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Arya R, Petito GT, Housekeeper J, Buroker J, Scholle C, Ervin B, Frink C, Horn PS, Liu W, Ruben M, Smith DF, Skoch J, Mangano FT, Greiner HM, Holland KD. Chronobiological Spatial Clusters of Cortical Regions in the Human Brain. J Clin Neurophysiol 2024:00004691-990000000-00176. [PMID: 39354656 DOI: 10.1097/wnp.0000000000001119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2024] Open
Abstract
PURPOSE We demonstrate that different regions of the cerebral cortex have different diurnal rhythms of spontaneously occurring high-frequency oscillations (HFOs). METHODS High-frequency oscillations were assessed with standard-of-care stereotactic electroencephalography in patients with drug-resistant epilepsy. To ensure generalizability of our findings beyond patients with drug-resistant epilepsy, we excluded stereotactic electroencephalography electrode contacts lying within seizure-onset zones, epileptogenic lesions, having frequent epileptiform activity, and excessive artifact. For each patient, we evaluated twenty-four 5-minute stereotactic electroencephalography epochs, sampled hourly throughout the day, and obtained the HFO rate (number of HFOs/minute) in every stereotactic electroencephalography channel. We analyzed diurnal rhythms of the HFO rates with the cosinor model and clustered neuroanatomic parcels in a standard brain space based on similarity of their cosinor parameters. Finally, we compared overlap among resting-state networks, described in the neuroimaging literature, and chronobiological spatial clusters discovered by us. RESULTS We found five clusters that localized predominantly or exclusively to the left perisylvian, left perirolandic and left temporal, right perisylvian and right parietal, right frontal, and right insular-opercular cortices, respectively. These clusters were characterized by similarity of the HFO rates according to the time of the day. Also, these chronobiological spatial clusters preferentially overlapped with specific resting-state networks, particularly default mode network (clusters 1 and 3), frontoparietal network (cluster 1), visual network (cluster 1), and mesial temporal network (cluster 2). CONCLUSIONS This is probably the first human study to report clusters of cortical regions with similar diurnal rhythms of electrographic activity. Overlap with resting-state networks attests to their functional significance and has implications for understanding cognitive functions and epilepsy-related mortality.
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Affiliation(s)
- Ravindra Arya
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, U.S.A
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, U.S.A
- Department of Computer Science, University of Cincinnati, Cincinnati, Ohio, U.S.A
| | - Gabrielle T Petito
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, U.S.A
| | - Jeremy Housekeeper
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, U.S.A
| | - Jason Buroker
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, U.S.A
| | - Craig Scholle
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, U.S.A
| | - Brian Ervin
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, U.S.A
| | - Clayton Frink
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, U.S.A
| | - Paul S Horn
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, U.S.A
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, U.S.A
| | - Wei Liu
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, U.S.A
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, U.S.A
- Division of Pulmonology, Center for Sleep Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, U.S.A
| | - Marc Ruben
- Division of Human Genetics, Circadian Biology Lab, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, U.S.A
| | - David F Smith
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, U.S.A
- Division of Pulmonology, Center for Sleep Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, U.S.A
- Division of Otolaryngology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, U.S.A.; and
| | - Jesse Skoch
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, U.S.A
- Division of Neurosurgery, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, U.S.A
| | - Francesco T Mangano
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, U.S.A
- Division of Neurosurgery, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, U.S.A
| | - Hansel M Greiner
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, U.S.A
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, U.S.A
| | - Katherine D Holland
- Division of Neurology, Comprehensive Epilepsy Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, U.S.A
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, U.S.A
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Jin BZ, Capiglioni M, Federspiel A, Ahmadli U, Schindler K, Kiefer C, Wiest R. Neuronal current imaging of epileptic activity: An MRI study in patients with a first unprovoked epileptic seizure. Epilepsia Open 2024; 9:1745-1757. [PMID: 38970780 DOI: 10.1002/epi4.13001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 06/07/2024] [Accepted: 06/11/2024] [Indexed: 07/08/2024] Open
Abstract
OBJECTIVE This study evaluates the performance of the novel MRI sequence stimulus-induced rotary saturation (SIRS) to map responses to interictal epileptic activity in the human cortex. Spin-lock pulses have been applied to indirectly detect neuronal activity through magnetic field perturbations. Following initial reports about the feasibility of the method in humans and animals with epilepsy, we aimed to investigate the diagnostic yield of spin-lock MR pulses in comparison with scalp-EEG in first seizure patients. METHODS We employed a novel method for measurements of neuronal activity through the detection of a resonant oscillating field, stimulus-induced rotary saturation contrast (SIRS) at spin-lock frequencies of 120 and 240 Hz acquired at a single 3T MRI system. Within a prospective observational study, we conducted SIRS experiments in 55 patients within 7 days after a suspected first unprovoked epileptic seizure and 61 healthy control subjects. In this study, we report on the analysis of data from a single 3T MRI system, encompassing 35 first seizure patients and 31 controls. RESULTS The SIRS method was applicable in all patients and healthy controls at frequencies of 120 and 240 Hz. We did not observe any significant age- or sex-related differences. Specificity of SIRS at 120 Hz was 90.3% and 93.5% at 240 Hz. Sensitivity was 17.1% at 120 Hz and 40.0% at 240 Hz. SIGNIFICANCE SIRS targets neuronal oscillating magnetic fields in patients with epilepsy. The coupling of presaturated spins to epilepsy-related magnetic field perturbations may serve as a-at this stage experimental-diagnostic test in first seizure patients to complement EEG findings as a standard screening test. PLAIN LANGUAGE SUMMARY Routine diagnostic tests carry several limitations when applied after a suspected first seizure. SIRS is a noninvasive MRI method to enable time-sensitive diagnosis of image correlates of epileptic activity with increased sensitivity compared to routine EEG.
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Affiliation(s)
- Baudouin Zongxin Jin
- Support Center of Advanced Neuroimaging, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Graduate School of Health Sciences, Medical Faculty, University of Bern, Bern, Switzerland
| | - Milena Capiglioni
- Support Center of Advanced Neuroimaging, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
| | - Andrea Federspiel
- Support Center of Advanced Neuroimaging, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
| | - Uzeyir Ahmadli
- Support Center of Advanced Neuroimaging, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
| | - Kaspar Schindler
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Claus Kiefer
- Support Center of Advanced Neuroimaging, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
| | - Roland Wiest
- Support Center of Advanced Neuroimaging, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
- Swiss Institute for Translational and Entrepreneurial Medicine, Sitem-Insel, Bern, Switzerland
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Kucewicz MT, Cimbalnik J, Garcia-Salinas JS, Brazdil M, Worrell GA. High frequency oscillations in human memory and cognition: a neurophysiological substrate of engrams? Brain 2024; 147:2966-2982. [PMID: 38743818 PMCID: PMC11370809 DOI: 10.1093/brain/awae159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/16/2024] [Accepted: 04/21/2024] [Indexed: 05/16/2024] Open
Abstract
Despite advances in understanding the cellular and molecular processes underlying memory and cognition, and recent successful modulation of cognitive performance in brain disorders, the neurophysiological mechanisms remain underexplored. High frequency oscillations beyond the classic electroencephalogram spectrum have emerged as a potential neural correlate of fundamental cognitive processes. High frequency oscillations are detected in the human mesial temporal lobe and neocortical intracranial recordings spanning gamma/epsilon (60-150 Hz), ripple (80-250 Hz) and higher frequency ranges. Separate from other non-oscillatory activities, these brief electrophysiological oscillations of distinct duration, frequency and amplitude are thought to be generated by coordinated spiking of neuronal ensembles within volumes as small as a single cortical column. Although the exact origins, mechanisms and physiological roles in health and disease remain elusive, they have been associated with human memory consolidation and cognitive processing. Recent studies suggest their involvement in encoding and recall of episodic memory with a possible role in the formation and reactivation of memory traces. High frequency oscillations are detected during encoding, throughout maintenance, and right before recall of remembered items, meeting a basic definition for an engram activity. The temporal coordination of high frequency oscillations reactivated across cortical and subcortical neural networks is ideally suited for integrating multimodal memory representations, which can be replayed and consolidated during states of wakefulness and sleep. High frequency oscillations have been shown to reflect coordinated bursts of neuronal assembly firing and offer a promising substrate for tracking and modulation of the hypothetical electrophysiological engram.
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Affiliation(s)
- Michal T Kucewicz
- BioTechMed Center, Brain & Mind Electrophysiology laboratory, Department of Multimedia Systems, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk 80-233, Poland
- Bioelectronics, Neurophysiology and Engineering Laboratory, Mayo Clinic, Departments of Neurology and Biomedical Engineering & Physiology, Mayo Clinic, Rochester, MN 55902, USA
| | - Jan Cimbalnik
- BioTechMed Center, Brain & Mind Electrophysiology laboratory, Department of Multimedia Systems, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk 80-233, Poland
- Department of Biomedical Engineering, St. Anne’s University Hospital in Brno & International Clinical Research Center, Brno 602 00, Czech Republic
- Brno Epilepsy Center, 1th Department of Neurology, St. Anne's University Hospital and Medical Faculty of Masaryk University, member of the ERN-EpiCARE, Brno 602 00, Czech Republic
| | - Jesus S Garcia-Salinas
- BioTechMed Center, Brain & Mind Electrophysiology laboratory, Department of Multimedia Systems, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk 80-233, Poland
| | - Milan Brazdil
- BioTechMed Center, Brain & Mind Electrophysiology laboratory, Department of Multimedia Systems, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk 80-233, Poland
- Brno Epilepsy Center, 1th Department of Neurology, St. Anne's University Hospital and Medical Faculty of Masaryk University, member of the ERN-EpiCARE, Brno 602 00, Czech Republic
- Behavioural and Social Neuroscience Research Group, CEITEC—Central European Institute of Technology, Masaryk University, Brno 625 00, Czech Republic
| | - Gregory A Worrell
- BioTechMed Center, Brain & Mind Electrophysiology laboratory, Department of Multimedia Systems, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk 80-233, Poland
- Bioelectronics, Neurophysiology and Engineering Laboratory, Mayo Clinic, Departments of Neurology and Biomedical Engineering & Physiology, Mayo Clinic, Rochester, MN 55902, USA
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Wilson W, Pittman DJ, Dykens P, Mosher V, Gill L, Peedicail J, George AG, Beers CA, Goodyear B, LeVan P, Federico P. The hemodynamic response to co-occurring interictal epileptiform discharges and high-frequency oscillations localizes the seizure-onset zone. Epilepsia 2024. [PMID: 39101302 DOI: 10.1111/epi.18071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 06/19/2024] [Accepted: 07/17/2024] [Indexed: 08/06/2024]
Abstract
OBJECTIVE To use intracranial electroencephalography (EEG) to characterize functional magnetic resonance imaging (fMRI) activation maps associated with high-frequency oscillations (HFOs) (80-250 Hz) and examine their proximity to HFO- and seizure-generating tissue. METHODS Forty-five patients implanted with intracranial depth electrodes underwent a simultaneous EEG-fMRI study at 3 T. HFOs were detected algorithmically from cleaned EEG and visually confirmed by an experienced electroencephalographer. HFOs that co-occurred with interictal epileptiform discharges (IEDs) were subsequently identified. fMRI activation maps associated with HFOs were generated that occurred either independently of IEDs or within ±200 ms of an IED. For all significant analyses, the Maximum, Second Maximum, and Closest activation clusters were identified, and distances were measured to both the electrodes where the HFOs were observed and the electrodes involved in seizure onset. RESULTS We identified 108 distinct groups of HFOs from 45 patients. We found that HFOs with IEDs produced fMRI clusters that were closer to the local field potentials of the corresponding HFOs observed within the EEG than HFOs without IEDs. In addition to the fMRI clusters being closer to the location of the EEG correlate, HFOs with IEDs generated Maximum clusters with greater z-scores and larger volumes than HFOs without IEDs. We also observed that HFOs with IEDs resulted in more discrete activation maps. SIGNIFICANCE Intracranial EEG-fMRI can be used to probe the hemodynamic response to HFOs. The hemodynamic response associated with HFOs that co-occur with IEDs better identifies known epileptic tissue than HFOs that occur independently.
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Affiliation(s)
- William Wilson
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Daniel J Pittman
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Perry Dykens
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Victoria Mosher
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Laura Gill
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Joseph Peedicail
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Antis G George
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Craig A Beers
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Bradley Goodyear
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Pierre LeVan
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Paolo Federico
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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Li Z, Zhao B, Hu W, Zhang C, Wang X, Liu C, Mo J, Guo Z, Yang B, Yao Y, Shao X, Zhang J, Zhang K. Practical measurements distinguishing physiological and pathological stereoelectroencephalography channels based on high-frequency oscillations in the human brain. Epilepsia Open 2024; 9:1287-1299. [PMID: 38808652 PMCID: PMC11296094 DOI: 10.1002/epi4.12950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 04/07/2024] [Accepted: 04/11/2024] [Indexed: 05/30/2024] Open
Abstract
OBJECTIVE The present study aimed to identify various distinguishing features for use in the accurate classification of stereoelectroencephalography (SEEG) channels based on high-frequency oscillations (HFOs) inside and outside the epileptogenic zone (EZ). METHODS HFOs were detected in patients with focal epilepsy who underwent SEEG. Subsequently, HFOs within the seizure-onset and early spread zones were defined as pathological HFOs, whereas others were defined as physiological. Three features of HFOs were identified at the channel level, namely, morphological repetition, rhythmicity, and phase-amplitude coupling (PAC). A machine-learning (ML) classifier was then built to distinguish two HFO types at the channel level by application of the above-mentioned features, and the contributions were quantified. Further verification of the characteristics and classifier performance was performed in relation to various conscious states, imaging results, EZ location, and surgical outcomes. RESULTS Thirty-five patients were included in this study, from whom 166 104 pathological HFOs in 255 channels and 53 374 physiological HFOs in 282 channels were entered into the analysis pipeline. The results revealed that the morphological repetitions of pathological HFOs were markedly higher than those of the physiological HFOs; this was also observed for rhythmicity and PAC. The classifier exhibited high accuracy in differentiating between the two forms of HFOs, as indicated by an area under the curve (AUC) of 0.89. Both PAC and rhythmicity contributed significantly to this distinction. The subgroup analyses supported these findings. SIGNIFICANCE The suggested HFO features can accurately distinguish between pathological and physiological channels substantially improving its usefulness in clinical localization. PLAIN LANGUAGE SUMMARY In this study, we computed three quantitative features associated with HFOs in each SEEG channel and then constructed a machine learning-based classifier for the classification of pathological and physiological channels. The classifier performed well in distinguishing the two channel types under different levels of consciousness as well as in terms of imaging results, EZ location, and patient surgical outcomes.
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Affiliation(s)
- Zilin Li
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical InstituteCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of NeurostimulationBeijingChina
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Chang Liu
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Zhihao Guo
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Bowen Yang
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Yuan Yao
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Xiaoqiu Shao
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical InstituteCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of NeurostimulationBeijingChina
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical InstituteCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of NeurostimulationBeijingChina
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Guisande N, Montani F. Rényi entropy-complexity causality space: a novel neurocomputational tool for detecting scale-free features in EEG/iEEG data. Front Comput Neurosci 2024; 18:1342985. [PMID: 39081659 PMCID: PMC11287776 DOI: 10.3389/fncom.2024.1342985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 06/21/2024] [Indexed: 08/02/2024] Open
Abstract
Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforward means of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed the most variation across different modes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders.
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Affiliation(s)
| | - Fernando Montani
- Instituto de Física de La Plata (IFLP), Consejo Nacional de Investigaciones Científicas y Técnicas – Universidad Nacional de La Plata (CONICET-UNLP), La Plata, Buenos Aires, Argentina
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Zhang Y, Daida A, Liu L, Kuroda N, Ding Y, Oana S, Monsoor T, Hussain SA, Qiao JX, Salamon N, Fallah A, Sim MS, Sankar R, Staba RJ, Engel J, Asano E, Roychowdhury V, Nariai H. Discovering Neurophysiological Characteristics of Pathological High-Frequency Oscillations in Epilepsy with an Explainable Deep Generative Model. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.10.24310189. [PMID: 39040207 PMCID: PMC11261948 DOI: 10.1101/2024.07.10.24310189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Interictal high-frequency oscillation (HFO) is a promising biomarker of the epileptogenic zone (EZ). However, objective definitions to distinguish between pathological and physiological HFOs have remained elusive, impeding HFOs' clinical applications. We employed self-supervised deep generative variational autoencoders to learn such discriminative HFO features directly from their morphologies in a data-driven manner. We studied a large retrospective cohort of 185 patients who underwent intracranial monitoring and analyzed 686,410 candidate HFO events collected from 18,265 brain contacts across diverse brain regions. The model automatically clustered HFOs into distinct morphological groups in the latent space. One cluster consisted of putative morphologically defined pathological HFOs (mpHFOs): HFOs in that cluster were observed to be associated with spikes and exhibited high signal intensity both in the HFO band (>80 Hz) at detection and in the sub-HFO band (10-80 Hz) surrounding the detection and were primarily localized in the seizure onset zone (SOZ). Moreover, resection of brain regions based on a higher prevalence of interictal mpHFOs better predicted postoperative seizure outcomes than current clinical standards based on SOZ removal. Our self-supervised, explainable, deep generative model distills pathological HFOs and thus potentially helps delineate the EZ purely from interictal intracranial EEG data.
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Affiliation(s)
- Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Atsuro Daida
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Lawrence Liu
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Naoto Kuroda
- Department of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, MI, USA
| | - Yuanyi Ding
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Shingo Oana
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Shaun A. Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Joe X Qiao
- Division of Neuroradiology, Department of Radiology, UCLA Medical Center, David Geffen School of Medicine, Los 6 Angeles, CA, USA
| | - Noriko Salamon
- Division of Neuroradiology, Department of Radiology, UCLA Medical Center, David Geffen School of Medicine, Los 6 Angeles, CA, USA
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Myung Shin Sim
- Department of Medicine, Statistics Core, University of California, Los Angeles, CA, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Richard J. Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Jerome Engel
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Neurobiology, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
- The Brain Research Institute, University of California, Los Angeles, CA, USA
| | - Eishi Asano
- Department of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, MI, USA
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children's Discovery and Innovation Institute, Los Angeles, CA, USA
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Shi W, Shaw D, Walsh KG, Han X, Eden UT, Richardson RM, Gliske SV, Jacobs J, Brinkmann BH, Worrell GA, Stacey WC, Frauscher B, Thomas J, Kramer MA, Chu CJ. Spike ripples localize the epileptogenic zone best: an international intracranial study. Brain 2024; 147:2496-2506. [PMID: 38325327 PMCID: PMC11224608 DOI: 10.1093/brain/awae037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 12/10/2023] [Accepted: 01/19/2024] [Indexed: 02/09/2024] Open
Abstract
We evaluated whether spike ripples, the combination of epileptiform spikes and ripples, provide a reliable and improved biomarker for the epileptogenic zone compared with other leading interictal biomarkers in a multicentre, international study. We first validated an automated spike ripple detector on intracranial EEG recordings. We then applied this detector to subjects from four centres who subsequently underwent surgical resection with known 1-year outcomes. We evaluated the spike ripple rate in subjects cured after resection [International League Against Epilepsy Class 1 outcome (ILAE 1)] and those with persistent seizures (ILAE 2-6) across sites and recording types. We also evaluated available interictal biomarkers: spike, spike-gamma, wideband high frequency oscillation (HFO, 80-500 Hz), ripple (80-250 Hz) and fast ripple (250-500 Hz) rates using previously validated automated detectors. The proportion of resected events was computed and compared across subject outcomes and biomarkers. Overall, 109 subjects were included. Most spike ripples were removed in subjects with ILAE 1 outcome (P < 0.001), and this was qualitatively observed across all sites and for depth and subdural electrodes (P < 0.001 and P < 0.001, respectively). Among ILAE 1 subjects, the mean spike ripple rate was higher in the resected volume (0.66/min) than in the non-removed tissue (0.08/min, P < 0.001). A higher proportion of spike ripples were removed in subjects with ILAE 1 outcomes compared with ILAE 2-6 outcomes (P = 0.06). Among ILAE 1 subjects, the proportion of spike ripples removed was higher than the proportion of spikes (P < 0.001), spike-gamma (P < 0.001), wideband HFOs (P < 0.001), ripples (P = 0.009) and fast ripples (P = 0.009) removed. At the individual level, more subjects with ILAE 1 outcomes had the majority of spike ripples removed (79%, 38/48) than spikes (69%, P = 0.12), spike-gamma (69%, P = 0.12), wideband HFOs (63%, P = 0.03), ripples (45%, P = 0.01) or fast ripples (36%, P < 0.001) removed. Thus, in this large, multicentre cohort, when surgical resection was successful, the majority of spike ripples were removed. Furthermore, automatically detected spike ripples localize the epileptogenic tissue better than spikes, spike-gamma, wideband HFOs, ripples and fast ripples.
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Affiliation(s)
- Wen Shi
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Dana Shaw
- Graduate Program in Neuroscience, Boston University, Boston, MA 02215, USA
| | - Katherine G Walsh
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Xue Han
- Center for Systems Neuroscience, Boston University, Boston, MA 02215, USA
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Uri T Eden
- Center for Systems Neuroscience, Boston University, Boston, MA 02215, USA
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
| | - Robert M Richardson
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Stephen V Gliske
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Julia Jacobs
- Department of Neuropediatrics and Muscle Disorders, Medical Center, University of Freiburg, Freiburg 79106, Germany
- Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
- Department of Neuroscience, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
- Hotchkiss Brain Institute and Alberta Children’s Hospital Research Institute, University of Calgary, Calgary T2N 1N4, AB, Canada
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, MN 55905, USA
| | - Gregory A Worrell
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, MN 55905, USA
| | - William C Stacey
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Birgit Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 0G4, Canada
- Analytical Neurophysiology Lab, Department of Neurology, Duke University Medical Center, Durham, NC 27710, USA
- Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC 27708, USA
| | - John Thomas
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 0G4, Canada
| | - Mark A Kramer
- Center for Systems Neuroscience, Boston University, Boston, MA 02215, USA
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA
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9
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Guo J, Wang Z, van 't Klooster MA, Van Der Salm SM, Leijten FS, Braun KP, Zijlmans M. Seizure Outcome After Intraoperative Electrocorticography-Tailored Epilepsy Surgery: A Systematic Review and Meta-Analysis. Neurology 2024; 102:e209430. [PMID: 38768406 PMCID: PMC11175635 DOI: 10.1212/wnl.0000000000209430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 03/12/2024] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Tailoring epilepsy surgery using intraoperative electrocorticography (ioECoG) has been debated, and modest number of epilepsy surgery centers apply this diagnostic method. We assessed the current evidence to use ioECoG-tailored epilepsy surgery for improving postsurgical outcome. METHODS PubMed and Embase were searched for original studies reporting on ≥10 cases who underwent ioECoG-tailored surgery for epilepsy, with a follow-up of at least 6 months. We used a random-effects model to calculate the overall rate of patients achieving favorable seizure outcome (FSO), defined as Engel class I, ILAE class 1, or seizure-free status. Meta-regression was used to investigate potential sources of heterogeneity. We calculated the odds ratio (OR) for estimating variables on FSO:ioECoG vs non-ioECoG-tailored surgery (if included studies contained patients with non-ioECoG-tailored surgery), ioECoG-tailored epilepsy surgery in children vs adults, temporal (TL) vs extratemporal lobe (eTL), MRI-positive vs MRI-negative, and complete vs incomplete resection of tissue that generated interictal epileptiform discharges (IEDs). A Bayesian network meta-analysis was conducted for underlying pathologies. We assessed the evidence certainty using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE). RESULTS Eighty-three studies (82 observational studies, 1 trial) comprising 3,631 patients with ioECoG-tailored surgery were included. The overall pooled rate of patients who attained FSO after ioECoG-tailored surgery was 74% (95% CI 71-77) with significant heterogeneity, which was predominantly attributed to pathologies and seizure outcome classifications. Twenty-two studies contained non-ioECoG-tailored surgeries. IoECoG-tailored surgeries reached a higher rate of FSO than non-ioECoG-tailored surgeries (OR 2.10 [95% CI 1.37-3.24]; p < 0.01; very low certainty). Complete resection of tissue that displayed IEDs in ioECoG predicted FSO better compared with incomplete resection (OR 3.04 [1.76-5.25]; p < 0.01; low certainty). We found insignificant difference in FSO after ioECoG-tailored surgery in children vs adults, TL vs eTL, or MRI-positive vs MRI-negative. The network meta-analysis showed that the odds of FSO was lower for malformations of cortical development than for tumors (OR 0.47 95% credible interval 0.25-0.87). DISCUSSION Although limited by low-quality evidence, our meta-analysis shows a relatively good surgical outcome (74% FSO) after epilepsy surgery with ioECoG, especially in tumors, with better outcome for ioECoG-tailored surgeries in studies describing both and better outcome after complete removal of IED areas.
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Affiliation(s)
- Jiaojiao Guo
- From the Department of Neurology and Neurosurgery (J.G., Z.W., M.A.K., S.M.V.D.S., F.S.L., K.P.B., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), the Netherlands
| | - Ziyi Wang
- From the Department of Neurology and Neurosurgery (J.G., Z.W., M.A.K., S.M.V.D.S., F.S.L., K.P.B., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), the Netherlands
| | - Maryse A van 't Klooster
- From the Department of Neurology and Neurosurgery (J.G., Z.W., M.A.K., S.M.V.D.S., F.S.L., K.P.B., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), the Netherlands
| | - Sandra M Van Der Salm
- From the Department of Neurology and Neurosurgery (J.G., Z.W., M.A.K., S.M.V.D.S., F.S.L., K.P.B., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), the Netherlands
| | - Frans S Leijten
- From the Department of Neurology and Neurosurgery (J.G., Z.W., M.A.K., S.M.V.D.S., F.S.L., K.P.B., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), the Netherlands
| | - Kees P Braun
- From the Department of Neurology and Neurosurgery (J.G., Z.W., M.A.K., S.M.V.D.S., F.S.L., K.P.B., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), the Netherlands
| | - Maeike Zijlmans
- From the Department of Neurology and Neurosurgery (J.G., Z.W., M.A.K., S.M.V.D.S., F.S.L., K.P.B., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), the Netherlands
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10
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Cai Z, Jiang X, Bagić A, Worrell GA, Richardson M, He B. Spontaneous HFO Sequences Reveal Propagation Pathways for Precise Delineation of Epileptogenic Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.02.592202. [PMID: 38746136 PMCID: PMC11092614 DOI: 10.1101/2024.05.02.592202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Epilepsy, a neurological disorder affecting millions worldwide, poses great challenges in precisely delineating the epileptogenic zone - the brain region generating seizures - for effective treatment. High-frequency oscillations (HFOs) are emerging as promising biomarkers; however, the clinical utility is hindered by the difficulties in distinguishing pathological HFOs from non- epileptiform activities at single electrode and single patient resolution and understanding their dynamic role in epileptic networks. Here, we introduce an HFO-sequencing approach to analyze spontaneous HFOs traversing cortical regions in 40 drug-resistant epilepsy patients. This data- driven method automatically detected over 8.9 million HFOs, pinpointing pathological HFO- networks, and unveiled intricate millisecond-scale spatiotemporal dynamics, stability, and functional connectivity of HFOs in prolonged intracranial EEG recordings. These HFO sequences demonstrated a significant improvement in localization of epileptic tissue, with an 818.47% increase in concordance with seizure-onset zone (mean error: 2.92 mm), compared to conventional benchmarks. They also accurately predicted seizure outcomes for 90% AUC based on pre-surgical information using generalized linear models. Importantly, this mapping remained reliable even with short recordings (mean standard deviation: 3.23 mm for 30-minute segments). Furthermore, HFO sequences exhibited distinct yet highly repetitive spatiotemporal patterns, characterized by pronounced synchrony and predominant inward information flow from periphery towards areas involved in propagation, suggesting a crucial role for excitation-inhibition balance in HFO initiation and progression. Together, these findings shed light on the intricate organization of epileptic network and highlight the potential of HFO-sequencing as a translational tool for improved diagnosis, surgical targeting, and ultimately, better outcomes for vulnerable patients with drug-resistant epilepsy. One Sentence Summary Pathological fast brain oscillations travel like traffic along varied routes, outlining recurrently visited neural sites emerging as critical hotspots in epilepsy network.
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11
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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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12
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Li Y, Cao D, Qu J, Wang W, Xu X, Kong L, Liao J, Hu W, Zhang K, Wang J, Li C, Yang X, Zhang X. Automatic Detection of Scalp High-Frequency Oscillations Based on Deep Learning. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1627-1636. [PMID: 38625771 DOI: 10.1109/tnsre.2024.3389010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2024]
Abstract
Scalp high-frequency oscillations (sHFOs) are a promising non-invasive biomarker of epilepsy. However, the visual marking of sHFOs is a time-consuming and subjective process, existing automatic detectors based on single-dimensional analysis have difficulty with accurately eliminating artifacts and thus do not provide sufficient reliability to meet clinical needs. Therefore, we propose a high-performance sHFOs detector based on a deep learning algorithm. An initial detection module was designed to extract candidate high-frequency oscillations. Then, one-dimensional (1D) and two-dimensional (2D) deep learning models were designed, respectively. Finally, the weighted voting method is used to combine the outputs of the two model. In experiments, the precision, recall, specificity and F1-score were 83.44%, 83.60%, 96.61% and 83.42%, respectively, on average and the kappa coefficient was 80.02%. In addition, the proposed detector showed a stable performance on multi-centre datasets. Our sHFOs detector demonstrated high robustness and generalisation ability, which indicates its potential applicability as a clinical assistance tool. The proposed sHFOs detector achieves an accurate and robust method via deep learning algorithm.
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13
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Přibylová L, Ševčík J, Eclerová V, Klimeš P, Brázdil M, Meijer HGE. Weak coupling of neurons enables very high-frequency and ultra-fast oscillations through the interplay of synchronized phase shifts. Netw Neurosci 2024; 8:293-318. [PMID: 38562290 PMCID: PMC10954350 DOI: 10.1162/netn_a_00351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 11/21/2023] [Indexed: 04/04/2024] Open
Abstract
Recently, in the past decade, high-frequency oscillations (HFOs), very high-frequency oscillations (VHFOs), and ultra-fast oscillations (UFOs) were reported in epileptic patients with drug-resistant epilepsy. However, to this day, the physiological origin of these events has yet to be understood. Our study establishes a mathematical framework based on bifurcation theory for investigating the occurrence of VHFOs and UFOs in depth EEG signals of patients with focal epilepsy, focusing on the potential role of reduced connection strength between neurons in an epileptic focus. We demonstrate that synchronization of a weakly coupled network can generate very and ultra high-frequency signals detectable by nearby microelectrodes. In particular, we show that a bistability region enables the persistence of phase-shift synchronized clusters of neurons. This phenomenon is observed for different hippocampal neuron models, including Morris-Lecar, Destexhe-Paré, and an interneuron model. The mechanism seems to be robust for small coupling, and it also persists with random noise affecting the external current. Our findings suggest that weakened neuronal connections could contribute to the production of oscillations with frequencies above 1000 Hz, which could advance our understanding of epilepsy pathology and potentially improve treatment strategies. However, further exploration of various coupling types and complex network models is needed.
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Affiliation(s)
- Lenka Přibylová
- Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Jan Ševčík
- Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Veronika Eclerová
- Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Petr Klimeš
- Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic
| | - Milan Brázdil
- Brno Epilepsy Center, Dept. of Neurology, St. Anne’s Univ. Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic, member of the ERN EpiCARE
- Behavioral and Social Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Hil G. E. Meijer
- Department of Applied Mathematics, Techmed Centre, University of Twente, Enschede, The Netherlands
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14
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Frauscher B, Rossetti AO, Beniczky S. Recent advances in clinical electroencephalography. Curr Opin Neurol 2024; 37:134-140. [PMID: 38230652 DOI: 10.1097/wco.0000000000001246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Abstract
PURPOSE OF REVIEW Clinical electroencephalography (EEG) is a conservative medical field. This explains likely the significant gap between clinical practice and new research developments. This narrative review discusses possible causes of this discrepancy and how to circumvent them. More specifically, we summarize recent advances in three applications of clinical EEG: source imaging (ESI), high-frequency oscillations (HFOs) and EEG in critically ill patients. RECENT FINDINGS Recently published studies on ESI provide further evidence for the accuracy and clinical utility of this method in the multimodal presurgical evaluation of patients with drug-resistant focal epilepsy, and opened new possibilities for further improvement of the accuracy. HFOs have received much attention as a novel biomarker in epilepsy. However, recent studies questioned their clinical utility at the level of individual patients. We discuss the impediments, show up possible solutions and highlight the perspectives of future research in this field. EEG in the ICU has been one of the major driving forces in the development of clinical EEG. We review the achievements and the limitations in this field. SUMMARY This review will promote clinical implementation of recent advances in EEG, in the fields of ESI, HFOs and EEG in the intensive care.
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Affiliation(s)
- Birgit Frauscher
- Department of Neurology, Duke University Medical Center & Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, North Carolina, USA
| | - Andrea O Rossetti
- Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund
- Aarhus University Hospital, Aarhus, Denmark
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15
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Latreille V, Avigdor T, Thomas J, Crane J, Sziklas V, Jones-Gotman M, Frauscher B. Scalp and hippocampal sleep correlates of memory function in drug-resistant temporal lobe epilepsy. Sleep 2024; 47:zsad228. [PMID: 37658793 PMCID: PMC10851866 DOI: 10.1093/sleep/zsad228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/22/2023] [Indexed: 09/05/2023] Open
Abstract
Seminal animal studies demonstrated the role of sleep oscillations such as cortical slow waves, thalamocortical spindles, and hippocampal ripples in memory consolidation. In humans, whether ripples are involved in sleep-related memory processes is less clear. Here, we explored the interactions between sleep oscillations (measured as traits) and general episodic memory abilities in 26 adults with drug-resistant temporal lobe epilepsy who performed scalp-intracranial electroencephalographic recordings and neuropsychological testing, including two analogous hippocampal-dependent verbal and nonverbal memory tasks. We explored the relationships between hemispheric scalp (spindles, slow waves) and hippocampal physiological and pathological oscillations (spindles, slow waves, ripples, and epileptic spikes) and material-specific memory function. To differentiate physiological from pathological ripples, we used multiple unbiased data-driven clustering approaches. At the individual level, we found material-specific cerebral lateralization effects (left-verbal memory, right-nonverbal memory) for all scalp spindles (rs > 0.51, ps < 0.01) and fast spindles (rs > 0.61, ps < 0.002). Hippocampal epileptic spikes and short pathological ripples, but not physiological oscillations, were negatively (rs > -0.59, ps < 0.01) associated with verbal learning and retention scores, with left lateralizing and antero-posterior effects. However, data-driven clustering failed to separate the ripple events into defined clusters. Correlation analyses with the resulting clusters revealed no meaningful or significant associations with the memory scores. Our results corroborate the role of scalp spindles in memory processes in patients with drug-resistant temporal lobe epilepsy. Yet, physiological and pathological ripples were not separable when using data-driven clustering, and thus our findings do not provide support for a role of sleep ripples as trait-like characteristics of general memory abilities in epilepsy.
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Affiliation(s)
- Véronique Latreille
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| | - Tamir Avigdor
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| | - John Thomas
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| | - Joelle Crane
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
- Department of Psychology, McGill University, Montreal, Canada
| | - Viviane Sziklas
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
- Department of Psychology, McGill University, Montreal, Canada
| | - Marilyn Jones-Gotman
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
- Department of Psychology, McGill University, Montreal, Canada
| | - Birgit Frauscher
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
- Analytical Neurophysiology (ANPHY) Lab, Duke University Medical Center, Durham, NC, USA
- Department of Neurology, Duke University Medical Center, Durham, NC, USA
- Department of Biomedical Engineering. Duke Pratt School of Engineering, Durham NC, USA
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16
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Lin J, Smith GC, Gliske SV, Zochowski M, Shedden K, Stacey WC. High frequency oscillation network dynamics predict outcome in non-palliative epilepsy surgery. Brain Commun 2024; 6:fcae032. [PMID: 38384998 PMCID: PMC10881100 DOI: 10.1093/braincomms/fcae032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 12/28/2023] [Accepted: 02/05/2024] [Indexed: 02/23/2024] Open
Abstract
High frequency oscillations are a promising biomarker of outcome in intractable epilepsy. Prior high frequency oscillation work focused on counting high frequency oscillations on individual channels, and it is still unclear how to translate those results into clinical care. We show that high frequency oscillations arise as network discharges that have valuable properties as predictive biomarkers. Here, we develop a tool to predict patient outcome before surgical resection is performed, based on only prospective information. In addition to determining high frequency oscillation rate on every channel, we performed a correlational analysis to evaluate the functional connectivity of high frequency oscillations in 28 patients with intracranial electrodes. We found that high frequency oscillations were often not solitary events on a single channel, but part of a local network discharge. Eigenvector and outcloseness centrality were used to rank channel importance within the connectivity network, then used to compare patient outcome by comparison with the seizure onset zone or a proportion within the proposed resected channels (critical resection percentage). Combining the knowledge of each patient's seizure onset zone resection plan along with our computed high frequency oscillation network centralities and high frequency oscillation rate, we develop a Naïve Bayes model that predicts outcome (positive predictive value: 100%) better than predicting based upon fully resecting the seizure onset zone (positive predictive value: 71%). Surgical margins had a large effect on outcomes: non-palliative patients in whom most of the seizure onset zone was resected ('definitive surgery', ≥ 80% resected) had predictable outcomes, whereas palliative surgeries (<80% resected) were not predictable. These results suggest that the addition of network properties of high frequency oscillations is more accurate in predicting patient outcome than seizure onset zone alone in patients with most of the seizure onset zone removed and offer great promise for informing clinical decisions in surgery for refractory epilepsy.
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Affiliation(s)
- Jack Lin
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Garnett C Smith
- Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Stephen V Gliske
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Michal Zochowski
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Physics and Biophysics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kerby Shedden
- Department of Statistics and Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - William C Stacey
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Division of Neurology, Ann Arbor VA Health System, Ann Arbor, MI 48109, USA
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17
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Frauscher B, Mansilla D, Abdallah C, Astner-Rohracher A, Beniczky S, Brazdil M, Gnatkovsky V, Jacobs J, Kalamangalam G, Perucca P, Ryvlin P, Schuele S, Tao J, Wang Y, Zijlmans M, McGonigal A. Learn how to interpret and use intracranial EEG findings. Epileptic Disord 2024; 26:1-59. [PMID: 38116690 DOI: 10.1002/epd2.20190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 10/21/2023] [Accepted: 11/29/2023] [Indexed: 12/21/2023]
Abstract
Epilepsy surgery is the therapy of choice for many patients with drug-resistant focal epilepsy. Recognizing and describing ictal and interictal patterns with intracranial electroencephalography (EEG) recordings is important in order to most efficiently leverage advantages of this technique to accurately delineate the seizure-onset zone before undergoing surgery. In this seminar in epileptology, we address learning objective "1.4.11 Recognize and describe ictal and interictal patterns with intracranial recordings" of the International League against Epilepsy curriculum for epileptologists. We will review principal considerations of the implantation planning, summarize the literature for the most relevant ictal and interictal EEG patterns within and beyond the Berger frequency spectrum, review invasive stimulation for seizure and functional mapping, discuss caveats in the interpretation of intracranial EEG findings, provide an overview on special considerations in children and in subdural grids/strips, and review available quantitative/signal analysis approaches. To be as practically oriented as possible, we will provide a mini atlas of the most frequent EEG patterns, highlight pearls for its not infrequently challenging interpretation, and conclude with two illustrative case examples. This article shall serve as a useful learning resource for trainees in clinical neurophysiology/epileptology by providing a basic understanding on the concepts of invasive intracranial EEG.
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Affiliation(s)
- B Frauscher
- Department of Neurology, Duke University Medical Center and Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, North Carolina, USA
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, Montreal, Québec, Canada
| | - D Mansilla
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, Montreal, Québec, Canada
- Neurophysiology Unit, Institute of Neurosurgery Dr. Asenjo, Santiago, Chile
| | - C Abdallah
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, Montreal, Québec, Canada
| | - A Astner-Rohracher
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - S Beniczky
- Danish Epilepsy Centre, Dianalund, Denmark
- Aarhus University, Aarhus, Denmark
| | - M Brazdil
- Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital and Medical Faculty of Masaryk University, Member of the ERN-EpiCARE, Brno, Czechia
- Behavioral and Social Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - V Gnatkovsky
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - J Jacobs
- Department of Paediatrics and Department of Neuroscience, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute and Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - G Kalamangalam
- Department of Neurology, University of Florida, Gainesville, Florida, USA
- Wilder Center for Epilepsy Research, University of Florida, Gainesville, Florida, USA
| | - P Perucca
- Epilepsy Research Centre, Department of Medicine (Austin Health), University of Melbourne, Melbourne, Victoria, Australia
- Bladin-Berkovic Comprehensive Epilepsy Program, Department of Neurology, Austin Health, Melbourne, Victoria, Australia
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
- Department of Neurology, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - P Ryvlin
- Department of Clinical Neurosciences, CHUV, Lausanne University Hospital, Lausanne, Switzerland
| | - S Schuele
- Department of Neurology, Feinberg School of Medicine, Northwestern Memorial Hospital, Chicago, Illinois, USA
| | - J Tao
- Department of Neurology, The University of Chicago, Chicago, Illinois, USA
| | - Y Wang
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Wilder Center for Epilepsy Research, University of Florida, Gainesville, Florida, USA
| | - M Zijlmans
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
| | - A McGonigal
- Department of Neurosciences, Mater Misericordiae Hospital, Brisbane, Queensland, Australia
- Mater Research Institute, Faculty of Medicine, University of Queensland, St Lucia, Queensland, Australia
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18
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Geller AS, Teale P, Kronberg E, Ebersole JS. Magnetoencephalography for Epilepsy Presurgical Evaluation. Curr Neurol Neurosci Rep 2024; 24:35-46. [PMID: 38148387 DOI: 10.1007/s11910-023-01328-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/13/2023] [Indexed: 12/28/2023]
Abstract
PURPOSE OF THE REVIEW Magnetoencephalography (MEG) is a functional neuroimaging technique that records neurophysiology data with millisecond temporal resolution and localizes it with subcentimeter accuracy. Its capability to provide high resolution in both of these domains makes it a powerful tool both in basic neuroscience as well as clinical applications. In neurology, it has proven useful in its ability to record and localize epileptiform activity. Epilepsy workup typically begins with scalp electroencephalography (EEG), but in many situations, EEG-based localization of the epileptogenic zone is inadequate. The complementary sensitivity of MEG can be crucial in such cases, and MEG has been adopted at many centers as an important resource in building a surgical hypothesis. In this paper, we review recent work evaluating the extent of MEG influence of presurgical evaluations, novel analyses of MEG data employed in surgical workup, and new MEG instrumentation that will likely affect the field of clinical MEG. RECENT FINDINGS MEG consistently contributes to presurgical evaluation and these contributions often change the plan for epilepsy surgery. Extensive work has been done to develop new analytic methods for localizing the source of epileptiform activity with MEG. Systems using optically pumped magnetometry (OPM) have been successfully deployed to record and localize epileptiform activity. MEG remains an important noninvasive tool for epilepsy presurgical evaluation. Continued improvements in analytic methodology will likely increase the diagnostic yield of the test. Novel instrumentation with OPM may contribute to this as well, and may increase accessibility of MEG by decreasing cost.
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Affiliation(s)
- Aaron S Geller
- Department of Neurology, CU Anschutz Medical School, Aurora, CO, USA.
| | - Peter Teale
- Department of Neurology, CU Anschutz Medical School, Aurora, CO, USA
| | - Eugene Kronberg
- Department of Neurology, CU Anschutz Medical School, Aurora, CO, USA
| | - John S Ebersole
- Department of Neurology, Atlantic Neuroscience Institute, Summit, NJ, USA
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19
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Chvojka J, Prochazkova N, Rehorova M, Kudlacek J, Kylarova S, Kralikova M, Buran P, Weissova R, Balastik M, Jefferys JGR, Novak O, Jiruska P. Mouse model of focal cortical dysplasia type II generates a wide spectrum of high-frequency activities. Neurobiol Dis 2024; 190:106383. [PMID: 38114051 DOI: 10.1016/j.nbd.2023.106383] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/04/2023] [Accepted: 12/13/2023] [Indexed: 12/21/2023] Open
Abstract
High-frequency oscillations (HFOs) represent an electrographic biomarker of endogenous epileptogenicity and seizure-generating tissue that proved clinically useful in presurgical planning and delineating the resection area. In the neocortex, the clinical observations on HFOs are not sufficiently supported by experimental studies stemming from a lack of realistic neocortical epilepsy models that could provide an explanation of the pathophysiological substrates of neocortical HFOs. In this study, we explored pathological epileptiform network phenomena, particularly HFOs, in a highly realistic murine model of neocortical epilepsy due to focal cortical dysplasia (FCD) type II. FCD was induced in mice by the expression of the human pathogenic mTOR gene mutation during embryonic stages of brain development. Electrographic recordings from multiple cortical regions in freely moving animals with FCD and epilepsy demonstrated that the FCD lesion generates HFOs from all frequency ranges, i.e., gamma, ripples, and fast ripples up to 800 Hz. Gamma-ripples were recorded almost exclusively in FCD animals, while fast ripples occurred in controls as well, although at a lower rate. Gamma-ripple activity is particularly valuable for localizing the FCD lesion, surpassing the utility of fast ripples that were also observed in control animals, although at significantly lower rates. Propagating HFOs occurred outside the FCD, and the contralateral cortex also generated HFOs independently of the FCD, pointing to a wider FCD network dysfunction. Optogenetic activation of neurons carrying mTOR mutation and expressing Channelrhodopsin-2 evoked fast ripple oscillations that displayed spectral and morphological profiles analogous to spontaneous oscillations. This study brings experimental evidence that FCD type II generates pathological HFOs across all frequency bands and provides information about the spatiotemporal properties of each HFO subtype in FCD. The study shows that mutated neurons represent a functionally interconnected and active component of the FCD network, as they can induce interictal epileptiform phenomena and HFOs.
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Affiliation(s)
- Jan Chvojka
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic; Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Natalie Prochazkova
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Monika Rehorova
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jan Kudlacek
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Salome Kylarova
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Michaela Kralikova
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Peter Buran
- Laboratory of Molecular Neurobiology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
| | - Romana Weissova
- Laboratory of Molecular Neurobiology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
| | - Martin Balastik
- Laboratory of Molecular Neurobiology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
| | - John G R Jefferys
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Ondrej Novak
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic.
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20
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Remakanthakurup Sindhu K, Phan C, Anis S, Riba A, Garner C, Magers AL, Tran N, Maser AL, Simon KC, Mednick SC, Shrey DW, Lopour BA. Physiological ripples during sleep in scalp electroencephalogram of healthy infants. Sleep 2023; 46:zsad247. [PMID: 37816242 PMCID: PMC10710989 DOI: 10.1093/sleep/zsad247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023] Open
Affiliation(s)
| | - Christopher Phan
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA
| | - Sara Anis
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA
| | - Aliza Riba
- Division of Neurology, Children’s Hospital of Orange County, Orange, CA, USA
| | - Cristal Garner
- Division of Neurology, Children’s Hospital of Orange County, Orange, CA, USA
| | - Amber L Magers
- Division of Neurology, Children’s Hospital of Orange County, Orange, CA, USA
| | - Nhi Tran
- Division of Neurology, Children’s Hospital of Orange County, Orange, CA, USA
| | - Amy L Maser
- Division of Neurology, Children’s Hospital of Orange County, Orange, CA, USA
| | - Katharine C Simon
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, USA
| | - Sara C Mednick
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, USA
| | - Daniel W Shrey
- Division of Neurology, Children’s Hospital of Orange County, Orange, CA, USA
- Department of Pediatrics, University of California, Irvine, Irvine, CA, USA
| | - Beth A Lopour
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA
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21
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Gerstl JVE, Kiseleva A, Imbach L, Sarnthein J, Fedele T. High frequency oscillations in relation to interictal spikes in predicting postsurgical seizure freedom. Sci Rep 2023; 13:21313. [PMID: 38042925 PMCID: PMC10693609 DOI: 10.1038/s41598-023-48764-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 11/30/2023] [Indexed: 12/04/2023] Open
Abstract
We evaluate whether interictal spikes, epileptiform HFOs and their co-occurrence (Spike + HFO) were included in the resection area with respect to seizure outcome. We also characterise the relationship between high frequency oscillations (HFOs) and propagating spikes. We analysed intracranial EEG of 20 patients that underwent resective epilepsy surgery. The co-occurrence of ripples and fast ripples was considered an HFO event; the co-occurrence of an interictal spike and HFO was considered a Spike + HFO event. HFO distribution and spike onset were compared in cases of spike propagation. Accuracy in predicting seizure outcome was 85% for HFO, 60% for Spikes, and 79% for Spike + HFO. Sensitivity was 57% for HFO, 71% for Spikes and 67% for Spikes + HFO. Specificity was 100% for HFO, 54% for Spikes and 85% for Spikes + HFO. In 2/2 patients with spike propagation, the spike onset included the HFO area. Combining interictal spikes with HFO had comparable accuracy to HFO. In patients with propagating spikes, HFO rate was maximal at the onset of spike propagation.
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Affiliation(s)
- Jakob V E Gerstl
- University College London Medical School, London, UK
- Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Alina Kiseleva
- Institute for Cognitive Neuroscience, HSE University, Myasnitskaya Ulitsa, 20, Moscow, Russian Federation, 101000
| | - Lukas Imbach
- Swiss Epilepsy Center, Klinik Lengg, Zurich, Switzerland
| | - Johannes Sarnthein
- Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Tommaso Fedele
- Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland.
- Institute for Cognitive Neuroscience, HSE University, Myasnitskaya Ulitsa, 20, Moscow, Russian Federation, 101000.
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22
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Li Z, Zhao B, Hu W, Zhang C, Wang X, Zhang J, Zhang K. Machine learning-based classification of physiological and pathological high-frequency oscillations recorded by stereoelectroencephalography. Seizure 2023; 113:58-65. [PMID: 37984126 DOI: 10.1016/j.seizure.2023.11.005] [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: 05/25/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/22/2023] Open
Abstract
OBJECTIVE High-frequency oscillations (HFOs) are an efficient indicator to locate the epileptogenic zone (EZ). However, physiological HFOs produced in the normal brain region may interfere with EZ localization. The present study aimed to build a machine learning-based classifier to distinguish the properties of each HFO event based on features in different domains. METHODS HFOs were detected in focal epilepsy patients from two different hospitals who underwent stereoelectroencephalography and subsequent resection surgery. Subsequently, 37 features in four different domains (time, frequency and time-frequency, entropy-based and nonlinear) were extracted for each HFO. After extraction, a fast correlation-based filter (FCBF) algorithm was applied for feature selection. The machine learning classifier was trained on the feature matrix with and without FCBF and then tested on the data set from patients in another hospital. RESULTS A dataset was compiled, consisting of 89,844 pathological HFOs and 23,613 physiological HFOs from 17 patients assigned to the training dataset. Additionally, 12,695 pathological HFOs and 5,599 physiological HFOs from 9 patients were assigned to the testing dataset. Four features (ripple band power, arithmetic mean, Petrosian fractal dimension and zero crossings) were obtained for classifier training after FCBF. The classifier showed an area under the curve (AUC) of 0.95/0.98 for FCBF/no FCBF features in the training dataset and AUC of 0.82/0.90 for FCBF/no FCBF features in the testing dataset. Our findings indicated that the classifier utilizing all features demonstrated superior performance compared to the one relying on FCBF-processed features. CONCLUSION Our classifier could reliably differentiate pathological HFOs from physiological ones, which could promote the development of HFOs in EZ localization.
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Affiliation(s)
- Zilin Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - 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
| | - 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
| | - 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
| | - 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.
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23
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Sheybani L, Vivekananda U, Rodionov R, Diehl B, Chowdhury FA, McEvoy AW, Miserocchi A, Bisby JA, Bush D, Burgess N, Walker MC. Wake slow waves in focal human epilepsy impact network activity and cognition. Nat Commun 2023; 14:7397. [PMID: 38036557 PMCID: PMC10689494 DOI: 10.1038/s41467-023-42971-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 10/27/2023] [Indexed: 12/02/2023] Open
Abstract
Slow waves of neuronal activity are a fundamental component of sleep that are proposed to have homeostatic and restorative functions. Despite this, their interaction with pathology is unclear and there is only indirect evidence of their presence during wakefulness. Using intracortical recordings from the temporal lobe of 25 patients with epilepsy, we demonstrate the existence of local wake slow waves (LoWS) with key features of sleep slow waves, including a down-state of neuronal firing. Consistent with a reduction in neuronal activity, LoWS were associated with slowed cognitive processing. However, we also found that LoWS showed signatures of a homeostatic relationship with interictal epileptiform discharges (IEDs): exhibiting progressive adaptation during the build-up of network excitability before an IED and reducing the impact of subsequent IEDs on network excitability. We therefore propose an epilepsy homeostasis hypothesis: that slow waves in epilepsy reduce aberrant activity at the price of transient cognitive impairment.
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Affiliation(s)
- Laurent Sheybani
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Umesh Vivekananda
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Roman Rodionov
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Fahmida A Chowdhury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Andrew W McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - James A Bisby
- Division of Psychiatry, University College London, London, UK
| | - Daniel Bush
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK.
| | - Neil Burgess
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK.
- Institute of Cognitive Neuroscience, University College London, London, UK.
| | - Matthew C Walker
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK.
- National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK.
- NIHR University College London Hospitals Biomedical Research Centre, London, UK.
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24
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Makhalova J, Madec T, Medina Villalon S, Jegou A, Lagarde S, Carron R, Scavarda D, Garnier E, Bénar CG, Bartolomei F. The role of quantitative markers in surgical prognostication after stereoelectroencephalography. Ann Clin Transl Neurol 2023; 10:2114-2126. [PMID: 37735846 PMCID: PMC10646998 DOI: 10.1002/acn3.51900] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/26/2023] [Accepted: 09/08/2023] [Indexed: 09/23/2023] Open
Abstract
OBJECTIVE Stereoelectroencephalography (SEEG) is the reference method in the presurgical exploration of drug-resistant focal epilepsy. However, prognosticating surgery on an individual level is difficult. A quantified estimation of the most epileptogenic regions by searching for relevant biomarkers can be proposed for this purpose. We investigated the performances of ictal (Epileptogenicity Index, EI; Connectivity EI, cEI), interictal (spikes, high-frequency oscillations, HFO [80-300 Hz]; Spikes × HFO), and combined (Spikes × EI; Spikes × cEI) biomarkers in predicting surgical outcome and searched for prognostic factors based on SEEG-signal quantification. METHODS Fifty-three patients operated on following SEEG were included. We compared, using precision-recall, the epileptogenic zone quantified using different biomarkers (EZq ) against the visual analysis (EZC ). Correlations between the EZ resection rates or the EZ extent and surgical prognosis were analyzed. RESULTS EI and Spikes × EI showed the best precision against EZc (0.74; 0.70), followed by Spikes × cEI and cEI, whereas interictal markers showed lower precision. The EZ resection rates were greater in seizure-free than in non-seizure-free patients for the EZ defined by ictal biomarkers and were correlated with the outcome for EI and Spikes × EI. No such correlation was found for interictal markers. The extent of the quantified EZ did not correlate with the prognosis. INTERPRETATION Ictal or combined ictal-interictal markers overperformed the interictal markers both for detecting the EZ and predicting seizure freedom. Combining ictal and interictal epileptogenicity markers improves detection accuracy. Resection rates of the quantified EZ using ictal markers were the only statistically significant determinants for surgical prognosis.
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Affiliation(s)
- Julia Makhalova
- APHM, Timone Hospital, Epileptology and Cerebral RhythmologyMarseilleFrance
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
- Aix Marseille Univ, CNRS, CRMBMMarseilleFrance
| | - Tanguy Madec
- APHM, Timone Hospital, Epileptology and Cerebral RhythmologyMarseilleFrance
| | - Samuel Medina Villalon
- APHM, Timone Hospital, Epileptology and Cerebral RhythmologyMarseilleFrance
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
| | - Aude Jegou
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
| | - Stanislas Lagarde
- APHM, Timone Hospital, Epileptology and Cerebral RhythmologyMarseilleFrance
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
| | - Romain Carron
- APHM, Timone Hospital, Functional, and Stereotactic NeurosurgeryMarseilleFrance
| | | | - Elodie Garnier
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
| | | | - Fabrice Bartolomei
- APHM, Timone Hospital, Epileptology and Cerebral RhythmologyMarseilleFrance
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
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25
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Ho A, Hannan S, Thomas J, Avigdor T, Abdallah C, Dubeau F, Gotman J, Frauscher B. Rapid eye movement sleep affects interictal epileptic activity differently in mesiotemporal and neocortical areas. Epilepsia 2023; 64:3036-3048. [PMID: 37714213 DOI: 10.1111/epi.17763] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/22/2023] [Accepted: 08/22/2023] [Indexed: 09/17/2023]
Abstract
OBJECTIVE Rapid eye movement (REM) sleep reduces the rate and extent of interictal epileptiform discharges (IEDs). Breakthrough epileptic activity during REM sleep is therefore thought to best localize the seizure onset zone (SOZ). We utilized polysomnography combined with direct cortical recordings to investigate the influences of anatomical locations and the time of night on the suppressive effect of REM sleep on IEDs. METHODS Forty consecutive patients with drug-resistant focal epilepsy underwent combined polysomnography and stereo-electroencephalography during presurgical evaluation. Ten-minute interictal epochs were selected 2 h prior to sleep onset (wakefulness), and from the first and second half of the night during non-REM (NREM) sleep and REM sleep. IEDs were detected automatically across all channels. Anatomic localization, time of night, and channel type (within or outside the SOZ) were tested as modulating factors. RESULTS Relative to wakefulness, there was a suppression of IEDs by REM sleep in neocortical regions (median = -27.6%), whereas mesiotemporal regions showed an increase in IEDs (19.1%, p = .01, d = .39). This effect was reversed when comparing the regional suppression of IEDs by REM sleep relative to NREM sleep (-35.1% in neocortical, -58.7% in mesiotemporal, p < .001, d = .39). Across all patients, no clinically relevant novel IED regions were observed in REM sleep versus NREM or wakefulness based on our predetermined thresholds (4 IEDs/min in REM, 0 IEDs/min in NREM and wakefulness). Finally, there was a reduction in IEDs in late (NREM: 1.08/min, REM: .61/min) compared to early sleep (NREM: 1.22/min, REM: .69/min) for both NREM (p < .001, d = .21) and REM (p = .04, d = .14). SIGNIFICANCE Our results demonstrate a spatiotemporal effect of IED suppression by REM sleep relative to wakefulness in neocortical but not mesiotemporal regions, and in late versus early sleep. This suggests the importance of considering sleep stage interactions and the potential influences of anatomical locations when using IEDs to define the epileptic focus.
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Affiliation(s)
- Alyssa Ho
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Sana Hannan
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - John Thomas
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Tamir Avigdor
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Chifaou Abdallah
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - François Dubeau
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Department of Neurology, Duke University Medical Center, Durham, North Carolina, USA
- Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, North Carolina, USA
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Sakakura K, Kuroda N, Sonoda M, Mitsuhashi T, Firestone E, Luat AF, Marupudi NI, Sood S, Asano E. Developmental atlas of phase-amplitude coupling between physiologic high-frequency oscillations and slow waves. Nat Commun 2023; 14:6435. [PMID: 37833252 PMCID: PMC10575956 DOI: 10.1038/s41467-023-42091-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
We investigated the developmental changes in high-frequency oscillation (HFO) and Modulation Index (MI) - the coupling measure between HFO and slow-wave phase. We generated normative brain atlases, using subdural EEG signals from 8251 nonepileptic electrode sites in 114 patients (ages 1.0-41.5 years) who achieved seizure control following resective epilepsy surgery. We observed a higher MI in the occipital lobe across all ages, and occipital MI increased notably during early childhood. The cortical areas exhibiting MI co-growth were connected via the vertical occipital fasciculi and posterior callosal fibers. While occipital HFO rate showed no significant age-association, the temporal, frontal, and parietal lobes exhibited an age-inversed HFO rate. Assessment of 1006 seizure onset sites revealed that z-score normalized MI and HFO rate were higher at seizure onset versus nonepileptic electrode sites. We have publicly shared our intracranial EEG data to enable investigators to validate MI and HFO-centric presurgical evaluations to identify the epileptogenic zone.
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Affiliation(s)
- Kazuki Sakakura
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
- Department of Neurosurgery, Rush University Medical Center, Chicago, IL, 60612, USA
- Department of Neurosurgery, University of Tsukuba, Tsukuba, 3058575, Japan
| | - Naoto Kuroda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, 9808575, Japan
| | - Masaki Sonoda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
- Department of Neurosurgery, Yokohama City University, Yokohama-shi, 2360004, Japan
| | - Takumi Mitsuhashi
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
- Department of Neurosurgery, Juntendo University, Tokyo, 1138421, Japan
| | - Ethan Firestone
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
- Department of Physiology, Wayne State University, Detroit, MI, 48201, USA
| | - Aimee F Luat
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
- Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
- Department of Pediatrics, Central Michigan University, Mount Pleasant, MI, 48858, USA
| | - Neena I Marupudi
- Department of Neurosurgery, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
| | - Sandeep Sood
- Department of Neurosurgery, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA
| | - Eishi Asano
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA.
- Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI, 48201, USA.
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27
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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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28
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Gotman J. Has recording of seizures become obsolete? Rev Neurol (Paris) 2023; 179:872-876. [PMID: 36906456 DOI: 10.1016/j.neurol.2023.01.726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/04/2023] [Indexed: 03/11/2023]
Abstract
Some patients with medically intractable epilepsy are considered for surgical treatment. In some surgical candidates, the investigation includes the placement of intracerebral electrodes and long-term monitoring to find the region of seizure onset. This region is the primary determinant of the surgical resection but about one-third of patients are not offered surgery after electrode implantation and among those operated only about 55% are seizure free after five years. This paper discusses why the primary reliance on the seizure onset maybe suboptimal and may be in part responsible for the relatively low surgical success rate. It also proposes to consider some interictal markers that may have advantages over seizure onset and may be easier to obtain.
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Affiliation(s)
- J Gotman
- Montreal Neurological Institute, McGill University, 3801 University Street, Montréal, Québec H3A 2B4, Canada.
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29
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Shafiei G, Fulcher BD, Voytek B, Satterthwaite TD, Baillet S, Misic B. Neurophysiological signatures of cortical micro-architecture. Nat Commun 2023; 14:6000. [PMID: 37752115 PMCID: PMC10522715 DOI: 10.1038/s41467-023-41689-6] [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: 01/24/2023] [Accepted: 09/11/2023] [Indexed: 09/28/2023] Open
Abstract
Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6800 time-series features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is co-localized with multiple micro-architectural features, including gene expression gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.
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Affiliation(s)
- Golia Shafiei
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ben D Fulcher
- School of Physics, The University of Sydney, Camperdown, NSW, 2006, Australia
| | - Bradley Voytek
- Department of Cognitive Science, Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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30
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Weiss SA, Fried I, Engel J, Sperling MR, Wong RKS, Nir Y, Staba RJ. Fast ripples reflect increased excitability that primes epileptiform spikes. Brain Commun 2023; 5:fcad242. [PMID: 37869578 PMCID: PMC10587774 DOI: 10.1093/braincomms/fcad242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/08/2023] [Accepted: 09/07/2023] [Indexed: 10/24/2023] Open
Abstract
The neuronal circuit disturbances that drive inter-ictal and ictal epileptiform discharges remain elusive. Using a combination of extra-operative macro-electrode and micro-electrode inter-ictal recordings in six pre-surgical patients during non-rapid eye movement sleep, we found that, exclusively in the seizure onset zone, fast ripples (200-600 Hz), but not ripples (80-200 Hz), frequently occur <300 ms before an inter-ictal intra-cranial EEG spike with a probability exceeding chance (bootstrapping, P < 1e-5). Such fast ripple events are associated with higher spectral power (P < 1e-10) and correlated with more vigorous neuronal firing than solitary fast ripple (generalized linear mixed-effects model, P < 1e-9). During the intra-cranial EEG spike that follows a fast ripple, action potential firing is lower than during an intra-cranial EEG spike alone (generalized linear mixed-effects model, P < 0.05), reflecting an inhibitory restraint of intra-cranial EEG spike initiation. In contrast, ripples do not appear to prime epileptiform spikes. We next investigated the clinical significance of pre-spike fast ripple in a separate cohort of 23 patients implanted with stereo EEG electrodes, who underwent resections. In non-rapid eye movement sleep recordings, sites containing a high proportion of fast ripple preceding intra-cranial EEG spikes correlate with brain areas where seizures begin more than solitary fast ripple (P < 1e-5). Despite this correlation, removal of these sites does not guarantee seizure freedom. These results are consistent with the hypothesis that fast ripple preceding EEG spikes reflect an increase in local excitability that primes EEG spike discharges preferentially in the seizure onset zone and that epileptogenic brain regions are necessary, but not sufficient, for initiating inter-ictal epileptiform discharges.
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Affiliation(s)
- Shennan A Weiss
- Department of Neurology, State University of New York Downstate, Brooklyn, NY 11203, USA
- Department of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, NY 11203, USA
- Department of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY 11203, USA
| | - Itzhak Fried
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Jerome Engel
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
- Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
- Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Michael R Sperling
- Departments of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Robert K S Wong
- Department of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, NY 11203, USA
| | - Yuval Nir
- Department of Physiology and Pharmacology, Sackler School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- The Sieratzki-Sagol Center for Sleep Medicine, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel
| | - Richard J Staba
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
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van Schalkwijk FJ, Weber J, Hahn MA, Lendner JD, Inostroza M, Lin JJ, Helfrich RF. An evolutionary conserved division-of-labor between archicortical and neocortical ripples organizes information transfer during sleep. Prog Neurobiol 2023:102485. [PMID: 37353109 DOI: 10.1016/j.pneurobio.2023.102485] [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: 11/16/2022] [Revised: 06/02/2023] [Accepted: 06/15/2023] [Indexed: 06/25/2023]
Abstract
Systems-level memory consolidation during sleep depends on the temporally precise interplay between cardinal sleep oscillations. Specifically, hippocampal ripples constitute a key substrate of the hippocampal-neocortical dialogue underlying memory formation. Recently, it became evident that ripples are not unique to archicortex, but constitute a wide-spread neocortical phenomenon. To date, little is known about the morphological similarities between archi- and neocortical ripples. Moreover, it remains undetermined if neocortical ripples fulfill distinct functional roles. Leveraging intracranial recordings from the human medial temporal lobe (MTL) and neocortex during sleep, our results reveal region-specific functional specializations, albeit a near-uniform morphology. While MTL ripples synchronize the memory network to trigger directional MTL-to-neocortical information flow, neocortical ripples reduce information flow to minimize interference. At the population level, MTL ripples confined population dynamics to a low-dimensional subspace, while neocortical ripples diversified the population response; thus, constituting an effective mechanism to functionally uncouple the MTL-neocortical network. Critically, we replicated the key findings in rodents, where the same division-of-labor between archi- and neocortical ripples was evident. In sum, these results uncover an evolutionary preserved mechanism where the precisely coordinated interplay between MTL and neocortical ripples temporally segregates MTL information transfer from subsequent neocortical processing during sleep.
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Affiliation(s)
- Frank J van Schalkwijk
- Hertie-Institute for Clinical Brain Research, Center for Neurology, University Medical Center Tübingen, Otfried-Müller Str. 27, 72076 Tübingen, Germany.
| | - Jan Weber
- Hertie-Institute for Clinical Brain Research, Center for Neurology, University Medical Center Tübingen, Otfried-Müller Str. 27, 72076 Tübingen, Germany; International Max Planck Research School for the Mechanisms of Mental Function and Dysfunction, University of Tübingen, Germany.
| | - Michael A Hahn
- Hertie-Institute for Clinical Brain Research, Center for Neurology, University Medical Center Tübingen, Otfried-Müller Str. 27, 72076 Tübingen, Germany.
| | - Janna D Lendner
- Hertie-Institute for Clinical Brain Research, Center for Neurology, University Medical Center Tübingen, Otfried-Müller Str. 27, 72076 Tübingen, Germany; Department of Anesthesiology and Intensive Care Medicine, University Medical Center Tübingen; Hoppe-Seyler-Str 3, 72076 Tübingen, Germany.
| | - Marion Inostroza
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.
| | - Jack J Lin
- Department of Neurology, University of California, Davis, 4860 Y St., Sacramento, CA 95817, USA; The Center for Mind and Brain, University of California, Davis, Davis, CA 95618, USA.
| | - Randolph F Helfrich
- Hertie-Institute for Clinical Brain Research, Center for Neurology, University Medical Center Tübingen, Otfried-Müller Str. 27, 72076 Tübingen, Germany.
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32
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Fabo D, Bokodi V, Szabó JP, Tóth E, Salami P, Keller CJ, Hajnal B, Thesen T, Devinsky O, Doyle W, Mehta A, Madsen J, Eskandar E, Erőss L, Ulbert I, Halgren E, Cash SS. The role of superficial and deep layers in the generation of high frequency oscillations and interictal epileptiform discharges in the human cortex. Sci Rep 2023; 13:9620. [PMID: 37316509 DOI: 10.1038/s41598-022-22497-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023] Open
Abstract
Describing intracortical laminar organization of interictal epileptiform discharges (IED) and high frequency oscillations (HFOs), also known as ripples. Defining the frequency limits of slow and fast ripples. We recorded potential gradients with laminar multielectrode arrays (LME) for current source density (CSD) and multi-unit activity (MUA) analysis of interictal epileptiform discharges IEDs and HFOs in the neocortex and mesial temporal lobe of focal epilepsy patients. IEDs were observed in 20/29, while ripples only in 9/29 patients. Ripples were all detected within the seizure onset zone (SOZ). Compared to hippocampal HFOs, neocortical ripples proved to be longer, lower in frequency and amplitude, and presented non-uniform cycles. A subset of ripples (≈ 50%) co-occurred with IEDs, while IEDs were shown to contain variable high-frequency activity, even below HFO detection threshold. The limit between slow and fast ripples was defined at 150 Hz, while IEDs' high frequency components form clusters separated at 185 Hz. CSD analysis of IEDs and ripples revealed an alternating sink-source pair in the supragranular cortical layers, although fast ripple CSD appeared lower and engaged a wider cortical domain than slow ripples MUA analysis suggested a possible role of infragranularly located neural populations in ripple and IED generation. Laminar distribution of peak frequencies derived from HFOs and IEDs, respectively, showed that supragranular layers were dominated by slower (< 150 Hz) components. Our findings suggest that cortical slow ripples are generated primarily in upper layers while fast ripples and associated MUA in deeper layers. The dissociation of macro- and microdomains suggests that microelectrode recordings may be more selective for SOZ-linked ripples. We found a complex interplay between neural activity in the neocortical laminae during ripple and IED formation. We observed a potential leading role of cortical neurons in deeper layers, suggesting a refined utilization of LMEs in SOZ localization.
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Affiliation(s)
- Daniel Fabo
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary.
| | - Virag Bokodi
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- Roska Tamás Doctoral School of Sciences and Technologies, Budapest, Hungary
| | - Johanna-Petra Szabó
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Budapest, Hungary
| | - Emilia Tóth
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- Department of Neurology, University of Texas, McGovern Medical School, Houston, TX, USA
| | - Pariya Salami
- Epilepsy Division, Department of Neurology, Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Corey J Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University Medical Center, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Boglárka Hajnal
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Budapest, Hungary
| | - Thomas Thesen
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, USA
- Department of Biomedical Sciences, College of Medicine, University of Houston, Houston, TX, USA
| | - Orrin Devinsky
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, USA
| | - Werner Doyle
- Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, USA
| | - Ashesh Mehta
- Department of Neurosurgery, Zucker School of Medicine at Hofstra/Northwell and Feinstein Institute for Medical Research, Manhasset, NY, USA
| | | | - Emad Eskandar
- Massachusetts General Hospital Neurosurgery Research, Boston, MA, USA
| | - Lorand Erőss
- Department of Functional Neurosurgery, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
| | - István Ulbert
- Epilepsy Unit, Department of Neurology, National Institute of Mental Health, Neurology and Neurosurgery, Amerikai Út 57. 1145, Budapest, Hungary
- Institute of Psychology, Eötvös Loránd Research Network, Budapest, Hungary
| | - Eric Halgren
- Department of Radiology, Neurosciences and Psychiatry, University of California, San Diego, San Diego, CA, USA
| | - Sydney S Cash
- Epilepsy Division, Department of Neurology, Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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Firestone E, Sonoda M, Kuroda N, Sakakura K, Jeong JW, Lee MH, Wada K, Takayama Y, Iijima K, Iwasaki M, Miyazaki T, Asano E. Sevoflurane-induced high-frequency oscillations, effective connectivity and intraoperative classification of epileptic brain areas. Clin Neurophysiol 2023; 150:17-30. [PMID: 36989866 PMCID: PMC10192072 DOI: 10.1016/j.clinph.2023.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/27/2023] [Accepted: 03/02/2023] [Indexed: 03/18/2023]
Abstract
OBJECTIVE To determine how sevoflurane anesthesia modulates intraoperative epilepsy biomarkers on electrocorticography, including high-frequency oscillation (HFO) effective connectivity (EC), and to investigate their relation to epileptogenicity and anatomical white matter. METHODS We studied eight pediatric drug-resistant focal epilepsy patients who achieved seizure control after invasive monitoring and resective surgery. We visualized spatial distributions of the electrocorticography biomarkers at an oxygen baseline, three time-points while sevoflurane was increasing, and at a plateau of 2 minimum alveolar concentration (MAC) sevoflurane. HFO EC was combined with diffusion-weighted imaging, in dynamic tractography. RESULTS Intraoperative HFO EC diffusely increased as a function of sevoflurane concentration, although most in epileptogenic sites (defined as those included in the resection); their ability to classify epileptogenicity was optimized at sevoflurane 2 MAC. HFO EC could be visualized on major white matter tracts, as a function of sevoflurane level. CONCLUSIONS The results strengthened the hypothesis that sevoflurane-activated HFO biomarkers may help intraoperatively localize the epileptogenic zone. SIGNIFICANCE Our results help characterize how HFOs at non-epileptogenic and epileptogenic networks respond to sevoflurane. It may be warranted to establish a normative HFO atlas incorporating the modifying effects of sevoflurane and major white matter pathways, as critical reference in epilepsy presurgical evaluation.
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Affiliation(s)
- Ethan Firestone
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Physiology, Wayne State University, Detroit, MI 48201, USA
| | - Masaki Sonoda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Neurosurgery, Yokohama City University Graduate School of Medicine, Yokohama 2360004, Japan
| | - Naoto Kuroda
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai 9808575, Japan
| | - Kazuki Sakakura
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Neurosurgery, University of Tsukuba, Tsukuba 3058575, Japan
| | - Jeong-Won Jeong
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA
| | - Min-Hee Lee
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA
| | - Keiko Wada
- Department of Anesthesiology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan; Department of Anesthesiology and Critical Care, Yokohama City University Graduate School of Medicine, Yokohama 2360004, Japan
| | - Yutaro Takayama
- Department of Neurosurgery, Yokohama City University Graduate School of Medicine, Yokohama 2360004, Japan; Department of Neurosurgery, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan
| | - Keiya Iijima
- Department of Neurosurgery, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan
| | - Masaki Iwasaki
- Department of Neurosurgery, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan
| | - Tomoyuki Miyazaki
- Department of Anesthesiology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan; Department of Physiology, Yokohama City University Graduate School of Medicine, Yokohama 2360004, Japan
| | - Eishi Asano
- Department of Pediatrics, Children's Hospital of Michigan, Detroit Medical Center,Wayne State University, Detroit, MI 48201, USA; Department of Neurology, Children's Hospital of Michigan, Detroit Medical Center, Wayne State University, Detroit, MI 48201, USA.
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Bernabei JM, Li A, Revell AY, Smith RJ, Gunnarsdottir KM, Ong IZ, Davis KA, Sinha N, Sarma S, Litt B. Quantitative approaches to guide epilepsy surgery from intracranial EEG. Brain 2023; 146:2248-2258. [PMID: 36623936 PMCID: PMC10232272 DOI: 10.1093/brain/awad007] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 12/11/2022] [Accepted: 12/28/2022] [Indexed: 01/11/2023] Open
Abstract
Over the past 10 years, the drive to improve outcomes from epilepsy surgery has stimulated widespread interest in methods to quantitatively guide epilepsy surgery from intracranial EEG (iEEG). Many patients fail to achieve seizure freedom, in part due to the challenges in subjective iEEG interpretation. To address this clinical need, quantitative iEEG analytics have been developed using a variety of approaches, spanning studies of seizures, interictal periods, and their transitions, and encompass a range of techniques including electrographic signal analysis, dynamical systems modeling, machine learning and graph theory. Unfortunately, many methods fail to generalize to new data and are sensitive to differences in pathology and electrode placement. Here, we critically review selected literature on computational methods of identifying the epileptogenic zone from iEEG. We highlight shared methodological challenges common to many studies in this field and propose ways that they can be addressed. One fundamental common pitfall is a lack of open-source, high-quality data, which we specifically address by sharing a centralized high-quality, well-annotated, multicentre dataset consisting of >100 patients to support larger and more rigorous studies. Ultimately, we provide a road map to help these tools reach clinical trials and hope to improve the lives of future patients.
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Affiliation(s)
- John M Bernabei
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Adam Li
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Andrew Y Revell
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Rachel J Smith
- Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Neuroengineering Program, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Kristin M Gunnarsdottir
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ian Z Ong
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A Davis
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nishant Sinha
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sridevi Sarma
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Brian Litt
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Frauscher B, Bénar CG, Engel JJ, Grova C, Jacobs J, Kahane P, Wiebe S, Zjilmans M, Dubeau F. Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy. Epilepsy Behav 2023; 143:109221. [PMID: 37119580 DOI: 10.1016/j.yebeh.2023.109221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 05/01/2023]
Abstract
Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to localize epileptic seizures. New signal analysis techniques and the prospects of artificial intelligence and big data will offer unprecedented opportunities to further advance the field in the near future, ultimately resulting in improved quality of life for many patients with drug-resistant epilepsy. This article summarizes selected presentations from Day 1 of the two-day symposium "Neurophysiology, Neuropsychology, Epilepsy, 2022: Hills We Have Climbed and the Hills Ahead". Day 1 was dedicated to highlighting and honoring the work of Dr. Jean Gotman, a pioneer in EEG, intracranial EEG, simultaneous EEG/ functional magnetic resonance imaging, and signal analysis of epilepsy. The program focused on two main research directions of Dr. Gotman, and was dedicated to "High-frequency oscillations, a new biomarker of epilepsy" and "Probing the epileptic focus from inside and outside". All talks were presented by colleagues and former trainees of Dr. Gotman. The extended summaries provide an overview of historical and current work in the neurophysiology of epilepsy with emphasis on novel EEG biomarkers of epilepsy and source imaging and concluded with an outlook on the future of epilepsy research, and what is needed to bring the field to the next level.
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Affiliation(s)
- B Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - C G Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - J Jr Engel
- David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - C Grova
- Multimodal Functional Imaging Lab, PERFORM Centre, Department of Physics, Concordia University, Montreal, QC, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, QC, Canada; Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
| | - J Jacobs
- Department of Pediatric and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - P Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institute Neurosciences, Department of Neurology, 38000 Grenoble, France
| | - S Wiebe
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - M Zjilmans
- Stichting Epilepsie Instellingen Nederland, The Netherlands; Brain Center, University Medical Center Utrecht, The Netherlands
| | - F Dubeau
- Montreal Neurological Institute and Hospital, Neurology and Neurosurgery Department, McGill University, Montreal, QC, Canada
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Weiss SA, Fried I, Engel J, Sperling MR, Wong RK, Nir Y, Staba RJ. Fast ripples reflect increased excitability that primes epileptiform spikes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.26.23287702. [PMID: 37034609 PMCID: PMC10081394 DOI: 10.1101/2023.03.26.23287702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
The neuronal circuit disturbances that drive interictal and ictal epileptiform discharges remains elusive. Using a combination of extraoperative macro- and micro-electrode interictal recordings in six presurgical patients during non-rapid eye movement (REM) sleep we found that, exclusively in the seizure onset zone, fast ripples (FR; 200-600Hz), but not ripples (80-200 Hz), frequently occur <300 msec before an interictal intracranial EEG (iEEG) spike with a probability exceeding chance (bootstrapping, p<1e-5). Such FR events are associated with higher spectral power (p<1e-10) and correlated with more vigorous neuronal firing than solitary FR (generalized linear mixed-effects model, GLMM, p<1e-3) irrespective of FR power. During the iEEG spike that follows a FR, action potential firing is lower than during a iEEG spike alone (GLMM, p<1e-10), reflecting an inhibitory restraint of iEEG spike initiation. In contrast, ripples do not appear to prime epileptiform spikes. We next investigated the clinical significance of pre-spike FR in a separate cohort of 23 patients implanted with stereo EEG electrodes who underwent resections. In non-REM sleep recordings, sites containing a high proportion of FR preceding iEEG spikes correlate with brain areas where seizures begin more than solitary FR (p<1e-5). Despite this correlation, removal of these sites does not guarantee seizure freedom. These results are consistent with the hypothesis that FR preceding EEG spikes reflect an increase in local excitability that primes EEG spike discharges preferentially in the seizure onset zone and that epileptogenic brain regions are necessary, but not sufficient, for initiating interictal epileptiform discharges.
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Affiliation(s)
- Shennan A Weiss
- Dept. of Neurology, State University of New York Downstate, Brooklyn, New York, 11203 USA
- Dept. of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York, 11203 USA
- Dept. of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY, USA
| | - Itzhak Fried
- Dept. of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Jerome Engel
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Michael R. Sperling
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Robert K.S. Wong
- Dept. of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York, 11203 USA
| | - Yuval Nir
- Department of Physiology and Pharmacology, Sackler School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- The Sieratzki-Sagol Center for Sleep Medicine, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel
| | - Richard J Staba
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
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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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Long S, Bruzzone M, Mitropanopoulos S, Kalamangalam G, Gunduz A. Identification and classification of pathology and artifacts for human intracranial cognitive research. Neuroimage 2023; 270:119961. [PMID: 36848970 PMCID: PMC10461234 DOI: 10.1016/j.neuroimage.2023.119961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 02/27/2023] Open
Abstract
Intracranial electroencephalography (iEEG) presents a unique opportunity to extend human neuroscientific understanding. However, typically iEEG is collected from patients diagnosed with focal drug-resistant epilepsy (DRE) and contains transient bursts of pathological activity. This activity disrupts performances on cognitive tasks and can distort findings from human neurophysiology studies. In addition to manual marking by a trained expert, numerous IED detectors have been developed to identify these pathological events. Even so, the versatility and usefulness of these detectors is limited by training on small datasets, incomplete performance metrics, and lack of generalizability to iEEG. Here, we employed a large annotated public iEEG dataset from two institutions to train a random forest classifier (RFC) to distinguish data segments as either 'non-cerebral artifact' (n = 73,902), 'pathological activity' (n = 67,797), or 'physiological activity' (n = 151,290). We found our model performed with an accuracy of 0.941, specificity of 0.950, sensitivity of 0.908, precision of 0.911, and F1 score of 0.910, averaged across all three event types. We extended the generalizability of our model to continuous bipolar data collected in a task-state at a different institution with a lower sampling rate and found our model performed with an accuracy of 0.789, specificity of 0.806, and sensitivity of 0.742, averaged across all three event types. Additionally, we created a custom graphical user interface to implement our classifier and enhance usability.
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Affiliation(s)
- Sarah Long
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, United States
| | - Maria Bruzzone
- Wilder Center for Epilepsy, Department of Neurology, University of Florida, United States
| | | | - Giridhar Kalamangalam
- Wilder Center for Epilepsy, Department of Neurology, University of Florida, United States
| | - Aysegul Gunduz
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, United States; Wilder Center for Epilepsy, Department of Neurology, University of Florida, United States.
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Guisande N, di Nunzio MP, Martinez N, Rosso OA, Montani F. Chaotic dynamics of the Hénon map and neuronal input-output: A comparison with neurophysiological data. CHAOS (WOODBURY, N.Y.) 2023; 33:043111. [PMID: 37097953 DOI: 10.1063/5.0142773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/10/2023] [Indexed: 06/19/2023]
Abstract
In this study, the Hénon map was analyzed using quantifiers from information theory in order to compare its dynamics to experimental data from brain regions known to exhibit chaotic behavior. The goal was to investigate the potential of the Hénon map as a model for replicating chaotic brain dynamics in the treatment of Parkinson's and epilepsy patients. The dynamic properties of the Hénon map were compared with data from the subthalamic nucleus, the medial frontal cortex, and a q-DG model of neuronal input-output with easy numerical implementation to simulate the local behavior of a population. Using information theory tools, Shannon entropy, statistical complexity, and Fisher's information were analyzed, taking into account the causality of the time series. For this purpose, different windows over the time series were considered. The findings revealed that neither the Hénon map nor the q-DG model could perfectly replicate the dynamics of the brain regions studied. However, with careful consideration of the parameters, scales, and sampling used, they were able to model some characteristics of neural activity. According to these results, normal neural dynamics in the subthalamic nucleus region may present a more complex spectrum within the complexity-entropy causality plane that cannot be represented by chaotic models alone. The dynamic behavior observed in these systems using these tools is highly dependent on the studied temporal scale. As the size of the sample studied increases, the dynamics of the Hénon map become increasingly different from those of biological and artificial neural systems.
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Affiliation(s)
- Natalí Guisande
- Instituto de Física de La Plata (IFLP), Universidad Nacional de La Plata, CONICET CCT-La Plata, Diagonal 113 entre 63 y 64, La Plata 1900, Buenos Aires, Argentina
| | - Monserrat Pallares di Nunzio
- Instituto de Física de La Plata (IFLP), Universidad Nacional de La Plata, CONICET CCT-La Plata, Diagonal 113 entre 63 y 64, La Plata 1900, Buenos Aires, Argentina
| | - Nataniel Martinez
- Instituto de Física de Mar del Plata, Universidad Nacional de Mar del Plata & CONICET, Mar del Plata 7600, Buenos Aires, Argentina
| | - Osvaldo A Rosso
- Instituto de Física de La Plata (IFLP), Universidad Nacional de La Plata, CONICET CCT-La Plata, Diagonal 113 entre 63 y 64, La Plata 1900, Buenos Aires, Argentina
- Instituto de Física, Universidade Federal de Alagoas (UFAL), BR 104 Norte km 97, 57072-970 Maceió, Brazil
| | - Fernando Montani
- Instituto de Física de La Plata (IFLP), Universidad Nacional de La Plata, CONICET CCT-La Plata, Diagonal 113 entre 63 y 64, La Plata 1900, Buenos Aires, Argentina
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Armonaite K, Nobili L, Paulon L, Balsi M, Conti L, Tecchio F. Local neurodynamics as a signature of cortical areas: new insights from sleep. Cereb Cortex 2023; 33:3284-3292. [PMID: 35858209 DOI: 10.1093/cercor/bhac274] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 06/02/2022] [Accepted: 06/04/2022] [Indexed: 11/13/2022] Open
Abstract
Sleep crucial for the animal survival is accompanied by huge changes in neuronal electrical activity over time, the neurodynamics. Here, drawing on intracranial stereo-electroencephalographic (sEEG) recordings from the Montreal Neurological Institute (MNI), we analyzed local neurodynamics in the waking state at rest and during the N2, N3, and rapid eye movement (REM) sleep phases. Higuchi fractal dimension (HFD)-a measure of signal complexity-was studied as a feature of the local neurodynamics of the primary motor (M1), somatosensory (S1), and auditory (A1) cortices. The key working hypothesis, that the relationships between local neurodynamics preserve in all sleep phases despite the neurodynamics complexity reduces in sleep compared with wakefulness, was supported by the results. In fact, while HFD awake > REM > N2 > N3 (P < 0.001 consistently), HFD in M1 > S1 > A1 in awake and all sleep stages (P < 0.05 consistently). Also power spectral density was studied for consistency with previous investigations. Meaningfully, we found a local specificity of neurodynamics, well quantified by the fractal dimension, expressed in wakefulness and during sleep. We reinforce the idea that neurodynamic may become a new criterion for cortical parcellation, prospectively improving the understanding and ability of compensatory interventions for behavioral disorders.
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Affiliation(s)
- Karolina Armonaite
- Faculty of Psychology, Uninettuno University, Corso V. Emanuele II, n. 39, 00186, Rome, Italy
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Via Palestro, n. 32, 00185, Rome, Italy
| | - Lino Nobili
- Child Neurology and Psychiatry, IRCCS Istituto Giannina Gaslini, Via Gerolamo Gaslini, n. 5, 16147, Genoa, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health (DINOGMI), University of Genoa, Largo Paolo Daneo, n. 3, 16132, Genoa, Italy
| | - Luca Paulon
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Via Palestro, n. 32, 00185, Rome, Italy
| | - Marco Balsi
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University, Via Eudossiana, n. 18, 00184, Rome
| | - Livio Conti
- Faculty of Engineering, Uninettuno University, Corso V. Emanuele II, n. 39, 00186, Rome, Italy
- INFN - Istituto Nazionale di Fisica Nucleare, Sezione Roma Tor Vergata, Via della Ricerca Scientifica, n.1, 00133, Rome, Italy
| | - Franca Tecchio
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Via Palestro, n. 32, 00185, Rome, Italy
- Faculty of Psychology, Uninettuno University, Corso V. Emanuele II, n. 39, 00186, Rome, Italy
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Thomas J, Kahane P, Abdallah C, Avigdor T, Zweiphenning WJEM, Chabardes S, Jaber K, Latreille V, Minotti L, Hall J, Dubeau F, Gotman J, Frauscher B. A Subpopulation of Spikes Predicts Successful Epilepsy Surgery Outcome. Ann Neurol 2023; 93:522-535. [PMID: 36373178 DOI: 10.1002/ana.26548] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 11/09/2022] [Accepted: 11/11/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVE Epileptic spikes are the traditional interictal electroencephalographic (EEG) biomarker for epilepsy. Given their low specificity for identifying the epileptogenic zone (EZ), they are given only moderate attention in presurgical evaluation. This study aims to demonstrate that it is possible to identify specific spike features in intracranial EEG that optimally define the EZ and predict surgical outcome. METHODS We analyzed spike features on stereo-EEG segments from 83 operated patients from 2 epilepsy centers (37 Engel IA) in wakefulness, non-rapid eye movement sleep, and rapid eye movement sleep. After automated spike detection, we investigated 135 spike features based on rate, morphology, propagation, and energy to determine the best feature or feature combination to discriminate the EZ in seizure-free and non-seizure-free patients by applying 4-fold cross-validation. RESULTS The rate of spikes with preceding gamma activity in wakefulness performed better for surgical outcome classification (4-fold area under receiver operating characteristics curve [AUC] = 0.755 ± 0.07) than the seizure onset zone, the current gold standard (AUC = 0.563 ± 0.05, p = 0.015) and the ripple rate, an emerging seizure-independent biomarker (AUC = 0.537 ± 0.07, p = 0.006). Channels with a spike-gamma rate exceeding 1.9/min had an 80% probability of being in the EZ. Combining features did not improve the results. INTERPRETATION Resection of brain regions with high spike-gamma rates in wakefulness is associated with a high probability of achieving seizure freedom. This rate could be applied to determine the minimal number of spiking channels requiring resection. In addition to quantitative analysis, this feature is easily accessible to visual analysis, which could aid clinicians during presurgical evaluation. ANN NEUROL 2023;93:522-535.
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Affiliation(s)
- John Thomas
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Philippe Kahane
- Grenoble Alpes University Hospital Center, Grenoble Alpes University, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France
| | - Chifaou Abdallah
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Tamir Avigdor
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Willemiek J E M Zweiphenning
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.,University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Stephan Chabardes
- Grenoble Alpes University Hospital Center, Grenoble Alpes University, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France
| | - Kassem Jaber
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Véronique Latreille
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Lorella Minotti
- Grenoble Alpes University Hospital Center, Grenoble Alpes University, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France
| | - Jeff Hall
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - François Dubeau
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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Zhao B, McGonigal A, Hu W, Zhang C, Wang X, Mo J, Zhao X, Ai L, Shao X, Zhang K, Zhang J. Interictal HFO and FDG-PET correlation predicts surgical outcome following SEEG. Epilepsia 2023; 64:667-677. [PMID: 36510851 DOI: 10.1111/epi.17485] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/09/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVE This study aimed to investigate the quantitative relationship between interictal 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and interictal high-frequency oscillations (HFOs) from stereo-electroencephalography (SEEG) recordings in patients with refractory epilepsy. METHODS We retrospectively included 32 patients. FDG-PET data were quantified through statistical parametric mapping (SPM) t test modeling with normal controls. Interictal SEEG segments with four, 10-min segments were selected randomly. HFO detection and classification procedures were automatically performed. Channel-based HFOs separating ripple (80-250 Hz) and fast ripple (FR; 250-500 Hz) counts were correlated with the surrounding metabolism T score at the individual and group level, respectively. The association was further validated across anatomic seizure origins and sleep vs wake states. We built a joint feature FR × T reflecting the FR and hypometabolism concordance to predict surgical outcomes in 28 patients who underwent surgery. RESULTS We found a negative correlation between interictal FDG-PET and HFOs through the linear mixed-effects model (R2 = .346 and .457 for ripples and FRs, respectively, p < .001); these correlations were generalizable to different epileptogenic-zone lobar localizations and vigilance states. The FR × T inside the resection volume could be used as a predictor for surgical outcomes with an area under the curve of 0.81. SIGNIFICANCE The degree of hypometabolism is associated with HFO generation rate, especially for FRs. This relationship would be meaningful for selection of SEEG candidates and for optimizing SEEG scheme planning. The concordance between FRs and hypometabolism inside the resection volume could provide prognostic information regarding surgical outcome.
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Affiliation(s)
- Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Aileen McGonigal
- Epilepsy Unit, Neurosciences Centre, Mater Hospital and Mater Research Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaobin Zhao
- Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lin Ai
- Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaoqiu Shao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
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Travnicek V, Klimes P, Cimbalnik J, Halamek J, Jurak P, Brinkmann B, Balzekas I, Abdallah C, Dubeau F, Frauscher B, Worrell G, Brazdil M. Relative entropy is an easy-to-use invasive electroencephalographic biomarker of the epileptogenic zone. Epilepsia 2023; 64:962-972. [PMID: 36764672 DOI: 10.1111/epi.17539] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 02/07/2023] [Accepted: 02/08/2023] [Indexed: 02/12/2023]
Abstract
OBJECTIVE High-frequency oscillations are considered among the most promising interictal biomarkers of the epileptogenic zone in patients suffering from pharmacoresistant focal epilepsy. However, there is no clear definition of pathological high-frequency oscillations, and the existing detectors vary in methodology, performance, and computational costs. This study proposes relative entropy as an easy-to-use novel interictal biomarker of the epileptic tissue. METHODS We evaluated relative entropy and high-frequency oscillation biomarkers on intracranial electroencephalographic data from 39 patients with seizure-free postoperative outcome (Engel Ia) from three institutions. We tested their capability to localize the epileptogenic zone, defined as resected contacts located in the seizure onset zone. The performance was compared using areas under the receiver operating curves (AUROCs) and precision-recall curves. Then we tested whether a universal threshold can be used to delineate the epileptogenic zone across patients from different institutions. RESULTS Relative entropy in the ripple band (80-250 Hz) achieved an average AUROC of .85. The normalized high-frequency oscillation rate in the ripple band showed an identical AUROC of .85. In contrast to high-frequency oscillations, relative entropy did not require any patient-level normalization and was easy and fast to calculate due to its clear and straightforward definition. One threshold could be set across different patients and institutions, because relative entropy is independent of signal amplitude and sampling frequency. SIGNIFICANCE Although both relative entropy and high-frequency oscillations have a similar performance, relative entropy has significant advantages such as straightforward definition, computational speed, and universal interpatient threshold, making it an easy-to-use promising biomarker of the epileptogenic zone.
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Affiliation(s)
- Vojtech Travnicek
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Petr Klimes
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Jan Cimbalnik
- International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Josef Halamek
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Pavel Jurak
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Benjamin Brinkmann
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Irena Balzekas
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Chifaou Abdallah
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - François Dubeau
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Greg Worrell
- Bioelectronics, Neurophysiology, and Engineering Laboratory, Departments of Neurology and Physiology & Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Milan Brazdil
- Department of Neurology, Brno Epilepsy Center, St. Anne's University Hospital and Medical Faculty of Masaryk University, Brno, Czech Republic.,Central European Institute of Technology, Masaryk University, Brno, Czech Republic
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Shafiei G, Fulcher BD, Voytek B, Satterthwaite TD, Baillet S, Misic B. Neurophysiological signatures of cortical micro-architecture. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.23.525101. [PMID: 36747831 PMCID: PMC9900796 DOI: 10.1101/2023.01.23.525101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6 800 timeseries features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is colocalized with multiple micro-architectural features, including genomic gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.
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Affiliation(s)
- Golia Shafiei
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ben D. Fulcher
- School of Physics, The University of Sydney, NSW 2006, Australia
| | - Bradley Voytek
- Department of Cognitive Science, Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
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Lisgaras CP, Oliva A, Mckenzie S, LaFrancois J, Siegelbaum SA, Scharman HE. Hippocampal area CA2 controls seizure dynamics, interictal EEG abnormalities and social comorbidity in mouse models of temporal lobe epilepsy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.15.524149. [PMID: 36711983 PMCID: PMC9882187 DOI: 10.1101/2023.01.15.524149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Temporal lobe epilepsy (TLE) is characterized by spontaneous recurrent seizures, abnormal activity between seizures, and impaired behavior. CA2 pyramidal neurons (PNs) are potentially important because inhibiting them with a chemogenetic approach reduces seizure frequency in a mouse model of TLE. However, whether seizures could be stopped by timing inhibition just as a seizure begins is unclear. Furthermore, whether inhibition would reduce the cortical and motor manifestations of seizures are not clear. Finally, whether interictal EEG abnormalities and TLE comorbidities would be improved are unknown. Therefore, real-time optogenetic silencing of CA2 PNs during seizures, interictal activity and behavior were studied in 2 mouse models of TLE. CA2 silencing significantly reduced seizure duration and time spent in convulsive behavior. Interictal spikes and high frequency oscillations were significantly reduced, and social behavior was improved. Therefore, brief focal silencing of CA2 PNs reduces seizures, their propagation, and convulsive manifestations, improves interictal EEG, and ameliorates social comorbidities. HIGHLIGHTS Real-time CA2 silencing at the onset of seizures reduces seizure durationWhen CA2 silencing reduces seizure activity in hippocampus it also reduces cortical seizure activity and convulsive manifestations of seizuresInterictal spikes and high frequency oscillations are reduced by real-time CA2 silencingReal-time CA2 silencing of high frequency oscillations (>250Hz) rescues social memory deficits of chronic epileptic mice.
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Weiss SA, Fried I, Wu C, Sharan A, Rubinstein D, Engel J, Sperling MR, Staba RJ. Graph theoretical measures of fast ripple networks improve the accuracy of post-operative seizure outcome prediction. Sci Rep 2023; 13:367. [PMID: 36611059 PMCID: PMC9825369 DOI: 10.1038/s41598-022-27248-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 12/28/2022] [Indexed: 01/09/2023] Open
Abstract
Fast ripples (FR) are a biomarker of epileptogenic brain, but when larger portions of FR generating regions are resected seizure freedom is not always achieved. To evaluate and improve the diagnostic accuracy of FR resection for predicting seizure freedom we compared the FR resection ratio (RR) with FR network graph theoretical measures. In 23 patients FR were semi-automatically detected and quantified in stereo EEG recordings during sleep. MRI normalization and co-registration localized contacts and relation to resection margins. The number of FR, and graph theoretical measures, which were spatial (i.e., FR rate-distance radius) or temporal correlational (i.e., FR mutual information), were compared with the resection margins and with seizure outcome We found that the FR RR did not correlate with seizure-outcome (p > 0.05). In contrast, the FR rate-distance radius resected difference and the FR MI mean characteristic path length RR did correlate with seizure-outcome (p < 0.05). Retesting of positive FR RR patients using either FR rate-distance radius resected difference or the FR MI mean characteristic path length RR reduced seizure-free misclassifications from 44 to 22% and 17%, respectively. These results indicate that graph theoretical measures of FR networks can improve the diagnostic accuracy of the resection of FR events for predicting seizure freedom.
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Affiliation(s)
- Shennan A. Weiss
- grid.262863.b0000 0001 0693 2202Department of Neurology, State University of New York Downstate, Brooklyn, USA ,grid.262863.b0000 0001 0693 2202Department of Physiology and Pharmacology, State University of New York Downstate, 450 Clarkson Avenue, MSC 1213, Brooklyn, NY 11203 USA ,grid.422616.50000 0004 0443 7226Department of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY USA
| | - Itzhak Fried
- grid.19006.3e0000 0000 9632 6718Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Chengyuan Wu
- grid.265008.90000 0001 2166 5843Department of Neuroradiology, Thomas Jefferson University, Philadelphia, USA ,grid.265008.90000 0001 2166 5843Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA 19107 USA
| | - Ashwini Sharan
- grid.265008.90000 0001 2166 5843Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA 19107 USA
| | - Daniel Rubinstein
- grid.265008.90000 0001 2166 5843Department of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, USA
| | - Jerome Engel
- grid.19006.3e0000 0000 9632 6718Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, USA ,grid.19006.3e0000 0000 9632 6718Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, USA ,grid.19006.3e0000 0000 9632 6718Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, USA ,grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, USA ,grid.19006.3e0000 0000 9632 6718David Geffen School of Medicine at UCLA, Brain Research Institute, Los Angeles, CA 90095 USA
| | - Michael R. Sperling
- grid.265008.90000 0001 2166 5843Department of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, USA
| | - Richard J. Staba
- grid.19006.3e0000 0000 9632 6718Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, USA
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Lisgaras CP, Scharfman HE. High-frequency oscillations (250-500 Hz) in animal models of Alzheimer's disease and two animal models of epilepsy. Epilepsia 2023; 64:231-246. [PMID: 36346209 PMCID: PMC10501735 DOI: 10.1111/epi.17462] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 11/07/2022] [Accepted: 11/07/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE To test the hypothesis that high-frequency oscillations (HFOs) between 250 and 500 Hz occur in mouse models of Alzheimer's disease (AD) and thus are not unique to epilepsy. METHODS Experiments were conducted in three mouse models of AD: Tg2576 mice that simulate a form of familial AD, presenilin 2 knock-out (PS2KO) mice, and the Ts65Dn model of Down's syndrome. We recorded HFOs using wideband (0.1-500 Hz, 2 kHz) intra-hippocampal and cortical surface electroencephalography (EEG) at 1 month until 24 months of age during wakefulness, slow wave sleep (SWS), and rapid eye movement (REM) sleep. In addition, interictal spikes (IISs) and seizures were analyzed for the possible presence of HFOs. Comparisons were made to the intra-hippocampal kainic acid and pilocarpine models of epilepsy. RESULTS We describe for the first time that hippocampal and cortical HFOs are a new EEG abnormality in AD mouse models. HFOs occurred in all transgenic mice but no controls. They were also detectable as early as 1 month of age and prior to amyloid beta plaque neuropathology. HFOs were most frequent during SWS (vs REM sleep or wakefulness). Notably, HFOs in the AD and epilepsy models were indistinguishable in both spectral frequency and duration. HFOs also occurred during IISs and seizures in the AD models, although with altered spectral properties compared to isolated HFOs. SIGNIFICANCE Our data demonstrate that HFOs, an epilepsy biomarker with high translational value, are not unique to epilepsy and thus not disease specific. Our findings also strengthen the idea of hyperexcitability in AD and its significant overlap with epilepsy. HFOs in AD mouse models may serve as an EEG biomarker, which is detectable from the scalp and thus amenable to noninvasive detection in people at risk for AD.
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Affiliation(s)
- Christos Panagiotis Lisgaras
- Departments of Child & Adolescent Psychiatry, Neuroscience & Physiology, and Psychiatry, and the Neuroscience Institute, New York University Langone Health, 550 First Ave., New York, NY 10016
- Center for Dementia Research, The Nathan Kline Institute for Psychiatric Research, New York State Office of Mental Health, 140 Old Orangeburg Road, Bldg. 35, Orangeburg, NY 10962
| | - Helen E. Scharfman
- Departments of Child & Adolescent Psychiatry, Neuroscience & Physiology, and Psychiatry, and the Neuroscience Institute, New York University Langone Health, 550 First Ave., New York, NY 10016
- Center for Dementia Research, The Nathan Kline Institute for Psychiatric Research, New York State Office of Mental Health, 140 Old Orangeburg Road, Bldg. 35, Orangeburg, NY 10962
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Zhang Y, Chung H, Ngo JP, Monsoor T, Hussain SA, Matsumoto JH, Walshaw PD, Fallah A, Sim MS, Asano E, Sankar R, Staba RJ, Engel J, Speier W, Roychowdhury V, Nariai H. Characterizing physiological high-frequency oscillations using deep learning. J Neural Eng 2022; 19:10.1088/1741-2552/aca4fa. [PMID: 36541546 PMCID: PMC10364130 DOI: 10.1088/1741-2552/aca4fa] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 11/22/2022] [Indexed: 11/23/2022]
Abstract
Objective.Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. However, HFOs can also be recorded in the healthy brain regions, which complicates the interpretation of HFOs. The present study aimed to characterize salient features of physiological HFOs using deep learning (DL).Approach.We studied children with neocortical epilepsy who underwent intracranial strip/grid evaluation. Time-series EEG data were transformed into DL training inputs. The eloquent cortex (EC) was defined by functional cortical mapping and used as a DL label. Morphological characteristics of HFOs obtained from EC (ecHFOs) were distilled and interpreted through a novel weakly supervised DL model.Main results.A total of 63 379 interictal intracranially-recorded HFOs from 18 children were analyzed. The ecHFOs had lower amplitude throughout the 80-500 Hz frequency band around the HFO onset and also had a lower signal amplitude in the low frequency band throughout a one-second time window than non-ecHFOs, resembling a bell-shaped template in the time-frequency map. A minority of ecHFOs were HFOs with spikes (22.9%). Such morphological characteristics were confirmed to influence DL model prediction via perturbation analyses. Using the resection ratio (removed HFOs/detected HFOs) of non-ecHFOs, the prediction of postoperative seizure outcomes improved compared to using uncorrected HFOs (area under the ROC curve of 0.82, increased from 0.76).Significance.We characterized salient features of physiological HFOs using a DL algorithm. Our results suggested that this DL-based HFO classification, once trained, might help separate physiological from pathological HFOs, and efficiently guide surgical resection using HFOs.
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Affiliation(s)
- Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Hoyoung Chung
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Jacquline P. Ngo
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Shaun A. Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Joyce H. Matsumoto
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Patricia D. Walshaw
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Myung Shin Sim
- Department of Medicine, Statistics Core, University of California, Los Angeles, CA, USA
| | - Eishi Asano
- Department of Pediatrics and Neurology, Children’s Hospital of Michigan, Wayne State University School of Medicine, Detroit, MI, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Richard J. Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Jerome Engel
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Neurobiology, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
- The Brain Research Institute, University of California, Los Angeles, CA, USA
| | - William Speier
- Department of Radiological Sciences, University of California, Los Angeles, CA, USA
- Department of Bioengineering, University of California, Los Angeles, CA, USA
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
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Wang Y, Yang Y, Li S, Su Z, Guo J, Wei P, Huang J, Kang G, Zhao G. Automatic Localization of Seizure Onset Zone Based on Multi-Epileptogenic Biomarkers Analysis of Single-Contact from Interictal SEEG. Bioengineering (Basel) 2022; 9:769. [PMID: 36550975 PMCID: PMC9774098 DOI: 10.3390/bioengineering9120769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/01/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
Successful surgery on drug-resistant epilepsy patients (DRE) needs precise localization of the seizure onset zone (SOZ). Previous studies analyzing this issue still face limitations, such as inadequate analysis of features, low sensitivity and limited generality. Our study proposed an innovative and effective SOZ localization method based on multiple epileptogenic biomarkers (spike and HFOs), and analysis of single-contact (MEBM-SC) to address the above problems. We extracted contacts epileptic features from signal distributions and signal energy based on machine learning and end-to-end deep learning. Among them, a normalized pathological ripple rate was designed to reduce the disturbance of physiological ripple and enhance the performance of SOZ localization. Then, a feature selection algorithm based on Shapley value and hypothetical testing (ShapHT+) was used to limit interference from irrelevant features. Moreover, an attention mechanism and a focal loss algorithm were used on the classifier to learn significant features and overcome the unbalance of SOZ/nSOZ contacts. Finally, we provided an SOZ prediction and visualization on magnetic resonance imaging (MRI). Ten patients with DRE were selected to verify our method. The experiment performed cross-validation and revealed that MEBM-SC obtains higher sensitivity. Additionally, the spike has better sensitivity while HFOs have better specificity, and the combination of these biomarkers can achieve the best performance. The study confirmed that MEBM-SC can increase the sensitivity and accuracy of SOZ localization and help clinicians to perform a precise and reliable preoperative evaluation based on interictal SEEG.
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Affiliation(s)
- Yiping Wang
- Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Ministry of Education, No. 10 Xitucheng Road, Haidian District, Beijing 100876, China
| | - Yanfeng Yang
- Department of Neurosurgery, Xuan Wu Hospital, Capital Medical University, No. 45 Changchun Street, Xuanwu District, Beijing 100053, China
| | - Si Li
- Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Ministry of Education, No. 10 Xitucheng Road, Haidian District, Beijing 100876, China
| | - Zichen Su
- Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Ministry of Education, No. 10 Xitucheng Road, Haidian District, Beijing 100876, China
| | - Jinjie Guo
- Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Ministry of Education, No. 10 Xitucheng Road, Haidian District, Beijing 100876, China
| | - Penghu Wei
- Department of Neurosurgery, Xuan Wu Hospital, Capital Medical University, No. 45 Changchun Street, Xuanwu District, Beijing 100053, China
| | - Jinguo Huang
- Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Ministry of Education, No. 10 Xitucheng Road, Haidian District, Beijing 100876, China
| | - Guixia Kang
- Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Ministry of Education, No. 10 Xitucheng Road, Haidian District, Beijing 100876, China
| | - Guoguang Zhao
- Department of Neurosurgery, Xuan Wu Hospital, Capital Medical University, No. 45 Changchun Street, Xuanwu District, Beijing 100053, China
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Perucca P, Gotman J. Delineating the epileptogenic zone: spikes versus oscillations. Lancet Neurol 2022; 21:949-951. [DOI: 10.1016/s1474-4422(22)00396-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 09/13/2022] [Indexed: 10/31/2022]
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