural grids from 19 children with medication-resistant neocortical epilepsy to: (i) replicate human expert annotation of artefacts and high-frequency oscillations with or without spikes, and (ii) discover epileptogenic high-frequency oscillations by designing a novel weakly supervised model. The ‘purification power’ of deep learning is then used to automatically relabel the high-frequency oscillations to distill epileptogenic high-frequency oscillations. Using 12 958 annotated high-frequency oscillation events from 19 patients, the model achieved 96.3% accuracy on artefact detection (F1 score = 96.8%) and 86.5% accuracy on classifying high-frequency oscillations with or without spikes (F1 score = 80.8%) using patient-wise cross-validation. Based on the algorithm trained from 84 602 high-frequency oscillation events from nine patients who achieved seizure-freedom after resection, the majority of such discovered epileptogenic high-frequency oscillations were found to be ones with spikes (78.6%, P < 0.001). While the resection ratio of detected high-frequency oscillations (number of resected events/number of detected events) did not correlate significantly with post-operative seizure freedom (the area under the curve = 0.76, P = 0.06), the resection ratio of epileptogenic high-frequency oscillations positively correlated with post-operative seizure freedom (the area under the curve = 0.87, P = 0.01). We discovered that epileptogenic high-frequency oscillations had a higher signal intensity associated with ripple (80–250 Hz) and fast ripple (250–500 Hz) bands at the high-frequency oscillation onset and with a lower frequency band throughout the event time window (the inverted T-shaped), compared to non-epileptogenic high-frequency oscillations. We then designed perturbations on the input of the trained model for non-epileptogenic high-frequency oscillations to determine the model’s decision-making logic. The model confidence significantly increased towards epileptogenic high-frequency oscillations by the artificial introduction of the inverted T-shaped signal template (mean probability increase: 0.285, P < 0.001), and by the artificial insertion of spike-like signals into the time domain (mean probability increase: 0.452, P < 0.001). With this deep learning-based framework, we reliably replicated high-frequency oscillation classification tasks by human experts. Using a reverse engineering technique, we distinguished epileptogenic high-frequency oscillations from others and identified its salient features that aligned with current knowledge.
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Affiliation(s)
- Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
| | - Qiujing Lu
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
| | - Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
| | - Shaun A. Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Joe X. Qiao
- Division of Neuroradiology, Department of Radiology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Noriko Salamon
- Division of Neuroradiology, Department of Radiology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Myung Shin Sim
- Department of Medicine, Statistics Core, University of California, Los Angeles, CA 90095, USA
| | - Eishi Asano
- Department of Pediatrics and Neurology, Children’s Hospital of Michigan, Wayne State University School of Medicine, Detroit, MI 48201, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA 90095, USA
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Richard J. Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Jerome Engel
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
- Department of Neurobiology, University of California, Los Angeles, CA 90095, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA 90095, USA
- The Brain Research Institute, University of California, Los Angeles, CA 90095, USA
| | - William Speier
- Department of Radiological Sciences, University of California, Los Angeles, CA 90095, USA
- Department of Bioengineering, University of California, Los Angeles, CA 90095, USA
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA 90095, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
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27
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Weiss SA, Staba RJ, Sharan A, Wu C, Rubinstein D, Das S, Waldman Z, Orosz I, Worrell G, Engel J, Sperling MR. Accuracy of high-frequency oscillations recorded intraoperatively for classification of epileptogenic regions. Sci Rep 2021; 11:21388. [PMID: 34725412 PMCID: PMC8560764 DOI: 10.1038/s41598-021-00894-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 10/19/2021] [Indexed: 11/10/2022] Open
Abstract
To see whether acute intraoperative recordings using stereo EEG (SEEG) electrodes can replace prolonged interictal intracranial EEG (iEEG) recording, making the process more efficient and safer, 10 min of iEEG were recorded following electrode implantation in 16 anesthetized patients, and 1-2 days later during non-rapid eye movement (REM) sleep. Ripples on oscillations (RonO, 80-250 Hz), ripples on spikes (RonS), sharp-spikes, fast RonO (fRonO, 250-600 Hz), and fast RonS (fRonS) were semi-automatically detected. HFO power and frequency were compared between the conditions using a generalized linear mixed-effects model. HFO rates were compared using a two-way repeated measures ANOVA with anesthesia type and SOZ as factors. A receiver-operating characteristic (ROC) curve analysis quantified seizure onset zone (SOZ) classification accuracy, and the scalar product was used to assess spatial reliability. Resection of contacts with the highest rate of events was compared with outcome. During sleep, all HFOs, except fRonO, were larger in amplitude compared to intraoperatively (p < 0.01). HFO frequency was also affected (p < 0.01). Anesthesia selection affected HFO and sharp-spike rates. In both conditions combined, sharp-spikes and all HFO subtypes were increased in the SOZ (p < 0.01). However, the increases were larger during the sleep recordings (p < 0.05). The area under the ROC curves for SOZ classification were significantly smaller for intraoperative sharp-spikes, fRonO, and fRonS rates (p < 0.05). HFOs and spikes were only significantly spatially reliable for a subset of the patients (p < 0.05). A failure to resect fRonO areas in the sleep recordings trended the most sensitive and accurate for predicting failure. In summary, HFO morphology is altered by anesthesia. Intraoperative SEEG recordings exhibit increased rates of HFOs in the SOZ, but their spatial distribution can differ from sleep recordings. Recording these biomarkers during non-REM sleep offers a more accurate delineation of the SOZ and possibly the epileptogenic zone.
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Affiliation(s)
- Shennan A Weiss
- Department of Neurology, State University of New York Downstate, Brooklyn, NY, 11203, USA.,Department of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, NY, 11203, USA.,Department of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY, USA
| | - Richard J Staba
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Ashwini Sharan
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Chengyuan Wu
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Daniel Rubinstein
- Department of Neurology and Neuroscience, Thomas Jefferson University, 901 Walnut St. Suite 400, Philadelphia, PA, 19107, USA
| | - Sandhitsu Das
- Penn Image Computing & Science Lab, University of Pennsylvania, Philadelphia, PA, 19143, USA
| | - Zachary Waldman
- Department of Neurology and Neuroscience, Thomas Jefferson University, 901 Walnut St. Suite 400, Philadelphia, PA, 19107, USA
| | - Iren Orosz
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Gregory Worrell
- Department of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), Rochester, USA.,Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, 55905, USA
| | - Jerome Engel
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.,Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.,Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Michael R Sperling
- Department of Neurology and Neuroscience, Thomas Jefferson University, 901 Walnut St. Suite 400, Philadelphia, PA, 19107, USA.
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Hashimoto H, Takahashi K, Kameda S, Yoshida F, Maezawa H, Oshino S, Tani N, Khoo HM, Yanagisawa T, Yoshimine T, Kishima H, Hirata M. Motor and sensory cortical processing of neural oscillatory activities revealed by human swallowing using intracranial electrodes. iScience 2021; 24:102786. [PMID: 34308292 PMCID: PMC8283146 DOI: 10.1016/j.isci.2021.102786] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/28/2021] [Accepted: 06/23/2021] [Indexed: 11/28/2022] Open
Abstract
Swallowing is attributed to the orchestration of motor output and sensory input. We hypothesized that swallowing can illustrate differences between motor and sensory neural processing. Eight epileptic participants fitted with intracranial electrodes over the orofacial cortex were asked to swallow a water bolus. Mouth opening and swallowing were treated as motor tasks, whereas water injection was treated as a sensory task. Phase-amplitude coupling between lower-frequency and high γ (HG) bands (75–150 Hz) was investigated. An α (10–16 Hz)-HG coupling appeared before motor-related HG power increases (burst), and a θ (5–9 Hz)-HG coupling appeared during sensory-related HG bursts. The peaks of motor-related coupling were 0.6–0.7 s earlier than that of HG power. The motor-related HG was modulated at the trough of the α oscillation, and the sensory-related HG amplitude was modulated at the peak of the θ oscillation. These contrasting results can help to elucidate the brain's sensory motor functions. Swallowing has two aspects; sensory input and motor output Phase-amplitude coupling showed differences of motor and sensory neural processing Coupling between the α and high γ band occurred before motor-related high γ activities Coupling between the θ and high γ band occurred during sensory-related high γ activities
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Affiliation(s)
- Hiroaki Hashimoto
- Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan.,Department of Neurosurgery, Otemae Hospital, Chuo-ku Otemae 1-5-34, Osaka, Osaka 540-0008, Japan.,Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Kazutaka Takahashi
- Department of Organismal Biology and Anatomy, The University of Chicago, 1027 E 57 St, Chicago, IL 60637, USA
| | - Seiji Kameda
- Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Fumiaki Yoshida
- Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan.,Department of Anatomy and Physiology, Saga Medical School Faculty of Medicine, Saga University, Nabeshima 5-1-1, Saga, Saga 849-8501, Japan
| | - Hitoshi Maezawa
- Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Satoru Oshino
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Naoki Tani
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Hui Ming Khoo
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Takufumi Yanagisawa
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Toshiki Yoshimine
- Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Haruhiko Kishima
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
| | - Masayuki Hirata
- Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan.,Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan.,Department of Neurosurgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka 565-0871, Japan
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29
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Yang J, Feng G, Chen M, Wang S, Tang F, Zhou J, Bao N, Yu J, Jiang G. Glucosamine promotes seizure activity via activation of the PI3K/Akt pathway in epileptic rats. Epilepsy Res 2021; 175:106679. [PMID: 34166966 DOI: 10.1016/j.eplepsyres.2021.106679] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 04/28/2021] [Accepted: 05/27/2021] [Indexed: 11/25/2022]
Abstract
CONTEXT Glucosamine is an amino monosaccharide with a small molecular weight and has a protective effect against various neurological diseases including multiple sclerosis and encephalomyelitis. Interestingly, low-dose glucosamine has exhibited anti-epilepsy activity. Recent studies have shown that the activation of the protein kinase B (Akt) signaling pathway may promote epilepsy. Glucosamine can increase the level of Akt phosphorylation in the brain tissue, which may aggravate epilepsy. Hence, we speculate that a higher dose of glucosamine may aggravate epilepsy via AKT signaling. OBJECTIVE To investigate the effect of glucosamine on the behavior and electrophysiology of epileptic rats through PI3K/Akt pathway. METHODS Glucose (2.0 g/kg) and glucosamine (0, 0.5, 1.0, and 2.0 g/kg) were added to 2 mL of drinking water, respectively. An acute seizure rat model of lithium-pilocarpine and PTZ-kindling were constructed to observe the effects of different doses of glucosamine on epileptic behavior and hippocampal electrical activity. Meanwhile, the changes in Akt were detected by western blot. RESULTS Epileptic seizures were induced by a single dose of pilocarpine or PTZ and 2.0 g/kg of glucosamine significantly prolonged the duration and severity of epileptic seizures, enhanced hippocampal electrical activity energy density, and increased phosphorylated AKT levels. A glucosamine dose of 2.0 g/kg also significantly increased the total onset energy density. Furthermore, 2.0 g/kg glucosamine facilitated the development of the chronic PTZ-kindling process. CONCLUSIONS Glucosamine may exacerbate acute and chronic epileptic seizures via activation of the PI3K/Akt pathway in rats with experimental epilepsy.
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Affiliation(s)
- Jin Yang
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, 63 Wenhua Road, Nanchong, 637000, China; Institute of Neurological Diseases, North Sichuan Medical College, 234 Fujiang Road, Nanchong, Sichuan, China
| | - Guibo Feng
- Department of General Medicine, Yongchuan Hospital of Chongqing Medical University, Chongqing, 402160, China
| | - Mingyue Chen
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, 63 Wenhua Road, Nanchong, 637000, China; Institute of Neurological Diseases, North Sichuan Medical College, 234 Fujiang Road, Nanchong, Sichuan, China
| | - Shenglin Wang
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, 63 Wenhua Road, Nanchong, 637000, China; Institute of Neurological Diseases, North Sichuan Medical College, 234 Fujiang Road, Nanchong, Sichuan, China
| | - Feng Tang
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, 63 Wenhua Road, Nanchong, 637000, China; Institute of Neurological Diseases, North Sichuan Medical College, 234 Fujiang Road, Nanchong, Sichuan, China
| | - Jing Zhou
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, 63 Wenhua Road, Nanchong, 637000, China; Institute of Neurological Diseases, North Sichuan Medical College, 234 Fujiang Road, Nanchong, Sichuan, China
| | - Nana Bao
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, 63 Wenhua Road, Nanchong, 637000, China; Institute of Neurological Diseases, North Sichuan Medical College, 234 Fujiang Road, Nanchong, Sichuan, China
| | - Juming Yu
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, 63 Wenhua Road, Nanchong, 637000, China; Institute of Neurological Diseases, North Sichuan Medical College, 234 Fujiang Road, Nanchong, Sichuan, China
| | - Guohui Jiang
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, 63 Wenhua Road, Nanchong, 637000, China; Institute of Neurological Diseases, North Sichuan Medical College, 234 Fujiang Road, Nanchong, Sichuan, China.
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30
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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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Hashimoto H, Khoo HM, Yanagisawa T, Tani N, Oshino S, Kishima H, Hirata M. Phase-amplitude coupling of ripple activities during seizure evolution with theta phase. Clin Neurophysiol 2021; 132:1243-1253. [PMID: 33867253 DOI: 10.1016/j.clinph.2021.03.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/25/2021] [Accepted: 03/09/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVE High-frequency activities (HFAs) and phase-amplitude coupling (PAC) are key neurophysiological biomarkers for studying human epilepsy. We aimed to clarify and visualize how HFAs are modulated by the phase of low-frequency bands during seizures. METHODS We used intracranial electrodes to record seizures of focal epilepsy (12 focal-to-bilateral tonic-clonic seizures and three focal-aware seizures in seven patients). The synchronization index, representing PAC, was used to analyze the coupling between the amplitude of ripples (80-250 Hz) and the phase of lower frequencies. We created a video in which the intracranial electrode contacts were scaled linearly to the power changes of ripple. RESULTS The main low frequency band modulating ictal-ripple activities was the θ band (4-8 Hz), and after completion of ictal-ripple burst, δ (1-4 Hz)-ripple PAC occurred. The ripple power increased simultaneously with rhythmic fluctuations from the seizure onset zone, and spread to other regions. CONCLUSIONS Ripple activities during seizure evolution were modulated by the θ phase. The PAC phenomenon was visualized as rhythmic fluctuations. SIGNIFICANCE Ripple power associated with seizure evolution increased and spread with fluctuations. The θ oscillations related to the fluctuations might represent the common neurophysiological processing involved in seizure generation.
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Affiliation(s)
- Hiroaki Hashimoto
- Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan; Department of Neurosurgery, Otemae Hospital, Osaka 540-0008, Japan; Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Suita 565-0871, Japan.
| | - Hui Ming Khoo
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan
| | - Takufumi Yanagisawa
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan
| | - Naoki Tani
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan
| | - Satoru Oshino
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan
| | - Haruhiko Kishima
- Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan
| | - Masayuki Hirata
- Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan; Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Suita 565-0871, Japan; Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan
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32
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Kuroda N, Sonoda M, Miyakoshi M, Nariai H, Jeong JW, Motoi H, Luat AF, Sood S, Asano E. Objective interictal electrophysiology biomarkers optimize prediction of epilepsy surgery outcome. Brain Commun 2021; 3:fcab042. [PMID: 33959709 PMCID: PMC8088817 DOI: 10.1093/braincomms/fcab042] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/09/2021] [Accepted: 02/02/2021] [Indexed: 12/12/2022] Open
Abstract
Researchers have looked for rapidly- and objectively-measurable electrophysiology biomarkers that accurately localize the epileptogenic zone. Promising candidates include interictal high-frequency oscillation and phase-amplitude coupling. Investigators have independently created the toolboxes that compute the high-frequency oscillation rate and the severity of phase-amplitude coupling. This study of 135 patients determined what toolboxes and analytic approaches would optimally classify patients achieving post-operative seizure control. Four different detector toolboxes computed the rate of high-frequency oscillation at ≥80 Hz at intracranial EEG channels. Another toolbox calculated the modulation index reflecting the strength of phase-amplitude coupling between high-frequency oscillation and slow-wave at 3–4 Hz. We defined the completeness of resection of interictally-abnormal regions as the subtraction of high-frequency oscillation rate (or modulation index) averaged across all preserved sites from that averaged across all resected sites. We computed the outcome classification accuracy of the logistic regression-based standard model considering clinical, ictal intracranial EEG and neuroimaging variables alone. We then determined how well the incorporation of high-frequency oscillation/modulation index would improve the standard model mentioned above. To assess the anatomical variability across non-epileptic sites, we generated the normative atlas of detector-specific high-frequency oscillation and modulation index. Each atlas allowed us to compute the statistical deviation of high-frequency oscillation/modulation index from the non-epileptic mean. We determined whether the model accuracy would be improved by incorporating absolute or normalized high-frequency oscillation/modulation index as a biomarker assessing interictally-abnormal regions. We finally determined whether the model accuracy would be improved by selectively incorporating high-frequency oscillation verified to have high-frequency oscillatory components unattributable to a high-pass filtering effect. Ninety-five patients achieved successful seizure control, defined as International League against Epilepsy class 1 outcome. Multivariate logistic regression analysis demonstrated that complete resection of interictally-abnormal regions additively increased the chance of success. The model accuracy was further improved by incorporating z-score normalized high-frequency oscillation/modulation index or selective incorporation of verified high-frequency oscillation. The standard model had a classification accuracy of 0.75. Incorporation of normalized high-frequency oscillation/modulation index or verified high-frequency oscillation improved the classification accuracy up to 0.82. These outcome prediction models survived the cross-validation process and demonstrated an agreement between the model-based likelihood of success and the observed success on an individual basis. Interictal high-frequency oscillation and modulation index had a comparably additive utility in epilepsy presurgical evaluation. Our empirical data support the theoretical notion that the prediction of post-operative seizure outcomes can be optimized with the consideration of both interictal and ictal abnormalities.
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Affiliation(s)
- Naoto Kuroda
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai 9808575, Japan
| | - Masaki Sonoda
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Neurosurgery, Yokohama City University, Yokohama 2360004, Japan
| | - Makoto Miyakoshi
- Swartz Centre for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA
| | - Hiroki Nariai
- Division of Paediatric Neurology, Department of Paediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Jeong-Won Jeong
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Neurology, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA
| | - Hirotaka Motoi
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Paediatrics, Yokohama City University Medical Centre, Yokohama 2320024, Japan
| | - Aimee F Luat
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Neurology, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA
| | - Sandeep Sood
- Department of Neurosurgery, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA
| | - Eishi Asano
- Department of Paediatrics, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA.,Department of Neurology, Children's Hospital of Michigan, Detroit Medical Centre, Wayne State University, Detroit, MI 48201, USA
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Uda T, Kuki I, Inoue T, Kunihiro N, Suzuki H, Uda H, Kawashima T, Nakajo K, Nakanishi Y, Maruyama S, Shibata T, Ogawa H, Okazaki S, Kawawaki H, Ohata K, Goto T, Otsubo H. Phase-amplitude coupling of interictal fast activities modulated by slow waves on scalp EEG and its correlation with seizure outcomes of disconnection surgery in children with intractable nonlesional epileptic spasms. J Neurosurg Pediatr 2021; 27:572-580. [PMID: 33636702 DOI: 10.3171/2020.9.peds20520] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 09/04/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Epileptic spasms (ESs) are classified as focal, generalized, or unknown onset ESs. The classification of ESs and surgery in patients without lesions apparent on MRI is challenging. Total corpus callosotomy (TCC) is a surgical option for diagnosis of the lateralization and possible treatment for ESs. This study investigated phase-amplitude coupling (PAC) of fast activity modulated by slow waves on scalp electroencephalography (EEG) to evaluate the strength of the modulation index (MI) before and after disconnection surgery in children with intractable nonlesional ESs. The authors hypothesize that a decreased MI due to surgery correlates with good seizure outcomes. METHODS The authors studied 10 children with ESs without lesions on MRI who underwent disconnection surgeries. Scalp EEG was obtained before and after surgery. The authors collected 20 epochs of 3 minutes each during non-rapid eye movement sleep. The MI of the gamma (30-70 Hz) amplitude and delta (0.5-4 Hz) phase was obtained in each electrode. MIs for each electrode were averaged in 4 brain areas (left/right, anterior/posterior quadrants) and evaluated to determine the correlation with seizure outcomes. RESULTS The median age at first surgery was 2.3 years (range 10 months-9.1 years). Two patients with focal onset ESs underwent anterior quadrant disconnection (AQD). TCC alone was performed in 5 patients with generalized or unknown onset ESs. Two patients achieved seizure freedom. Three patients had residual generalized onset ESs. Disconnection surgeries in addition to TCC consisted of TCC + posterior quadrant disconnection (PQD) (1 patient); TCC + AQD + PQD (1 patient); and TCC + AQD + hemispherotomy (1 patient). Seven patients became seizure free with a mean follow-up period of 28 months (range 5-54 months). After TCC, MIs in 4 quadrants were significantly lower in the 2 seizure-free patients than in the 6 patients with residual ESs (p < 0.001). After all 15 disconnection surgeries in 10 patients, MIs in the 13 target quadrants for each disconnection surgery that resulted in freedom from seizures were significantly lower than in the 26 target quadrants in patients with residual ESs (p < 0.001). CONCLUSIONS In children with nonlesional ESs, PAC for scalp EEG before and after disconnection surgery may be a surrogate marker for control of ESs. The MI may indicate epileptogenic neuronal modulation of the interhemispheric corpus callosum and intrahemispheric subcortical network for ESs. TCC may be a therapeutic option to disconnect the interhemispheric modulation of epileptic networks.
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Affiliation(s)
- Takehiro Uda
- 1Department of Neurosurgery, Osaka City University Graduate School of Medicine.,Departments of2Pediatric Neurosurgery and
| | - Ichiro Kuki
- 3Pediatric Neurology, Osaka City General Hospital, Osaka, Japan; and
| | - Takeshi Inoue
- 3Pediatric Neurology, Osaka City General Hospital, Osaka, Japan; and
| | | | - Hiroharu Suzuki
- 4Department of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Hiroshi Uda
- 1Department of Neurosurgery, Osaka City University Graduate School of Medicine.,Departments of2Pediatric Neurosurgery and
| | - Toshiyuki Kawashima
- 1Department of Neurosurgery, Osaka City University Graduate School of Medicine
| | - Kosuke Nakajo
- 1Department of Neurosurgery, Osaka City University Graduate School of Medicine
| | | | - Shinsuke Maruyama
- 4Department of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Takashi Shibata
- 4Department of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Hiroshi Ogawa
- 4Department of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Shin Okazaki
- 3Pediatric Neurology, Osaka City General Hospital, Osaka, Japan; and
| | - Hisashi Kawawaki
- 3Pediatric Neurology, Osaka City General Hospital, Osaka, Japan; and
| | - Kenji Ohata
- 1Department of Neurosurgery, Osaka City University Graduate School of Medicine
| | - Takeo Goto
- 1Department of Neurosurgery, Osaka City University Graduate School of Medicine
| | - Hiroshi Otsubo
- 4Department of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada
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Rampp S, Rössler K, Hamer H, Illek M, Buchfelder M, Doerfler A, Pieper T, Hartlieb T, Kudernatsch M, Koelble K, Peixoto-Santos JE, Blümcke I, Coras R. Dysmorphic neurons as cellular source for phase-amplitude coupling in Focal Cortical Dysplasia Type II. Clin Neurophysiol 2021; 132:782-792. [PMID: 33571886 DOI: 10.1016/j.clinph.2021.01.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 01/04/2021] [Accepted: 01/11/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVE Reliable localization of the epileptogenic zone is necessary for successful epilepsy surgery. Neurophysiological biomarkers include ictal onsets and interictal spikes. Furthermore, the epileptic network shows oscillations with potential localization value and pathomechanistic implications. The cellular origin of such markers in invasive EEG in vivo remains to be clarified. METHODS In the presented pilot study, surgical brain samples and invasive EEG recordings of seven patients with surgically treated Focal Cortical Dysplasia (FCD) type II were coregistered using a novel protocol. Dysmorphic neurons and balloon cells were immunohistochemically quantified. Evaluated markers included seizure onset, spikes, and oscillatory activity in delta, theta, gamma and ripple frequency bands, as well as sample entropy and phase-amplitude coupling between delta, theta, alpha and beta phase and gamma amplitude. RESULTS Correlations between histopathology and neurophysiology provided evidence for a contribution of dysmorphic neurons to interictal spikes, fast gamma activity and ripples. Furthermore, seizure onset and phase-amplitude coupling in areas with dysmorphic neurons suggests preserved connectivity is related to seizure initiation. Balloon cells showed no association. CONCLUSIONS Phase-amplitude coupling, spikes, fast gamma and ripples are related to the density of dysmorphic neurons and localize the seizure onset zone. SIGNIFICANCE The results of our pilot study provide a new powerful tool to address the cellular source of abnormal neurophysiology signals to leverage current and novel biomarkers for the localization of epileptic activity in the human brain.
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Affiliation(s)
- Stefan Rampp
- Department of Neurosurgery, University Hospital Erlangen, Germany; Department of Neurosurgery, University Hospital Halle, Germany.
| | - Karl Rössler
- Department of Neurosurgery, University Hospital Erlangen, Germany; Department of Neurosurgery, University Hospital Vienna, Austria
| | - Hajo Hamer
- Epilepsy Center, Department of Neurology, University Hospital Erlangen, Germany
| | - Margit Illek
- Department of Neurosurgery, University Hospital Erlangen, Germany
| | | | - Arnd Doerfler
- Department of Neuroradiology, University Hospital Erlangen, Germany
| | - Tom Pieper
- Hospital for Neuropediatrics and Neurological Rehabilitation, Epilepsy Center for Children and Adolescents, Schön Klinik Vogtareuth, Germany
| | - Till Hartlieb
- Hospital for Neuropediatrics and Neurological Rehabilitation, Epilepsy Center for Children and Adolescents, Schön Klinik Vogtareuth, Germany
| | - Manfred Kudernatsch
- Epilepsy Center and Department of Neurosurgery, Schön Klinik Vogtareuth, Germany; Research Institute, Rehabilitation, Transition, Palliation, PMU Salzburg, Salzburg, Austria
| | - Konrad Koelble
- Department of Neuropathology, University Hospital Erlangen, Germany
| | - Jose Eduardo Peixoto-Santos
- Department of Neuropathology, University Hospital Erlangen, Germany; Department of Neurology and Neurosurgery, Paulista School of Medicine, UNIFESP, Brazil
| | - Ingmar Blümcke
- Department of Neuropathology, University Hospital Erlangen, Germany
| | - Roland Coras
- Department of Neuropathology, University Hospital Erlangen, Germany
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Hao J, Cui Y, Niu B, Yu L, Lin Y, Xia Y, Yao D, Guo D. Roles of Very Fast Ripple (500-1000[Formula: see text]Hz) in the Hippocampal Network During Status Epilepticus. Int J Neural Syst 2020; 31:2150002. [PMID: 33357153 DOI: 10.1142/s0129065721500027] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Very fast ripples (VFRs, 500-1000[Formula: see text]Hz) are considered more specific than high-frequency oscillations (80-500[Formula: see text]Hz) as biomarkers of epileptogenic zones. Although VFRs are frequent abnormal phenomena in epileptic seizures, their functional roles remain unclear. Here, we detected the VFRs in the hippocampal network and tracked their roles during status epilepticus (SE) in rats with pilocarpine-induced temporal lobe epilepsy (TLE). All regions in the hippocampal network exhibited VFRs in the baseline, preictal, ictal and postictal states, with the ictal state containing the most VFRs. Moreover, strong phase-locking couplings existed between VFRs and slow oscillations (1-12[Formula: see text]Hz) in the ictal and postictal states for all regions. Further investigation indicated that during VFRs, the build-up of slow oscillations in the ictal state began from the temporal lobe and then spread through the whole hippocampal network via two different pathways, which might be associated with the underlying propagation of epileptiform discharges in the hippocampal network. Overall, we provide a functional description of the emergence of VFRs in the hippocampal network during SE, and we also establish that VFRs may be the physiological representation of the pathological alterations in hippocampal network activity during SE in TLE.
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Affiliation(s)
- Jianmin Hao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic, Science and Technology of China, Chengdu Sichuan 611731, P. R. China
| | - Yan Cui
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic, Science and Technology of China, Chengdu Sichuan 611731, P. R. China
| | - Bochao Niu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic, Science and Technology of China, Chengdu Sichuan 611731, P. R. China
| | - Liang Yu
- Department of Neurology, Sichuan Academy of Medical, Sciences and Sichuan Provincial People's Hospital, Chengdu Sichuan, P. R. China
| | - Yuhang Lin
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic, Science and Technology of China, Chengdu Sichuan 611731, P. R. China
| | - Yang Xia
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic, Science and Technology of China, Chengdu Sichuan 611731, P. R. China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic, Science and Technology of China, Chengdu Sichuan 611731, P. R. China.,School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan 450001, P. R. China
| | - Daqing Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic, Science and Technology of China, Chengdu Sichuan 611731, P. R. China
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Nariai H, Hussain SA, Bernardo D, Motoi H, Sonoda M, Kuroda N, Asano E, Nguyen JC, Elashoff D, Sankar R, Bragin A, Staba RJ, Wu JY. Scalp EEG interictal high frequency oscillations as an objective biomarker of infantile spasms. Clin Neurophysiol 2020; 131:2527-2536. [PMID: 32927206 DOI: 10.1016/j.clinph.2020.08.013] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 07/25/2020] [Accepted: 08/04/2020] [Indexed: 12/21/2022]
Abstract
OBJECTIVE To investigate the diagnostic utility of high frequency oscillations (HFOs) via scalp electroencephalogram (EEG) in infantile spasms. METHODS We retrospectively analyzed interictal slow-wave sleep EEGs sampled at 2,000 Hz recorded from 30 consecutive patients who were suspected of having infantile spasms. We measured the rate of HFOs (80-500 Hz) and the strength of the cross-frequency coupling between HFOs and slow-wave activity (SWA) at 3-4 Hz and 0.5-1 Hz as quantified with modulation indices (MIs). RESULTS Twenty-three patients (77%) exhibited active spasms during the overnight EEG recording. Although the HFOs were detected in all children, increased HFO rate and MIs correlated with the presence of active spasms (p < 0.001 by HFO rate; p < 0.01 by MIs at 3-4 Hz; p = 0.02 by MIs at 0.5-1 Hz). The presence of active spasms was predicted by the logistic regression models incorporating HFO-related metrics (AUC: 0.80-0.98) better than that incorporating hypsarrhythmia (AUC: 0.61). The predictive performance of the best model remained favorable (87.5% accuracy) after a cross-validation procedure. CONCLUSIONS Increased rate of HFOs and coupling between HFOs and SWA are associated with active epileptic spasms. SIGNIFICANCE Scalp-recorded HFOs may serve as an objective EEG biomarker for active epileptic spasms.
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Affiliation(s)
- Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, California, USA.
| | - Shaun A Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, California, USA
| | - Danilo Bernardo
- Department of Neurology, Division of Epilepsy, University of California, San Francisco, San Francisco, CA, USA
| | - Hirotaka Motoi
- Department of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Masaki Sonoda
- Department of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Naoto Kuroda
- Department of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Eishi Asano
- Department of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Jimmy C Nguyen
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, California, USA
| | - David Elashoff
- Department of Medicine, Statistics Core, University of California, Los Angeles, Los Angeles, California, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, California, USA
| | - Anatol Bragin
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, California, USA
| | - Richard J Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, California, USA
| | - Joyce Y Wu
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, California, USA
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Shibata T, Otsubo H. Phase-amplitude coupling of delta brush unveiling neuronal modulation development in the neonatal brain. Neurosci Lett 2020; 735:135211. [PMID: 32593774 DOI: 10.1016/j.neulet.2020.135211] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 06/15/2020] [Accepted: 06/24/2020] [Indexed: 10/24/2022]
Abstract
INTRODUCTION Delta brushes are an indicator of brain maturity on a neonatal EEG. We investigated phase-amplitude coupling (PAC) between slow delta waves and superimposed alpha-beta activity in delta brushes to elucidate the spatiotemporal developments of the delta brush with post-menstrual weeks (PMW). METHODS The subjects were 18 neurologically intact patients (seven girls). We analyzed EEG within 42 PMW. Patients were divided into four age groups as follows: PMW ≤30w; 31-34 w; 35-38 w; and 39-42 w. We selected up to three epochs of 2-minute EEG segments including delta brushes. We calculated the modulation index (MI), direct mean vector length (dMVL), and mean of phase angle of coupling by PAC between slow waves (0.5-1.5 Hz) and fast activities (8-25 Hz) in four regions (F: Fp1 and Fp2, C: C3 and C4, T: T3 and T4, O: O1 and O2). RESULTS We collected data from 18 patients and 31 epochs between 29 and 42 PMW, which comprised one, four, five, and eight patients, and two, seven, eight, and 14 epochs in the ≤30w, 31-34 w, 35-38 w, and 39-42 w groups, respectively. There were significant differences in the dMVL between the four regions in age groups ≤30w (P = 0.033) and 31-34w (0.017). Both MI and dMVL showed that delta brushes became higher in the occipital region from 32 to 36 PMW. The mean phase angle of coupling concentrated around either 0° or 180° for all age groups. CONCLUSIONS PAC analysis revealed the spatiotemporal relations of alpha-beta activities that are modulated by slow delta waves in neonatal delta brushes. The delta brushes appeared to be at a maximum around 32-36 PMW with the predominant occipital distribution. The PAC of the delta brush might represent the cortical neuronal fast activity that is modulated by slow delta waves of subcortical regions during a particular neonatal period.
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Affiliation(s)
- Takashi Shibata
- Division of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Hiroshi Otsubo
- Division of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada.
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Zhao B, Hu W, Zhang C, Wang X, Wang Y, Liu C, Mo J, Yang X, Sang L, Ma Y, Shao X, Zhang K, Zhang J. Integrated Automatic Detection, Classification and Imaging of High Frequency Oscillations With Stereoelectroencephalography. Front Neurosci 2020; 14:546. [PMID: 32581688 PMCID: PMC7287040 DOI: 10.3389/fnins.2020.00546] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 05/04/2020] [Indexed: 11/13/2022] Open
Abstract
Objective During presurgical evaluation for focal epilepsy patients, the evidence supporting the use of high frequency oscillations (HFOs) for delineating the epileptogenic zone (EZ) increased over the past decade. This study aims to develop and validate an integrated automatic detection, classification and imaging pipeline of HFOs with stereoelectroencephalography (SEEG) to narrow the gap between HFOs quantitative analysis and clinical application. Methods The proposed pipeline includes stages of channel inclusion, candidate HFOs detection and automatic labeling with four trained convolutional neural network (CNN) classifiers and HFOs sorting based on occurrence rate and imaging. We first evaluated the initial detector using an open simulated dataset. After that, we validated our full algorithm in a 20-patient cohort against three assumptions based on previous studies. Classified HFOs results were compared with seizure onset zone (SOZ) channels for their concordance. The receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) were calculated representing the prediction ability of the labeled HFOs outputs for SOZ. Results The initial detector demonstrated satisfactory performance on the simulated dataset. The four CNN classifiers converged quickly during training, and the accuracies on the validation dataset were above 95%. The localization value of HFOs was significantly improved by HFOs classification. The AUC values of the 20 testing patients increased after HFO classification, indicating a satisfactory prediction value of the proposed algorithm for EZ identification. Conclusion Our detector can provide robust HFOs analysis results revealing EZ at the individual level, which may ultimately push forward the transitioning of HFOs analysis into a meaningful part of the presurgical evaluation and surgical planning.
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Affiliation(s)
- Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yao Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chang Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaoli Yang
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Lin Sang
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Yanshan Ma
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Xiaoqiu Shao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
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Interictal scalp fast ripple occurrence and high frequency oscillation slow wave coupling in epileptic spasms. Clin Neurophysiol 2020; 131:1433-1443. [PMID: 32387963 DOI: 10.1016/j.clinph.2020.03.025] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 02/27/2020] [Accepted: 03/12/2020] [Indexed: 12/26/2022]
Abstract
OBJECTIVE Intracranial high frequency oscillation (HFO) occurrence rate (OR) and slow wave activity (SWA) coupling are potential markers of epileptogenicity in epileptic spasms (ES). Scalp ripple (R) detection and SWA coupling have been described in ES; however, the feasibility of scalp fast ripple (FR) detection and measurement of scalp FR coupling to SWA is not known. We evaluated interictal scalp R and FR OR and SWA coupling in pre-treatment EEG in children with short-term treatment-refractory ES compared to short-term treatment non-refractory ES. METHODS We retrospectively identified children with ES and identified HFOs using a semi-automated HFO detector on pre-treatment scalp EEG during sleep. We evaluated HFO OR and event-triggered modulation index (MI) to quantify R (100-250 Hz) and FR (250-600 Hz) coupling strength with different SWA passbands (0.5-1, 1-2, 2-3, 3-4, and 4-8 Hz). We used HFO phasor transform and circular statistics to evaluate phase coupling angle distributions. RESULTS We identified 15 children with ES with pre-treatment EEG recorded at 2000 Hz. Thirteen out of 15 patients had HFOs and were included for analysis. There were six treatment responders and seven nonresponders three months after treatment initiation. Responders and nonresponders were similar in age (6.1 vs 7.2 mo), ES diagnosis duration (0.7 vs 2.6 mo), and HFO OR (R: 1.07 vs 2.30/min, FR: 0.43 vs 1.96/min). No differences between responders and nonresponders were seen in HFO MI at different SWA. Coupling of R and FR to 2-3 Hz SWA demonstrated increased incidence rate ratio in nonresponders relative to responders at distinct phase coupling angle distributions. CONCLUSIONS This study demonstrates the feasibility of interictal scalp R and FR detection and quantification of scalp R and FR coupling to SWA in ES. SIGNIFICANCE HFO phase coupling with SWA may be useful as a marker of potential treatment refractoriness in patients with ES.
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40
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Sudhakar SK, Ahmed OJ. More Is More: Potential Benefits of Characterizing High-Frequency Activity Over Long Durations. Epilepsy Curr 2019; 19:397-399. [PMID: 31526032 PMCID: PMC6891179 DOI: 10.1177/1535759719875469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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41
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Cox R, Rüber T, Staresina BP, Fell J. Heterogeneous profiles of coupled sleep oscillations in human hippocampus. Neuroimage 2019; 202:116178. [PMID: 31505272 PMCID: PMC6853182 DOI: 10.1016/j.neuroimage.2019.116178] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 09/04/2019] [Accepted: 09/06/2019] [Indexed: 11/24/2022] Open
Abstract
Cross-frequency coupling of sleep oscillations is thought to mediate memory consolidation. While the hippocampus is deemed central to this process, detailed knowledge of which oscillatory rhythms interact in the sleeping human hippocampus is lacking. Combining intracranial hippocampal and non-invasive electroencephalography from twelve neurosurgical patients, we characterized spectral power and coupling during non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. Hippocampal coupling was extensive, with the majority of channels expressing spectral interactions. NREM consistently showed delta–ripple coupling, but ripples were also modulated by slow oscillations (SOs) and sleep spindles. SO–delta and SO–theta coupling, as well as interactions between delta/theta and spindle/beta frequencies also occurred. During REM, limited interactions between delta/theta and beta frequencies emerged. Moreover, oscillatory organization differed substantially between i) hippocampus and scalp, ii) sites along the anterior-posterior hippocampal axis, and iii) individuals. Overall, these results extend and refine our understanding of hippocampal sleep oscillations. Sleep oscillations in human hippocampus exhibit cross-frequency coupling during non-rapid eye movement sleep Coupling occurs between various frequency pairs, including slow oscillation, delta, theta, spindle, beta, and ripple bands Oscillatory organization varies between hippocampus and scalp, sites along the hippocampal axis, and individuals
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Affiliation(s)
- Roy Cox
- Department of Epileptology, University of Bonn, Bonn, Germany.
| | - Theodor Rüber
- Department of Epileptology, University of Bonn, Bonn, Germany; Epilepsy Center Frankfurt Rhine-Main, Department of Neurology, Goethe University Frankfurt, Frankfurt am Main, Germany; Center for Personalized Translational Epilepsy Research (CePTER), Goethe University Frankfurt, Frankfurt am Main, Germany
| | | | - Juergen Fell
- Department of Epileptology, University of Bonn, Bonn, Germany
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Yu H, Zhu L, Cai L, Wang J, Liu C, Shi N, Liu J. Variation of functional brain connectivity in epileptic seizures: an EEG analysis with cross-frequency phase synchronization. Cogn Neurodyn 2019; 14:35-49. [PMID: 32015766 DOI: 10.1007/s11571-019-09551-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 07/22/2019] [Accepted: 08/02/2019] [Indexed: 11/26/2022] Open
Abstract
Frequency coupling in nervous system is believed to be associated with normal and impaired brain functions. However, most of the existing experiments have been concentrated on the coupling strength within frequency bands, while the coupling strength between different bands is ignored. In this work, we apply phase synchronization index (PSI) to investigate the cross-frequency coupling (CFC) of Electroencephalogram (EEG) signals. The PSI matrixes for the multi-channel EEG signals are calculated from interictal to ictal period in each sliding time window. The results show that CFC changes obviously once seizure occurs between the different bands, and such alteration is earlier than the appearance of clinical symptoms in seizure. Considering the similar role of the within-frequency coupling (WFC), we further reconstruct multi-layered brain networks, including CFC networks and WFC networks. The graph metrics are applied to investigate the variation of network structure of the epileptic brain. Significant decreases/increases of the local/global efficiency are found in δ-β, δ-α, θ-α and δ-θ bands from the CFC network, while WFC network shows a significant decline in the local efficiency in θ and α bands. These findings suggest that CFC may provide a new perspective to observe the alteration of brain structure when seizure occurs, and the investigation of functional connectivity across the full frequency spectrum can give us a deeper understanding of epileptic brains.
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Affiliation(s)
- Haitao Yu
- 1School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Lin Zhu
- 1School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Lihui Cai
- 1School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Jiang Wang
- 1School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Chen Liu
- 1School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Nan Shi
- 2Department of Neurology, Tangshan Gongren Hospital, Tangshan, 063000 Hebei China
| | - Jing Liu
- 2Department of Neurology, Tangshan Gongren Hospital, Tangshan, 063000 Hebei China
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CRF Mediates Stress-Induced Pathophysiological High-Frequency Oscillations in Traumatic Brain Injury. eNeuro 2019; 6:ENEURO.0334-18.2019. [PMID: 31040158 PMCID: PMC6514440 DOI: 10.1523/eneuro.0334-18.2019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 04/01/2019] [Accepted: 04/20/2019] [Indexed: 01/19/2023] Open
Abstract
It is not known why there is increased risk to have seizures with increased anxiety and stress after traumatic brain injury (TBI). Stressors cause the release of corticotropin-releasing factor (CRF) both from the hypothalamic pituitary adrenal (HPA) axis and from CNS neurons located in the central amygdala and GABAergic interneurons. We have previously shown that CRF signaling is plastic, becoming excitatory instead of inhibitory after the kindling model of epilepsy. Here, using Sprague Dawley rats we have found that CRF signaling increased excitability after TBI. Following TBI, CRF type 1 receptor (CRFR1)-mediated activity caused abnormally large electrical responses in the amygdala, including fast ripples, which are considered to be epileptogenic. After TBI, we also found the ripple (120-250 Hz) and fast ripple activity (>250 Hz) was cross-frequency coupled with θ (3-8 Hz) oscillations. CRFR1 antagonists reduced the incidence of phase coupling between ripples and fast ripples. Our observations indicate that pathophysiological signaling of the CRFR1 increases the incidence of epileptiform activity after TBI. The use for CRFR1 antagonist may be useful to reduce the severity and frequency of TBI associated epileptic seizures.
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Weiss SA, Waldman Z, Raimondo F, Slezak D, Donmez M, Worrell G, Bragin A, Engel J, Staba R, Sperling M. Localizing epileptogenic regions using high-frequency oscillations and machine learning. Biomark Med 2019; 13:409-418. [PMID: 31044598 DOI: 10.2217/bmm-2018-0335] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Pathological high frequency oscillations (HFOs) are putative neurophysiological biomarkers of epileptogenic brain tissue. Utilizing HFOs for epilepsy surgery planning offers the promise of improved seizure outcomes for patients with medically refractory epilepsy. This review discusses possible machine learning strategies that can be applied to HFO biomarkers to better identify epileptogenic regions. We discuss the role of HFO rate, and utilizing features such as explicit HFO properties (spectral content, duration, and power) and phase-amplitude coupling for distinguishing pathological HFO (pHFO) events from physiological HFO events. In addition, the review highlights the importance of neuroanatomical localization in machine learning strategies.
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Affiliation(s)
- Shennan A Weiss
- Departments of Neurology & Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Zachary Waldman
- Departments of Neurology & Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Federico Raimondo
- Department of Computer Science, Faculty of Exact & Natural Sciences, University of Buenos Aires, Buenos Aires, Argentina.,Institute of Research in Computer Science, National Scientific & Technical Research Council, University of Buenos Aires, Buenos Aires, Argentina
| | - Diego Slezak
- Department of Computer Science, Faculty of Exact & Natural Sciences, University of Buenos Aires, Buenos Aires, Argentina.,Institute of Research in Computer Science, National Scientific & Technical Research Council, University of Buenos Aires, Buenos Aires, Argentina
| | - Mustafa Donmez
- Departments of Neurology & Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Gregory Worrell
- Department of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), Mayo Clinic, Rochester, MN 55905, USA
| | - Anatol Bragin
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Jerome Engel
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Richard Staba
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Michael Sperling
- Departments of Neurology & Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
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Ahmed OJ, Sudhakar SK. High-Frequency Activity During Stereotyped Low-Frequency Events Might Help to Identify the Seizure Onset Zone. Epilepsy Curr 2019; 19:1535759719842236. [PMID: 31012323 PMCID: PMC6610385 DOI: 10.1177/1535759719842236] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Stereotyped high-frequency oscillations discriminate seizure onset zones and critical functional cortex in focal epilepsy. Liu S, Gurses C, Sha Z, Quach MM, Sencer A, Bebek N, et al. Brain. 2018;141(3):713-730. doi:10.1093/brain/awx374. PMID: 29394328 . High-frequency oscillations in local field potentials recorded with intracranial electroencephalogram are putative biomarkers of seizure-onset zones in epileptic brain. However, localized 80- to 500-Hz oscillations can also be recorded from normal and nonepileptic cerebral structures. When defined only by rate or frequency, physiological high-frequency oscillations are indistinguishable from pathological ones that limit their application in epilepsy presurgical planning. We hypothesized that pathological high-frequency oscillations occur in a repetitive fashion with a similar waveform morphology that specifically indicates seizure onset zones. We investigated the waveform patterns of automatically detected high-frequency oscillations in 13 patients with epilepsy and 5 control subjects, with an average of 73 subdural and intracerebral electrodes recorded per patient. The repetitive oscillatory waveforms were identified using a pipeline of unsupervised machine learning techniques and were then correlated with independently clinician-defined seizure onset zones. Consistently in all patients, the stereotypical high-frequency oscillations with the highest degree of waveform similarity were localized within the seizure onset zones only, whereas the channels generating high-frequency oscillations embedded in random waveforms were found in the functional regions independent of the epileptogenic locations. The repetitive waveform pattern was more evident in fast ripples compared to ripples, suggesting a potential association between waveform repetition and the underlying pathological network. Our findings provided a new tool for the interpretation of pathological high-frequency oscillations that can be efficiently applied to distinguish seizure onset zones from functionally important sites, which is a critical step toward the translation of these signature events into valid clinical biomarkers.
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Li L, Bragin A, Staba R, Engel J. Unit firing and oscillations at seizure onset in epileptic rodents. Neurobiol Dis 2019; 127:382-389. [PMID: 30928646 DOI: 10.1016/j.nbd.2019.03.027] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 03/04/2019] [Accepted: 03/26/2019] [Indexed: 01/27/2023] Open
Abstract
Epileptic seizures result from a variety of pathophysiological processes, evidenced by different electrographic ictal onset patterns, as seen on direct brain recordings. The two most common electrographic patterns of focal ictal onset in patients are hypersynchronous (HYP) and low-voltage fast (LVF). Whereas LVF ictal onsets were believed to result from disinhibition; based on similarities with absence seizures, focal HYP ictal onsets were believed to result from increased synchronizing inhibition. Recent findings, however, suggest the differences between these seizure onset types are more complicated and, in some cases, the opposite of these concepts are true. The following review presents evidence that a reduction of tonic inhibition on small pathologically interconnected neuron (PIN) clusters generating pathological high-frequency oscillations (pHFOs), which reflect abnormal synchronously bursting neurons may be the cause of HYP ictal onsets. Increased inhibition preceding LVF ictal onsets are discussed in other reviews in this issue. We postulate that neuronal cell loss following epileptogenic insults can result in structural reorganization, giving rise to small PIN clusters, which generate pHFOs. These clusters have a heterogeneous distribution and are spatially stable over time. Studies have demonstrated that a transient reduction in tonic inhibition causes these clusters to increase in size. This could result in consolidation and synchronization of pHFOs until a critical mass leads to propagation of HYP ictal discharges. Viewed within a network neuroscience framework, local disturbances such as PIN clusters are likely to contribute to large-scale brain network alterations: a better understanding of these epileptogenic networks promises to elucidate mechanisms of ictogenesis, epileptogenesis, and certain comorbidities of epilepsy.
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Affiliation(s)
- Lin Li
- Department of Neurology, University of California, Los Angeles, CA, USA
| | - Anatol Bragin
- Department of Neurology, University of California, Los Angeles, CA, USA; Brain Research Institute, University of California, Los Angeles, CA, USA
| | - Richard Staba
- Department of Neurology, University of California, Los Angeles, CA, USA
| | - Jerome Engel
- Department of Neurology, University of California, Los Angeles, CA, USA; Brain Research Institute, University of California, Los Angeles, CA, USA; Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
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Phase Coherent Currents Underlying Neocortical Seizure-Like State Transitions. eNeuro 2019; 6:eN-NWR-0426-18. [PMID: 30923739 PMCID: PMC6437657 DOI: 10.1523/eneuro.0426-18.2019] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 02/25/2019] [Accepted: 03/01/2019] [Indexed: 02/07/2023] Open
Abstract
In the epileptic brain, phase amplitude cross-frequency coupling (CFC) features have been used to objectively classify seizure-related states, and the inter-seizure state has been demonstrated as being random, in contrast to the seizure state being predictable; however, the excitatory and inhibitory networks underlying their dynamics remain unclear. Therefore, the objectives of this study are to classify the dynamics of seizure sub-states labeling seizure-like event (SLE) onset and termination intervals using CFC features and to obtain their underlying excitatory/inhibitory cellular correlates. SLEs were induced in mouse neocortical brain slices using a low-magnesium perfusate, and were recorded in Layer II/III using simultaneous local field potential (LFP) and whole-cell voltage clamp electrodes. Classification of onset and termination of SLE transitions was investigated using CFC features in conjunction with an unsupervised two-state hidden Markov model (HMM). γ-Distributions of their durations indicated that both are predictable. Furthermore, omitting 4 Hz from the HMM classifier switched both SLE sub-states from statistically deterministic to random without changing the dynamics of the SLE state. These results were generalized to 4-aminopyridine (4-AP)-induced SLEs and human seizure traces. Only during these sub-states, both excitatory and inhibitory currents coupled with the field. Where excitatory currents phase locked to a broad range of frequencies between 1 and 12 Hz, inhibitory currents dominantly phase locked at 4 Hz. We conclude that inhibition underlies the predictability of neocortical CFC-defined SLE transition sub-states.
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Yin C, Zhang X, Chen Z, Li X, Wu S, Lv P, Wang Y. Detection and localization of interictal ripples with magnetoencephalography in the presurgical evaluation of drug-resistant insular epilepsy. Brain Res 2018; 1706:147-156. [PMID: 30408475 DOI: 10.1016/j.brainres.2018.11.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 09/29/2018] [Accepted: 11/03/2018] [Indexed: 01/15/2023]
Abstract
Precise noninvasive presurgical localization of insular epilepsy is important. The objective of the present study was to detect and localize interictal high-frequency oscillations (HFOs) in patients with insular epilepsy at the source levels using magnetoencephalography (MEG). We investigated whether HFOs can delineate epileptogenic areas. We analysed MEG data with new accumulated source imaging (HFOs, 80-250 Hz ripples during spikes) and conventional dipole modelling (spikes) methods for localizing epileptic foci. We evaluated the relationship of the resection of focal brain regions containing interictal HFOs and the spikes with the postsurgical seizure outcome. Interictal HFOs were localized in the insular epileptogenic zone (EZ) in 18 out of 21 patients undergoing surgical treatment for clinically diagnosed insular epilepsy. While dipole clusters of spikes were involved in the insular EZ in 15 patients. Both the HFOs and the dipole cluster were localized in the insula in 14 patients. The seizure-free percentage was 87% for the resection of brain regions generating HFOs, whereas 80% for the resection of brain regions generating spikes. There was a much higher chance of freedom from seizures with complete resection of the HFO-generating regions than with partial resection or no resection (P = 0.031). No such difference was seen for spike-generating regions. Our results suggest that HFOs from insular epilepsy could be noninvasively detected and quantitatively assessed with MEG technology. MEG HFOs (ripples during spikes) may be valuable for the localization of the epileptogenic zone in insular epilepsy.
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Affiliation(s)
- Chunli Yin
- Department of Neurology, Hebei Medical University, Shijiazhuang 050017, China
| | - Xiating Zhang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Beijing Key Laboratory of Neuromodulation, Beijing 100053, China; Center of Epilepsy, Beijing Institute for Brain Disorders, Capital Medical University, Beijing 100053, China
| | - Zheng Chen
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Beijing Key Laboratory of Neuromodulation, Beijing 100053, China; Center of Epilepsy, Beijing Institute for Brain Disorders, Capital Medical University, Beijing 100053, China
| | - Xin Li
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Beijing Key Laboratory of Neuromodulation, Beijing 100053, China; Center of Epilepsy, Beijing Institute for Brain Disorders, Capital Medical University, Beijing 100053, China
| | - Siqi Wu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Beijing Key Laboratory of Neuromodulation, Beijing 100053, China; Center of Epilepsy, Beijing Institute for Brain Disorders, Capital Medical University, Beijing 100053, China
| | - Peiyuan Lv
- Department of Neurology, Hebei Medical University, Shijiazhuang 050017, China; Department of Neurology, Hebei General Hospital, Shijiazhuang 050051, China.
| | - Yuping Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Beijing Key Laboratory of Neuromodulation, Beijing 100053, China; Center of Epilepsy, Beijing Institute for Brain Disorders, Capital Medical University, Beijing 100053, China.
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Waldman ZJ, Camarillo-Rodriguez L, Chervenova I, Berry B, Shimamoto S, Elahian B, Kucewicz M, Ganne C, He XS, Davis LA, Stein J, Das S, Gorniak R, Sharan AD, Gross R, Inman CS, Lega BC, Zaghloul K, Jobst BC, Davis KA, Wanda P, Khadjevand M, Tracy J, Rizzuto DS, Worrell G, Sperling M, Weiss SA. Ripple oscillations in the left temporal neocortex are associated with impaired verbal episodic memory encoding. Epilepsy Behav 2018; 88:33-40. [PMID: 30216929 PMCID: PMC6240385 DOI: 10.1016/j.yebeh.2018.08.018] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 08/14/2018] [Accepted: 08/15/2018] [Indexed: 11/30/2022]
Abstract
BACKGROUND We sought to determine if ripple oscillations (80-120 Hz), detected in intracranial electroencephalogram (iEEG) recordings of patients with epilepsy, correlate with an enhancement or disruption of verbal episodic memory encoding. METHODS We defined ripple and spike events in depth iEEG recordings during list learning in 107 patients with focal epilepsy. We used logistic regression models (LRMs) to investigate the relationship between the occurrence of ripple and spike events during word presentation and the odds of successful word recall following a distractor epoch and included the seizure onset zone (SOZ) as a covariate in the LRMs. RESULTS We detected events during 58,312 word presentation trials from 7630 unique electrode sites. The probability of ripple on spike (RonS) events was increased in the SOZ (p < 0.04). In the left temporal neocortex, RonS events during word presentation corresponded with a decrease in the odds ratio (OR) of successful recall, however, this effect only met significance in the SOZ (OR of word recall: 0.71, 95% confidence interval (CI): 0.59-0.85, n = 158 events, adaptive Hochberg, p < 0.01). Ripple on oscillation (RonO) events that occurred in the left temporal neocortex non-SOZ also correlated with decreased odds of successful recall (OR: 0.52, 95% CI: 0.34-0.80, n = 140, adaptive Hochberg, p < 0.01). Spikes and RonS that occurred during word presentation in the left middle temporal gyrus (MTG) correlated with the most significant decrease in the odds of successful recall, irrespective of the location of the SOZ (adaptive Hochberg, p < 0.01). CONCLUSION Ripples and spikes generated in the left temporal neocortex are associated with impaired verbal episodic memory encoding. Although physiological and pathological ripple oscillations were not distinguished during cognitive tasks, our results show an association of undifferentiated ripples with impaired encoding. The effect was sometimes specific to regions outside the SOZ, suggesting that widespread effects of epilepsy outside the SOZ may contribute to cognitive impairment.
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Affiliation(s)
- Zachary J. Waldman
- Dept. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, PA USA 19107
| | | | - Inna Chervenova
- Dept. of Pharmacology & Experimental Therapeutics, Thomas Jefferson University, Philadelphia, PA USA 19107
| | - Brent Berry
- Dept. of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL).,Dept. of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN USA 55905
| | - Shoichi Shimamoto
- Dept. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, PA USA 19107
| | - Bahareh Elahian
- Dept. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, PA USA 19107
| | - Michal Kucewicz
- Dept. of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL).,Dept. of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN USA 55905
| | - Chaitanya Ganne
- Dept. of Neurology, Thomas Jefferson University, Philadelphia, PA USA 19107
| | - Xiao-Song He
- Dept. of Neurology, Thomas Jefferson University, Philadelphia, PA USA 19107
| | - Leon A. Davis
- Dept. of Psychology, Mayo Clinic, Rochester, MN USA 55905
| | - Joel Stein
- Department of Radiology, Mayo Clinic, Rochester, MN USA 55905
| | - Sandhitsu Das
- Penn Image Computing and Science Laboratory, Department of Radiology, Mayo Clinic, Rochester, MN USA 55905.,Penn Memory Center, Department of Neurology, Mayo Clinic, Rochester, MN USA 55905
| | - Richard Gorniak
- Dept. of Radiology, Thomas Jefferson University, Philadelphia, PA USA 19107
| | - Ashwini D. Sharan
- Dept. of Neurosurgery, Thomas Jefferson University, Philadelphia, PA USA 19107
| | - Robert Gross
- Emory University, Dept. of Neurosurgery, Atlanta, GA USA 30322
| | - Cory S. Inman
- Emory University, Dept. of Neurosurgery, Atlanta, GA USA 30322
| | - Bradley C. Lega
- University of Texas Southwestern Medical Center, Dept. of Neurosurgery, Dallas, TX USA 75390
| | - Kareem Zaghloul
- Surgical Neurology Branch, NINDS, NIH, Bethesda, MD USA 20892
| | - Barbara C. Jobst
- Dartmouth-Hitchcock Medical Center, Dept. of Neurology, Lebanon, NH USA 03756
| | - Katheryn A. Davis
- Dept. of Neurology, University of Pennsylvania, Philadelphia, PA USA 19104
| | - Paul Wanda
- Dept. of Psychology, Mayo Clinic, Rochester, MN USA 55905
| | - Mehraneh Khadjevand
- Dept. of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL).,Dept. of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN USA 55905
| | - Joseph Tracy
- Dept. of Neurology, Thomas Jefferson University, Philadelphia, PA USA 19107
| | | | - Gregory Worrell
- Dept. of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL).,Dept. of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN USA 55905
| | - Michael Sperling
- Dept. of Neurology, Thomas Jefferson University, Philadelphia, PA USA 19107
| | - Shennan A. Weiss
- Dept. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, PA USA 19107
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Jiang G, Pu T, Li Z, Zhang X, Zhou R, Cao X, Yu J, Wang X. Lithium affects rat hippocampal electrophysiology and epileptic seizures in a dose dependent manner. Epilepsy Res 2018; 146:112-120. [DOI: 10.1016/j.eplepsyres.2018.07.021] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 07/20/2018] [Accepted: 07/27/2018] [Indexed: 12/14/2022]
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